Quant Letter
The week in quantitative finance research: 90 papers from arXiv, SSRN and RePEc, screened and ranked, 19–25 Sep 2026.
This week balances methodological rigor with practical market insights. Tail risk estimation and time-series validation trade-offs address foundational modeling challenges, while label engineering and LLM look-ahead bias expose common pitfalls in factor and AI development. Key reads: Semi-Discrete Optimal Transport, Time-Series Validation Trade-Offs Revisited, and Label Engineering for Stock Selection.
If you read five papers this week, read these.
Proposes semi-discrete optimal transport to capture heavy tails in financial returns, maintaining stable tail ratio estimates across diverse neural generators when standard Lipschitz methods fail.
A survey examines how AI transforms information production, intermediation, and market structure, with implications for efficiency, competition and financial stability.
Reshaping the prediction target through location, scale and shape transformations raises long-short Sharpe from 0.68 to 1.69, with label choice mattering more than model choice.
Topics drawing unusually many papers this week.
1.8% of this week's 791 new papers, 3.0x its share over the previous 2-3 weeks (0.6%).
3.0% of this week's 791 new papers, 2.2x its share over the previous 2-3 weeks (1.4%).
1.8% of this week's 791 new papers, 2.3x its share over the previous 2-3 weeks (0.8%).
3.2% of this week's 791 new papers, 1.7x its share over the previous 2-3 weeks (1.8%).
3.0% of this week's 791 new papers, 1.7x its share over the previous 2-3 weeks (1.8%).
What the quant community starred on GitHub this week, from a panel of 10,589 developers who follow financial machine learning (244 of the 860 we checked starred something). Counts only: we never publish who starred what.
Open-source platform for tracking real-time stock prices and company insights.
★ 19.2k+3,593 this weekTypeScript
AI trading bot making one Jev trade decision per Monad block.
★ 2,395+1,462 this weekTypeScriptNew repo
Collection of 23,000+ agent skills for empirical research in social sciences.
★ 4,374+500 this weekStata
Anthropic's financial services implementation.
★ 37.5k+2,589 this weekPython
Autonomous financial deep research framework with agentic capabilities.
★ 176+33 this weekPythonNew repo
Calibrated typed-decision models for finance and trading applications.
★ 18+18 this weekPythonNew repo
Open-source self-hosted privacy-first personal finance manager.
★ 3,783+181 this weekPython
Unified API for 100+ crypto exchanges and prediction markets.
★ 44.1k+123 this weekPython
Non-autoregressive decision engine for typed choices over text in 100+ languages.
★ 24.0k+23.7k this weekPythonNew repo
Fastest and cheapest web agent for automation tasks.
★ 20.1k+14.0k this weekPythonNew repo
Google's open agentic orchestration runtime for agent systems.
★ 11.1k+9,108 this weekGo
Decision models built on Qwen3.5/3.8 you can train locally.
★ 6,886+6,653 this weekPythonNew repo
Contrastive language model implementation.
★ 1,065+1,065 this weekPythonNew repo
Turn any LLM into a Jev-style decision model with typed decisions.
★ 590+590 this weekPythonNew repo
Of the 1,631 finance papers we featured as new at least a year ago, 34% are now published, and 8 of our early picks have 100+ citations. FinGPT: Open-Source Financial Large Language Models was featured 5 days after release; it has 492 citations.
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Quantitative finance and ML-for-finance preprints
30 papers · 29 figures
Proposes semi-discrete optimal transport to capture heavy tails in financial returns, maintaining stable tail ratio estimates across diverse neural generators when standard Lipschitz methods fail.
Fanfare 4Risk, Credit & BankingRyan M. Engel et al.PDF
A dual-perspective graph neural network framework predicts corporate defaults from buyer-seller transaction networks, improving approval rates by 7-11 percentage points without increasing default risk.
Fanfare 3Risk, Credit & BankingJunghoon Kim et al.PDF
Develops a deep learning scheme for optimal stopping under simultaneous model and discount-rate ambiguity, with application to American option valuation under uncertainty.
Fanfare 2Risk, Credit & BankingNacira Agram et al.PDF
Proposes a statistical referee that judges investment factors proposed by language-model agents using out-of-sample market outcomes, ensuring false-discovery control at any stopping time.
