Quantitative Finance · Diting Lab

Generalised Kessler Syndrome and the Persian Gulf: Extending orbital debris cascade theory to supply chains and analysing maritime restrictions' impact on Gulf states

2026-10-29T00:00:00+09:00

The Generalised Kessler Syndrome and the Persian Gulf arXiv · Oct 29, 2026The paper generalises the Kessler syndrome, a runaway cascade of orbital debris, to supply chains, identifying an additive channel where failures trigger further failures and a subtractive channel that erodes containment and recovery capacity. When damage outpaces repair, the system irreversibly settles into a new state. Applied to the Persian Gulf, it argues that maritime supply restrictions from spring 2026 are poorly offset by air and road alternatives, unevenly depleting inventories and impairing logistics itself. The author formulates the problem as a Kelly portfolio allocation under a weakest-link payoff, concluding that the optimal strategy favours lagging inputs over leading ones.

Information Flow and Market Risk: Transformer-Based Sentiment and Volatility Evidence

2026-10-29T00:00:00+09:00

Information Flow and Market Risk: Transformer-Based Evidence on Sentiment and Volatility Dynamics arXiv · Oct 29, 2026This study tests whether transformer-based financial news sentiment improves equity volatility forecasts beyond conventional market predictors. Using eleven US stocks, it compares models incorporating the VIX, AAII investor sentiment, and ModernFinBERT news sentiment through walk-forward testing. A naive persistence benchmark achieves the lowest average QLIKE loss at all horizons, and text sentiment yields no general forecasting advantage.

Early buyers and fast wallets on pump.fun: a measurement study

2026-10-29T00:00:00+09:00

Early-Buyer Groups and Fast-Wallet Teams on a Bonding-Curve Launchpad: A Prospective Measurement Study of pump.fun on Solana arXiv · Oct 29, 2026This paper studies coordinated wallet groups on the pump.fun token launchpad on Solana, using records of about 200,000 token launches and over 15 million curve trades. The authors identify 23,710 wallet groups, whose activity is highly concentrated and splits into serial creators and long-lived rings of early buyers. They report that group members buy earlier than other buyers, but treat this as an association, and find that following the earliest group signal loses money out of sample.

Why Bounded Rationality Persists in Financial Markets: An Evolutionary Game Model

2026-10-29T00:00:00+09:00

Persistence of Bounded Rationality in Financial Markets: An Evolutionary Game-Theoretic Model arXiv · Oct 29, 2026This paper models a financial market as two trader types, relatively rational and boundedly rational, whose shares change over time according to relative performance. When the two types complement each other and their friction costs differ moderately, a unique stable mix exists in which boundedly rational trading persists. Lowering the cost of disciplined trading, such as through algorithmic and AI trading, shifts the mix toward rational trading but does not eliminate bounded rationality unless the cost advantage passes an explicit threshold.

Right numbers, wrong questions: testing LLMs on vague financial queries

2026-10-28T00:00:00+09:00

Right Numbers, Wrong Questions: Evaluating LLMs on Underspecified Financial Queries arXiv · Oct 28, 2026The paper examines how financial assistants can answer a question with a real figure from their data while addressing a different question than the user intended, a failure the authors call frame collapse. Using Indian mutual funds, where product variants and tax rules are formally defined so correct answers can be computed for each reading, the authors test models on ambiguous data labels, missing investor facts, and alternative interpretations. They find models often use the user's label, list options in a layout-dependent order, and assume defaults or outdated tax rules. Because such answers match the data, data-based checks miss these errors. The paper also proposes controls for financial assistants.

Decorrelating AI Advice Layers to Reduce Synchronized Liquidation in Agentic Wealth Management

2026-10-27T00:00:00+09:00

Decorrelating the Advice Layer: A Calibrated Fragility Metric and Tested Mitigations for Synchronized Liquidation in Agentic Wealth Management arXiv · Oct 27, 2026This paper models conversational AI agents that run continuous market analysis, allocation, and execution for wealth management clients, calibrated to model concentration, retail turnover, and 33 years of equity returns. It finds that the effective number of independent decision-makers is bounded, and that synchronized automation rather than model concentration drives market-wide tail losses. A fragility index built from aggregate flow, public returns, and impact estimates forecasts tail liquidation out of sample better than concentration or correlation measures. Dispersing overlay parameters, staging execution, and adding a participation breaker together cut tail liquidation by 63 percent. ★★★★ Notable: The authors propose an aggregate-flow fragility index and report that synchronized automation, not model concentration, drives tail liquidation in simulations and historical replays.

Calibrating Passive Market Impact Using a Queue-Sensitive Propagator Model

2026-10-28T00:00:00+09:00

Calibration of Passive Market Impact with a Queue-Sensitive Propagator Model arXiv · Oct 28, 2026This paper proposes a framework for calibrating passive market impact, the price response to liquidity-providing limit orders, separately from the impact of liquidity-consuming market orders. It builds on a model combining Hawkes-driven market-order flow with queue-reactive limit-order and cancellation dynamics. The authors show that prices decompose into a universal propagator term driven by signed order-flow imbalance and a state-dependent correction reflecting how volume at the best quotes affects price sensitivity. Calibrated on high-frequency order-book data, the passive-impact term explains substantial price variation beyond the classical propagator and improves model fit and predicted impact curves.

What language models add to healthcare stock rankings, and what they could have known

2026-10-28T00:00:00+09:00

What Language Models Add to Healthcare-Equity Rankings, and What They Could Have Known arXiv · Oct 28, 2026This study evaluates whether non-market features and language-model representations improve market-based rankings of US healthcare firms from 2020 to 2025. Adding balance-sheet and attention features raises the hybrid model's rank information coefficient at one, three and twelve months, with the twelve-month neutral IC rising from 0.063 to 0.093. Language models contribute most clearly at three months, though the findings are exploratory.

CADENCE: A Model Predicting Business Cash-Flow Timing and Amounts for Credit Underwriting

2026-10-28T00:00:00+09:00

CADENCE: Distributional Next-Transaction Forecasting for Business Cash-Flow Underwriting arXiv · Oct 28, 2026The paper introduces CADENCE, a compact causal Transformer that predicts the amount and a nonparametric timing distribution of each business cash flow's next payment. It uses transaction semantics from an external labeler and a frozen text encoder, along with calendar-aware inputs. Compared with a production FFT baseline, it reports gains in one-day timing accuracy and next-event F1, and improvements in KS, approval rate, and loss rate in an end-to-end credit evaluation.

Simultaneous return spillover networks and systemic risk among European banks

2026-10-28T00:00:00+09:00

Contemporaneous Transmission Networks and Systemic Risk in European Banking arXiv · Oct 28, 2026This paper examines how contemporaneous equity-return transmission shapes systemic exposure among 51 listed European banks. Using a recursive non-Gaussian network model on volatility-standardized returns, the authors build two market-capitalization-weighted measures: the Systemic Impact Score, capturing a bank's influence on others, and the Systemic Vulnerability Score, capturing its exposure to shocks from elsewhere. The study also assesses the reproducibility of these roles and the limits of identification.

Optimal quote placement for market makers under a state-dependent Hawkes order flow model

2026-10-27T00:00:00+09:00

Optimal Order Placement under a State-Dependent Hawkes Process Limit Order Book model arXiv · Oct 27, 2026The paper studies how a market maker should place quotes when order flow follows a state-dependent Hawkes process. It links market-order counts to execution volumes through a block-based linear demand model and formulates a continuous-time, discrete-state dynamic program. Under a quadratic terminal inventory penalty, it derives inventory-dependent quotes and backward recursions for the value function coefficients. Numerical tests on LOBSTER data show higher aggregate terminal wealth than fixed-spread benchmarks at one and three ticks, but lower than the two-tick benchmark.

Pricing Catastrophe Bonds in Incomplete Markets Using Riemannian Geometry and Financial Entropy

2026-10-27T00:00:00+09:00

An Application of Geometric Riemannian Pricing Algorithm for Valuing Catastrophe Bonds in Incomplete Markets: Market-Consistent Financial Entropy and the Split of Frequency and Severity Risk Premia arXiv · Oct 27, 2026This paper proposes a framework for pricing catastrophe bonds in incomplete markets by optimizing financial entropy with Riemannian geometry to select the pricing measure. It uses a compound Poisson and generalized Pareto loss model with Wang distortion and derives closed-form expressions for entropy and the Riemannian metric. Applied to a hypothetical earthquake bond calibrated to Golden State Re II (Series 2014-1) spreads, the study compares risk-neutral and physical trigger intensities and splits the risk premium between frequency and severity components.

Deposits Should Not Reduce Returns: Supplier Income Monotonicity and Rate Curve Design in Pooled Lending

2026-10-27T00:00:00+09:00

Depositing More Should Not Earn Less: Supplier Income Monotonicity and Rate Curve Design in Pooled Lending Markets arXiv · Oct 27, 2026The paper studies utilization-based interest rate models in which adding liquidity can lower a supplier's total interest income even as its balance rises. It gives the condition under which a supplier's income does not decrease with a marginal deposit and derives the resulting constraint on admissible rate curves. The authors also check how often the problematic case occurred on two public Drift markets.

Simple, low-complexity ETF portfolios for private investors

2026-10-27T00:00:00+09:00

Simple Portfolios that Deliver Consistent Decent Returns arXiv · Oct 27, 2026This empirical study tests portfolio construction methods for private investors using liquid US ETFs, with monthly trading costs and no short selling or leverage. The portfolios are built as convex optimization problems using interpretable inputs estimated from public data. The authors report that simple portfolios of as few as three ETFs deliver consistent returns and outperform a standard 60/40 allocation over the past two decades.

Risk measures for infinite-server queues driven by a Cox-BESQ process

2026-10-27T00:00:00+09:00

Risk measures for infinite-server queues driven by a Cox-BESQ process arXiv · Oct 27, 2026This paper proposes an infinite-server queue model whose arrival intensity follows a Cox process driven by a squared Bessel (BESQ) process. Using the affine structure of the dynamics, it derives closed-form joint moment-generating functions for the arrival intensity, the number of customers, and the time integral of the customer count, extendable to two or more dates. These explicit functions are used to construct risk measures, such as joint moments conditional on a variable exceeding a threshold, computed via Fourier representations. Numerical experiments illustrate the practical implementation of the method.

Event definition and branching ratio: Hawkes estimates of price-change clustering in consolidated quotes

2026-10-27T00:00:00+09:00

Event definition and the branching ratio: Hawkes estimates of price-change clustering in the consolidated quote feed arXiv · Oct 27, 2026This paper fits Hawkes models to price changes in the consolidated US quote feed, where raw estimates show a sub-millisecond kernel and a near-critical branching ratio. The authors find that in large-tick stocks about half of NBBO changes are locked or depleted quotes resolved within a millisecond, which the model misreads as excitation. Defining events as a mid-price level persisting for one millisecond removes the sub-millisecond component.

FinLLM-KG: Knowledge-Graph-Enhanced LLMs for Financial Sentiment in Algorithmic Trading

2026-10-27T00:00:00+09:00

FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading arXiv · Oct 27, 2026This paper proposes FinLLM-KG, a framework that combines causal large language models with a financial knowledge graph built from SEC 10-K filings. It supplies company-level context at inference time without updating model weights, either as a profile or as relevance-ranked factual triples. Sentiment scores are attributed to individual sentences and graph facts to make predictions more explainable. Simulations reportedly show consistent portfolio performance gains over a state-of-the-art baseline.

Separating liquidity-driven from strategic loan defaults using deep learning

2026-09-29T00:00:00+09:00

Disentangling Liquidity-Constrained from Strategic Default: A Deep Learning Approach arXiv · Sep 29, 2026This paper applies a deep neural network to a proprietary commercial mortgage panel from 1998 to 2021 to distinguish liquidity-constrained default from strategic default, two explanations for delinquency that are hard to tell apart with standard models. Among delinquent loans, property net operating income is found to matter more for default rates than loan-to-value. The estimated thresholds remain stable across the 2008 financial crisis and the COVID-19 shock in 2020 and 2021.

Modelling Regime Shifts in Electricity Intraday Markets with State-Dependent Hawkes Processes

2026-10-06T00:00:00+09:00

Modelling Regime Shifts in Continuous Intraday Electricity Markets with State-dependent Hawkes Processes arXiv · Oct 6, 2026This paper proposes a multivariate state-dependent Hawkes process to model order flow across liquidity regimes in the continuous intraday electricity market. Market states are identified with a Liquidity Stress Index built from bid-ask spread, order book volume, and mid-price volatility. Calibrated on Dutch XBID hourly order data from EPEX SPOT for January-March 2024, the estimates show strongly self-exciting, near-critical order flow, with liquidity stress mainly strengthening same-side transaction self-excitation.

FinAutoRubric: Expert-guided automatic rubric generation for evaluating financial research agents

2026-09-29T00:00:00+09:00

FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents arXiv · Sep 29, 2026This paper presents FinAutoRubric, a framework for evaluating finance research agents. Experts specify reusable evaluation guidance, and agents and code use it to generate, review, and validate query-specific rubrics, with reusable criteria stored in a Task Bank. On three expert-authored finance benchmarks, its rubrics match expert scoring about as closely as the strongest compared generator, agree with human grading, and were preferred by in-house analysts in blind review. The authors also release the 100-query FinAutoRubric Benchmark.

LiveOption: Testing AI Agents on Structured Option Trading with Nonlinear Payoffs

2026-09-27T00:00:00+09:00

LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs arXiv · Sep 27, 2026This paper introduces LiveOption, a framework for evaluating large language model agents in option trading, a market more complex than simple stock trading. It provides a reproducible environment under realistic execution and capital constraints, covering portfolio overlays, earnings-event trading, and 0DTE intraday trading. The authors also propose a hierarchical metric suite assessing action validity, decision quality, risk, and outcomes. Experiments show current agents often fail to achieve competitive returns in most scenarios.

RICE-Alpha: LLM Agent Stock Forecasting with Event Graphs and Reliability Correction

2026-09-28T00:00:00+09:00

RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting arXiv · Sep 28, 2026This paper presents RICE-Alpha, a point-in-time stock-scoring framework that uses historical news events to forecast stock performance. It separates a multi-view base forecast from a reliability-calibrated residual correction based on how past events continued, using event graphs to preserve issuer-specific chronology. Tested on daily Nasdaq-100 and Hang Seng Index data from 2024 to 2026, it reportedly outperformed the evaluated LLM-based agents and momentum baselines. The authors conclude that historical event continuation adds information when it is temporally grounded, reliability-calibrated, and applied as a residual correction.

Deep kernel hedging: a paper combining neural features with kernel methods for derivative hedging

2026-09-28T00:00:00+09:00

Deep kernel hedging arXiv · Sep 28, 2026This paper introduces a deep kernel hedging framework in which the hedging function lies in a reproducing kernel Hilbert space whose kernel is parameterized by a neural network embedding, and it handles path-dependent inputs via truncated time-augmented signature features. The authors derive a generalized representer theorem that reduces the empirical optimization to a finite-dimensional problem, and they use scalable random Fourier feature approximations with convergence guarantees. Experiments on synthetic and real data reportedly show competitive and robust hedging performance, especially in low-data regimes.

Validity Without Ground Truth: Applying Stated-Preference Economics to Evaluating Language Models

2026-10-08T00:00:00+09:00

Validity Without Ground Truth: What Stated-Preference Economics Offers the Evaluation of Language Models arXiv · Oct 8, 2026This paper argues that the validity framework from stated-preference economics, covering content, construct, and criterion validity, reliability, incentive compatibility, and consequentiality, can evaluate language models when no correct answer exists. The authors test six models on a published water-quality stated-preference valuation survey (Vossler et al. 2023), using economic-theory predictions such as downward-sloping demand and willingness to pay responding to scope and income. Two older models fail the most basic test, while the two newest pass every scorable theoretical validity test but diverge on convergent validity. The authors stress that passing validity tests shows coherence, not correctness.

Representation risk: pretrained image encoders can change predictive conclusions

2026-09-29T00:00:00+09:00

Representation Risk in Pretrained Image Encoders arXiv · Sep 29, 2026The paper argues that the pretrained image encoder used to turn pictures into features is a source of model uncertainty, which the authors call representation risk. Ten frozen encoders were compared across tasks including house prices, racehorse performance, histology, chest radiographs, facial age, and rice disease, with the encoder chosen on locked validation data. The results show no single encoder is best everywhere, and the authors recommend benchmarking candidates, selecting on validation data, and reporting paired and split-level uncertainty. The workflow is implemented in the LOOKAGAIN-ML software package. ★★★★ Notable: Proposes representation risk, showing that ten frozen image encoders across six applications yield large validation-selected test gaps, e.g., house-price R² rising from 0.396 to 0.629.

A spread-gated Hawkes model of best bid and ask dynamics, applied to limit order sizing

2026-09-29T00:00:00+09:00

A Spread-Gated Hawkes-Flocking Model for Best Bid and Ask Dynamics, with an Application to Limit Order Placement arXiv · Sep 29, 2026The paper models the joint dynamics of the best bid and ask prices with a spread-gated Hawkes-flocking process that distinguishes four types of best-quote movements and includes a spread-dependent cross-side excitation term. The authors prove non-explosion on finite horizons, provide an O(N) recursive likelihood, and validate maximum likelihood estimation by simulation. On intraday data for INTC and MSFT, removing the cross-side term is rejected and the full model fits substantially better by AIC and BIC. As an application, they derive a closed-form optimal size for a single-period limit order placed at the best or second-best quote.

