# ML-Quant > ML-Quant ranks new research on machine learning in quantitative finance every week. Each Friday it screens every new arXiv, SSRN and RePEc paper, ranks the best 30 per venue with a one-sentence summary and the paper's key figure, tracks every featured paper's citations and journal publication on Semantic Scholar, and reports what about 10,600 quant developers starred on GitHub. 132 issues since May 2023; 6,396 featured papers. Every page has a Markdown twin: append `index.md` to its URL. Our summaries are CC BY 4.0; please link back. JSON for everything: https://www.ml-quant.com/api/v1/index.json ## This week - [Quant Letter No. 132, 2026-09-25](https://www.ml-quant.com/issues/2026-09-25/index.md): 90 ranked papers with summaries, top picks, rising topics, GitHub radar - [GitHub radar](https://www.ml-quant.com/radar/index.md): what quant developers starred this week - [Track record](https://www.ml-quant.com/track-record/index.md): what became of every featured paper (citations, journals) ## Topics - [Crypto & DeFi](https://www.ml-quant.com/topics/crypto-defi/index.md): 294 papers. Crypto assets, DeFi, stablecoins and blockchain markets. - [LLMs & Text](https://www.ml-quant.com/topics/llms-text/index.md): 577 papers. Large language models, agents, sentiment and text as data in finance. - [Derivatives & Volatility](https://www.ml-quant.com/topics/derivatives-volatility/index.md): 868 papers. Option pricing, volatility models and forecasting, hedging and implied surfaces. - [Trading, Microstructure & Execution](https://www.ml-quant.com/topics/trading-microstructure-execution/index.md): 538 papers. Order books, market making, execution, high-frequency data and trading signals. - [Portfolio & Allocation](https://www.ml-quant.com/topics/portfolio-allocation/index.md): 609 papers. Portfolio construction, allocation, rebalancing and risk budgeting, from Markowitz to deep RL. - [Risk, Credit & Banking](https://www.ml-quant.com/topics/risk-credit-banking/index.md): 363 papers. Credit risk, default prediction, banking, systemic risk and risk measures. - [Asset Pricing & Factors](https://www.ml-quant.com/topics/asset-pricing-factors/index.md): 249 papers. Factor models, anomalies, the cross-section of returns and what survives publication. - [Macro-Finance & Rates](https://www.ml-quant.com/topics/macro-finance-rates/index.md): 258 papers. Rates, the yield curve, monetary policy, inflation and macro-finance. - [Econometrics & Forecasting](https://www.ml-quant.com/topics/econometrics-forecasting/index.md): 297 papers. Forecasting, time series, econometrics and nowcasting. - [ML & AI Methods](https://www.ml-quant.com/topics/ml-ai-methods/index.md): 1107 papers. Machine-learning methods applied to finance: deep learning, boosting, RL and new architectures. - [Corporate Finance](https://www.ml-quant.com/topics/corporate-finance/index.md): 169 papers. Firms, governance, IPOs, M&A and corporate decisions. - [Other](https://www.ml-quant.com/topics/other/index.md): 1067 papers. Everything that doesn't fit the other topics: economics, policy and the odd surprise. ## Papers by venue - [arXiv](https://www.ml-quant.com/papers/arxiv/index.md): Quantitative-finance and ML-for-finance preprints from arXiv. - [SSRN](https://www.ml-quant.com/papers/ssrn/index.md): Working papers in finance and economics from SSRN. - [RePEc](https://www.ml-quant.com/papers/repec/index.md): Economics working papers from RePEc's NEP field reports. - [Machine learning](https://www.ml-quant.com/papers/ml/index.md): The general machine-learning papers the letter carried in 2023-25. ## Recent issues - [No. 132: September 2026, Week 4](https://www.ml-quant.com/issues/2026-09-25/index.md): 2026-09-25 - [No. 131: May 2026, Week 3](https://www.ml-quant.com/issues/2026-05-20/index.md): 2026-05-20 - [No. 130: April 2026, Week 3](https://www.ml-quant.com/issues/2026-04-16/index.md): 2026-04-16 - [No. 129: April 2026, Week 1](https://www.ml-quant.com/issues/2026-04-03/index.md): 2026-04-03 - [No. 128: March 2026, Week 1](https://www.ml-quant.com/issues/2026-03-04/index.md): 2026-03-04 - [No. 127: February 2026, Week 2](https://www.ml-quant.com/issues/2026-02-12/index.md): 2026-02-12 - [No. 126: February 2026, Week 1](https://www.ml-quant.com/issues/2026-02-02/index.md): 2026-02-02 - [No. 125: January 2026, Week 3](https://www.ml-quant.com/issues/2026-01-16/index.md): 2026-01-16 ## Data - [API index](https://www.ml-quant.com/api/v1/index.json): every endpoint - [OpenAPI](https://www.ml-quant.com/openapi.json) - [Track record data](https://www.ml-quant.com/api/v1/track-record.json) - [Search index](https://www.ml-quant.com/api/v1/search.json): every paper, topic, issue and repo in one file - [RSS](https://www.ml-quant.com/feed.xml) - [MCP server](https://www.ml-quant.com/mcp): Streamable HTTP, read-only, no auth ## Optional - [All issues](https://www.ml-quant.com/issues/index.md) - [Library](https://www.ml-quant.com/library/index.md): news, podcasts, blogs, videos, repos - [llms-full.txt](https://www.ml-quant.com/llms-full.txt): the latest issues and the track record in one file - [For agents](https://www.ml-quant.com/agents/index.md) - [About](https://www.ml-quant.com/about/index.md) --- # Quant Letter: September 2026, Week 4: Weekly quantitative finance newsletter *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.