Fanfare 4ML & AI MethodsBo Qu et al.PDF
Proposes a multi-agent system with post-training that automates alpha factor mining locally, using diverse research paths and joint optimization to broaden exploration while maintaining prediction quality.
Fanfare 3ML & AI MethodsQingzhuo Wang et al.PDF
Introduces a replayable environment combining 1,568 resolved prediction-market questions with 18.8M dated news articles to benchmark and train language-model forecasting agents on historical data.
Fanfare 3ML & AI MethodsLiqin Ye et al.PDF
Proves that training sufficiency, test coverage, and temporal causality cannot be maximized simultaneously in time-series validation, pricing each constraint explicitly.
Fanfare 4Econometrics & ForecastingJiayu LiPDF
A hierarchical multi-task framework jointly predicts price movement, volatility and volume using liquidity-aware signals, outperforming neural and tree-based baselines on Chinese equity indices.
Fanfare 3Econometrics & ForecastingHengyi Yang et al.PDF
Jointly reconciling hourly price and spread forecasts improves intraday electricity price prediction accuracy by up to 19.7% and battery-arbitrage profits by up to 10.4%.
Fanfare 3Econometrics & ForecastingArkadiusz Lipiecki et al.PDF
A regime-switching volatility-in-mean VAR reveals that the drivers of growth and inflation tails differ from median dynamics, with macroeconomic uncertainty playing a larger role in downside risk.
Fanfare 3Econometrics & ForecastingHaroon Mumtaz and Sofia VelascoPDF
A stochastic nested fixed-point estimator reduces memory and computational cost for random-coefficients logit demand models, enabling estimation on 100 million markets in hours.
Fanfare 3Econometrics & ForecastingZhentong Lu et al.PDF
Augmenting HAR with a negative moving-average component approximates rough dynamics, outperforming classical models out-of-sample and matching continuous-time rough model accuracy.
Fanfare 3Econometrics & ForecastingMikkel Bennedsen et al.PDF
Introduces a network realized GARCH-Itô model that identifies dynamic volatility transmission among assets using high-frequency data, outperforming recursive forecasts on sector ETFs.
Fanfare 2Econometrics & ForecastingXinyu SongPDF
The research extends adversarial reinforcement learning for market making to handle self-exciting order arrivals and price impact, using an LSTM module to improve robustness in complex microstructure environments.
Fanfare 3Trading, Microstructure & ExecutionHao Yang and Zhenguo XuPDF
Develops a nested optimization model for market making and rebate design in option markets, showing how exchanges can set fees to incentivize liquidity provision and improve market depth.
Fanfare 2Trading, Microstructure & ExecutionSamuel N. Cohen et al.PDF
Extends the Almgren-Chriss optimal execution framework to enforce intertemporal cash constraints, reducing peak cash drawdown while maintaining implementation shortfall in multi-asset rebalancing.
Fanfare 2Trading, Microstructure & ExecutionRyuji Hashimoto and Namid R. StillmanPDF
Experiments show that algorithm design features like warnings, pre-configured strategies, and LLM advice raise market prices by increasing starting prices and fostering cooperative algorithm designs.
Fanfare 2Trading, Microstructure & ExecutionAdrian Hillenbrand et al.PDF
Introduces a market-friction-aware framework that converts timestamped financial news into auditable trading decisions while accounting for execution timing, transaction costs, and liquidity constraints.
Fanfare 3LLMs & TextKemal KirtacPDF
The study shows that how monetary policy sentiment unfolds across a press conference, not just its average tone, predicts rate changes and shapes forecaster expectations at the ECB and Fed.
Fanfare 3LLMs & TextMartin Feldkircher et al.PDF
A benchmark reveals that language models answer financial questions above 90 percent with clarification but only 28.9 percent when they must elicit it themselves, exposing model ambiguity resolution.
Fanfare 3LLMs & TextXinyu Wang et al.PDF
Develops a two-stage framework that fine-tunes language models for listwise asset ranking using Spearman rank correlation rewards, achieving a Sharpe ratio of 0.636 on asset allocation.