When Hedging Changes the Payoff: Option Replication Under Price Impact and Trading Costs

2026-09-29T00:00:00+09:00

When Hedging Changes the Payoff: Option Replication with Price Impact and Execution Costs arXiv · Sep 29, 2026The paper studies how hedging a derivative by trading the underlying asset can change the payoff it was meant to replicate, once trading causes price impact and execution costs. In a binomial model replication is characterized by a fixed-point equation, and in continuous time a nonlinear pricing PDE is derived. For monotone convex Lipschitz payoffs such as calls and puts, exact replication is established under midpoint execution costs, and numerical experiments illustrate the effects.

Comparing two ways to split tail risk among components

2026-09-29T00:00:00+09:00

A tale of two allocations: Risk capital contributions versus risk contributions in the tail arXiv · Sep 29, 2026The paper compares two proportional measures of how much each risk component contributes to aggregate tail risk: the share of tail risk capital allocated under Conditional Tail Expectation (CTE), and the expected realized risk share under Geometric Tail Expectation (GTE). It establishes when the two allocations agree, how they are ordered when they differ, and how they separate asymptotically in the extreme tail. The difference is shown to be a normalized tail covariance.

Testing whether a Pepsi–Coca-Cola pairs trade still works after its original fit period.

2026-09-29T00:00:00+09:00

From Cointegration to Out-of-Sample Failure: A Pairs-Trading Case Study on PEP-KO arXiv · Sep 29, 2026This paper tests whether a pairs trading strategy between PepsiCo and The Coca-Cola Company, based on cointegration, is statistically robust and economically exploitable. The authors estimate the spread's mean-reversion parameters over 2013–2018, fix them, and optimise a threshold trading strategy in-sample over 2018–2023. The strategy is assessed with transaction-cost and parameter sensitivity tests, walk-forward validation, and adjusted and deflated Sharpe ratios, then evaluated out-of-sample from 2023 onward. The results show that weakening mean-reversion in the spread undermines the strategy's out-of-sample effectiveness.

Optimal Liquidation Near Support and Resistance under Multi-Skew Brownian Motion

2026-09-28T00:00:00+09:00

Optimal Liquidation with Support and Resistance Levels under Multi-Skew Brownian Motion arXiv · Sep 28, 2026The paper solves a perpetual liquidation problem for a geometric multi-skew Brownian motion with local-time pushes at a support level and a resistance level, a technical-analysis-style model that depends only on price. A closed-form criterion yields three geometries: the continuation band lies below resistance, straddles it, or has its upper edge at resistance. Selling into resistance emerges as a conclusion rather than an assumption. Support parameters are identified exactly from exercise behaviour, while resistance strength is identifiable only within an interval with a closed-form endpoint.

Oracle-parametrized AMMs: pricing rules that use external price feeds

2026-09-28T00:00:00+09:00

Oracle-Parametrized Constant Function Market Makers: From Price Feeds to Pricing Rules arXiv · Sep 28, 2026This paper introduces oracle-parametrized automated market makers (OP-AMMs), whose quoted prices depend on both pool reserves and an external oracle price, suited to settings like tokenized securities where price discovery happens off-chain. Under a strict oracle-contraction condition, the authors show the quoted price lies between the oracle price and an implicit autarkic price set by the reserves, and derive a loss-versus-rebalancing decomposition. The analysis extends to stale and discrete-update oracles and sandwich attacks, and a backtest on one-second SPY NBBO data illustrates the trade-offs.

CZAR Loss: A Loss Function Designed for Financial Log-Return Prediction

2026-09-29T00:00:00+09:00

Introducing the CZAR Loss: A Tailored Objective Function for Financial Log-Return Predictions arXiv · Sep 29, 2026The paper argues that standard symmetric regression losses, such as mean squared and mean absolute error, are poorly aligned with financial return prediction, since they make a constant zero forecast nearly optimal and penalize models with weak directional skill. It introduces the CZAR (Composite Zero-Agnostic Return) loss, a piecewise quadratic function that is convex in the prediction and has closed-form gradient and Hessian for use in gradient-boosted libraries. In idealized tests and a LightGBM experiment on intraday BTC log-returns, the authors report reduced zero-returns bias and better directional accuracy on large-magnitude returns.

RADAR: Retrieval-Augmented Diffusion for Portfolio Optimization with Stochastic Discount Factors

2026-09-28T00:00:00+09:00

Retrieval-Augmented Diffusion Modeling for Stochastic Discount Factor Portfolios arXiv · Sep 28, 2026This paper studies portfolio optimization within the stochastic discount factor framework by learning market state representations that capture underlying financial risk structures. The authors propose RADAR, which retrieves similar historical market regimes to build context-dependent noise distributions and applies conditional diffusion to denoise multimodal inputs. The authors report state-of-the-art results on key risk-adjusted metrics and economically meaningful signals for asset returns and correlations.

From Word Counts to Context: Topic Models for Asset Pricing

2026-09-28T00:00:00+09:00

From Word Counts to Context: Topic Models for Asset Pricing arXiv · Sep 28, 2026This paper compares a sentence transformer with k-means clustering against Latent Dirichlet Allocation (LDA) for generating topics from 394,661 news articles used in a financial portfolio construction pipeline. Topic coherence is measured by NPMI and financial performance by the Sharpe ratio. The sentence transformer approach shows higher observed scores on both measures, though the authors note the tests do not establish outperformance. They suggest stricter tests using only information available at each date, and broader datasets, are needed.

Controlling Option Greeks: Portfolios with Built-in Hedging Bias

2026-09-28T00:00:00+09:00

Taming the Greeks: Option Portfolios with Inductive Biases arXiv · Sep 28, 2026This paper presents an end-to-end deep learning framework for systematic options trading that embeds hedging behavior by explicitly controlling portfolio-level risk exposures. It adds a differentiable risk-sensitivity penalty to the training objective to keep portfolios neutral on selected risk dimensions, trained on historical data. Tests on Nasdaq 100 equity options report that a well-calibrated penalty improves out-of-sample risk-adjusted performance and reduces realized directional exposure compared with an unregularized baseline.

EverMine: Studying Self-Evolving Research Skills in Long-Term Alpha Discovery

2026-09-27T00:00:00+09:00

EverMine: Dissecting the Self-Evolution of Research Capabilities in Long-Horizon Alpha Research arXiv · Sep 27, 2026This paper introduces EverMine, a framework for testing whether self-evolving agents keep improving their accumulated research skills during long-horizon alpha factor discovery. It splits the research state into history, the current factor portfolio, and reusable capabilities, and compares runs with fixed or evolving capabilities under matched resource limits. Across 18 long trajectories and 48 continuation branches, capability evolution showed no consistent gain, while tuning existing factor structures still helped. The authors also find that a candidate's value depends on the evolving portfolio and the order in which candidates are submitted.

How much screening can offset slower quote updates for liquidity providers

2026-10-15T00:00:00+09:00

How Much Screening Buys a Second? Speed, Selection, and the Maker's Spread arXiv · Oct 15, 2026The paper studies the spread a liquidity provider needs to break even when counterparties can exploit stale quotes, comparing faster repricing with screening of informed flow on one scale. Empirically, across four USDC-WETH pools benchmarked against Binance ETH-USDT, one-second price innovations are sharply non-Gaussian, with 73% of seconds showing no price change. Moving from twelve-second to one-second cadence sheds 84.5% of the expected shock, so a twelve-second maker must screen at least that share of informed flow to match. ★★★★ Notable: The authors give a break-even condition placing repricing speed and informed-flow screening on one scale; on four USDC-WETH pools, 73% of one-second shocks are zero and a 12-second maker must screen a

Efficient frontier computed with a standard LASSO solver

2026-09-29T00:00:00+09:00

The Efficient Frontier from a LASSO Solver arXiv · Sep 29, 2026This note numerically checks Schmelzer and Hastie's result that Markowitz's Critical Line Algorithm and the LASSO path trace the same curve, using scikit-learn's lars_path with no portfolio software. It tests long-short portfolios under a leverage cap, fixed leverage with varying risk appetite, and the classical long-only fully invested frontier, and reports agreement with independent quadratic-programming solvers. The script, checks, Fama-French returns and an R cross-check are published so the results can be reproduced.

Non-perturbative option pricing for rough volatility using quantum-field-theory effective action

2026-09-29T00:00:00+09:00

Dyson-Schwinger Effective-Action Methods for Rough Volatility: A Correlation-Response Architecture for Calibration, Exotics and Risk arXiv · Sep 29, 2026The paper proposes a non-perturbative framework for stochastic-volatility option pricing based on the two-particle-irreducible effective action and Dyson-Schwinger gap equations. It approximates the joint law of state variables with a self-consistent Gaussian and handles both Markovian and rough (Volterra) models, including nonzero correlation. Reported benchmarks show close agreement with Monte Carlo references and comparisons against Hagan's expansion and rough SABR.

Buying at support: optimal entry under multi-skew Brownian price motion

2026-10-04T00:00:00+09:00

Buying at Support: The Entry Problem under Multi-Skew Brownian Motion arXiv · Oct 4, 2026The paper studies when to buy a share that will later be sold optimally, when the price follows a geometric multi-skew Brownian motion whose skew levels represent support and resistance. The buying reward is the exit premium from a companion liquidation problem. The entry problem reduces to a finite one, and with one support and one resistance, the optimal rule is to buy exactly at the support, with an explicit value.

Robust enhanced index tracking portfolio selection under uncertain return distributions

2026-10-03T00:00:00+09:00

Robust enhanced index tracking portfolio selection under distributional uncertainty arXiv · Oct 3, 2026This paper studies building portfolios that aim to beat a benchmark index when the true joint distribution of asset and index losses is unknown. The authors propose robust models, guided by Pareto-optimality, that minimize a convex combination of worst-case mean loss and downside risk under a worst-case mean return constraint. Empirical tests on real market data report better out-of-sample cumulative wealth and Sharpe, Sortino, and Omega ratios than the tracked index, an equal-weighted portfolio, and other robust models.

Cities of Signals: Compression and Novelty Geometry Explained

2026-10-03T00:00:00+09:00

Cities of Signals: Compression, Separation, and the Geometry of Novelty arXiv · Oct 3, 2026The paper treats each daily cross-sectional forecast as a point on a sphere and defines its "city" as the direction of its time-averaged vector, a compressed summary of signal history. It proves probability bounds on when the angle between two cities conservatively estimates the angle between their histories. The authors test these results on positive-IC signal pairs from AlphaNova's May 2026 competition, finding agreement that is mostly positive but not fully explained by the uniform null model.

Optimal design of catastrophe bonds and their use for climate change risk

2026-10-01T00:00:00+09:00

Optimal Catastrophe Bond Design and its Applications to Climate Change Risk arXiv · Oct 1, 2026This paper treats the payout function of catastrophe bonds as a decision variable and studies their optimal design from the sponsor's perspective. It shows that a piecewise linear indemnity function is optimal under expected-disutility and spectral-risk-measure frameworks, reducing the design problem to a one-dimensional choice of attachment point. Calibrated with U.S. severe storm loss data, the optimal designs are reported to achieve substantial risk reductions across most risk criteria.

Stochastic Knothe-Rosenblatt: fast calibration of stochastic local volatility models

2026-09-30T00:00:00+09:00

Stochastic Knothe-Rosenblatt: Light-speed Calibration of Stochastic Local Volatility Models arXiv · Sep 30, 2026The paper notes that European option smiles pin down an asset's risk-neutral marginal distributions but not how they are linked across maturities. It proposes a modular calibration overlay for existing stochastic and path-dependent volatility models, building a martingale that exactly matches all prescribed marginals while staying as close as possible to the reference dynamics in an adapted Knothe-Rosenblatt sense. The authors cover numerical implementation, convergence to the SLV model, benchmarks on Heston and Bergomi dynamics, and a multi-asset extension.

Axient: A canonical protocol graph for leveraged event markets with single-authority state and recoverable execution

2026-10-02T00:00:00+09:00

Axient: Canonical Protocol-Graph Composition for Leveraged Event Markets: Single State Authority, Atomic Composition, Durable Sagas, and Exactly-Once Recovery arXiv · Oct 2, 2026This paper addresses leveraged event-market protocols, where individually correct contracts may still lack a single authoritative financial execution path. It proposes a canonical protocol graph in which each financial domain has one storage authority and composition components coordinate transitions. The authors formally prove properties such as atomic closure, loss conservation and priority, and exactly-once recovery, and evaluate the implementation through twelve Financial Interaction Assertions and a reproducible evidence chain.

Social welfare and price discovery in double auction markets explained

2026-10-01T00:00:00+09:00

Social welfare and price discovery in double auction markets arXiv · Oct 1, 2026Double auctions are known from lab experiments to drive prices toward competitive equilibrium, but no theory explained this. The paper studies dynamic double auctions in a pure exchange economy where agents bid indifference prices implied by their holdings and preferences. It shows that Walras equilibria coincide with the fixed points of the double auction, and that repeated auctions produce bounded sequences of allocations and prices whose cluster points are Walras equilibria with transfers.

Shared Models, Selective Trading, and Order Flow: A study of whether diverse language models still differ in the orders they submit when news framing changes in synthetic markets.

2026-10-02T00:00:00+09:00

Shared Models, Selective Trading, and Order Flow arXiv · Oct 2, 2026The paper uses synthetic markets with a fixed mix of three language-model families to examine how news presentation affects the orders they submit. It reports that Qwen's share of submitted orders shifted by 48 percentage points in a financing event with little change in net order counts, and Mistral's share shifted by 40 points in a workforce-reduction event where net counts reversed sign. An analytical decomposition shows selection can improve or worsen price accuracy even when aggregate demand sensitivity is unchanged.

Spouse-Protected Tontines: A Retirement Pool That Keeps Paying the Surviving Spouse

2026-09-30T00:00:00+09:00

Spouse-Protected Tontines: Household Decumulation via Neural-Network Optimization arXiv · Sep 30, 2026This paper proposes a tontine design in which the first death in a household does not transfer money to the pool; instead, the account continues to pay the surviving spouse. The authors set up a multidimensional withdrawal and rebalancing problem that trades off expected real payments against the CVaR of terminal shortfalls, solved with a neural-network policy parameterization. Numerical experiments calibrated to Australia show the spouse-only phase is a first-order, persistent household event, and that book-level diversification removes most of the excess upper-tail cost of spouse continuation.

Multi-period bond portfolio optimization with transaction costs via a Markov Decision Process

2026-09-30T00:00:00+09:00

Multiperiod bond portfolio optimization with transaction costs using a Markov Decision process arXiv · Sep 30, 2026The paper addresses how bank treasury portfolios should allocate across bonds of different maturities under interest-rate risk, noting that static allocations can suffer large losses when rates rise. It proposes a simulation-based multi-period framework that models yield curves with a Dynamic Nelson-Siegel parameterization and vector autoregressive factor dynamics, approximated by a discrete-state Markov chain. Within a finite-horizon Markov Decision Process with proportional rebalancing costs, the investor maximizes expected terminal wealth, with the optimal policy found by backward induction. The authors also quantify the error from truncating the transition kernel, finding that mean terminal wealth is barely affected while drawdown and tail statistics are substantially distorted.

AlphaPADI: Automatically discovering stock prediction formulas by generating formula pools with diffusion

2026-10-04T00:00:00+09:00

AlphaPADI: Formulaic Alpha Discovery via Pool-Aware Hierarchical Discrete Diffusion arXiv · Oct 4, 2026This paper studies formulaic alpha discovery, which seeks symbolic formulas that predict cross-sectional stock returns. The authors propose AlphaPADI, a framework with grammar-constrained buffer initialization, pool-aware hierarchical discrete diffusion, and reward-guided pool refinement that considers both predictive performance and diversity among formulas. According to the paper, experiments on Chinese and U.S. stock markets show AlphaPADI outperforms the evaluated baselines in predictive and portfolio performance.

Comparing learned and hand-crafted monotone temporal risk aggregation under equal admissibility

2026-10-04T00:00:00+09:00

Measuring Learned Monotone Temporal Aggregation at Matched Admissibility arXiv · Oct 4, 2026The paper studies risk scores that must be monotone non-decreasing in every exposure input, a constraint drawn from financial regulation. The authors build a recurrent network whose state uses classical risk statistics, keeping it monotone, and compare it with hand-crafted aggregates under the same constraint. They find that learned and engineered aggregation are substitutes within the separable family, but the learned approach fails on consecutivity, time localization, and the exposure floor. Each incumbent approach has advantages on different axes, and none dominates on all three.

Stock-JEPA: Learning Equity Market Representations by Revising a Financial Prior

2026-10-04T00:00:00+09:00

STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets arXiv · Oct 4, 2026This paper proposes Stock-JEPA, a framework for learning representations from low signal-to-noise equity market data. It anchors a latent space to fixed statistics from a simple financial model describing multi-horizon returns and risk, then learns a context-based predictor of the revision needed to reach future representations. The authors report that it outperforms 13 baselines on China and U.S. equity universes across five evaluation metrics.

FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining

2026-10-03T00:00:00+09:00

FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining arXiv · Oct 3, 2026This paper introduces FactorBench, a benchmark that compares about 5,000 factors produced by nine automated mining methods across five equity markets. It evaluates factor validity, temporal generalization, predictiveness beyond risk and style exposures, pool distinctness including similarity to Alpha101, and the quality of composite signals and after-cost portfolio performance. The authors find that no single mining paradigm consistently outperforms the others.