* ## Top picks ### 1. [Tail Risk via Semi-Discrete Optimal Transport](https://arxiv.org/abs/2609.27785) · arXiv ### 2. [Artificial Intelligence and Financial Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7515878) · SSRN ### 3. [Agentic AI Systems Beat Asset Pricing Benchmarks](https://econpapers.repec.org/RePEc:nbr:nberwo:35431) · RePEc ### 4. [Frozen Referee for Agent Factor Mining](https://arxiv.org/abs/2609.27051) · arXiv ### 5. [Label Engineering for Stock Selection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7494298) · SSRN ## What's rising ### Option pricing: 14 papers this week, 3.0× the usual 4.6 ### Anomalies: 24 papers this week, 2.2× the usual 11 ### Jump processes: 14 papers this week, 2.3× the usual 6.1 ### Monte Carlo: 25 papers this week, 1.7× the usual 14.5 ### Implied volatility: 24 papers this week, 1.7× the usual 14.1 ## GitHub radar ### Quant repos rising #### [Open-Dev-Society/OpenStock](https://github.com/Open-Dev-Society/OpenStock): Open-source platform for tracking real-time stock prices and company insights. (8 quants, 19.2k stars, +3,593 this week) #### [jarrodwatts/jev-trader](https://github.com/jarrodwatts/jev-trader): AI trading bot making one Jev trade decision per Monad block. (4 quants, 2,395 stars, +1,462 this week, new repo) #### [brycewang-stanford/Auto-Empirical-Research-Skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills): Collection of 23,000+ agent skills for empirical research in social sciences. (4 quants, 4,374 stars, +500 this week) #### [anthropics/financial-services](https://github.com/anthropics/financial-services): Anthropic's financial services implementation. (4 quants, 37.5k stars, +2,589 this week) #### [Yijia-Xiao/FinanceHarness](https://github.com/Yijia-Xiao/FinanceHarness): Autonomous financial deep research framework with agentic capabilities. (2 quants, 176 stars, +33 this week, new repo) #### [caiovicentino/eikos](https://github.com/caiovicentino/eikos): Calibrated typed-decision models for finance and trading applications. (2 quants, 18 stars, +18 this week, new repo) #### [securo-finance/securo](https://github.com/securo-finance/securo): Open-source self-hosted privacy-first personal finance manager. (2 quants, 3,783 stars, +181 this week) #### [ccxt/ccxt](https://github.com/ccxt/ccxt): Unified API for 100+ crypto exchanges and prediction markets. (2 quants, 44.1k stars, +123 this week) ### What quants are playing with #### [NandhaKishorM/laya](https://github.com/NandhaKishorM/laya): Non-autoregressive decision engine for typed choices over text in 100+ languages. (28 quants, 24.0k stars, +23.7k this week, new repo) #### [browser-use/jev-ultrafast](https://github.com/browser-use/jev-ultrafast): Fastest and cheapest web agent for automation tasks. (19 quants, 20.1k stars, +14.0k this week, new repo) #### [google/ax](https://github.com/google/ax): Google's open agentic orchestration runtime for agent systems. (19 quants, 11.1k stars, +9,108 this week) #### [jaredpalmer/kev](https://github.com/jaredpalmer/kev): Decision models built on Qwen3.5/3.8 you can train locally. (13 quants, 6,886 stars, +6,653 this week, new repo) #### [Contrastive-LM/CLM](https://github.com/Contrastive-LM/CLM): Contrastive language model implementation. (8 quants, 1,065 stars, +1,065 this week, new repo) #### [nokia-applied-research/AnyJev](https://github.com/nokia-applied-research/AnyJev): Turn any LLM into a Jev-style decision model with typed decisions. (7 quants, 590 stars, +590 this week, new repo) ## Our track record ### 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](https://arxiv.org/abs/2306.06031) was featured 5 days after release; it has 492 citations. ## arXiv __[Tail Risk via Semi-Discrete Optimal Transport](https://arxiv.org/abs/2609.27785)__: 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. (2026-09-24, fanfare: 4) ![Variational method for optimal transport](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-01-001a7c5a.png) __[Frozen Referee for Agent Factor Mining](https://arxiv.org/abs/2609.27051)__: 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. (2026-09-24, fanfare: 4) ![Persistence and monetisation by family](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-02-0501a54b.png) __[Time-Series Validation Trade-Offs Revisited](https://arxiv.org/abs/2609.29530)__: Proves that training sufficiency, test coverage, and temporal causality cannot be maximized simultaneously in time-series validation, pricing each constraint explicitly. (2026-09-25, fanfare: 4) ![The feasible region of strictly causal schemes, \alpha+\beta\leq 1 (Theorem 1 (c)), and the coordinates of the standard](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-03-d6b3b9ad.png) __[Adversarial RL for Hawkes Market Making](https://arxiv.org/abs/2609.22785)__: 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. (2026-09-22, fanfare: 3) ![Kernel density estimates of terminal wealth in G19](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-04-51074eeb.png) __[MFAST Framework for News-Based Trading](https://arxiv.org/abs/2609.23703)__: 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. (2026-09-22, fanfare: 3) ![MFAST system architecture for market-friction-aware news-based trading](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-05-fe752553.png) __[Universal Diffusion Models for Volatility Surfaces](https://arxiv.org/abs/2609.22893)__: 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. (2026-09-22, fanfare: 3) ![Conditional FiLM denoiser architecture](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-06-686203ce.png) __[AlphaDiverse: Multi-Agent Alpha Factor Mining](https://arxiv.org/abs/2609.29014)__: 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. (2026-09-25, fanfare: 3) ![AlphaDiverse overview](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-07-00e00707.png) __[Forecast-Dojo: LLM Forecasting Benchmark](https://arxiv.org/abs/2609.28876)__: 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. (2026-09-25, fanfare: 3) ![Overview of