Fanfare 2LLMs & TextNingyuan Deng et al.PDF
A universal diffusion model trained on pooled data from 50 stocks learns to jointly generate implied volatility surface changes and stock returns, extrapolating well to unseen stocks.
Fanfare 3Derivatives & VolatilityMingzhi Yang et al.PDF
Develops a surface-driven stochastic volatility framework for commodity options using daily volatility surface factors, recovering vol-of-vol and leverage parameters from smile dynamics.
Fanfare 2Derivatives & VolatilityArthur Steve Tchoneteck et al.PDF
Tree-based machine learning outperforms neural networks at detecting systematic option-pricing deviations from Black-Scholes using 2.6 million real contracts, with domain-expert features crucial.
Fanfare 2Derivatives & VolatilityJuli Huang et al.PDF
Formulates a stochastic control problem for actively allocating between equal-weighted and market portfolios based on a diversity-dispersion model, outperforming passive strategies during market bubbles.
Fanfare 3Portfolio & AllocationBrian Ceco et al.PDF
Proposes a KKT-based decision-focused learning method that trains mean-variance portfolio models by directly minimizing downstream portfolio loss while preserving all constraints.
Fanfare 3Portfolio & AllocationKensei Nosaka et al.PDF
Shows that mean-variance portfolio selection and the constrained LASSO trace identical piecewise-linear solution paths, mapping their parametrizations exactly.
Fanfare 2Portfolio & AllocationThomas Schmelzer and Trevor HastiePDF
Characterizes optimal consumption and investment strategies in markets with stochastic volatility clocks using infinite-horizon backward SDEs, extending to rough and hyper-rough regimes.
Fanfare 2Portfolio & AllocationEduardo Abi Jaber et al.PDF
Meta-analysis of 1,613 size-premium estimates across 31 countries finds that stronger rule of law is associated with larger size premia, contrary to intuition.
Fanfare 3Asset Pricing & FactorsJiri Schwarz et al.PDF
Introduces a large-scale benchmark on 1.35 billion Ethereum transactions to evaluate DeFi representations under temporal, protocol, and contract drift, revealing model degradation on unseen pools.
Fanfare 3Crypto & DeFiKemal Kirtac and Carsten MaplePDF
New working papers in finance, economics and ML
30 papers
A survey examines how AI transforms information production, intermediation, and market structure, with implications for efficiency, competition and financial stability.
Fanfare 4ML & AI MethodsÁlvaro Cartea et al.
Reshaping the prediction target through location, scale and shape transformations raises long-short Sharpe from 0.68 to 1.69, with label choice mattering more than model choice.
Fanfare 4ML & AI MethodsTony Guida and Guillaume Coqueret
Testing whether a large language model ranks stocks by forecasting or memory, the study finds a significant information-coefficient gap of 0.185 inside versus outside its training window, suggesting substantial look-ahead contamination.
Fanfare 3ML & AI MethodsBach Nguyen
The study measures recall versus forecasting in an LLM's stock rankings by comparing cross-sectional information coefficients inside and outside the training window.
Fanfare 3ML & AI MethodsBach Nguyen
The paper builds a closed-loop system where an LLM proposes equity factors penalized for execution costs and shows that accounting for trading costs dramatically improves net performance.
Fanfare 3ML & AI MethodsRaghuram Nagireddy
Q-learning pricing agents in simulated duopolies reach supracompetitive outcomes with no communication, achieving collusion indices of 0.778 and 40% profit gains over competitive benchmarks.
Fanfare 4Trading, Microstructure & ExecutionVladislav Dolgov
Analysis of Kalshi's regulated Bitcoin and Ethereum futures reveals that 39-48% of notional trades are mechanical fixed-size orders that vanish when fees are charged, indicating costless artificial volume rather than legitimate trading.
Fanfare 3Trading, Microstructure & ExecutionAndre Guettler
Hungary's 2015 adoption of payment-versus-payment settlement reduced currency excess returns by ten basis points, demonstrating settlement risk is a priced friction limiting arbitrage.
Fanfare 3Trading, Microstructure & ExecutionSeungduck Lee et al.