Explaining Neural Calibration of Volatility Models Using Shapley Values

2026-10-02T00:00:00+09:00

Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models arXiv · Oct 2, 2026This paper uses two complementary Shapley-based explainable AI methods, SHAP and νSHAP, to analyze neural network calibration of the Heston and rough Heston stochastic volatility models. It compares several architectures and finds that short maturities and smile wings dominate parameter inference, with a stable attribution pattern across architectures. The authors show that νSHAP explanations can guide a large reduction in input dimensionality for the rough Heston model while keeping calibration accuracy comparable to using the full implied volatility surface.

Agent Policy-Value Audit: Splitting Trade Mix from Event Picking

2026-10-03T00:00:00+09:00

Agent Policy-Value Audit: Separating Transition Composition from Event Selection in Financial LLM Agents arXiv · Oct 3, 2026The paper argues that financial LLM agents are often judged by return differences against a baseline, which cannot tell event-selection skill apart from passive market exposure. The authors propose an audit that keeps each type of action change at its observed count and randomly reassigns them across eligible events, using the average payoff as a composition benchmark. Applied to 723 earnings events at 44 U.S. consumer-facing firms, the agent's gross value of +15.2 bps per event splits into a +25.8 composition benchmark and a -10.6 selection value, showing no detectable outperformance over matched random assignment.

When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in a Solved Broker-Trader Game

2026-10-03T00:00:00+09:00

When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game arXiv · Oct 3, 2026This paper places a proximal policy optimisation (PPO) agent in an analytically solved continuous-time broker-trader game, where it chooses the broker's trading speed. With stochastic uninformed order flow, the tested PPO networks remain inaccurate, and their critics do not reliably rank nearby actions. Under partial information, a causal certainty-equivalent controller based on observable history stays closer to the reference than PPO. Freezing the analytical policy and training PPO to adjust it after a cost change closes 2.22% of the gap to the changed-cost reference.

FinNextAssist: A Deep Research Assistant for Professional Finance

2026-10-02T00:00:00+09:00

FinNextAssist: Towards Professional Financial Deep Research Assistant arXiv · Oct 2, 2026This paper presents FinNextAssist, an end-to-end deep research framework for professional financial analysis. It splits the research process into four stages: task planning, evidence compilation, reasoning, and report assembly, and adds two lightweight sub-agents, TabAgent for cross-market financial table understanding and HeteroAgent for interpreting heterogeneous financial data across modalities. The authors report experiments on three benchmarks showing it outperforms strong proprietary and open-source deep research agents, with ablations confirming each component's contribution.

MintEval: Do LLMs Build the Trading Strategy Requested? A Behaviour-Matching Benchmark for Language-to-Strategy Code

2026-10-02T00:00:00+09:00

MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code arXiv · Oct 2, 2026This paper introduces MintEval, a benchmark that checks whether code generated by large language models from plain-language trading descriptions behaves like the strategy requested. Generated and reference programs are run bar by bar on identical market data and frictions, and compared by their trading actions rather than code similarity or profit. The results show that even a frontier model silently fails on a substantial share of tasks, and that an existing LLM judge accepts all of these failures. ★★★★ Notable: Proposes MintEval, a bar-by-bar behavioural-equivalence benchmark for LLM-generated trading strategy code; the authors report 79.2% of correctly read implementations diverge on over 10% of active bars

Event History Over Scale: Compact Transformers for Low-Latency Order Book Forecasting

2026-10-02T00:00:00+09:00

Event History Over Scale: Compact Transformers for Low-Latency Limit Order Book Forecasting arXiv · Oct 2, 2026The paper introduces MBOFormer, a 7,203-parameter causal transformer, and MBOFusion, a 14,371-parameter extension with a slow temporal-context branch, for short-horizon price-trend prediction from limit order books. Both models use market-by-order (level-3) histories of order submissions, cancellations, and executions, and are compared with level-2 snapshot-based baselines. The authors report the highest mean macro-F1 in eleven of twelve settings across three markets and four horizons, with sub-millisecond median inference and state dictionaries under 70 kB.

Multi-agent AI as a nested principal-agent problem in private wealth management across Switzerland, Germany and Austria

2026-10-02T00:00:00+09:00

Multi-Agent AI as a Nested Principal-Agent Problem in Private Wealth Management: Mandate Representation and Evidence Control in Switzerland, Germany and Austria arXiv · Oct 2, 2026The paper proposes a model-independent framework that combines nested principal-agent delegation, in which a manager hands work to AI acting for the client, with constrained joint maximisation of client and manager outcomes. Legal duties, mandate requirements and evidence sufficiency determine admissibility, with Switzerland, Germany and Austria as the legal context. Simulations using constructed mandates and public-market data show how weights and omitted terms can change selected services. These are illustrative examples, and professional and field studies are needed to assess oversight and client outcomes.

Axient: A Manifest-Bound Evidence Framework for On-Chain Financial Protocols

2026-10-02T00:00:00+09:00

Axient: Manifest-Bound Evidence for On-Chain Financial Protocols: Seven-Layer Derivation, Correlation, Tamper Rejection, and Reproducible Claim Promotion arXiv · Oct 2, 2026This paper presents an evidence architecture for hybrid on-chain financial protocols, in which a sealed execution manifest binds system identities, roles, schemas and release identity. Seven evidence layers are linked by a common correlation identity and must satisfy a conjunctive rule before a claim is promoted. The authors formalize the evidence graph, prove that copied hashes cannot establish independent derivation, and report a registered local case study showing a reviewable chain from financial assertions to a replay-bound package.

Same Ranking, Different Scale: Checking LLM Judges for Job-Related AI Measurement

2026-10-02T00:00:00+09:00

Right Order, Wrong Scale: Auditing LLM Judges for Occupational AI Measurement arXiv · Oct 2, 2026This paper introduces O*NET-BENCH, an audit suite built from an existing survey of 45,796 worker ratings, to test 33 LLM judge configurations on whether AI responses meet workplace requirements. Many judges agree with human raters on response rankings, yet their estimated acceptance rates range from 3.0% to 97.9%, compared with 61.1% for occupation-matched workers. The authors conclude that ranking agreement alone is insufficient for occupational measurement and that judges should be validated against the acceptance rates and aggregates they will be used to estimate.

Expected Utility Regret Rule: Minimax and Bayes-Optimal Portfolio Selection

2026-10-02T00:00:00+09:00

Expected Utility Regret Rule: Minimax and Bayes Optimal Portfolio Choice arXiv · Oct 2, 2026This paper studies portfolio choice, aiming to recommend portfolios that maximize an investor's expected utility of wealth. It proposes the Expected Utility Regret (EUR) rule, which jointly selects a portfolio class and estimates its weights. In a regular parametric return model, a single EUR rule attains both minimax and Bayes lower bounds without using the prior distribution. Mean-variance and risk-parity portfolios are shown to be special cases of this framework. ★★★★ Notable: The authors propose an EUR rule that, without a prior, reportedly attains both minimax and Bayes regret lower bounds including leading constants, recovering mean-variance and risk-parity portfolios as

Verifying Agent Claims by Evidence, Not Aggregate Scores

2026-10-01T00:00:00+09:00

Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents arXiv · Oct 1, 2026This paper proposes a claim-specific verification audit for modular agents that plan, act, check and refine, scoring the evidence behind each conclusion rather than an overall task score. Each conclusion receives one of four verdicts (supported, unsupported, unresolved, or not evaluated) along with its supporting evidence and scope. The authors apply oracle policies and component replacement to a constrained portfolio-allocation agent in a synthetic market with hidden regimes. The main contribution is the protocol itself, and the empirical findings apply only to the agent and environment studied.

PPO-HRAP: A Risk-Controlled Trading Policy Blending PPO with Regime-Aware Exposure

2026-10-01T00:00:00+09:00

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading arXiv · Oct 1, 2026This paper proposes PPO-HRAP, a reinforcement learning trading method that combines Proximal Policy Optimization with an interpretable, volatility-aware regime prior. Its reward blends portfolio log return, a VIX-conditioned drawdown penalty, target-exposure deviation, and turnover cost. On a held-out 2020-2022 SPY test window, it reports 27.62% total return and reduces maximum drawdown from 34.10% (Buy and Hold) to 18.47%. The authors note high turnover remains, and cross-asset results on QQQ and DIA are single-run only.

Pricing American options under stochastic local volatility and stochastic correlation using RBSDEs

2026-10-01T00:00:00+09:00

On the Pricing of American Options under Stochastic Local Volatility and Stochastic Correlation via the RBSDE Framework arXiv · Oct 1, 2026This paper studies American option pricing in stochastic local volatility (SLV) models extended with stochastic correlation driven by an additional stochastic process. The authors derive the corresponding reflected forward-backward stochastic differential equations (RBSDEs) and solve them with data-driven numerical methods for pricing and hedging. They also provide a convergence analysis of the method and numerical experiments assessing the extended models and the accuracy and efficiency of the approach.

Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep RL Trading Games

2026-10-01T00:00:00+09:00

Beyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games arXiv · Oct 1, 2026This paper studies a two-player, finite-horizon liquidation game in which independent reinforcement learning agents reach costs below the Nash benchmark. The authors identify a learned punishment mechanism: when one agent deviates, the other accelerates liquidation, costing the deviator more than it gains while leaving the punisher's average payoff largely unchanged. Two formal checks show the punishment outweighs the deviation gain and that the change in trading explains the loss, supporting a collusive interpretation.

QuantCode Model: Language Models for Executable Trading Code

2026-09-30T00:00:00+09:00

QuantCode Model: Specializing Language Models for Executable Algorithmic Trading Code arXiv · Sep 30, 2026This paper studies how to adapt large language models to turn natural-language trading strategy descriptions into executable Backtrader code. The authors compare continued pretraining on trading-framework code with supervised fine-tuning on agent-validated request-to-code pairs, evaluated on the 400-task QuantCode-Bench. Supervised fine-tuning gave the larger gain, raising Judge Pass for Qwen3.6-35B-A3B to 58.2%. The authors also report that domain specialization can degrade structured tool-calling ability, which targeted recovery training partly restores.|

Rank Collapse of the Predictor Jacobian in Decision-Focused Learning

2026-09-30T00:00:00+09:00

Jacobian Rank Collapse in Decision-Focused Learning arXiv · Sep 30, 2026This paper studies how a rank restriction in the predictor Jacobian limits the parameter-update directions available in decision-focused learning (DFL). Rank-one Jacobians make per-example gradients nearly collinear, and the authors show that such local statements do not imply common minimizers or collinear batch updates. In experiments, DFL gains over MSE stayed below 1.8% in 38 equity configurations, while full-capacity SPO+ reduced regret in shortest-path and knapsack tasks, with only the knapsack result surviving multiple-comparison correction.

Say, Echo, Do: Strategic Narratives and Revealed Positioning in Financial Markets

2026-09-30T00:00:00+09:00

Say, Echo, Do: Strategic Narratives and Revealed Positioning in Financial Markets arXiv · Sep 30, 2026This paper studies how institutional statements (Say), media repetition (Echo), and actual trading positions (Do) relate in financial markets, asking when text signals should be followed or faded. A linear-quadratic model identifies when an informed institution talks an asset down while buying it. The authors propose methods to detect echo articles, learn return-aligned embeddings, and measure who moved first. In simulated markets with known ground truth, echo sentiment predicted returns with a significantly negative sign in all 29 markets.

A Generalized Langevin Model of Latent Liquidity and Concave Price Impact

2026-09-30T00:00:00+09:00

A Generalized Langevin Model of Latent Liquidity and Concave Price Impact arXiv · Sep 30, 2026The paper models market impact as the response to submitted order flow net of counterflow from latent traders, who become active when price moves exceed their individual thresholds. Order flow depletes this latent pool, whose recovery follows a generalized Langevin equation with memory over several time scales. For an undepleted pool, the model yields an intermediate square-root regime between linear small- and large-order limits without assuming a square-root law directly. Numerical experiments show that depletion narrows this regime, and calibration to market data is deferred to a companion paper.

Information Games: How Rivalry Reshapes Firm Positioning in Language-Model Space

2026-09-30T00:00:00+09:00

Information Games: Strategic Crowding and Firm Repositioning in Language-Model Space arXiv · Sep 30, 2026The paper introduces ESCAPE, a rational-share game in which firms with different capability costs position themselves in a language-model representation space, and proves a unique equilibrium. It derives an exact reallocation formula separating opportunity effects from crowding effects, showing that competition need not push firms apart. Rivalry mainly changes how differently firms respond to the same opportunity shift. Corporate news data show that peer structure is persistent but changes over time.

From Intraday Orderbook to Imbalance Price: How Cross-Market Trading Shapes Balancing Prices

2026-09-30T00:00:00+09:00

From Intraday Orderbook to Imbalance Price: Understanding Cross-Market Interaction arXiv · Sep 30, 2026As variable renewables make power scheduling more uncertain, this paper uses probabilistic modeling to study how intraday orderbook information reflects later imbalance price formation in Germany and Austria. It compares orderbook representations such as VWAP and last mid price, including neighboring products and products from the neighboring country. The authors find VWAP with neighboring products performs best in both countries, while adding fundamental features reduces test loss in Germany but increases it in Austria.

Det-MASO: Diversity-Regularized Multi-Agent Sequential Optimization for Factor Generation

2026-10-26T00:00:00+09:00

Det-MASO: Diversity-Regularized Multi-Agent Sequential Optimization for Factor Generation arXiv · Oct 26, 2026This paper proposes Det-MASO, a framework for generating alpha factors in quantitative investing. It adds a differentiable log-determinant penalty on the inter-agent correlation matrix to the joint objective, discouraging agents from producing similar outputs while keeping computational cost linear in the number of assets. The authors report that the method outperforms baselines and improves the Sharpe ratio by 0.33.

Do round-number prices act as barriers? Order book study of liquidity and price departures

2026-10-26T00:00:00+09:00

Do Round Numbers Hold Prices? Occupation Densities and Depth-Conditioned Hazards in the Limit Order Book arXiv · Oct 26, 2026Prices often cluster at round numbers, which is sometimes read as evidence those levels act as barriers. This paper uses limit order book data and a tick-grid framework to separate barrier effects from a mechanical explanation, where deeper resting liquidity makes prices slower to leave. Analyzing five Nasdaq stocks throughout 2025, it finds round levels attract more liquidity but do not show longer depth-adjusted durations. Intel and SoFi show directional effects, with departures biased away from round levels.

Finite bid-ask spread from replenishment placed away from the quote

2026-10-06T00:00:00+09:00

A Finite Bid--Ask Spread from Replenishment Displaced from the Quote arXiv · Oct 6, 2026This paper gives an analytic account of a finite bid-ask spread in a two-field reaction-diffusion order book model that tracks bid and ask densities over operational time. A market-making source withholds order placement over a finite interval around the reaction boundary, representing replenishment displaced from the quote. Under a quasistationary, weak-overlap approximation, the quoted spread is the sub-threshold core of the placement-free interval. Market orders remove density at the quote, while replenishment arrives outside it and must diffuse back, producing a delayed and attenuated quote-level response.

A functional representation of credit behavior for default probability modeling

2026-10-06T00:00:00+09:00

A Functional Representation of Credit Behavior for Probability of Default Modeling arXiv · Oct 6, 2026This paper proposes a framework that models probability of default using functional data analysis, representing credit variables as continuous functions. It shows that commonly used variables such as available funds, utilization rate, and overdraft can be derived from three time-observed quantities: account type, account balance, and credit limit. A relative time framework aligns recurring banking-day cycles across borrowers, and the approach is evaluated with functional logistic regression. The model reportedly matches XGBoost's predictive performance while outperforming standard logistic regression and retaining the interpretability of linear models.

Ex ante and scenario-conditional value of information in dynamic portfolio choice

2026-10-10T00:00:00+09:00

Ex Ante and Scenario-Conditional Value of Information in Dynamic Portfolio Choice arXiv · Oct 10, 2026This paper studies portfolio decisions under uncertainty about expected returns, where investors update beliefs as new information arrives. It introduces scenario-conditional expected utility and value of information, comparing a fixed-belief investor, a price observer, and an attentive investor with limited attention capacity in a finite-horizon Gaussian model. Ex ante, learning and attention are valuable, but scenario-conditionally learning brings both a correction benefit and a fluctuation cost, so the ranking can reverse.

Tail instability and scale control in neural network models that generate Treasury yield scenarios

2026-10-10T00:00:00+09:00

Tail Instability and Scale Control in Conditional Normalizing Flows for Treasury Yield-Curve Scenarios arXiv · Oct 10, 2026This paper compares two conditional normalizing flow models that generate scenarios for daily changes in eleven Treasury yield maturities. On a new 60-date window, the original model produced extreme samples with an absolute standardized coordinate of 34.3 million, while a stabilized Gaussian-base version reached 14.72. The authors find that numerical stability did not translate into better prediction, as historical simulation scored better on distributional measures and coverage on the new window.