Forecast-Dojo](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-08-1e9c90b3.png) __[Active Portfolio Allocation with SPT](https://arxiv.org/abs/2609.27113)__: 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. (2026-09-24, fanfare: 3) ![Top row: A simulated path of market diversity and dispersion under the calibrated mean-reverting SDD model (left), and](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-09-7374f4a8.png) __[Decision-Focused Learning for Portfolio Optimization](https://arxiv.org/abs/2609.21427)__: Proposes a KKT-based decision-focused learning method that trains mean-variance portfolio models by directly minimizing downstream portfolio loss while preserving all constraints. (2026-09-21, fanfare: 3) __[DefaultGNN for Corporate Default Prediction](https://arxiv.org/abs/2609.25542)__: 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. (2026-09-23, fanfare: 3) ![Overall framework of DefaultGNN . View-specific embeddings learned from multiplex transaction networks are fused via](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-11-5ffc58b8.png) __[Sentiment Arcs in Central Bank Communication](https://arxiv.org/abs/2609.25034)__: 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. (2026-09-23, fanfare: 3) ![Seed phrase validation: PCA of seed embeddings](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-12-acbff833.png) __[Trust, Rule of Law, and the Size Premium](https://arxiv.org/abs/2609.26212)__: 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. (2026-09-23, fanfare: 3) ![Bayesian Model-Averaged Coefficient Summary](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-13-322c707a.png) __[Multi-Task Learning for Stock Forecasting](https://arxiv.org/abs/2609.25617)__: 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. (2026-09-23, fanfare: 3) ![Cumulative long-short returns of APO and three portfolio-construction baselines](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-14-4d7feed8.png) __[Temporal Hierarchy Forecasting for Electricity](https://arxiv.org/abs/2609.23223)__: 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%. (2026-09-22, fanfare: 3) ![Top two panels: German (DE) and Spanish (ES) day-ahead electricity prices from 5 January 2018 to 31 December 2025](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-15-82628f04.png) __[Macroeconomic Tail Risk Drivers](https://arxiv.org/abs/2609.26994)__: 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. (2026-09-24, fanfare: 3) ![Time series of GNP growth, inflation, and credit spread showing tail risk distributions with regime shading.](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-16-bc38253e.png) __[ETH-TraceBench: Ethereum DeFi Benchmark](https://arxiv.org/abs/2609.23659)__: 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. (2026-09-22, fanfare: 3) ![ETH-TraceBench benchmark construction and evaluation pipeline](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-17-ae28e506.png) __[FinInteract Benchmark for Ambiguous Financial QA](https://arxiv.org/abs/2609.24002)__: 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. (2026-09-22, fanfare: 3) ![Per-category model performance on clarification capability across entity, metric, temporal, and recognition policy ambiguities.](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-18-653e8945.png) __[Stochastic Nested Fixed Point BLP Estimation](https://arxiv.org/abs/2609.23998)__: 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. (2026-09-22, fanfare: 3) ![Small-Sample Distribution of the BLP Estimator](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-19-bb02e026.png) __[Rough HAR Model for Realized Variance](https://arxiv.org/abs/2609.21587)__: Augmenting HAR with a negative moving-average component approximates rough dynamics, outperforming classical models out-of-sample and matching continuous-time rough model accuracy. (2026-09-21, fanfare: 3) ![Autocorrelation functions comparing fBm, IOU, Rough AR, and Rough HAR models](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-20-0922e21e.png) __[Optimal Liquidity Provision and Rebate Design](https://arxiv.org/abs/2609.26606)__: 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. (2026-09-23, fanfare: 2) ![Limit order execution intensities for ask and bid sides across spread ticks](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-21-bda870db.png) __[Surface-Driven Stochastic Volatility for Commodities](https://arxiv.org/abs/2609.27138)__: 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. (2026-09-24, fanfare: 2) ![Time series of daily CME CVOL soybean surface indicators: ATM volatility, skew, skew ratio, and convexity from 2013–2025.](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-22-8ffaed0a.png) __[Network Realized GARCH-Itô Models](https://arxiv.org/abs/2609.29515)__: Introduces a network realized GARCH-Itô model that identifies dynamic volatility transmission among assets using high-frequency data, outperforming recursive forecasts on sector ETFs. (2026-09-25, fanfare: 2) ![Connectedness across market regimes](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-23-782d0f3b.png) __[FinRankGRPO: LLM Portfolio Ranking](https://arxiv.org/abs/2609.24175)__: 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. (2026-09-22, fanfare: 2) ![The two-stage construction framework of our FinRankGRPO, Stage 1 is SFT in high quality distill CoT datasets, Stage 2](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-24-ba73f686.png) __[Optimal Execution Under Cash Constraints](https://arxiv.org/abs/2609.27786)__: Extends the Almgren-Chriss optimal execution framework to enforce intertemporal cash constraints, reducing peak cash drawdown while maintaining implementation shortfall in multi-asset