Semantic and tonal shifts across sequential Federal Reserve communications generate significant intraday price movements and abnormal volume, revealing incomplete information absorption at initial announcement.
Fanfare 2Trading, Microstructure & ExecutionAihui Wei
European defence stocks repriced sharply starting November 2021, two to three months before Russia's invasion, delivering 26% alpha and reflecting release of ESG-exclusion constraints.
Fanfare 4Asset Pricing & FactorsAndrej Bajic and Miloš Starović
Factor momentum strategies earn 49 basis points per month extra return following quarters of rapid margin-debt growth, a predictability that persists after publication and reflects limits to arbitrage correction.
Fanfare 3Asset Pricing & FactorsWeijian Sun and Yu Xia
Momentum profits concentrate among firms with high price delay, a measure of information friction, directly supporting theories that gradual information incorporation drives momentum.
Fanfare 3Asset Pricing & FactorsBharat Raj Parajuli
Comparing firms across the ECB's 2014 negative rate adoption shows treated European firms had higher valuations but reduced leverage, suggesting cash-flow and discount-rate channels dominate tax-shield effects.
Fanfare 2Asset Pricing & FactorsArun Upadhyay et al.
High-frequency analysis reveals contractionary forward guidance immediately cuts bank lending, while expansionary guidance produces weak stimulus, driven by binding capital constraints.
Fanfare 3Risk, Credit & BankingElliot Spears
Banks engaged in securitization contract lending more sharply after monetary tightening because their investor base demands higher returns and cuts risk exposure when rates rise.
Fanfare 3Risk, Credit & BankingDorian Henricot and Enrico Sette
Using SEC filings from 2013-2021, the paper finds hedge fund returns show heterogeneous sensitivity to interest rates, with effects varying by strategy, leverage, and derivative exposure.
Fanfare 3Risk, Credit & BankingAyelen Banegas
Combining quantile-connectedness, tail-risk measures, and network analysis, the research shows tail connectedness exceeds median levels and lower-tail effects persist longer, with the VIX alone reliably predicting next-week systemic risk.
Fanfare 2Risk, Credit & BankingOguzhan Ozcelebi et al.
Japan's 2016 negative-rate policy reduced lending from low-profitability banks holding reserves, consistent with lower expected returns on bank assets rather than deposit-side stress.
Fanfare 2Risk, Credit & BankingKoji Takahashi and Kiyotaka Nakashima
The study develops a time-varying signature asset model for structural credit that improves calibration across CDS maturities and equity option prices, especially for high-yield firms.
Fanfare 2Risk, Credit & BankingMatthias Arnsdorf et al.
Analysis of 284 distressed exchanges from 2009-2022 reveals over 50% of firms face subsequent default, with large illiquid creditors trapped in a prisoner's dilemma explaining high acceptance rates.
Fanfare 3Risk, Credit & BankingRafal Sieradzki and Edward I. Altman
Using factor selection, the study finds mean active alpha of plus 9 basis points monthly for mutual funds, reversing the no-alpha conclusion when benchmarks are tailored to each fund.
Fanfare 3Portfolio & AllocationDmitry Malakhov
The research proposes a data-driven portfolio method that blends tail-risk models and projects onto valid mixtures, providing bounds on Expected Shortfall regret without Wasserstein assumptions.
Fanfare 2Portfolio & AllocationZheqi Fan
The paper compares machine learning option pricing trained on pricing errors versus implied-volatility errors using 8.67 million S&P 500 index-option observations from 1997 through 2025.
Fanfare 3Derivatives & VolatilityPanayiotis C. Andreou et al.
Leveraged hedge fund positions amplify sovereign bond yield sensitivity to monetary shocks by over a quarter through directional rebalancing, with effects scaling to position intensity.
Fanfare 3Derivatives & VolatilityFelix Hermes
A cubic quantile framework forecasts Value-at-Risk and Expected Shortfall more reliably than GARCH benchmarks across eight equity indices without requiring a parametric density.
Fanfare 3Derivatives & VolatilityLaura Garcia-Jorcano et al.
A deep operator network maps volatility surfaces to option prices under the Heston model 15,000 times faster than finite-difference methods while reducing dynamic hedging variance by over 59% under transaction costs.
