Research Directions
Methods and pitfalls of machine learning in return prediction, feature engineering, and portfolio construction.
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This 9-page paper accepted by ICAIF '26 proposes a diagnostic protocol to examine the true source of incremental information from LLM embeddings of r/WallStreetBets posts for volatility prediction. The study covers panel data of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample test.
Read originalThis arXiv paper evaluates the reliability and investment consequences of TimeGAN-generated scenarios in Rachev ratio portfolio allocation. Using 12 ETFs, it compares historical, substitution, and hybrid strategies under delayed execution and transaction costs. The study finds that generated scenarios underestimate volatility and tail losses and distort dependencies between assets; extending training only reduces reconstruction loss without improving cross-asset correlation accuracy. Substitution strategies increase turnover and are sensitive to the lower bound of the objective function denominator, and adjusting this lower bound does not consistently improve realized downside risk.
Read originalResearchers used proprietary panel data of commercial mortgages from 1998 to 2021 and adopted deep neural networks (DNN) to distinguish liquidity-constrained default from strategic default. The study found that, under delinquency conditions, property net operating income (NOI) has a more significant impact on default rates than loan-to-value ratio (LTV), and the threshold for dividing the two types of defaults remained stable during the 2008 financial crisis and the 2020-2021 COVID-19 commercial real estate shock.
Read originalPublished at ICAIF 2026, this study finds that accurate option prices do not necessarily mean accurate recovery of the underlying risk-neutral density. By comparing synthetic simulation benchmarks with out-of-sample NIFTY option benchmarks, the research reveals that the two-component lognormal mixture model demonstrates overall pricing performance on synthetic benchmarks.
Read originalThis arXiv paper proposes proximal equilibrium operators (PEOs), constructed via finite-dimensional deep equilibrium layers, that can solve a family of convex optimization problems on separable Hilbert spaces. The paper proves that when input loss functions lie in a suitable infinite-dimensional set, PEOs can uniformly approximate corresponding solutions to arbitrary precision, with rank and width growing only logarithmically with the inverse of approximation error, and depth growing linearly. The related theory and trainability have been validated across three application domains: nonlinear PDEs, stochastic optimal control, and financial portfolio optimization and hedging.
Read originalThis arXiv paper reproduces Calvano et al.'s 2020 simulated Bertrand duopoly Q-learning pricing experiment and challenges the conventional punishment-mechanism explanation for why independently trained tabular Q-learning agents converge to supracompetitive prices.
Read originalA study of European firms from 2016 to 2022, combining panel regression, machine learning, and explainable AI methods, finds a nonlinear relationship between board gender diversity and emissions performance: emissions performance begins to improve significantly when female director representation reaches a minimum threshold of approximately 22%, and further increases in female representation beyond an optimal level of approximately 35% do not yield additional emissions performance gains.
Read originalResearch shows that large language model pricing agents can deviate from the repeated Bertrand competition benchmark where prices converge to marginal costs in homogeneous goods markets, with their performance depending on deployment decisions. Whether LLM pricing agents can maintain prices above marginal costs in self-play is directly related to deployment choices: whether they set prices period-by-period based directly on raw market history including competitor prices, or periodically output probability distributions based on summaries of their own profit history.
Read originalThe research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.
Read originalResearchers propose OrderFlowDiT, a conditional diffusion Transformer trained with an unbalanced optimal transport (UOT) auxiliary loss, to predict the conditional distribution of limit order submissions over the next five minutes. Across 1,950 validation conditions covering 30 U.S. stocks, adding UOT reduces the full-field transport cost by 45.8% and the transport energy score by 26.1% compared with training using only mean squared error, with improvements in both metrics for every stock.
Read originalThis arXiv paper proposes a NoLBERT encoder pre-trained only on texts from 1976 to 1995, comparing it with LDA, MiniLM, and FinBERT on 1.26 million news articles from 2012 to 2024 to predict returns of the 1000 largest U.S. stocks.
Read originalThis paper proposes a multivariate state-dependent Hawkes process (SDHP) to model cross-product liquidity dynamics between quarter-hourly and hourly contracts in the Dutch EPEX SPOT continuous intraday market. The model is calibrated using Level 3 order book data from the first month of each quarter in 2024, dividing trading sessions into normal and stress states. The study finds that approximately 95% of orders are driven by their own order flow, with cross-excitation primarily flowing from quarter-hourly contracts to hourly contracts, and this dominance significantly weakens in stress states.
Read originalThe Bank for International Settlements (BIS) released Working Paper No. 1386, which proposes an LLM-based reasoning pipeline to identify firms' actions in response to geoeconomic pressure from earnings call transcripts and summarize the underlying logic. Compared with traditional NLP methods, it can extract implicit conditional relationships in the text rather than merely counting keywords.
Read originalMoontower has released this issue, mentioning the Metagame event held at Lighthaven in Berkeley from November 6 to 8. Moontower readers can enjoy a $50 discount on regular tickets. This issue also shares AI-related thoughts, TIPS investment analysis, crude oil market learning resources, and pre-sale information for "How I Invest Volume 2", which will be shipped on October 20.
Read originalQuantocracy released its latest roundup of quantitative research links on October 14, 2026, featuring multiple research pieces and content related to the quantitative field. These include an out-of-sample evaluation study of Hidden Markov Model (HMM) market state identification, an analysis of the impact of complexity on strategy performance, and research on cross-sectional momentum ranking methods. The related research covers backtest results across different sample periods and discussions of strategy logic.
Read originalQuantpedia released a study comparing four four-state Gaussian HMMs that only differ in training start year (1970, 1980, 1990, 2000), evaluating market state recognition performance on the same 2010-2023 test set.
Read originalGoogle DeepMind is advancing the widespread deployment of Gemini 4 Argon while expanding the coverage of trusted cyber defenders in the Fairwind Program. The model solved all 12 scenarios in end-to-end simulated cyberattack tests, a significant improvement from 5 in the previous generation. Its refusal rate for violating requests nearly doubled compared to the previous generation, and violations accounted for only 2% of attempts in coding benchmark tests.
Read originalWhen evaluating AI heat risk models used in company valuations, investors should focus on whether they fully cover the entire transmission chain from heat hazards to asset exposure, operational sensitivity, financial impact, and ultimately valuation. Existing heat risk estimates from different institutions show significant discrepancies and often omit key impact dimensions such as employee productivity. Investors can reference the case of Électricité de France (EDF) and require companies to disclose quantifiable operational thresholds, financial sensitivities, and adaptation costs to determine whether heat-related losses are already reflected in company value.
Read originalAuthor Kris Longmore shares content from his 72-minute interview on The Algorithmic Advantage podcast, looking back on an early experience at a Sydney traders' meetup where he was unable to answer a proprietary trader's question about his own trading edge. He later joined that proprietary firm as a researcher and partner, and summarized relevant insights after leaving to trade independently.
Read originalQuantocracy compiled recent quantitative content on October 12, 2026, covering an introduction to Quant Observer, a quantitative tools directory platform; a historical effectiveness analysis of excess returns from insider buying; a backtest of volatility clustering effects in S&P 500 constituents from 2006 to 2025; a guide to building a multi-asset, multi-timeframe trading pipeline; and AI answers on the replicability of empirical finance research.
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