rebalancing. (2026-09-24, fanfare: 2) ![Joint distributions of peak cash drawdown and combined IS for Ours and AC free](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-25-f1c496e3.png) __[Machine Learning Detects Black-Scholes Deviations](https://arxiv.org/abs/2609.27764)__: 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. (2026-09-24, fanfare: 2) ![Model comparison across three representational regimes](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-26-812392bb.png) __[Rule-Based Pricing Algorithms in Digital Markets](https://arxiv.org/abs/2609.26861)__: 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. (2026-09-24, fanfare: 2) ![Average Market Price by LLM Model Configuration](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-27-92f54243.png) __[Critical Line Algorithm and Constrained LASSO](https://arxiv.org/abs/2609.25704)__: Shows that mean-variance portfolio selection and the constrained LASSO trace identical piecewise-linear solution paths, mapping their parametrizations exactly. (2026-09-23, fanfare: 2) ![Proposition 4 checked along the path](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-28-f60037a5.png) __[Optimal Investment under Integrated Variance Clocks](https://arxiv.org/abs/2609.26349)__: Characterizes optimal consumption and investment strategies in markets with stochastic volatility clocks using infinite-horizon backward SDEs, extending to rough and hyper-rough regimes. (2026-09-23, fanfare: 2) ![Optimal Investment under Integrated Variance Clocks](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-29-368f83d5.png) __[Deep Learning Reflected BSDE under Paired Ambiguity](https://arxiv.org/abs/2609.23768)__: Develops a deep learning scheme for optimal stopping under simultaneous model and discount-rate ambiguity, with application to American option valuation under uncertainty. (2026-09-22, fanfare: 2) ![Training diagnostics showing value estimates and control processes converging across multiple scenarios with bounded discount rate ambiguity.](https://www.ml-quant.com/issues/2026-09-25/figures/arxiv-30-ad24fd37.png) ## SSRN __[Artificial Intelligence and Financial Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7515878)__: A survey examines how AI transforms information production, intermediation, and market structure, with implications for efficiency, competition and financial stability. (2026-09-24, fanfare: 4) __[Label Engineering for Stock Selection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7494298)__: 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. (2026-09-22, fanfare: 4) __[Reinforcement Learning Agents Enable Collusion](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7500483)__: 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. (2026-09-24, fanfare: 4) __[Defence Sector Repricing Before Ukraine](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7477998)__: 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. (2026-09-19, fanfare: 4) __[Forward Guidance and Bank Credit Supply](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7514178)__: High-frequency analysis reveals contractionary forward guidance immediately cuts bank lending, while expansionary guidance produces weak stimulus, driven by binding capital constraints. (2026-09-23, fanfare: 3) __[LLM Stock Rankings and Look-Ahead Bias](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7490302)__: 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. (2026-09-22, fanfare: 3) __[Training-Data Leakage in LLM Stock Signals](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7485818)__: The study measures recall versus forecasting in an LLM's stock rankings by comparing cross-sectional information coefficients inside and outside the training window. (2026-09-21, fanfare: 3) __[Zero Fees Drive Fake Volume in Crypto Futures](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7512338)__: 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. (2026-09-23, fanfare: 3) __[FOMC Semantic Novelty and Financial Stress](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7519200)__: 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. (2026-09-24, fanfare: 3) __[LASSO Benchmarks Reveal Mutual Fund Alpha](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7508299)__: 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. (2026-09-23, fanfare: 3) __[Margin Debt Growth and Factor Momentum](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7512099)__: 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. (2026-09-23, fanfare: 3) __[LLM Factor Search with Transaction Cost Penalties](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7498983)__: 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. (2026-09-21, fanfare: 3) __[Training Option Models on Prices not Volatility](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7498639)__: 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. (2026-09-22, fanfare: 3) __[Securitization Amplifies Rate Transmission](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7515879)__: 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. (2026-09-24, fanfare: 3) __[Hedge Fund Leverage Amplifies Bond Volatility](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7506360)__: 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. (2026-09-23, fanfare: 3) __[Price Delay and Momentum Profits](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7518623)__: Momentum profits concentrate among firms with high price delay, a measure of information friction, directly supporting theories that gradual information incorporation drives momentum. (2026-09-24, fanfare: 3) __[Industry Networks Predict Market Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7486138)__: 