Fanfare 2Derivatives & VolatilityWonChan Cho
Decomposing yield sensitivity without assuming rational expectations reveals that expectations rather than risk premia drive short- and medium-term bond yields, with systematic inconsistencies across horizons.
Fanfare 3Macro-Finance & RatesPooya Molavi et al.
A 100-basis-point contractionary monetary shock lowers inflation and sales across 20 euro-area economies, with transmission strength varying by bank asset-risk exposure and assets-to-GDP ratio rather than a simple weak-strong taxonomy.
Fanfare 2Macro-Finance & RatesShi Hongyan
Semantic surprises extracted from Federal Reserve statements predict subsequent financial-stress dynamics and reduce forecast error by up to 23%, particularly when initial stress is high or during recessions.
Fanfare 3LLMs & TextFengtian Yang et al.
Using production, employment, and sales data across 426 industries, the research shows that upstream industry signals predict aggregate monthly stock returns with 23.8% out-of-sample R-squared.
Fanfare 3Econometrics & ForecastingElham Ghorbani and Rasoul Foroughfard
Working papers curated by RePEc's NEP field reports
30 papers · 15 figures
Optimized AI systems analyzing earnings call transcripts double explained variation in stock returns versus standard benchmarks while improving interpretability through human-readable decision rules.
Fanfare 4ML & AI MethodsRalph S. J. Koijen and Bradford LevyPDF
Tree-based machine learning models predict the full distribution of financial market stress 27% better than traditional time-series methods, with macro uncertainty and monetary policy expectations as key drivers.
Fanfare 3ML & AI MethodsInaki Aldasoro et al.PDF
Q-learning and large language model investors generate systematically different behaviors in fund redemption settings, with Q-learning showing excessive coordination and amplified fragility under default risk.
Fanfare 3ML & AI MethodsKartik Anand et al.PDF
Using model-free skewness measures from currency options, the study shows that skewness risk is priced in currency returns and explains variation across a broad cross-section of currency portfolios.
Fanfare 3Asset Pricing & FactorsJunye Li et al.PDF
The paper shows that portfolio holdings contain all information needed for asset pricing and develops asset embeddings analogous to word embeddings to represent firms and predict valuations.
Fanfare 3Asset Pricing & FactorsXavier Gabaix et al.PDF
A two-country model shows that uncertainty shocks tighten intermediary constraints, widening credit spreads, appreciating the dollar, and raising currency risk premia globally.
Fanfare 3Asset Pricing & FactorsOzge Akinci and Ṣebnem Kalemli-ÖzcanPDF
Focusing on dollar-lira trading, the paper shows that higher crash risk significantly increases carry trade expected returns, accounting for 46–77% of compensation through Shapley decomposition.
Fanfare 3Asset Pricing & FactorsMerve Mavus Kutuk and Sweder van WijnbergenPDF
Stock returns are dampened by rate insurance: falling rates cushion payoff risk in bad times while rising rates in good times hedge duration exposure.
Fanfare 3Asset Pricing & FactorsOlivier WangPDF
The research identifies common risk factors spanning stocks, corporate bonds, and options linked to economic indicators, revealing significant market segmentation and cross-asset hedging opportunities.
Fanfare 2Asset Pricing & FactorsZhongtian Chen et al.PDF
Traders place 15% weight on USDA crop reports relative to private priors when forming price expectations, with this anchoring weight rising when private analyst disagreement increases.
Fanfare 2Asset Pricing & FactorsHosung Nam et al.PDF
A nonlinear factor constructed from credit spreads and equity volatility prices global corporate bond returns, explaining up to 13% of three-month-ahead return variation across markets.
Fanfare 3Risk, Credit & BankingNina Boyarchenko and Leonardo EliasPDF
UK firm survey data validates that external borrowers face larger cost-of-capital increases and cut investment more than internal funders when rates rise, accounting for a quarter of monetary policy's total effect.
Fanfare 3Risk, Credit & BankingKrishan Shah et al.PDF
Analysis of 550 million US credit card accounts shows that despite high charge-off rates, card lenders earn 1.5% alpha and 6.8% return on assets through pricing power and non-interest income.
