In briefA Model That Ranks Market Sectors by Expected Risk-Adjusted Performance
SummaryThis 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.
In briefAuDQ: Auditable Data Quality via Agentic Rule Discovery
SummaryThe 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.
Jacopo Biggiogera, Nicolas Colombo, Krati Saxena et al.
In briefConflicting History Hurts Foundation Model Return Forecasts
SummaryThis 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.
In briefAmortized neural filtering for calibrated posteriors in statistical arbitrage
SummaryThis 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.
In briefCompressing prediction market belief states with topology to forecast events
SummaryThis 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.
In briefReconstructing Arbitrage-Free Implied Volatility Surfaces with Additive Martingales
SummaryThis 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.
In briefWhen Better Opportunities Backfire: How Human Workers Respond When AI Is Deployed in Parallel for Customer Acquisition
SummaryThis 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.
In briefLOBGrad: Probabilistic forecasting of limit order books using conditional diffusion models
SummaryThe 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.
In briefMore Mentions, Less Information? Checking Where Reddit Text Signals Come From in Volatility Forecasting
SummaryThe 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.