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. (2026-09-19, fanfare: 3) __[Expectations Drive Term Structure Sensitivity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7497046)__: 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. (2026-09-21, fanfare: 3) __[Hedge Fund Returns and Interest Rate Risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7493702)__: 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. (2026-09-20, fanfare: 3) __[Settlement Risk Prices Currency Excess Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7201787)__: 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. (2026-09-24, fanfare: 3) __[Tail Risk Forecasting with Cubic Distributions](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7504480)__: 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. (2026-09-24, fanfare: 3) __[Physics-Constrained Neural Operators for Option Pricing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7498326)__: 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. (2026-09-21, fanfare: 2) __[Systemic Risk in Global Banking Networks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7493706)__: 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. (2026-09-20, fanfare: 2) __[Negative Rates Cut Bank Lending via Asset Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7489554)__: 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. (2026-09-19, fanfare: 2) __[Banking Structure and Euro-Area Monetary Transmission](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7519040)__: 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. (2026-09-24, fanfare: 2) __[Fed Communication Divergence and High-Frequency Trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7479619)__: Semantic and tonal shifts across sequential Federal Reserve communications generate significant intraday price movements and abnormal volume, revealing incomplete information absorption at initial announcement. (2026-09-19, fanfare: 2) __[Negative Rates and Firm Valuations](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7512572)__: 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. (2026-09-23, fanfare: 2) __[Signature-Based Structural Credit Models](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7498599)__: 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. (2026-09-22, fanfare: 2) __[Minimax Portfolio Optimization Under Tail Risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7486600)__: 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. (2026-09-21, fanfare: 2) __[Distressed Debt Exchanges and Creditor Trilemma](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7502204)__: 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. (2026-09-22, fanfare: 3) ## RePEc __[Agentic AI Systems Beat Asset Pricing Benchmarks](https://econpapers.repec.org/RePEc:nbr:nberwo:35431)__: Optimized AI systems analyzing earnings call transcripts double explained variation in stock returns versus standard benchmarks while improving interpretability through human-readable decision rules. (2026-09-17, fanfare: 4) ![Progress on Explaining Asset Prices Around Earnings Announcements](https://www.ml-quant.com/issues/2026-09-25/figures/repec-01-1af3061c.png) __[Stablecoins and the Mundell-Fleming Trilemma](https://econpapers.repec.org/RePEc:fip:fednsr:103702)__: Wallet-level stablecoin data shows crisis countries experience inflows during banking restrictions; this endogenizes capital mobility and tightens monetary policy constraints. (2026-09-14, fanfare: 4) ![Hump-shaped curve showing equilibrium enforcement rises then falls with stablecoin adoption.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-02-f9565d96.png) __[Skewness Risk in Currency Markets](https://econpapers.repec.org/RePEc:cpr:ceprdp:20587)__: 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. (2026-09-17, fanfare: 3) __[Global Credit Cycle Factor Pricing](https://econpapers.repec.org/RePEc:cpr:ceprdp:21268)__: 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. (2026-09-16, fanfare: 3) __[Asset Embeddings from Portfolio Holdings](https://econpapers.repec.org/RePEc:cpr:ceprdp:20082)__: 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. (2026-09-18, fanfare: 3) __[Intermediary Constraints and Global Risk Pricing](https://econpapers.repec.org/RePEc:fip:fedgif:103716)__: A two-country model shows that uncertainty shocks tighten intermediary constraints, widening credit spreads, appreciating the dollar, and raising currency risk premia globally. (2026-09-14, fanfare: 3) ![Model responses to uncertainty shock: credit spreads, exchange rates, and risk premiums over time.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-06-ee191d19.png) __[Carry Trade Returns and Crash Risk](https://econpapers.repec.org/RePEc:cpr:ceprdp:20745)__: 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. (2026-09-17, fanfare: 3) __[Predicting Market Stress with Random Forests](https://econpapers.repec.org/RePEc:cpr:ceprdp:20439)__: 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. (2026-09-17, fanfare: 3) __[Credit Channel of Monetary Policy in Practice](https://econpapers.repec.org/RePEc:boe:boeewp:023260)__: 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. (2026-09-14, fanfare: 3) ![Distribution of reported impacts of higher interest rates on sales, employment and investment, in 2023 Q3](https://www.ml-quant.com/issues/2026-09-25/figures/repec-09-5afeeb7e.png) __[Monetary Policy Shocks Impair Innovation Financing](https://econpapers.repec.org/RePEc:boe:boeewp:023581)__: 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. (2026-09-21, fanfare: 3) ![Persistent productivity loss over 12 years, larger for non-borrowers than borrowers post-shock](https://www.ml-quant.com/issues/2026-09-25/figures/repec-10-37560323.png) __[AI Architecture and Financial Stability](https://econpapers.repec.org/RePEc:cpr:ceprdp:20681)__: 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. (2026-09-17, fanfare: 3) __[Hedge Fund Demand Inelasticity in Repo](https://econpapers.repec.org/RePEc:zbw:safewp:343098)__: 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. (2026-09-17, fanfare: 3) ![Scatter plot showing relationship between hedge fund repo positions and mispricing measures across market segments.