Fanfare 3Risk, Credit & BankingItamar Drechsler et al.PDF
A database of 3,984 historical US bank runs shows runs are more likely in weak banks but often occur in strong banks; failures concentrate in fundamentally weak institutions.
Fanfare 3Risk, Credit & BankingSergio A. Correia et al.PDF
The study measures liquidity risk from solvency hedging in sterling repo and swaps, finding that pre-crisis LASH risk predicted pension fund bond sales during the 2022 UK market stress.
Fanfare 2Risk, Credit & BankingLaura Alfaro et al.PDF
Analysis of 20 years of US junk bonds and emerging market sovereign debt reveals surprisingly similar average returns, Sharpe ratios, default frequencies, and haircuts across the two asset classes.
Fanfare 2Risk, Credit & BankingGita Gopinath et al.PDF
Expansionary central bank collateral policy surprises reduce bank default risk and volatility while compressing government bond spreads, transmitting effects distinctly from asset purchases.
Fanfare 2Risk, Credit & BankingPia Hüttl et al.PDF
Dutch pension funds use interest rate swaps more aggressively when underfunded, exposing themselves to margin calls exceeding 6% of assets and forcing procyclical sales of government bonds.
Fanfare 2Risk, Credit & BankingKristy Jansen et al.PDF
The study develops a general equilibrium model of financial intermediation showing that liquidity regulation alone cannot achieve efficient allocations and requires complementary policies like bank size limits.
Fanfare 2Risk, Credit & BankingMadalen Castells-Jauregui et al.PDF
European banks with assets exceeding half of home GDP enjoy at least 30 percent lower credit spreads, and this implicit subsidy persists and depends on sovereign fiscal strength.
Fanfare 2Risk, Credit & BankingLaura Deen and Daniel DimitrovPDF
Monetary tightening reduces R&D more sharply among firms lacking cash-flow-based borrowing, generating persistent 0.12% output loss that younger, high-patent firms bear disproportionately.
Fanfare 3Macro-Finance & RatesAydan Dogan and Ozgen OzturkPDF
Swedish data reveals that financially constrained firms adjust prices less to monetary shocks, materially dampening aggregate inflation response to policy changes.
Fanfare 3Macro-Finance & RatesMichael Bauer et al.PDF
In response to US monetary tightening, financial channels dominate for small open economies: credit spreads widen and output falls despite currency depreciation.
Fanfare 3Macro-Finance & RatesAmbrogio Cesa-Bianchi et al.PDF
Using German sovereign bond repo data, the research shows hedge funds are price-elastic in cash markets but highly inelastic in repo, inheriting elasticity from their cash-market counterparties.
Fanfare 3Trading, Microstructure & ExecutionAndrea Poinelli et al.PDF
The study finds that algorithmic trading lowers realized volatility but increases tail co-movement and asymmetry in China's corn and soybean futures markets.
Fanfare 2Trading, Microstructure & ExecutionChenguang Xu and Xinyue HePDF
Tree-based models partition the option surface by moneyness and maturity to forecast volatility, reducing one-month-ahead errors by 13 percent versus benchmark models.
Fanfare 3Derivatives & VolatilityHyung Joo Kim and Dong Hwan OhPDF
Financial and macroeconomic news topics systematically predict implied volatility in corn and soybean markets, with program trading and 2008 crisis topics most robust at short horizons.
Fanfare 2Derivatives & VolatilityHongqiang Yan et al.PDF
Introducing coefficient-specific penalization into multivariate GARCH equations reduces complexity and improves out-of-sample covariance forecasts across bonds, equities, and commodities.
Fanfare 2Derivatives & VolatilityYongdeng Xu et al.PDF
Wallet-level stablecoin data shows crisis countries experience inflows during banking restrictions; this endogenizes capital mobility and tightens monetary policy constraints.
Fanfare 4Crypto & DeFiPablo D. Azar et al.PDF
Corporate leverage affects how monetary tightening transmits to the real economy: equity holders lose wealth while safe-asset holders are cushioned, raising the sacrifice ratio.
Fanfare 3Portfolio & AllocationCharles Goodhart et al.PDF