In briefWhat FLARE's Published Scores Actually Measure
SummaryThis 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.
In briefOptimal investing when a green transition's timing is uncertain
SummaryThis 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.
In briefTokenised Sukuk: a verifiable system for asset evidence and Shariah trading rules
SummaryThe 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.
WhyEncodes Shariah tradability rules as on-chain policies and compares them with identity-only baselines, revealing trade-offs, though evaluated only on a synthetic instrument.
In briefTesting whether deep learning across many convertible bonds can beat simple price persistence for next-day forecasts
SummaryThe 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.
In briefBuilding implied volatility surfaces and forecasting with survey sampling, tempered inverse-probability learning, and activity clocks
SummaryThis 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.
In briefMeasuring High-Frequency Trading in China and Its Impact on Market Quality
SummaryThe 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.
WhyProposes an order-level HFT proxy and uses cross-channel latency variation in a difference-in-differences design, yielding strong novelty and credible causal evidence.
In briefContinuity of conditional distributions and stratified sampling for derivative pricing
SummaryStratified 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.
In briefOptimal collective investment when future regulations are uncertain
SummaryThis 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.