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-12-0bcdc6e3.png) __[Household Portfolios and Monetary Transmission](https://econpapers.repec.org/RePEc:cxv:wpaper:2602)__: 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. (2026-09-21, fanfare: 3) __[Financial Constraints and Monetary Price Response](https://econpapers.repec.org/RePEc:hhs:rbnkwp:0468)__: Swedish data reveals that financially constrained firms adjust prices less to monetary shocks, materially dampening aggregate inflation response to policy changes. (2026-09-14, fanfare: 3) ![Impulse Response Functions of Monthly Macro Variables](https://www.ml-quant.com/issues/2026-09-25/figures/repec-14-80a043c2.png) __[Machine Learning for Implied Volatility Forecasting](https://econpapers.repec.org/RePEc:fip:fedgfe:103519)__: 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. (2026-09-17, fanfare: 3) ![Autocorrelation Function of Implied Volatilities](https://www.ml-quant.com/issues/2026-09-25/figures/repec-15-ca11c478.png) __[Capital Flows and Exchange Rates Policy](https://econpapers.repec.org/RePEc:boe:boeewp:023263)__: In response to US monetary tightening, financial channels dominate for small open economies: credit spreads widen and output falls despite currency depreciation. (2026-09-14, fanfare: 3) ![Impulse responses showing GDP, exports, exchange rate, and credit spread with and without financial frictions.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-16-1d62efd5.png) __[Rate Insurance in Equity and Bond Returns](https://econpapers.repec.org/RePEc:nbr:nberwo:35636)__: Stock returns are dampened by rate insurance: falling rates cushion payoff risk in bad times while rising rates in good times hedge duration exposure. (2026-09-13, fanfare: 3) ![Rate insurance effect comparing corporate bonds and equities across duration periods 1988-2019.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-17-31b661f5.png) __[Common Factors Across Stocks, Bonds, Options](https://econpapers.repec.org/RePEc:nbr:nberwo:35579)__: 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. (2026-09-13, fanfare: 2) ![Cumulative returns of the first five common factors (F C)](https://www.ml-quant.com/issues/2026-09-25/figures/repec-18-154327bc.png) __[Credit Card Banking Economics and Profitability](https://econpapers.repec.org/RePEc:nbr:nberwo:35607)__: 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. (2026-09-12, fanfare: 3) __[Bank Runs History and Economic Consequences](https://econpapers.repec.org/RePEc:nbr:nberwo:35504)__: 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. (2026-09-14, fanfare: 3) __[Algorithmic Trading in Agricultural Futures](https://econpapers.repec.org/RePEc:ags:aaea26:404354)__: The study finds that algorithmic trading lowers realized volatility but increases tail co-movement and asymmetry in China's corn and soybean futures markets. (2026-09-25, fanfare: 2) __[LASH Risk and Interest Rate Movements](https://econpapers.repec.org/RePEc:cpr:ceprdp:20158)__: 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. (2026-09-18, fanfare: 2) __[High-Yield Corporate and Sovereign Bonds Converge](https://econpapers.repec.org/RePEc:cpr:ceprdp:20100)__: 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. (2026-09-18, fanfare: 2) __[Economic News Drives Agricultural Volatility](https://econpapers.repec.org/RePEc:ags:asea26:404810)__: 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. (2026-09-23, fanfare: 2) __[USDA Reports Anchor Commodity Price Expectations](https://econpapers.repec.org/RePEc:ags:aaea26:404411)__: 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. (2026-09-23, fanfare: 2) __[Collateral Policy Surprises Stabilize Banking](https://econpapers.repec.org/RePEc:zbw:bubdps:343110)__: Expansionary central bank collateral policy surprises reduce bank default risk and volatility while compressing government bond spreads, transmitting effects distinctly from asset purchases. (2026-09-21, fanfare: 2) ![Scatter plots showing collateral policy surprise correlation with CDS spreads across multiple financial indicators.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-26-c83c53c6.png) __[Adaptive LASSO-MGARCH Volatility Forecasting](https://econpapers.repec.org/RePEc:cdf:wpaper:2026/4)__: Introducing coefficient-specific penalization into multivariate GARCH equations reduces complexity and improves out-of-sample covariance forecasts across bonds, equities, and commodities. (2026-09-16, fanfare: 2) ![Time evolution of returns for the eight assets](https://www.ml-quant.com/issues/2026-09-25/figures/repec-27-b4d64442.png) __[Pension Funds' Swap-Driven Liquidity Risk](https://econpapers.repec.org/RePEc:cpr:ceprdp:21095)__: 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. (2026-09-16, fanfare: 2) __[A Theory of Bank Liquidity Requirements](https://econpapers.repec.org/RePEc:ecb:ecbwps:20263252)__: 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. (2026-09-17, fanfare: 2) ![Supply and demand curves showing equilibrium cash determination in financial markets.](https://www.ml-quant.com/issues/2026-09-25/figures/repec-29-d78f5b03.png) __[Too-Big-to-Fail