Diffusive Market Impact: A Consistent Microfoundation

2026-10-08T00:00:00+09:00

Diffusive Market Impact: A Consistent Microfoundation arXiv · Oct 8, 2026This paper provides a microfoundation for diffusive market impact, which explains the square-root law of market impact and its shift to a linear regime at low trading rates. In a model with one trader and many arbitrageurs who progressively eliminate predictable returns, impact returns become white. The authors also discuss the economic meaning of the impact phase and find that under a biexponential trade flow, impact decline falls within a 50%–60% band.

Realized Tail-Risk Expectile Models for Stable, Accurate VaR and ES Forecasts

2026-10-09T00:00:00+09:00

Realized Tail-Risk Conditional Autoregressive Expectile Models: Accurate and Stable Forecasting Performance arXiv · Oct 9, 2026This paper proposes a realized tail-risk conditional autoregressive expectile (RTCARE) model for jointly forecasting value-at-risk (VaR) and expected shortfall (ES). It adds realized tail measures, such as realized quantile and realized ES built from intraday high-frequency returns, to the dynamics and measurement equations of the conditional expectile. Parameters are estimated by Markov chain Monte Carlo, and forecasts are assessed in numerical examples and an empirical study using U.S. stock returns. The RTCARE models perform competitively and often better than existing alternatives across the tail levels considered, with stable results across Monte Carlo replications and rolling forecast evaluations.

Adding transaction-text behavioural features to credit risk scorecards

2026-10-09T00:00:00+09:00

Enhancing credit risk scorecards with transactional-based behavioural features arXiv · Oct 9, 2026This paper examines whether payment narratives in transaction data can serve as an alternative source for credit risk modelling. Using natural language processing and regular expressions, the authors convert unstructured transaction descriptions into interpretable spending categories with a newly developed classification algorithm. Adding these features to a scorecard significantly increased its Gini coefficient compared with a scorecard without them.

Entropy-regularized dynamic portfolio selection under no-shorting constraints

2026-10-09T00:00:00+09:00

Entropy Regularization under No-Shorting: Structure Preservation and Convexity in Dynamic Mean-Variance Portfolio Selection arXiv · Oct 9, 2026This paper studies entropy regularization for discrete-time dynamic mean-variance portfolio selection with no-shorting constraints. It introduces a state-dependent temperature matched to the problem's homogeneity, which preserves the piecewise-quadratic value function and finite-dimensional Bellman recursion. The authors prove that a sufficiently small common temperature keeps the Bellman structure and convexity over the whole finite horizon, and the original solution is recovered as the temperature vanishes.

Using a supervised neural network to initialize reinforcement learning for timing exits from SPX short-straddle options

2026-10-09T00:00:00+09:00

MLP-Initialized Reinforcement Learning for Exit Timing in SPX Short-Straddle Portfolios arXiv · Oct 9, 2026This paper studies when to exit SPX short-straddle option portfolios using two stages. A multilayer perceptron is first trained on ex-post optimal hold or exit labels from historical data, then used to initialize reinforcement-learning actors fine-tuned with a risk-aware trading reward. Out-of-sample tests over 2024 to 2026 show the supervised MLP substantially outperforms passive holding and fixed-time or profit-and-loss exit rules.

FLEX Options and Market Outcomes: How Customized Exchange-Traded Options Affect Regular Options and Stocks

2026-10-09T00:00:00+09:00

FOMO: FLEX Options and Market Outcomes arXiv · Oct 9, 2026This paper examines whether activity in FLEX options, exchange-cleared contracts with customizable strikes, expirations, exercise styles and settlement terms, leaves measurable effects on regular listed options and underlying equities. Using data from March 2025 to March 2026 covering 41 underlyings and 53 event dates, the authors find a significant post-event rise in listed-option open interest and show FLEX activity concentrates in high implied-volatility environments. They conclude FLEX activity mainly serves as an observable marker of institutional volatility demand and open-interest positioning rather than a predictor of stock returns.

Funding Debt Relief: How Intermediary Liquidity Shapes Mortgage Buyouts

2026-10-09T00:00:00+09:00

The Financing of Debt Relief: Intermediary Liquidity and the Value of Borrower Information arXiv · Oct 9, 2026This paper studies how intermediary liquidity affects mortgage resolution, using 1.9 million Ginnie Mae buyout options from 2019 to 2022. When funding markets froze in 2020, nonbank issuers repurchased far fewer delinquent loans than depositories servicing loans in the same pools, and shifted away from large balances. About $19 billion of repurchases were delayed, and most were completed later.

Multi-period mean-expectile portfolio optimization under Wasserstein ambiguity, with reformulation and ground-metric effects

2026-10-08T00:00:00+09:00

Multi-period Mean-Expectile Portfolio Optimization under Wasserstein Ambiguity: Reformulation, Degeneracy and the Role of the Ground Metric arXiv · Oct 8, 2026This paper studies multi-period portfolio optimization using expectiles as the risk measure under Wasserstein ambiguity sets. The authors derive an envelope theorem that characterizes the worst-case expectile as the unique root of a worst-case expectation, enabling standard Wasserstein duality and a reformulation into four parametric linear programs. Experiments on 90 FTSE constituents over 3,341 out-of-sample trading days show the expectile model outperforms a matched CVaR model in all nine parameter cells.

Weighted selection from elliptical distributions: a stochastic representation for portfolio separation

2026-10-08T00:00:00+09:00

Weighted selection from elliptical distributions: a stochastic representation and an application to portfolio separation arXiv · Oct 8, 2026The paper represents weighted-selection elliptical distributions as an affine combination of q selection variables plus an elliptical term whose direction alone is independent. This representation suffices for q+2 fund separation via first-order stochastic dominance. It relaxes the three-fund assumptions of Simaan (1993).

A Hawkes-based microfoundation for a multitype inverse-Gaussian random clock

2026-10-08T00:00:00+09:00

A Hawkes Microfoundation for Multitype Inverse Gaussian Subordinators arXiv · Oct 8, 2026The paper derives a multitype inverse-Gaussian subordinator as the limit of event counts and integrated intensities of nearly critical multivariate linear Hawkes processes. The limit is a multivariate pure-jump subordinator whose dependence comes from microscopic cross-excitation. In the diagonal case it reduces to independent classical inverse-Gaussian subordinators. ★★★★ Notable: The authors give an event-level Hawkes microfoundation showing joint convergence of counts and integrated intensities to a multitype inverse-Gaussian subordinator, explain the mechanism via clusters b

Linking Stochastic Differential Equations to a Hierarchy of Generalized Beta Distributions

2026-10-08T00:00:00+09:00

From a Hierarchy of Stochastic Differential Equations to a Hierarchy of Generalized Beta Distributions arXiv · Oct 8, 2026The paper introduces a mean-reverting stochastic differential equation with a three-component noise term that generates a hierarchy of stationary distributions. The top level is a modified-Beta distribution, and its one- and two-parameter reductions yield compact-support, power-law, and exponential-type families. The authors build two generalizations, applying a power transformation either before or after obtaining the stationary hierarchy, and show these routes agree on some lower branches but generally differ at the top level. They derive density and distribution functions for both routes and compare them with the traditional Generalized Beta framework.

A study revisiting Bonart's explanation of the square-root impact law

2026-10-07T00:00:00+09:00

On Bonart's interpretation of the Square-Root Impact Law arXiv · Oct 7, 2026This paper examines theoretical explanations of the square-root impact law, which links price impact to the square root of traded volume. The authors reformulate Bonart's argument based on diffusive prices and white increments, emphasizing that the market must whiten each participant's stationary stream of metaorders rather than isolated ones. They build a multi-agent propagator model and note that its falsifiable prediction, that isolated metaorders should not follow the law, seems at odds with known empirical data.

Robust portfolio risk management using CVaR with a reward-penalty mechanism

2026-10-07T00:00:00+09:00

Conditional value-at-risk under reward-penalty mechanism with applications to robust portfolio management arXiv · Oct 7, 2026This paper proposes robust portfolio selection models that add a reward-penalty mechanism to portfolio management, assuming the joint loss distribution of risky assets is uncertain but lies within a multivariate distribution set. It derives an explicit closed-form expression for the worst-case conditional value-at-risk and uses it to find allocations that minimize this worst-case CVaR. Experiments with real market data suggest the models can outperform several existing related models, and that combining downside risk with portfolio loss can improve risk management and returns. The results also show a trade-off between raising expected return and controlling worst-case CVaR.

Exponential investors facing weakly mean-reverting prices

2026-10-07T00:00:00+09:00

Exponential investors with weakly mean-reverting prices arXiv · Oct 7, 2026This paper studies a continuous-time market where asset prices show weak (sublinear) mean reversion and a nonzero drift. For an investor maximizing expected exponential utility, the authors show the certainty equivalent grows as O(T^{2β+1}), where 0<β<1 is the mean-reversion strength. An explicit asymptotically optimal strategy is also provided.

Convex Order in Higher Dimensions: Projection Tests, Counterexamples, and Gaussian Mixtures

2026-10-06T00:00:00+09:00

Convex Order Beyond Dimension One: Projection Tests, Counterexamples and Gaussian Mixtures arXiv · Oct 6, 2026This paper studies convex order, a way to compare random vectors with the same mean, in dimensions greater than one. It shows that checking all one-dimensional projections is not sufficient in general and builds radial counterexamples in every dimension at least two. The authors prove that projection tests do suffice for linear transformations of a common radially distributed vector, including centred Gaussian distributions. They also settle when a centred Gaussian is dominated by a mixture of two centred Gaussians, showing this holds exactly when all one-dimensional projections are ordered. ★★★★ Notable: The authors show that projection tests are necessary and sufficient for multivariate convex order between a centred Gaussian and a two-component centred Gaussian mixture, and construct radial countere

Continuous Latent Stress Spectrum in Limit Order Book Dynamics

2026-10-05T00:00:00+09:00

Latent Continuum of Regimes in Limit Order Book Dynamics arXiv · Oct 5, 2026The paper analyses high-frequency limit order book data for EURO STOXX 50 index futures over 987 trading days from 2022 to 2025, testing whether market states form distinct regimes or a continuum discretised into apparent regimes. It finds an effective state dimension of about 1.4, with one dominant latent coordinate explaining 83.66% to 85.27% of covariance-state variation and tracking VSTOXX without regime labels. Fine-scale continuous information remains predictive, with forecasting peaking near ten minutes and a maximum pooled out-of-sample R2 of 0.7483. ★★★★ Notable: Using Riemannian geometry, the authors argue limit-order-book states form a continuum rather than discrete regimes; across 987 trading days and 17 methods, the dominant coordinate explains 83.66%–85.2

Error bounds for option pricing under volatility uncertainty

2026-10-06T00:00:00+09:00

A posteriori error bounds for the Uncertain Volatility Model arXiv · Oct 6, 2026The paper develops a posteriori primal-dual bounds for numerical approximations of European option prices in the Uncertain Volatility Model. A lower bound comes from a feedback control induced by the Hessian of a smooth candidate value function, while an upper bound comes from a dual representation based on a matrix-valued Gamma field. The authors also quantify discretization errors and test candidates from policy-gradient and physics-informed neural networks. The experiments indicate that second-order derivative accuracy is important for tight bounds.

Measuring Trade Direction on a Prediction Market: Settlement-Based Validation and Trading-Cost Measurement on Polymarket

2026-10-05T00:00:00+09:00

Measuring Trade Direction in a Prediction Market: Settlement Ground Truth and Trading-Cost Measurement on Polymarket arXiv · Oct 5, 2026This paper validates the trade-direction labels of Polymarket's public trade prints against the taker leg of on-chain settlement records. It compares Lee-Ready, tick-test and retrospective bulk volume classification, and shows how sign errors shift measured effective spread, realised spread and midpoint impact. The authors caution that these delivered-quote accounting measures do not identify causal impact or private information.

Comparing run-off triangles and regression for estimating credit card loss given default

2026-10-05T00:00:00+09:00

Comparing two approaches for modelling the loss given default of credit cards: Run-off triangles vs regression arXiv · Oct 5, 2026The paper benchmarks run-off triangles, a common banking method, against a two-stage regression model for estimating credit card loss given default (LGD). The regression approach can reproduce the typical U-shaped LGD distribution, which run-off triangles cannot. Aggregated regression estimates also track empirical loss rates over time much more closely. The authors suggest the regression approach is likely better under IFRS 9, which prioritises accuracy.

A Dirichlet mixed-membership model for exact multivariate credibility in insurance pricing

2026-10-05T00:00:00+09:00

A Dirichlet Mixed-Membership Model for Exact Multivariate Distributional Credibility arXiv · Oct 5, 2026This paper proposes a Dirichlet mixed-membership model (DMMM) that represents policyholder risk as a stable composition over latent risk classes. Baseline characteristics set the a priori assessment, and repeated multivariate experience updates the a posteriori assessment. The posterior predictive distribution is written exactly as a convex combination of portfolio and experience components, extending classical credibility beyond the conditional mean. The framework is studied through a simulation and a vehicle telematics application.

Speculative Imbalance Positions by Non-Physical Traders and Their Effect on Great Britain's Electricity Balancing

2026-10-20T00:00:00+09:00

Speculative Imbalance Positions by Non-Physical Traders: Impacts on Electricity System Balancing in Great Britain arXiv · Oct 20, 2026This paper provides the first empirical analysis of speculative imbalance positions taken by non-physical traders in Great Britain's electricity system from 2021 to 2024. Using counterfactual simulations that remove the aggregate non-physical trader imbalance, the authors measure effects on balancing volumes, balancing costs and supplier imbalance costs. They find these positions reduced balancing costs by 5.9 to 6.7 percent and supplier imbalance costs by 65 to 75 percent, suggesting consumer bills would have been higher without them.

AlphaRLM: Training Language Models to Build Market Factors from Finance Literature

2026-10-10T00:00:00+09:00

AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery arXiv · Oct 10, 2026This paper introduces AlphaRLM, a reinforcement-learning framework that trains LLMs to construct executable symbolic trading factors directly from financial literature, without requiring input factor expressions. It combines Literature-Guided Operator Assembly (LEGO) with rewards for executable validity and grouped-return separation. Experiments on CSI 300, CSI 1000, and S&P 500 report that AlphaRLM outperforms several factor-mining and LLM-based baselines in annualized excess return.

Too Late to Prune: How Timing of Generative AI Access Affects Team Creative Choices

2026-10-11T00:00:00+09:00

Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions arXiv · Oct 11, 2026This paper examines when generative AI's options are most valuable to creative teams. In a randomized experiment, 148 three-person teams used the same language model at different stages of a timed product-design task. Moving the same 15 minutes of access from the search stage to the selection stage lowered expert-rated creativity by 0.62 standard deviations, with the difference located at the screening step.

Exact phase-type point processes for measuring and predicting event streams

2026-10-10T00:00:00+09:00

Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams arXiv · Oct 10, 2026The paper proposes a point-process framework built on a phase-type (Erlang-chain) state space, giving exact likelihoods for both measuring market reactions and predicting the next event. Applied to six markets around 670 scheduled macroeconomic releases from 2022 to 2026, the measurement model finds that the direct reaction to CPI, payrolls, PPI and the FOMC statement is absorbed in about a second, while the market's own echoes stretch half the response to 16-137 seconds. The prediction model, EPT-X, is reported to achieve the best test likelihood on several event-stream datasets and to match the best published model on StackOverflow. ★★★★ Notable: The authors propose a multiscale phase-type Hawkes process with a concave likelihood, a duality-gap certificate for the global optimum, and closed-form shock responses with a stability criterion, appl

Early warning of pension sponsor distress using public filings and dynamic investment limits

2026-10-09T00:00:00+09:00

Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps arXiv · Oct 9, 2026The paper presents a transparent early-warning pipeline built on public data, centred on a Sponsor Vulnerability Index (SVI) that uses SEC EDGAR balance-sheet fundamentals and two macro series. On 36 U.S. sponsors (1,418 firm-quarters), it reaches an out-of-fold AUC-ROC of 0.887 under leave-one-firm-out validation. It also adds a stress-augmented Monte Carlo VaR and a self-investment cap that falls as the SVI signals distress, and reports the method's failure modes.

CorrFlow: Exact, permutation-equivariant generation of valid correlation matrices via flow matching

2026-10-09T00:00:00+09:00

CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope arXiv · Oct 9, 2026This paper presents CorrFlow, a method for generating correlation matrices that separates keeping outputs valid from learning the data distribution. A generalized Fisher transform maps correlation matrices to unconstrained coordinates, where a graph flow-matching network learns the empirical distribution and is permutation equivariant. On 80×80 Taiwan equity matrices, CorrFlow produced 100% valid draws without repair and a lower feature sliced-Wasserstein distance than CorrGAN and image diffusion. The authors note the results do not establish superiority over fully tuned image models or isolate each component's contribution.

Selecting Sparse Features for Financial Regression Using Monte Carlo Tree Search

2026-10-09T00:00:00+09:00

L₀ Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression arXiv · Oct 9, 2026This paper proposes a feature selection method for high-dimensional financial regression that uses Monte Carlo Tree Search with Generalized Nested Rollout Policy Adaptation. Candidate feature subsets are scored by out-of-sample validation performance rather than training error. On a task explaining weekly NASDAQ-100 returns from 1,072 engineered features, the method reaches an out-of-sample R² of 0.6961, compared with 0.6313 for an optimized Lasso baseline.

