In briefLineageEvo: a method that uses factor evolution lineages to guide LLM-based mining of market prediction factors.
SummaryThis 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.
In briefDistributions or Quantiles? A Convex Theory of Worst-Case Risk
SummaryThis 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.
WhyUnifies worst-case spectral risk analysis under a convex variational theory, deriving closed-form solutions for several ambiguity models with strong analytical support and moderate field impact.
Jianjun Gao, Jonathan Yu-Meng Li, Zhen Wang et al.
In briefWhat launch-time contract details reveal about prediction-market trading volume and disputes
SummaryThe 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.
In briefJaxMARL-BC: Fast Multi-Agent Reinforcement Learning for Macroeconomic Models
SummaryThis 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.
Daniele Montagnani, Andrea Coletta, Antonietta Mira et al.
In briefDSINC method extended beyond Heston to 3/2 and other non-affine stochastic-volatility models
SummaryThis 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.
In briefHow a state-space model for limit order books internally represents market dynamics
SummaryThe 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.
Valentin Mohl, Alexandre Bismuth, Aramis Marti-Shahandeh et al.