Premium in European Banking](https://econpapers.repec.org/RePEc:dnb:dnbwpp:868)__: 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. (2026-09-17, fanfare: 2) ![Time-varying estimate of TBTF wedge, scaled to Dec 2024](https://www.ml-quant.com/issues/2026-09-25/figures/repec-30-671485e1.png) --- --- title: Track record url: https://www.ml-quant.com/track-record/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-09-26 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt --- # Track record What became of every paper Quant Letter featured: citations and journal publications from Semantic Scholar, refreshed weekly. - Finance papers featured when new: 2396 - Now published (of those featured at least a year ago): 26% - Cited 100+ times: 10 - Median days from release to feature: 5.0 ## We called it: most cited, featured within two weeks of release - [FinGPT: Open-Source Financial Large Language Models](https://arxiv.org/abs/2306.06031): 492 citations; featured 2023-06-14, 5 days after release; not yet published - [FinMem: A Performance-Enhanced LLM Trading Agent With Layered Memory and Character Design](https://arxiv.org/abs/2311.13743): 253 citations; featured 2023-11-29, 6 days after release; IEEE Transactions on Big Data - [HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction](https://arxiv.org/abs/2408.04948): 231 citations; featured 2024-08-15, 6 days after release; Proceedings of the 5th ACM International Conference on AI in Finance - [TradingAgents: Multi-Agents LLM Financial Trading Framework](http://arxiv.org/abs/2412.20138v1): 220 citations; featured 2025-01-01, 4 days after release; not yet published - [TKAN: Temporal Kolmogorov-Arnold Networks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4825654): 189 citations; featured 2024-05-15, 3 days after release; not yet published - [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://arxiv.org/abs/2306.12659): 140 citations; featured 2023-06-28, 6 days after release; not yet published - [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](http://arxiv.org/abs/2401.05799): 138 citations; featured 2024-01-17, 6 days after release; ACM Transactions on Management Information Systems - [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://arxiv.org/abs/2307.10485): 124 citations; featured 2023-07-26, 7 days after release; not yet published - [Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis](https://arxiv.org/abs/2305.07972): 106 citations; featured 2023-05-18, 5 days after release; Annual Meeting of the Association for Computational Linguistics - [ESG Reputation Risk Matters: An Event Study Based on Social Media Data](https://arxiv.org/abs/2307.11571): 106 citations; featured 2023-07-26, 5 days after release; not yet published - [A Scoping Review of ChatGPT Research in Accounting and Finance](http://dx.doi.org/10.1016/j.accinf.2024.100715): 99 citations; featured 2024-12-12, 11 days after release; International Journal of Accounting Information Systems - [The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research](https://arxiv.org/abs/2305.11528): 97 citations; featured 2023-05-24, 5 days after release; not yet published ## Now out in journals - [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://arxiv.org/abs/2402.03659): Proceedings of the ACM Web Conference 2024, 83 citations - [Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment](https://arxiv.org/abs/2308.00016): Conference on Empirical Methods in Natural Language Processing, 81 citations - [Using Large Language Models for Qualitative Analysis can Introduce Serious Bias](https://arxiv.org/abs/2309.17147): Sociological Methods & Research, 78 citations - [LSTM-ARIMA as a hybrid approach in algorithmic investment strategies](https://arxiv.org/abs/2406.18206): Knowl. Based Syst., 61 citations - [Extracting Financial Data from Unstructured Sources: Leveraging Large Language Models](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4567607): J. Inf. Syst., 51 citations - [Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction](https://arxiv.org/abs/2402.00299): European Journal of Operational Research, 49 citations - [ChatGPT-Based Investment Portfolio Selection](https://arxiv.org/abs/2308.06260): Operations Research Forum, 46 citations - [Bubble economics](https://arxiv.org/abs/2311.03638): Journal of Mathematical Economics, 43 citations - [Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research](http://arxiv.org/abs/2411.04788v1): Proceedings of the 5th ACM International Conference on AI in Finance, 42 citations - [Energy Security and Resilience: Reviewing Concepts and Advancing Planning Perspectives for Transforming Integrated Energy Systems](http://arxiv.org/abs/2504.18396v1): Energy Policy, 42 citations ## Most cited featured papers - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/pdf/2312.00752.pdf): 9205 citations (preprint), featured 2024-06-05 - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://arxiv.org/abs/2402.03300): 9003 citations (preprint), featured 2024-02-07 - [Mistral 7B](https://arxiv.org/abs/2310.06825): 3920 citations (preprint), featured 2023-10-16 - [Depth Anything V2](https://arxiv.org/abs/2406.09414): 2228 citations (Neural Information Processing Systems), featured 2024-06-20 - [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://arxiv.org/abs/2406.01574): 2199 citations (Neural Information Processing Systems), featured 2024-10-09 - [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://arxiv.org/abs/2408.03314): 2189 citations (preprint), featured 2024-08-07 - [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://arxiv.org/abs/2405.21060): 1953 citations (International Conference on Machine