How AI shopping assistants affect retail revenue and product range choices

2026-10-09T00:00:00+09:00

AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization arXiv · Oct 9, 2026This paper studies how a retailer should choose its product range when AI shopping assistants shape which items customers consider. Ignoring AI mediation is shown to cause average revenue losses of 2.52% to 11.37% relative to the Plackett-Luce optimum, while a simple deterministic approximation based on fitted AI utility orderings loses at most 0.53% on average. The authors also prove that, for a fixed number of customer segments, the optimization problem admits a fully polynomial-time approximation scheme.

A distributional framework for characterizing and managing risk in large language models by measuring how their repeated responses vary.

2026-10-09T00:00:00+09:00

A Distributional Framework for Behavioral Characterization and Risk Management of Large Language Models arXiv · Oct 9, 2026The paper notes that large language models are inherently stochastic, so repeated runs under identical conditions can yield different responses. It proposes a framework applicable across the model lifecycle, estimating empirical response distributions via Monte Carlo simulation and studying how they shift under semantic interventions. The authors illustrate it with borrower classification and music album recommendation tasks, linking it to model risk management by measuring robustness and control effectiveness. Because it relies only on observed responses, it can also be applied to black-box models accessed through commercial APIs.

Market Impact Estimation via Causal Foundation Models: A causal simulator trained on synthetic markets estimates how large trades causally move prices.

2026-10-08T00:00:00+09:00

Market Impact Estimation via Causal Foundation Models arXiv · Oct 8, 2026The paper presents a proof-of-concept Causal Market Simulator, a continuous-time causal PFN trained on a synthetic market where hidden informational flow confounds flow and price. On this prior, the naive estimator's bias follows a closed form (up to 68% overestimation), and the model tracks the unbiased oracle at high confounding. The authors report zero-shot responses to metaorders in an ABIDES order book and to flow bursts in crypto perpetuals and Nasdaq equities, consistent with but not proof of causal transfer. A recurrent state-space variant better captures impact accruing over execution duration.

Forecasting Resale Prices of Used Phones and Laptops: A Benchmark of Statistical and Deep Learning Models

2026-10-08T00:00:00+09:00

Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark arXiv · Oct 8, 2026This paper presents the first multi-horizon benchmark for predicting resale prices of used smartphones and laptops. Using daily listing data from Polish online marketplaces (January 2022 to March 2025), it compares eleven statistical and deep learning models across horizons from 1 to 365 days. N-BEATS achieves the lowest error beyond 30 days, with a MAPE of 8.51% at 365 days versus 14.94% for the best statistical baseline. At 1 to 7 days, all models perform similarly and a naive baseline remains competitive.

SOTA: Option-Implied Return Distribution-Guided Stock Options Trading Agents

2026-10-08T00:00:00+09:00

SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions arXiv · Oct 8, 2026The paper presents SOTA (Stock Options Trading Agents), an agentic framework that selects structured option strategies by abstracting the large contract universe into strategy-level decisions, with deterministic resolvers handling implementation. It post-trains Qwen3.8-27B using supervised fine-tuning followed by reinforcement learning. Evaluated on options for nine large-cap U.S. equities and SPY against rule-based and machine-learning selectors, SOTA reports an 18.3% six-month out-of-sample return, a Sharpe ratio of 1.60, and an 8.96% maximum drawdown. The authors also find news plays an asymmetric role: it improves frontier-teacher trajectories, but retaining news during reinforcement learning lowers out-of-sample return to -2.7%.

OOM-RL II: Reality as Oracle, Not Debugger — Provenance-Constrained Diagnosis in Evolving Agent-Engineered Systems

2026-10-08T00:00:00+09:00

OOM-RL II: Reality Is an Oracle, Not a Debugger Provenance-Constrained Diagnosis in Continually Evolving Agent-Engineered Systems arXiv · Oct 8, 2026The paper examines a human-directed, agent-engineered quantitative trading system to show what external outcome feedback can and cannot establish. Over one year the account gained and beat a broad market index, yet annual alpha was not statistically distinguishable from zero under the main retrospective specification, and engineering records show changes during the period with incomplete recommendation-to-runtime binding. The authors introduce an outcome–diagnosis gap, distinguish frozen instances, pre-specified adaptive procedures, and ad-hoc development, and propose a prospective production-binding protocol that has not yet been prospectively validated.

DSTNet: A Causal Multi-Horizon Forecasting Model for Financial Time Series

2026-10-07T00:00:00+09:00

DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting arXiv · Oct 7, 2026DSTNet is a forecasting model that keeps the recent evolution of wavelet filter-bank magnitudes from seven technical indicators over a twenty-day lookback, using only causal information. It outputs one, three, five, and ten day forecasts in one pass. Tested on seven equity indices and gold against nine learned baselines and a random-walk benchmark, it beats persistence only at longer horizons in MAE and MAPE, while one-day results are inconclusive. A downstream allocation test does not show an equity-timing advantage.

Residual Learning for Deeper Neural Asset Pricing Models

2026-10-07T00:00:00+09:00

Residual Learning in Empirical Asset Pricing arXiv · Oct 7, 2026This paper applies residual learning to neural network asset pricing models, preserving and refining shallow benchmark models so that the networks can be made deeper. Out of sample, value-weighted long-short portfolios built from deep residual models achieve a Sharpe ratio of 2.07, versus 1.92 for the corresponding shallow models and 0.89 for deep feedforward models. The authors conclude that model depth provides additional economic value in asset pricing and that the approach can scale such models.

FinVector-Market-4B: a controlled test of LoRA tuning for structured finance tasks

2026-10-06T00:00:00+09:00

FinVector-Market-4B: A Controlled Study of LoRA Adaptation for Structured Financial Tasks arXiv · Oct 6, 2026This paper fine-tunes the Qwen3.5-4B model with rank-16 LoRA on 22,000 examples for structured financial tasks, then compares it with the base model on a 600-example benchmark. Supplying a JSON schema alone raised the base model's JSON validity from 0% to 91.3%. Under matched explicit prompting, several task scores also improved. The authors note that the reported macro-F1 drop reflects a changed label set, and that filing overlap and other issues limit the generalization claims.

Learned Monotone Recurrent Features in Regulated Credit Scoring

2026-10-06T00:00:00+09:00

Learned Monotone Recurrent Features in Governed Credit Scoring: The Price of the Frame and the Necessity of Macro Conditioning arXiv · Oct 6, 2026This paper examines whether learned monotone recurrent features add value to regulated credit scoring, testing five production-scale credit datasets. The authors extend the monotonicity guarantee with proofs to vector inputs and macro-conditioned decay gates. They find that learned feature value rises with governance strictness, and that conditioning the recurrence on macroeconomic series, rather than using them as inputs, yields crisis-cohort AUC gains.

Adversarial training for deep hedging when markets shift over time

2026-10-06T00:00:00+09:00

Adversarial Training for Deep Hedging in Nonstationary Markets arXiv · Oct 6, 2026This paper proposes WRAP, a drift-aware adversarial training framework for deep hedging based on a two-budget distributionally robust optimization formulation. It lets an adversary reweight observed trading trajectories under a φ-divergence constraint and perturb their paths under an optimal-transport constraint, to handle mismatch between historical training data and future market conditions. Experiments on stationary and nonstationary Heston dynamics and a generalized affine diffusion (GAD) show that reweighting and transport each help, with joint adversarial training giving the largest gains under nonstationarity.

Expert-verified AI study materials and who gains from them in a university course

2026-10-05T00:00:00+09:00

Verified, not generated: expert-verified AI study materials and the distribution of learning gains in a university course arXiv · Oct 5, 2026This paper studies a compulsory first-year economics course in which one cohort received AI-generated podcasts, FAQs and quiz-based study guides checked by a graduate teaching assistant. A difference-in-differences analysis of 340 examination marks links access to a 2.34-mark average advantage on a 50-mark component. Most of the average effect originates in the bottom quintile, and the authors argue that evaluations reporting only mean effects cannot show whether the intended students benefit.

Synthetic cultural agents built from group-level preference anchors

2026-10-06T00:00:00+09:00

Synthetic Cultural Agents from Aggregate Anchors arXiv · Oct 6, 2026The paper proposes building synthetic survey respondents by mapping the signs of six Global Preferences Survey coordinates to group-specific choice policies, each fitted as a parameter-efficient adapter with Direct Preference Optimization. On candidate World Values Survey items, the adapters recover the imposed labels and separate the two GPS-sign groups on a sixteen-country development panel. However, agreement between the adapters and human WVS responses remains unresolved.

Who Keeps the Gains from AI Assistants? Seller Responses and Non-Users in a Simulated Rental Market

2026-10-05T00:00:00+09:00

Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation arXiv · Oct 5, 2026This paper uses an agent-based rental market simulation with language models to study how sellers' responses shape the gains from personal AI assistants and their effects on consumers without one. With frozen sellers, executing assistants cut adopters' spending by 13.7 USD per renter-day, and adaptation recovered about a third. The predicted burden on non-users did not materialise, with a mean spending change near zero. The authors argue assistants should be evaluated at the market level. ★★★★ Notable: Agent-based language-model market simulation shows seller adaptation claws back about a third of assistant gains, and the unassisted burden forecast fails.

Combinatorial Arbitrage in Prediction Markets: a study of arbitrage across logically related prediction-market contracts

2026-10-16T00:00:00+09:00

Combinatorial Arbitrage in Prediction Markets arXiv · Oct 16, 2026The paper develops a framework to identify and quantify arbitrage among logically related prediction-market contracts, proving that prices outside the convex hull of admissible payoff vectors admit arbitrage and using integer programming to find optimal arbitrage portfolios. Using venue metadata and semantic analysis of resolution criteria, the authors recover 1.9 million logical relations covering more than 255,000 contracts. Replaying full-depth Polymarket order books, they find that an ideal arbitrageur earns a risk-free return of 3.5% a week on settled portfolios after spreads and fees. Opportunities persist longer and attract less observed participation where relation discovery and execution are more costly.

Dynamic Programming for Value-at-Risk Decision Processes in Continuous Spaces

2026-10-17T00:00:00+09:00

Dynamic Programming for Value-at-Risk Markov Decision Processes in Continuous Spaces arXiv · Oct 17, 2026This paper studies Markov decision processes whose objective is Value-at-Risk (VaR), a quantile-based risk measure. Earlier work gave dynamic programming equations for finite state and action spaces, but the general case was unresolved. The authors show these equations are valid in continuous spaces, for both finite and infinite horizons. They apply the framework to an optimal execution problem with a VaR objective and characterize the optimal dynamic strategies.

GROMM: A risk-neutral option market model that fits European option prices without arbitrage.

2026-10-12T00:00:00+09:00

GROMM: Generative Risk-neutral Option Market Models arXiv · Oct 12, 2026The paper presents a statically and dynamically arbitrage-free model of European option markets, built on the SANOS framework to smoothly interpolate option prices, and proves its dynamics have a unique strong solution. It serves as a stochastic local volatility representation of an option price surface rather than a pricing model. Tested on S&P Index option data from January 2022 to August 2025, the model's initial state fits within bid/ask for over 95% of relevant prices. The authors also propose a range of BHW tests for checking the absence of arbitrage.

Dynamic portfolio selection using range VaR and log returns

2026-10-17T00:00:00+09:00

Dynamic portfolio selection under range value-at-risk with logarithmic returns arXiv · Oct 17, 2026This paper studies continuous-time portfolio selection in which risk is measured by Range Value-at-Risk (RVaR) and performance is evaluated using logarithmic returns. The dynamic problem is reduced to a static quantile problem, and the optimal terminal wealth is characterized through a convex envelope. The RVaR solutions reduce to their VaR and Expected Shortfall counterparts in limiting parameter cases.

Optimal trading filters and how much predictive signals are worth in trading

2026-10-17T00:00:00+09:00

Optimal Trading Filters and the Value of Predictive Signals arXiv · Oct 17, 2026The paper solves a quadratic trading problem with transient price impact using Wiener-Hopf factorization. The optimal policy is expressed as a conditional forecast composed with an explicit causal linear filter. Results are given for exponential resilience and power-law impact decay, and the net value under trading costs is characterized. ★★★★ Notable: Uses Wiener-Hopf factorization to derive optimal trading under transient impact, yielding fractional trading filters and characterizing value with and without trading costs, mainly for quantitative fi

Rachev-Ratio Portfolio Allocation Using TimeGAN-Generated Scenarios: Replacement Versus Supplementation

2026-10-17T00:00:00+09:00

Rachev-Ratio Portfolio Optimization with TimeGAN Scenarios: Replacement versus Supplementation arXiv · Oct 17, 2026This study tests how well scenarios from a TimeGAN model work in portfolio allocation using 12 exchange-traded funds. It compares historical, replacement, and hybrid policies across five training seeds, with delayed execution and transaction costs included. The generated scenarios understate volatility and tail losses and distort dependence between assets, and longer training improves reconstruction loss without improving correlation accuracy. Some hybrid configurations earn higher average net returns, but the confidence intervals for generated-versus-historical differences include zero at the reference block length.

Choosing Trade Duration and Execution Schedule Under Concave Impact and Hurst Scaling

2026-10-17T00:00:00+09:00

From Alpha to Execution: Trading Horizons, Regimes, and Schedules under Concave Impact and Hurst Scaling arXiv · Oct 17, 2026This paper studies order execution under concave power-law market impact and Hurst-scaled price fluctuations. It selects the execution horizon by maximizing the Sharpe ratio of the trade's realized P&L, then derives the execution schedule shape via convex programming. The authors report that an interior optimal horizon exists only under certain sign conditions, and the objective is nearly flat over a wide window. Under Hurst-scaled cost, the schedule is front-loaded and tapers smoothly to zero, saving 10-17% of envelope cost versus a constant-rate (TWAP) schedule.

Modelling Cross-Product Liquidity in Intraday Electricity Markets with Hawkes Processes

2026-10-17T00:00:00+09:00

Cross-product Modelling in Continuous Intraday Electricity Markets via Hawkes Processes arXiv · Oct 17, 2026This paper introduces a multivariate State-Dependent Hawkes Process to model how Quarter-Hourly and Hourly intraday continuous electricity contracts influence each other's liquidity. Trading sessions are split into 'Normal' and 'Stressed' regimes using bid-ask spread, mid-price volatility and queue imbalance. The model is calibrated on level 3 limit order book data from the Dutch EPEX SPOT market for the first month of each quarter of 2024. Results show roughly 95% of orders are reactions to earlier orders, and under stress the leadership of the Quarter-Hourly contract weakens while the cross-contract lag shrinks sharply.

Basis-Only Funding Is Not Dividend Fair: Settlement Design for Crypto Equity Perpetuals

2026-10-16T00:00:00+09:00

Basis-Only Funding Is Not Dividend Fair: Settlement Design in Crypto Equity Perpetuals arXiv · Oct 16, 2026The paper examines perpetual futures on dividend-paying equities listed on crypto venues. It argues that the common premium-index funding mechanism, which sets funding from the basis, cannot replicate shareholder cash flows for traders entering and exiting at arbitrary times, because funding serves both basis control and settlement. A predictable dividend behaves as a measure-theoretic atom, so fairness requires a discrete event-based settlement. Exchange-native data are cited to show one venue posts such a discrete funding payment at ex-dividend dates while a standard venue does not.

Collusion Without Deterrence: Auditing Punishment in Q-Learning Pricing

2026-10-15T00:00:00+09:00

Algorithmic Collusion Without Deterrence: Auditing the Punishment Mechanism in Q-Learning Pricing Agents arXiv · Oct 15, 2026The paper replicates a simulated Bertrand duopoly in which independent Q-learning pricing agents converge to above-competitive prices. Auditing frozen policies, the authors find the reward-punishment explanation does not hold: a single attractor covers most of the state space, and re-pairing agents collapses coordination by 80%. They also report that the one-period undercut is unprofitable in 92% of pairs, but optimal deviations earn 17.5% on average, so collusion persists largely because deviation is never attempted. ★★★★ Notable: Frozen-policy audits question the punishment-mechanism account of algorithmic collusion: one-period undercuts are unprofitable in 92% of pairs, yet dynamic-programming optimal deviations profit 17.5%

Executed and Recoverable Credit: How Action Order Shapes LLM Borrowing in a Simulated Lending Market

2026-10-12T00:00:00+09:00

Requested, Executed and Recoverable Credit: Shared Action Ordering among LLM Borrowers in a Simulated Lending Market arXiv · Oct 12, 2026This paper tests whether the order in which shared actions occur changes how much credit is actually executed in a simulated collateralized lending market with three small open-weight language models. An exact randomization test rejects the claim that common-market net debt is unaffected by complete-order assignment (two-sided p = 2/8,100). All 24 markets increased debt, and no market experienced liquidation or realized bad debt. The authors also report that reference-priced collateral narrows the observed order gap under fixed requests.

Six Gaps Between Backtests and Live Trading in an Intraday System

2026-10-15T00:00:00+09:00

Where Backtests and Live Trading Part Ways: Six Defects from One Intraday System arXiv · Oct 15, 2026The paper reports six defects found by comparing a US equity intraday trading system's live days with its backtest replay. Three stem from data issues, two from code, and one from configuration. For each defect, the authors describe the symptom, error size, cause, and the test or control now used to prevent it. The paper makes no claim about whether the system is profitable.

Valuing American commodity options with a two-factor price model and an efficient numerical scheme

2026-10-15T00:00:00+09:00

Pricing American Commodity Options Under the Schwartz Two-Factor Model Using the Alternating Direction Explicit (ADE) Method arXiv · Oct 15, 2026This paper studies how to value American commodity options under the Schwartz two-factor model. It uses a double-sweep alternating direction explicit (ADE) finite difference method and compares it with Saul'yev's single-sweep variant. The authors derive the governing PDE, map the semi-infinite domain to a rectangle, and set boundary conditions using Fichera theory. The solver is checked against a closed-form European solution and Monte Carlo, then benchmarked against Longstaff-Schwartz Monte Carlo for accuracy and convergence. Following feedback from a commodities trader, the framework was extended to exercise on futures prices and to use seasonal parameters.

Loss-versus-rebalancing with external transaction costs

2026-10-15T00:00:00+09:00

Loss-versus-rebalancing with external proportional transaction costs arXiv · Oct 15, 2026This paper studies liquidity provision in automated market makers when trading on external markets incurs proportional transaction costs. It compares the pool with a self-financing benchmark that holds the same risky inventory and trades at external bid and ask prices. Under continuous prices and pure arbitrage, the two have identical liquidation wealth whenever the pool fee or external spread is positive. The paper also constructs pool designs that implement decreasing inventory schedules and constant no-trade boundaries.

Neural Estimation of Irreversibility in Market Order Book Paths

2026-10-12T00:00:00+09:00

Neural Estimation of Irreversibility in Real and Simulated Limit Order Book Paths arXiv · Oct 12, 2026The paper defines a time irreversibility rate for coarse-grained limit order book paths as the forward-to-reverse log-density ratio per event. It estimates this rate from three weeks of Nasdaq order data using order-invariant and order-sensitive neural networks. Real paths show more irreversibility than a surrogate preserving the one-step transition table, while a calibrated queue-reactive simulator matches the overall rate but shows almost no ordering-based increment. The authors propose the ordering and scale profile of path irreversibility as a benchmark for order book simulators.

Regime Detection in Latent Factor Models: A Bayesian Decision-Theoretic Approach

2026-10-15T00:00:00+09:00

Regime Detection in Latent Factor Models -- A Bayesian Decision-Theoretic Approach arXiv · Oct 15, 2026This paper studies when an agent should act on inferred regime changes in models with latent factors and hidden regimes. It frames online regime detection as a Bayesian decision problem that balances false alarms, detection delay, and misclassification, solved approximately with particle filtering and Monte Carlo dynamic programming. In numerical experiments on Heston and regime-switching Vasicek models, the latent-information procedure outperforms feasible plug-in rules.

Data-driven dynamic portfolio selection using vine copula simulation and reinforcement learning

2026-10-14T00:00:00+09:00

Data-Driven Dynamic Portfolio Selection with Vine Copula Simulation and Reinforcement Learning arXiv · Oct 14, 2026This paper proposes a dynamic portfolio selection method that combines vine copula simulation with reinforcement learning, for an investor with constant relative risk aversion who penalises conditional value-at-risk (CVaR) of losses. A conditional vine model is fitted to historical returns to generate scenarios and paths for training a recurrent portfolio policy, and stability bounds are derived for parameter errors. On a seven-asset panel of US and Chinese equities and gold, an ensemble of ten trained policies reports a 34.13% compound annual growth rate and a Sharpe ratio of 2.31 over 22 monthly holding periods, exceeding six benchmarks.

The Missile and the Toll: Two-Way Flow Through the Strait of Hormuz

2026-10-14T00:00:00+09:00

The Missile and the Toll: Two-Way Flow Through the Strait of Hormuz arXiv · Oct 14, 2026This paper models a Stackelberg escalation game in which an enforcer picks an attack intensity and a toll schedule for the Strait of Hormuz, while ships choose to pay, cross exposed, wait, reroute, or exit. The authors argue that attacks create willingness to pay and tolls capture it. The model predicts asymmetry across trade types. Payments would be clandestine, but its implications can be tested against freight rates, insurance premia, and incident reports.

A GLE-LSV model with state-dependent leverage and colored noise

2026-10-14T00:00:00+09:00

A GLE-LSV model with state-dependent leverage and colored noise arXiv · Oct 14, 2026This paper studies a local stochastic volatility model in which the variance factor follows a generalized Langevin equation with colored noise, and past returns affect variance through a memory kernel with state-dependent amplitudes. It proves existence, uniqueness, and non-explosion for kernels that are finite sums of exponentials. Under structural and nondegeneracy conditions, it shows that an exact reduced Markovian state has a smooth density, so the Dupire conditional expectation can be computed without a stochastic PDE. The paper also covers premiums under the minimal entropy martingale measure, the squared VIX, and a regularized calibration problem with bid-ask constraints.

Risk Measures for the Climate Transition Age: Why Symmetric Risk Budgets Fall Short

2026-10-13T00:00:00+09:00

Risk Measures for the Climate Transition Age: Why Symmetric Risk Budgets are unlikely to mitigate two-dimensionally asymmetric Climate Change risks arXiv · Oct 13, 2026This paper argues that common risk measures such as variance, standard deviation and tracking error penalise outperformance as heavily as underperformance, which conflicts with climate transition investing. The authors propose downside-only alternatives, including semi-variance, standard risk and trailing error. They also suggest specific mandate design changes that would let institutional investors pursue transition opportunities within risk-aware frameworks.

Portfolio decarbonization when carbon and transition information is incomplete

2026-10-13T00:00:00+09:00

Portfolio Decarbonization under Incomplete Carbon and Transition Information arXiv · Oct 13, 2026This paper studies how an investor can dynamically decarbonize a portfolio when firms' carbon intensities and the transition state are only partly observable. The investor maximizes expected utility of terminal wealth minus a penalty on perceived cumulative financed emissions. In a numerical example, incomplete information has limited effects when prior beliefs are well specified, but biased beliefs cause welfare losses and opposite portfolio tilts that affect the chance of exceeding the emissions target differently.

On integrability of local utility maximizing strategies

2026-10-13T00:00:00+09:00

On integrability of local utility maximizing strategies arXiv · Oct 13, 2026This paper studies local utility maximizing strategies in stochastic control, where a local optimizer is derived from a global criterion such as expected utility. The existence of such a local optimizer can be established under very mild conditions, so the key question is when it has good integrability properties. The authors show that the reasonable asymptotic elasticity condition of Kramkov and Schachermayer (1999), applied to the local utility function, plays a central role.

Structured SDEs for Realized GARCH: A Continuous-Time Volatility Model

2026-10-13T00:00:00+09:00

Structured SDEs for Realized GARCH: Continuous-Time Volatility Modelling and Component-Level Analysis arXiv · Oct 13, 2026This paper proposes a continuous-time stochastic volatility model for forecasting next-day integrated variance. It feeds a jointly evolving realized-measure state into latent variance dynamics and adds correlated Brownian innovations linked to returns. Parameters are estimated via simulation-based predictive learning, with Monte Carlo simulation used for forecasts. Across 17 equity indices, the model shows strong out-of-sample performance against several benchmarks, with realized-measure feedback contributing most of the gain and return-linked Brownian dependence adding smaller complementary improvements.

Backtesting Expected Shortfall without VaR forecasts in time-series models

2026-10-13T00:00:00+09:00

E-Backtesting Expected Shortfall without Value-at-Risk Forecasts under Time-series Models arXiv · Oct 13, 2026This paper proposes two e-backtesting methods for Expected Shortfall that use only observed losses and ES forecasts, with no Value-at-Risk forecast required. One targets iid losses, and the other targets time series with an AR-GARCH conditional VaR structure. Under the stated assumptions, both methods control false rejections at any stopping time. Simulations and an application to daily NASDAQ losses compare the methods with VaR-ES e-processes and fixed-sample ES backtests. ★★★★ Notable: The paper proposes sequential e-backtests for Expected Shortfall that use only observed losses and ES forecasts, with no VaR forecast needed, extending the approach to AR-GARCH conditional VaR time se

Designing Independent Variation: Guided Iteration for Price Experiments in Correlated Panels

2026-10-13T00:00:00+09:00

Designing Independent Variation: A Guided Iteration Methodology for Price Experiments in Short, Correlated Panels arXiv · Oct 13, 2026This paper argues that estimation error in short, correlated pricing panels is driven mainly by trajectory dispersion rather than sampling noise, so only independently assigned price variation can resolve the identification shortfall. It proposes a guided iteration method that uses a cyclic price-perturbation scheme under a revenue-at-risk budget to decide how much variation to generate, where, and for how long. The method is applied to Dominick's retail data, with theoretical results and empirical comparisons reported.

Dirichlet sub-mixtures: a new view of multiplicative risk models with Pareto margins

2026-10-13T00:00:00+09:00

Dirichlet sub-mixtures: A new reading of multiplicative background risk models with Pareto margins arXiv · Oct 13, 2026The paper builds a class of multiplicative background risk models with Pareto margins by combining Marshall-Olkin exponential and additive gamma risk factor structures, each including idiosyncratic and systemic background factors. Under an admissibility condition, the joint survival function is shown to be a finite sub-mixture of Pareto survival functions, with Dirichlet-probability weights over orderings that sum to at most one. The authors also study tail behavior, stating that asymptotic tail dependence and exceedance decay rates are governed by the gamma risk factors alone.

The Buffett Effect: How Famous Investors' Stock Disclosures Move Markets

2026-10-13T00:00:00+09:00

The Buffett Effect arXiv · Oct 13, 2026This paper examines whether disclosed new stock positions by recognizable investors trigger larger abnormal returns than similar disclosures from non-recognizable managers. The authors build a recognition index from Wikipedia pageviews and news mentions and compare 24 well-known funds with 24 matched control funds from 2000 to 2025. They find a positive disclosure premium that grows with recognition, and portfolios copying the most recognized investors earn significant alpha lasting months.

4Cast Lab: An Open-Source App for Machine Learning and Statistical Analysis

2026-10-12T00:00:00+09:00

4Cast Lab: Open-Source App for Machine Learning & Statistical Analysis arXiv · Oct 12, 20264Cast Lab is a free, open-source application for machine learning, econometrics and statistical analysis that runs on the user's own computer without installation or programming. Its Forecasting mode trains eight learning algorithms under leakage-safe validation for time-ordered data and compares selected models with alternatives and naive benchmarks via formal tests. Its Estimation mode covers applied econometrics from robust OLS to ARIMA, VAR/VECM and GARCH. The paper describes version 4.6.2 and reports validation of 1,234 numerical results against reference libraries and NIST certified values.

A theory model of household spending that compares with neighbors and resists cutting back, showing how group consumption shapes saving and investment decisions

2026-10-12T00:00:00+09:00

A Mean Field Model of Consumption Inertia and Keeping Up with the Joneses arXiv · Oct 12, 2026This paper studies how households choose consumption and investment when their well-being depends on spending relative to the population's geometric average, with spending cuts hurting more than increases help. The authors show that a mean field equilibrium exists under a weak interaction condition, and in a complete market with common risk it is an exact Nash equilibrium of the finite game. In a baseline example, ignoring inherited spending standards costs up to 17% of wealth, while small errors about the reference group cost less than 0.5%.

From Economic Explanation to Better Machine-Learning Portfolio Performance

2026-10-11T00:00:00+09:00

From Economic Explanation to Performance Improvement in Machine-Learning Portfolios arXiv · Oct 11, 2026This paper proposes a local first-order attribution-propagation framework that links feature exposures in an allocation function to local sensitivities of portfolio performance metrics. The authors relate these attributions to Shapley values defined for portfolio objectives and derive statistical inference for Sharpe-ratio attributions. An attribution-guided exposure intervention can be applied after training without retraining the model. Empirically, the interventions yield statistically significant improvements in net Sharpe ratios across different model classes.

Strategic Origins of Network Structure: Game-Graph Equilibria and Mechanistic Inference

2026-10-18T00:00:00+09:00

Strategic Origins of Network Structure arXiv · Oct 18, 2026The paper introduces the game-graph equilibrium, a stable rest point of a payoff-driven partner-choice dynamic analogous to Nash equilibrium, presenting network structure as the record of strategic interaction. The authors prove that two-type games have exactly four network topologies and generalize this to K^K classes for K-type interactions. They also develop a mechanistic inference method that constrains a block model to a game-theoretic manifold to recover generating dynamics from observed networks, applying it to real networks such as international trade.

Conditional evidence weights for gradient-boosted credit models: estimation, additivity, and adverse action reasons

2026-10-17T00:00:00+09:00

Conditional Weight of Evidence for Gradient-Boosted Credit Models: Estimation, Additivity and Its Relation to Adverse Action Reasons arXiv · Oct 17, 2026The paper defines conditional weight of evidence, which measures how observing a credit characteristic changes default log odds once the rest of an applicant's credit record is known. Its sign shows whether the information is adverse or favorable for that applicant, and its size shows its strength. The authors estimate it with gradient-boosted trees and test accuracy in a bureau-style simulation. The measure is not an adverse action reason.

Where Data Value Comes From: Consumer Disclosure, Access Design, and Platform Monetization

2026-10-16T00:00:00+09:00

The Source of Data Value: Consumer Disclosure, Access Design, and Platform Monetization arXiv · Oct 16, 2026The paper develops a game-theoretic model of a commissioned platform, two differentiated sellers, and privacy-sensitive consumers to study how consumer disclosure rights shape sellers' data purchases and the platform's incentive to provide data. Under mandatory collection, the platform can monetize defensive adoption even when bilateral personalization reduces seller revenue. Under voluntary disclosure with anonymous purchasing and positive privacy costs, unilateral disclosure is unattractive in the baseline. The authors also discuss welfare effects and suggest capping anonymous offers at their no-data benchmark.

Reading News Without Hindsight: NoLBERT Topics and Stock Returns

2026-10-16T00:00:00+09:00

Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns arXiv · Oct 16, 2026The paper builds news topics with NoLBERT, an encoder pretrained only on 1976–1995 text and adapted by contrastive fine-tuning, and compares it with LDA, MiniLM and FinBERT readers. The topics are used to forecast returns for the 1,000 largest US stocks over 2022–2024, a held-out period after training on 2013–2019 and validation on 2020–2021. The best validation-selected experiment loses money at every rebalancing frequency. An equal-weight combination of all readers and models earns a monthly net Sharpe ratio of 1.12, significant against zero but not against a price-only benchmark. ★★★★ Notable: The authors build news topics with NoLBERT, pretrained only on 1976-1995 text to avoid hindsight, compare it with LDA, MiniLM and FinBERT on 1.26 million 2012-2024 articles, and forecast top-1,000 US

OrderFlowDiT: A diffusion model that forecasts limit-order supply, with better price and volume accuracy

2026-10-16T00:00:00+09:00

OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting arXiv · Oct 16, 2026The paper studies how much limit-order volume will be submitted at each price over the next five minutes, given the current order book and recent order flow. It introduces OrderFlowDiT, a conditional Diffusion Transformer trained with an unbalanced optimal-transport auxiliary loss, and a full-field evaluation framework that scores price placement, total volume, and uncertainty together. On 1,950 validation conditions across 30 U.S. equities, adding the transport loss cut full-field transport cost by 45.8% and the transport energy score by 26.1% versus mean squared error alone, with both improving for every stock.

How language model agents change price competition

2026-10-15T00:00:00+09:00

How language model agents reshape price competition arXiv · Oct 15, 2026This paper studies LLM pricing agents in repeated Bertrand competition, a standard benchmark for algorithmic pricing. LLMs adapt faster than standard online learners, but this advantage largely disappears against full-information learners. Depending on deployment choices, some agents can sustain supracompetitive prices while others move toward the competitive region, producing dynamic pricing regimes. ★★★★ Notable: The paper reports that LLM pricing agents can escape the Bertrand Nash outcome, with results depending on deployment: agents using raw market histories sustain supracompetitive prices, while those usi

How momentum, commentary tone, and earnings surprise shape LLM financial forecasts

2026-10-16T00:00:00+09:00

Momentum, Commentary Tone, and Earnings Surprise in LLM Financial Forecasts arXiv · Oct 16, 2026The paper builds synthetic earnings announcements that independently vary 20-day momentum, uninformative commentary tone, and whether actual EPS beats a manipulated consensus. It tests six instruction-tuned language model checkpoints from four families on 2,016 prompts each. The authors measure matched changes in conditional answer-prefix scores rather than calibrated market probabilities. Tone and momentum often exceed the EPS contrast, though answer-encoding and other controls qualify these results. The study measures observable input reliance and numerical recognition, not internal reasoning or real return predictability.

When Does “Post-Call” Begin? An Availability Contract for Earnings-Call NLP Benchmarks

2026-10-15T00:00:00+09:00

When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks arXiv · Oct 15, 2026The paper argues that financial NLP benchmarks can suffer a timing mismatch within events, where the prediction target starts before the text or audio input is complete. It proposes an Availability Contract requiring benchmarks to state the input boundary, availability evidence, outcome start, and supported claims. Applied to 552 earnings calls from 24 firms, the authors show that changing the timing proxy flips return signs for 9.4% of calls and changes quintiles for 28.3%.

Auction-based allocation of routes in capacity-constrained networks

2026-10-14T00:00:00+09:00

Auction-based allocation of routes in capacity-constrained networks arXiv · Oct 14, 2026This paper studies how to allocate sets of routes between end nodes in capacity-constrained flow networks with transfer costs. The authors introduce an origin-destination bidding language, where participants bid on bounded conic combinations of inlet, outlet and quantity licences, and routing is determined by a network-flow winner-determination problem. Under a restricted model, the VCG payment rule is shown to be core-stable. For the general model, where source access may depend on the individual consumer, they propose a nodal pricing rule based on shadow prices of flow-conservation constraints, which is always core-stable but not dominant-strategy incentive compatible.

Structured neural solver for option pricing under stochastic volatility

2026-10-14T00:00:00+09:00

Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing arXiv · Oct 14, 2026This paper introduces BeTINN, a structured conditional neural architecture that extends time-induced modulation with multi-directional time-parameter conditioning and a joint residual pathway. It is evaluated on the Heston stochastic-volatility PDE, improving joint accuracy for normalized option value and key sensitivity metrics over the authors' baselines. The authors also find that validation physics loss does not fully determine derivative fidelity, so derivative-aware evaluation is recommended when sensitivities matter.

How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accounting

2026-10-13T00:00:00+09:00

How Well Do LLMs Perform in Peer Review? Evidence from Finance and Accounting arXiv · Oct 13, 2026The paper has GPT-4o referee papers from the 2024 AFA and NBER finance meetings and compares its verdicts with publication in top finance journals over the next two years. Papers scored one standard deviation higher were about 44% more likely to be published, net of author reputation. The predictive information came from judgements of correctness and exposition, not originality or significance. The authors conclude LLMs can check whether research is right but not whether it matters.

Reinforcement learning with Hawkes-structured networks for market-making strategies

2026-10-12T00:00:00+09:00

Reinforcement Learning with Hawkes-Structured Networks for Market Making arXiv · Oct 12, 2026This paper studies market making when order arrivals are clustered and self-exciting, modeled with non-Markovian Hawkes processes. The authors propose a model-free reinforcement learning framework and a Hawkes-Structured Network that encodes order flow history with a learnable mixture-of-exponentials filter. Experiments with several kernel types show the network earns higher trading rewards than general-purpose neural architectures and implicitly recovers order-flow intensities.

Dynamic industry-network survival model for mortgage default risk

2026-10-14T00:00:00+09:00

Discrete-Time Survival Modelling with Dynamic Industrial Graphs for Mortgage Credit Risk arXiv · Oct 14, 2026This paper proposes a discrete-time survival model that measures economic proximity by industrial composition similarity and rebuilds dynamic industrial graphs annually to capture changing cross-area dependence in mortgage default risk. It combines a conditional autoregressive process with a first-order autoregressive process and estimates the model using Integrated Nested Laplace Approximation. Tested on U.S. mortgage loan-level data, the dynamic model outperforms static industrial graph specifications in in-sample fit and out-of-sample discrimination.

StochLOB: Robust limit order book trend prediction using heavy-tailed noise in training

2026-10-15T00:00:00+09:00

StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction arXiv · Oct 15, 2026This paper proposes StochLOB, a training-only score-matching framework that replaces the usual Gaussian noise in diffusion models with jump-Lévy and clipped α-stable noise to better reflect heavy-tailed, bursty limit order book order flow. The deployed predictor remains deterministic and needs no reverse-time diffusion sampling at inference. On chronologically held-out Coinbase LOB data, the variants stay competitive on uncorrupted BTC and altcoin horizons and improve several tail-window stress diagnostics. An exploratory A-share transfer study on Feishu data is also included.

Scenario matching for credit dependence stress tests with correlation constraints and empirical risk bounds

2026-10-26T00:00:00+09:00

Scenario Matching for Credit Dependence Stress Tests: Temporal correlation constraints and empirical risk bounds arXiv · Oct 26, 2026The paper pairs two sets of simulated market and credit paths by minimizing the absolute error in temporal correlation relative to a target, with each market path matched to exactly one credit path so both full-scenario distributions are preserved. A perfect-matching test checks whether a uniform correlation-error tolerance is feasible and finds its smallest value, and further assignments yield exact empirical bounds on mean loss and exceedance probability. A synthetic bond-forward credit stress test illustrates matching precision and tail results. The method does not depend on the scenario generator and can be used to examine dependence assumptions in existing risk engines.

Checking Whether Portfolio RL Agents Follow Their Intended Financial Goal

2026-10-25T00:00:00+09:00

Certifying Reward Alignment in Portfolio Reinforcement Learning arXiv · Oct 25, 2026This paper proposes a decision audit for portfolio reinforcement learning that separates reward misspecification from learning error, checking whether a learned policy implements its stated mean-variance objective. The method is tested in controlled markets and on historical US industry portfolios. The authors report that it quantifies whether a learned decision implements its financial objective, separately from backtest performance.

Institutional Traders' Intraday Forecasting Skill and Liquidity Provision

2026-10-22T00:00:00+09:00

Intraday Forecasting Ability and Liquidity Provision of Institutional Traders arXiv · Oct 22, 2026This study uses high-frequency exchange data to examine how institutional traders' forecasting ability and liquidity provision relate to their proprietary intraday profits. It finds persistent forecasting skill, with flow-following strategies based on these traders' executions yielding annualized Sharpe ratios of 16 to 63 across trading frequencies. Higher forecasting ability and more passive executions within a position are both associated with higher profit and loss per matched position.

Directionally Correct Expert Views in Portfolio Optimization

2026-10-22T00:00:00+09:00

Directionally Correct Views in Portfolio Optimization arXiv · Oct 22, 2026This paper, inspired by the Black-Litterman framework, asks whether incorporating expert views improves portfolio allocation. In a simple Markowitz setting, the authors show that even a small amount of directionally correct information can materially improve investment decisions. They also discuss using entropy-based ideas to add expert views to end-to-end portfolio models. Empirically, simpler Markowitz-type models perform as well as an end-to-end model once directionally correct experts are included.

Modeling how liquidity is distributed in concentrated-liquidity trading pools over time

2026-10-22T00:00:00+09:00

Dynamics of the Liquidity Profile in Concentrated Liquidity Provision: An SPIDE Approach arXiv · Oct 22, 2026The paper models how liquidity is spread across price ranges in Uniswap V3 and similar pools, using a stochastic partial integro-differential equation (SPIDE). The equation combines an operator that keeps liquidity concentrated near the market price with an integral operator capturing redistribution from providers repositioning. The authors prove a mild solution exists and is unique, and reduce the model to linear stochastic equations for level, slope, and curvature. Calibration on three Uniswap pools shows lower active-band forecast error than a martingale benchmark on two pools consistent with the stationary specification, while the third pool's out-of-sample results suggest nonstationarity.

Arithmetic Models for Pricing Commodity Futures, Vanilla, and Calendar Spread Options

2026-10-20T00:00:00+09:00

Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options arXiv · Oct 20, 2026The paper proposes an arithmetic multi-factor stochastic volatility model for commodity futures and options, giving a low-dimensional Markovian representation of the futures curve and semi-closed-form prices for vanilla, basket, and calendar spread options. It is estimated with an unscented Kalman filter on WTI crude oil futures, vanilla options, and calendar spread options from 2013 to 2024. The authors find three volatility factors are needed, and that including calendar spread options improves the model's treatment of dependence between adjacent futures contracts. The arithmetic model is also reported to fit better than its geometric counterpart.

Amortized neural filtering for calibrated posteriors in statistical arbitrage

2026-10-19T00:00:00+09:00

Amortized Neural Filtering for Calibrated Posteriors in Statistical Arbitrage arXiv · Oct 19, 2026This paper addresses how statistical arbitrage trading ignores the uncertainty in estimating residual spread states. It introduces a Neural Filter Estimator trained on simulated mean-reverting paths that returns posterior distributions over each spread's latent state in one forward pass. On Korean equities, the conventional point estimate overforecasts returns by about a thousand basis points per holding period, and the amortized methods reduce that gap by roughly two to eight times.

Probability-weighting functions from return distribution changes: three orthogonal channels and information-theoretic indices

2026-10-20T00:00:00+09:00

Probability-Weighting Functions for Normal Tempered-Stable Laws: Orthogonal Channels and Information-Theoretic Indices arXiv · Oct 20, 2026The paper builds probability-weighting functions from changes between prior and posterior normal tempered-stable return distributions rather than a parametric family. Scale, skewness and tail thickness are isolated one at a time with variance held fixed, each producing a distinct sign pattern in the deviation from identity. Three information-theoretic indices summarize the distortions, and the framework is applied to S&P 500 ETF daily returns from 2015 to 2025.

Conflicting History Hurts Foundation Model Return Forecasts

2026-10-19T00:00:00+09:00

Conflicting History in Foundation Model Return Forecasts arXiv · Oct 19, 2026This paper examines how older return data can mislead foundation-model forecasters when the relationship between past and next returns changes. Across five forecasting procedures and three simulated processes, each run 1,024 times, conflicting history lowered the chance of beating a zero forecast in every higher-signal comparison. An exploratory study on 56 assets found that shortening inputs from 256 to 64 returns increased mean absolute error for every model.

Compressing prediction market belief states with topology to forecast events

2026-10-19T00:00:00+09:00

Topological Compression of Prediction Market Belief States for Event Forecasting arXiv · Oct 19, 2026This paper applies Topological Data Analysis and Persistent Homology to forecasting models built on prediction market data, testing whether market belief states form a structure whose shape offers predictive insight. The authors conclude that single-parameter persistent homology improves the predictive ability and calibrated accuracy of linear models. They also report that two-parameter multipersistence yields similar benefits for nonlinear models.

More Mentions, Less Information? Checking Where Reddit Text Signals Come From in Volatility Forecasting

2026-10-19T00:00:00+09:00

More Mentions, Less Information? Diagnosing Identity and Alignment in Reddit Embeddings for Volatility Forecasting arXiv · Oct 19, 2026The paper forecasts log realized variance for stocks using frozen LLM embeddings of r/WallStreetBets posts, reaching an out-of-sample R² of 0.13. The authors test this against a HAR benchmark, text-free firm-identity controls, and shuffled day-text alignment to isolate the sources of the signal. They find most standalone fit comes from firm or firm-year information, with embeddings adding a small 1.22% MSE reduction over HAR that is largely eliminated by alignment shuffling and disappears in 2020–2022.

Equilibria and Learning in Competitive Market Making: A game-theoretic study of market makers and a non-strategic trader, proving pure-strategy Nash equilibria exist and examining learning under discrete prices.

2026-10-22T00:00:00+09:00

Equilibria and Learning in Competitive Market Making arXiv · Oct 22, 2026The paper proposes a market-making game among N ≥ 2 strategic market makers and one non-strategic representative trader. Because quotes are continuous and payoffs are non-convex and discontinuous, standard finite-game and classical continuous existence results do not apply. Under minimal assumptions on the asset value distribution, the authors show at least one pure-strategy Nash equilibrium always exists, and that the equilibrium bid-ask spread widens strictly with asset value volatility. With discretized prices and an added regularity condition, they show time-averaged play of no-internal-regret learners approaches the continuous Nash price envelope as tick size vanishes.

Joules-to-GDP: AI Economic Value per Unit of Energy

2026-10-22T00:00:00+09:00

Joules-to-GDP: How Much Economic Value Do AI Systems Produce per Unit of Energy? arXiv · Oct 22, 2026This paper measures how much economic value an AI system creates per joule of energy consumed, using two metrics: net dollar value and time saving. The authors study 220 public GDPval tasks across 44 occupations, running five open-weight models locally and evaluating four accelerator types. They find that occupations and models that rank highest on success, dollar value, or time saving do not necessarily deliver the greatest economic return per joule.

The Delegation Divide: An Open Index of AI-Agent Delegability by Occupation and Country

2026-10-22T00:00:00+09:00

The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries arXiv · Oct 22, 2026The paper introduces the Agent Delegability Index (ADI), in which two language models rate all 18,796 O*NET 30.2 task statements against a frozen, preregistered rubric of delegation levels and barriers. Tasks are mapped to ISCO-08 occupation groups and weighted by ILOSTAT employment across 146 countries. The authors find that delegable employment rises with income, from 22.5% in low-income to 48.7% in high-income countries, and that ADI correlates strongly with existing generative AI exposure measures at the occupation level. ★★★★ Notable: The authors build an AI-agent delegability index covering 146 countries, reporting delegable employment rising from 22.5% in low-income to 48.7% in high-income countries, and high correlation with pri

When News Gets Old: Portfolio Allocation with Reinforcement Learning and LLMs

2026-10-22T00:00:00+09:00

When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models arXiv · Oct 22, 2026This paper studies how aging financial news affects portfolio decisions, comparing keeping old news with timing information against filtering it out before selection. A fixed large language model extracts signals from public SEC disclosures, and these treatments are tested in reinforcement learning policies and LLMs that select portfolios directly. Over historical tests from 2023 to 2025, measured after trading costs, filtering old news gave the LLM the highest average net return of 18.43% per year when news delivery was delayed by four weeks, exceeding all RL strategies that used news. RL choices responded to timing information in a controlled test, but adding it showed no consistent benefit in historical returns.

Governed self-evolution for autonomous investing: a multi-agent AI system with a protected core

2026-10-22T00:00:00+09:00

Governed Self-Evolution for Autonomous Investing: A Multi-Agent LLM System with an Invariant Core arXiv · Oct 22, 2026The paper presents Steward, a multi-agent LLM system for autonomous investing with nine specialised agents, a seven-layer shared memory built by code, and nightly and quarterly self-review. Self-edits are blocked from an invariant core covering order arithmetic, risk rules and discipline-agent prompts. Reported results include a five-month live deployment over 102 trading days and a 2015–2026 backtest.

LineageEvo: a method that uses factor evolution lineages to guide LLM-based mining of market prediction factors.

2026-10-21T00:00:00+09:00

Lineage-Aware Search Control for LLM-Based Evolutionary Factor Mining arXiv · Oct 21, 2026This paper addresses LLM-based evolutionary mining of formulaic alpha factors, where search tends to concentrate on a few structurally similar factors. The authors propose LineageEvo, which controls search at the lineage level by conditioning LLM variation on lineage-derived operator priors and by allocating search budget across lineages to limit concentration. Experiments on CSI 500 and S&P 500 report that LineageEvo achieves the strongest predictive performance on both markets, while LSPS yields a more balanced lineage distribution and broader factor pool.

What launch-time contract details reveal about prediction-market trading volume and disputes

2026-10-21T00:00:00+09:00

What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk arXiv · Oct 21, 2026The paper analyzes 802,863 resolved Polymarket markets listed from January 2023 to May 2026, using only information available at listing, such as titles, descriptions, resolution sources, and metadata. Contract information predicts trading volume well, with market topic contributing the most. Performance drops sharply when platform changes and new listing formats appear in later periods. Dispute risk is ranked better than chance but is too imprecise for predicting individual disputes.

JaxMARL-BC: Fast Multi-Agent Reinforcement Learning for Macroeconomic Models

2026-10-21T00:00:00+09:00

JaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in Seconds arXiv · Oct 21, 2026This paper presents JaxMARL-BC, a JAX-accelerated simulator that trains MARL-BC economies 75 to 724 times faster than the reference implementation and scales to 200,000 independent learning households. The authors verify its correctness against known DSGE limiting cases and demonstrate it through a proof-of-concept calibration of an extended model with heterogeneous returns on capital, completed in two hours rather than over a month. The tool is released as open source.

DSINC method extended beyond Heston to 3/2 and other non-affine stochastic-volatility models

2026-10-21T00:00:00+09:00

DSINC beyond Heston: the 3/2 and other non-affine stochastic-volatility models arXiv · Oct 21, 2026This paper extends the damped-sinc (DSINC) method for pricing American options from the affine Heston class to general one-factor stochastic-volatility models. It shows that the collapse identity, which reduces early-exercise integration to a single spectral integral, still holds for these models. Tests on 3/2, SABR, GARCH and lognormal-volatility models agree with fine finite-difference benchmarks to within one basis point of the strike.

How a state-space model for limit order books internally represents market dynamics

2026-10-21T00:00:00+09:00

Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books arXiv · Oct 21, 2026The study examines what an 8.2M-parameter Mamba-3 state-space model learns after being trained autoregressively on tokenized limit order book message streams from eight large-cap US stocks. The authors report that order-flow imbalance is decodable from the model's internal state with R² ≈ 0.59, and that this representation extends to unseen stocks and to traded volume and volatility. They also report that steering the internal state shifts the model's next-message predictions in line with the implied feature.

Occupational Uniqueness and Wage Growth

2026-10-20T00:00:00+09:00

Occupational Uniqueness and Wage Growth arXiv · Oct 20, 2026The paper proposes a framework that separates changes in the number of tasks an occupation performs from changes in occupational uniqueness, meaning how distinct its task mix is relative to other occupations. Using 196,563 task statements from the Dictionary of Occupational Titles and O*NET covering 1977 to 2018, the authors find that occupations becoming less unique are associated with lower wage growth. The wage associations of task additions and removals depend on how widely those tasks are performed elsewhere.

Stronger AI breakthroughs accelerate the exhaustion of their own ideas

2026-10-20T00:00:00+09:00

Stronger artificial intelligence breakthroughs hasten their own exhaustion arXiv · Oct 20, 2026The paper analyzes 36 years of computer science, including 7.2 million papers, 73,089 peer reviews and 3.7 million researchers. It finds that attention narrowed sharply after the Transformer, and that each major breakthrough was followed by steeper exhaustion of ideas. The authors call for institutions that protect diverse attention in AI. ★★★★★ Notable: Analyzing 7.2M papers, 73,089 reviews and 3.7M researchers, the authors argue breakthroughs deplete diverse attention, reporting a similar cycle in structural biology and simulations.

LOB-Impact Benchmark: Market Impact Emerging in Generative Order Book Models

2026-10-20T00:00:00+09:00

LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models arXiv · Oct 20, 2026This paper introduces the LOB-Impact benchmark for testing whether generative limit order book models reproduce realistic market impact. The authors inject execution-style metaorders into order flow generated by five autoregressive models across five NASDAQ stocks and measure price responses using empirical impact estimators. Models trained only for next-token prediction show emergent impact, with exponents between 0.63 and 1.21, slightly above the square-root law's 0.5. No impact decay during relaxation was observed, which the authors tentatively attribute to short context windows; the benchmark is released as open source.

Exact risk analysis of teacher training, fixed features, and calibration for candidate-scoring models

2026-10-20T00:00:00+09:00

Teacher Supervision, Fixed Representations and Calibration: Exact Risk Calculations for Candidate-Scoring Decision Models arXiv · Oct 20, 2026The paper studies three training choices that determine what a candidate-scoring decision model's probabilities mean: soft targets from a teacher model, a frozen pretrained backbone, and temperature calibration. It analyzes these in Gaussian surrogate models where finite-sample risk can be computed exactly, and tests them on 231,521 CFPB credit card complaints about whether a complaint ends in monetary relief. The authors find that a student inherits a teacher's bias, and that calibration corrects only the probability scale, so probabilities must be checked against realized outcomes.

Multi-agent reinforcement learning for market making on Polymarket order books

2026-10-20T00:00:00+09:00

Cooperative Multi-Agent Reinforcement Learning for Market Making on Prediction-Market Order Books arXiv · Oct 20, 2026This paper studies market makers trained with multi-agent reinforcement learning on Polymarket, a prediction market with thin order books. The authors reconstruct order-level LOBSTER-format data from Polymarket and train agents on 417 real markets, splitting the maker into a quoter and a risk manager with reward shaping. Their redesigned single-agent maker quotes more often and has smaller held-out losses than the retrained baseline. However, none of the configurations achieves positive average portfolio value in periodic held-out evaluation, and each was trained with one seed.

A Model That Ranks Market Sectors by Expected Risk-Adjusted Performance

2026-10-19T00:00:00+09:00

Regime-Aware Interpretable Ranking for Sector Rotation arXiv · Oct 19, 2026This paper addresses sector rotation in benchmark-relative portfolio management by ranking sectors according to expected future risk-adjusted active performance. The authors propose RegimeRank, an interpretable model that uses expert-defined temporal signal features, conditions them on learned macro factors via cross-attention, and captures cross-sector dependencies with self-attention. On weekly S&P 500 data from 2002 to 2024, it outperforms representative ranking and machine-learning baselines across multiple ranking metrics.

AuDQ: Auditable Data Quality via Agentic Rule Discovery

2026-10-19T00:00:00+09:00

AuDQ: Auditable Data Quality through Agentic Rule Discovery arXiv · Oct 19, 2026The paper proposes an ontology-grounded data quality layer built into the code repository, where rules are versioned, linked to governance texts, and replayable through immutable execution traces. The authors implement the agentic rule-discovery core with Full-Agentic and Guided acquisition modes and a human-in-the-loop review interface. Preliminary experiments on finance and retail industry datasets compare whether fully autonomous divide-and-conquer rule discovery matches human expert rules in quality and diversity.

Reconstructing Arbitrage-Free Implied Volatility Surfaces with Additive Martingales

2026-10-19T00:00:00+09:00

Arbitrage-Free Implied Volatility Surface Reconstruction via Additive Martingales arXiv · Oct 19, 2026This paper introduces AddMart, a framework that rebuilds a continuous implied volatility surface from a few option quotes. It is designed to satisfy static no-arbitrage conditions by its architecture rather than through regularization. Tests on ten real option datasets show competitive accuracy with no material static arbitrage violations. The method also directly supports estimating VaR, CVaR, and crash probabilities.

When Better Opportunities Backfire: How Human Workers Respond When AI Is Deployed in Parallel for Customer Acquisition

2026-10-19T00:00:00+09:00

When Better Opportunities Backfire: Service Worker Responses to Parallel AI Deployment in Customer Acquisition arXiv · Oct 19, 2026This paper studies what happens when AI voice agents handle low-intent sales leads while human workers receive high-intent ones. Using over half a million outreach records from a Chinese online health-insurance brokerage and a regression-discontinuity-in-time design, the authors find that human-facing leads improve early in the workday. Yet human workers acquire fewer customers precisely where lead quality rises most, as they become less willing to persist with their current lead.

LOBGrad: Probabilistic forecasting of limit order books using conditional diffusion models

2026-10-19T00:00:00+09:00

Probabilistic Limit Order Book Forecasting with Conditional Diffusion Models arXiv · Oct 19, 2026The paper introduces LOBGrad, a conditional DDPM framework that pairs an autoregressive RNN encoder with a diffusion generator to forecast limit order book dynamics probabilistically. It establishes an excess-risk oracle inequality for the one-step noise-prediction objective under exponential mixing and regularity assumptions, and shows that under contractive dynamics the dependence on conditioning-window length reduces to latent-state dimension. Experiments on BTC/USDT, GOOGL, and META show it outperforms its encoder-free diffusion ablation on all reported queue metrics and reproduces key LOB stylized facts.

What FLARE's Published Scores Actually Measure

2026-10-24T00:00:00+09:00

What FLARE's Published Numbers Actually Measure arXiv · Oct 24, 2026This paper audits the scoring pipeline of FLARE, the financial benchmark behind PIXIU and FinBen, and finds that in many cells the score depends on the answer-extraction rule rather than the model. On identical generations, two extraction rules differ in accuracy by up to 0.4216, and no leaderboard states which rule it used. The authors argue that reading a FLARE score requires ambiguity rate, coverage, and a majority-class floor, and they release floorbench to compute these from existing generations.

Optimal investing when a green transition's timing is uncertain

2026-10-24T00:00:00+09:00

Optimal portfolio choice under green transition timing risk arXiv · Oct 24, 2026This paper studies an investor's optimal portfolio choice when a possible future green transition, whose timing is uncertain, affects stock prices. The transition is modeled as a jump in which green stocks rise in value and brown stocks fall. The investor is ambiguous about timing and designs a strategy robust to this risk, with preferences that penalize ambiguity. A case study with one green and one brown stock shows that a transition-aware investor shifts holdings toward the green stock and away from the brown stock.

Testing whether deep learning across many convertible bonds can beat simple price persistence for next-day forecasts

2026-10-24T00:00:00+09:00

Cross-Bond Panel Learning for Next-Day Price Forecasting of Chinese Convertible Bonds: A Reality Check arXiv · Oct 24, 2026The paper uses a daily panel of 73,806 observations from 100 Chinese A-share convertible bonds (2022–2025) to test whether cross-bond neural networks beat a random walk baseline. Under strict temporal splitting and dependence-aware inference, the price-change MLPs show slightly higher out-of-sample RMSE than the random walk, negative out-of-sample R², and non-significant test results. The authors conclude that without contemporaneous equity market information or explicit contractual event conditioning, deep panel models do not deliver next-day predictability beyond persistence.

Building implied volatility surfaces and forecasting with survey sampling, tempered inverse-probability learning, and activity clocks

2026-10-24T00:00:00+09:00

Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks arXiv · Oct 24, 2026This paper uses split-adjusted NVIDIA equity and options data from 2018 to 2025 to study how to compile implied volatility surfaces and forecast derivative risk. It combines finite-population survey sampling, tempered inverse-probability sequence learning, and causal activity clocks. The findings show that the sampling design should match the target measure, short-horizon prices are not forecastable beyond a random walk with drift among the tested models, and forward implied volatility shows descriptive mean-reversion that is not statistically significant under session-clustered tests.

Measuring High-Frequency Trading in China and Its Impact on Market Quality

2026-10-24T00:00:00+09:00

Tracking High-Frequency Trading Activity in China and Its Effects on Market Quality arXiv · Oct 24, 2026The paper proposes an order-level proxy for high-frequency trading based on limit orders priced exactly at the best opposite-side quote. Using Shenzhen Stock Exchange data, the authors validate the proxy through its links to quote responsiveness, cancellation speed, and mid-quote returns. A difference-in-differences design using order dissemination latency differences at market open finds that lower latency raises the share of such orders, which increases short-horizon price variation, wide-spread incidence, and adverse-selection exposure. Realized spreads decline and pricing error falls modestly. ★★★★ Notable: Proposes an order-level HFT proxy based on exact-match orders and, using cross-channel latency variation at Shenzhen's open, reports that lower latency raises active exact-match shares and increases s

Whether digital investment and energy use are reshaping the Kitchin business cycle, tested with U.S. and Irish data

2026-10-23T00:00:00+09:00

The Digital-Energy Recomposition of the Kitchin Cycle: Exploratory Evidence from U.S. IT and Irish Data-Centre Series (1995-2025) arXiv · Oct 23, 2026This paper uses quarterly U.S. and Irish data from 1995 to 2025 to explore whether IT investment and data-centre electricity use are reshaping the Kitchin cycle around energy and compute assets. It reports a structural break in the U.S. IT-GDP correlation in 2014Q3, and finds that the strong Irish data-centre electricity and GDP correlation largely disappears when GNP replaces GDP. The authors state that all coefficients are exploratory given small samples.

Tokenised Sukuk: a verifiable system for asset evidence and Shariah trading rules

2026-10-24T00:00:00+09:00

Tokenised Sukuk: A Verifiable Architecture for Asset Evidence and Programmable Shariah Compliance arXiv · Oct 24, 2026The paper argues that security-token standards check only who holds a token, not what backs a sukuk, so an identity-only token can keep trading after non-debt assets are sold. The authors encode the 2023 Securities Commission Malaysia Shariah Advisory Council ruling on Sukuk Wakalah bi al-Istithmar as issuance and transfer policies that read signed asset evidence. In 26 scenarios on a synthetic instrument, the identity-only baseline wrongly allowed 13 operations that the full policy denies, while the full policy matched every expected outcome. ★★★★ Notable: Encodes a Malaysian Shariah ruling on sukuk tradability as on-chain policy versus identity-only baselines; across 26 synthetic scenarios the baseline wrongly allowed 13 operations at about 35,834 gas

Deep catastrophic-risk-aware reinforcement learning: training neural policies with extreme value theory to control rare-event losses

2026-10-20T00:00:00+09:00

Deep catastrophic-risk-aware reinforcement learning arXiv · Oct 20, 2026This paper extends an Extreme Value Theory approach for CVaR optimization to high-dimensional neural-network policies. It uses a peaks-over-threshold representation and method-of-moments estimation to obtain a differentiable tail-risk objective whose finite-sample gradient can be computed via automatic differentiation. In a deep-hedging study with rare extreme market moves, EVT-trained policies behaved similarly to empirical-CVaR-trained ones under normal conditions but had lower hedging losses in extreme-scenario evaluation.

Survey on quantifying uncertainty in financial foundation models

2026-10-23T00:00:00+09:00

Uncertainty Quantification for Financial Foundation Models: A Survey arXiv · Oct 23, 2026This survey examines uncertainty in financial foundation models used for question answering, disclosure analysis, forecasting, and decision-making. It proposes a source-aware framework that separates six uncertainty sources across evidence and context, model and inference, and outcome and action, and a mechanism-based taxonomy for assessing methods across financial tasks. The authors map finance-specific empirical studies onto this taxonomy. They find that most studies address single parts of a workflow, with little work linking evidence and reasoning reliability to downstream prediction and constrained action.

Prospect Theory in LLMs: Meta-Analysis of Loss Aversion and Related Biases

2026-10-23T00:00:00+09:00

Prospect Theory in Large Language Models: A Meta-Analysis of Loss Aversion, Diminishing Sensitivity, and Probability Weighting arXiv · Oct 23, 2026This paper meta-analyzes 102 fitted estimates from 11 studies covering 32 language models to test whether LLMs show the same prospect-theory properties as people. Loss aversion is modest, with a pooled λ of 1.26, far below the strongest human estimate. Probability weighting shows a human-like inverse S shape, but diminishing sensitivity depends on the measurement method used.

Identifying level-k reasoning in repeated games: strategies, beliefs, and cognitive ability

2026-10-22T00:00:00+09:00

Identifying level-k reasoning in repeated games: Strategies, beliefs, and cognitive ability arXiv · Oct 22, 2026This paper identifies level-k reasoning operating at the level of supergame strategies in repeated games. The authors build a model covering both strategy choices and beliefs about others' strategies. Using elicited strategies and strategy beliefs from the indefinitely repeated Prisoner's Dilemma and a new balance-optimal error tolerance measure, they classify a substantial share of subjects as level-1 or level-2. They also find that cognitive ability and experience predict higher-level reasoning.

Continuity of conditional distributions and stratified sampling for derivative pricing

2026-10-24T00:00:00+09:00

On the continuity of conditional distributions and stratification for derivative pricing arXiv · Oct 24, 2026Stratified sampling reduces Monte Carlo estimator variance by partitioning the sample space using a low-dimensional factor and sampling within each stratum, which suits path-dependent claims in high-dimensional models. The paper studies constructing neural-network stratification factors for derivative pricing under proportional allocation in continuous-time models. It establishes continuity results for conditional expectations and distributions, and gives counterexamples showing the limits of this theory. Combined with neural network density results, it yields a universal approximation theorem for admissible factors, and numerical experiments show the learned factors outperform all benchmarks considered.

Optimal collective investment when future regulations are uncertain

2026-10-24T00:00:00+09:00

Optimal collective investment under regulatory uncertainty arXiv · Oct 24, 2026This paper studies optimal collective investment under a Value-at-Risk-type constraint that reflects uncertainty about future regulatory requirements. The constraint includes several wealth thresholds and requires a minimum weighted average probability of meeting them. The authors characterize the optimal terminal wealth and show that welfare losses can be unevenly distributed among investors with different capital contributions.

Balancing Risk Pricing, Coverage and Anti-Discrimination in Insurance Tariffs

2026-10-23T00:00:00+09:00

Risk Adequacy and Anti-Discrimination in Insurance Pricing: Classification, Aggregation, and Transfers arXiv · Oct 23, 2026The paper proposes a framework for insurance tariff design built on risk classification, aggregation of benchmark risk prices within classes, and balanced transfers. It evaluates tariffs on risk adequacy, portfolio coverage, and anti-discrimination, and frames the trade-offs as a multi-objective problem with a Pareto frontier. The authors prove several mathematical characterizations of these trade-offs and illustrate the results with a numerical motor-insurance study.

Optimal order size for market makers under a state-dependent Hawkes process model

2026-10-23T00:00:00+09:00

Optimal Size in Market Making under State-Dependent Hawkes Process arXiv · Oct 23, 2026The paper models a market maker's executed volume as the product of average order size, an exponential demand function, and a Prony-series multiplier that depends on quote depth, with market-order counts built from a state-dependent Hawkes model. An empirical study on limit-order-book data examines calibrated models and sensitivity to inventory restrictions. In retrospective evaluations conditioned on realised order-flow paths, the selected calibration achieves higher terminal utility than a baseline while respecting the inventory bound, and the authors separate the effects of trading revenue from inventory control.

Distributions or Quantiles? A Convex Theory of Worst-Case Risk

2026-10-21T00:00:00+09:00

Distributions or Quantiles? A Convex Variational Theory of Worst-Case Risk arXiv · Oct 21, 2026This paper develops a convex variational theory for worst-case risk evaluation using spectral risk measures, unifying and extending existing analyses. It uses a representation principle that picks the quantile or distribution formulation preserving convexity of the ambiguity set, and optimality conditions identify the worst-case law and its risk value. The theory yields closed-form or semi-closed-form solutions for several models, including Wasserstein and Kullback–Leibler ambiguity sets, and supplies projection-based algorithms with fixed-grid convergence. In a high-dimensional portfolio application, an exact projection identity under a Gaussian reference reduces robust portfolio selection to a second-order cone program. ★★★★ Notable: The authors propose a convex variational theory for worst-case spectral risk, deriving closed or semi-closed forms for Wasserstein and KL ambiguity sets and a portfolio SOCP reduction.

Context-rich trading strategy using actor-critic deep reinforcement learning with sentiment for stock trading

2026-10-23T00:00:00+09:00

Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading arXiv · Oct 23, 2026This paper proposes a deep reinforcement learning approach to stock trading that partially and deeply incorporates sentiment analysis to capture external factors such as social sentiment and political instability. It compares actor-critic agents using only policy optimization (A2C, PPO) with those combining policy optimization and Q-learning (DDPG, TD3, SAC). The authors report that agents combining both principles consistently improve in profit maximization and risk-averse trading when sentiment is deeply infused.

Multichoice Modeling: Exploring Decision Configuration Space Under Uncertainty

2026-10-22T00:00:00+09:00

Multichoice Modeling: Exploring Decision Configuration Space under Uncertainty arXiv · Oct 22, 2026This paper proposes multichoice modeling, a framework that makes large spaces of interdependent decision configurations explicit and explorable under uncertainty. The authors treat such problems as contingent performance landscapes, drawing on rugged landscape logic, and combine variance-based sensitivity analysis with conditional distribution analysis in a single simulation. Three case studies (a manufacturing process upgrade, M&A strategy, and mortgage design) show how the approach reveals unexpected complementarities and near-best configurations with different risk profiles.