Learning), featured 2024-06-05 - [Octo: An Open-Source Generalist Robot Policy](https://arxiv.org/abs/2405.12213): 1880 citations (Robotics: Science and Systems Conference), featured 2024-05-22 - [AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration](https://arxiv.org/abs/2306.00978): 1800 citations (GetMobile: Mobile Computing and Communications), featured 2024-07-24 - [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://arxiv.org/abs/2412.05271): 1775 citations (preprint), featured 2024-12-12 - [Qwen2.5-Coder Technical Report](https://arxiv.org/abs/2409.12186): 1558 citations (preprint), featured 2024-09-25 - [s1: Simple test-time scaling](https://arxiv.org/pdf/2501.19393): 1462 citations (Conference on Empirical Methods in Natural Language Processing), featured 2025-02-05 - [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434): 1459 citations (preprint), featured 2024-05-22 - [Mastering Diverse Domains through World Models](https://arxiv.org/abs/2301.04104): 1418 citations (preprint), featured 2024-04-24 - [MemGPT: Towards LLMs as Operating Systems](https://arxiv.org/abs/2310.08560): 1373 citations (preprint), featured 2023-10-16 - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](http://dx.doi.org/10.48550/arxiv.2303.08896): 1236 citations (Conference on Empirical Methods in Natural Language Processing), featured 2023-10-16 - [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734): 1173 citations (Neural Information Processing Systems), featured 2024-07-10 - [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://arxiv.org/abs/2404.02905): 1169 citations (Neural Information Processing Systems), featured 2024-06-12 - [A Simple and Effective Pruning Approach for Large Language Models](https://arxiv.org/pdf/2306.11695.pdf): 981 citations (International Conference on Learning Representations), featured 2024-05-08 - [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://arxiv.org/abs/2310.00426): 967 citations (International Conference on Learning Representations), featured 2024-01-03 - [TinyLlama: An Open-Source Small Language Model](https://arxiv.org/abs/2401.02385): 902 citations (preprint), featured 2024-01-09 - [TÜLU 3: Pushing Frontiers in Open Language Model Post-Training](https://arxiv.org/abs/2411.15124): 888 citations (preprint), featured 2025-02-05 - [Simple and Effective Masked Diffusion Language Models](https://arxiv.org/abs/2406.07524): 850 citations (Neural Information Processing Systems), featured 2024-06-12 - [Training Large Language Models to Reason in a Continuous Latent Space](https://arxiv.org/abs/2412.06769): 742 citations (preprint), featured 2024-12-12 - [Fourier Neural Operator with Learned Deformations for PDEs on General Geometries](https://arxiv.org/abs/2207.05209): 727 citations (J. Mach. Learn. Res.), featured 2024-05-08 - [KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization](https://arxiv.org/abs/2401.18079): 701 citations (Neural Information Processing Systems), featured 2024-04-10 - [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://arxiv.org/abs/2501.09747): 673 citations (Robotics), featured 2025-01-23 - [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://arxiv.org/abs/2311.17245): 660 citations (Neural Information Processing Systems), featured 2024-04-03 - [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://arxiv.org/pdf/2310.10642.pdf): 627 citations (International Conference on Learning Representations), featured 2023-10-18 - [Scalable Extraction of Training Data from (Production) Language Models](https://arxiv.org/abs/2311.17035): 619 citations (preprint), featured 2023-12-06 - [Ferret: Refer and Ground Anything Anywhere at Any Granularity](https://arxiv.org/abs/2310.07704): 604 citations (International Conference on Learning Representations), featured 2023-10-16 - [BLINK: Multimodal Large Language Models Can See but Not Perceive](https://arxiv.org/pdf/2404.12390.pdf): 603 citations (European Conference on Computer Vision), featured 2024-04-24 - [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://arxiv.org/abs/2311.18259): 603 citations (2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2024-05-01 - [Continuous 3D Perception Model with Persistent State](https://arxiv.org/abs/2501.12387): 593 citations (2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2025-01-23 - [Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks](https://arxiv.org/abs/2404.02151): 580 citations (International Conference on Learning Representations), featured 2024-06-20 - [TOFU: A Task of Fictitious Unlearning for LLMs](https://arxiv.org/abs/2401.06121): 579 citations (preprint), featured 2024-01-17 - [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://arxiv.org/abs/2303.09535): 574 citations (2023 IEEE/CVF International Conference on Computer Vision (ICCV)), featured 2023-10-16 - [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://arxiv.org/abs/2305.00050): 571 citations (Trans. Mach. Learn. Res.), featured 2024-08-21 - [LlaVA-CoT: Let Vision Language Models Reason Step-By-Step](https://arxiv.org/abs/2411.10440): 566 citations (2025 IEEE/CVF International Conference on Computer Vision (ICCV)), featured 2024-11-20 - [Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models](https://arxiv.org/abs/2405.01535): 541 citations (Conference on Empirical Methods in Natural Language Processing), featured 2024-05-08 - [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635): 539 citations (International Conference on Machine Learning), featured 2023-12-13 - [Llemma: An Open Language Model For Mathematics](https://arxiv.org/abs/2310.10631): 497 citations (International Conference on Learning Representations), featured 2023-10-18 - [FinGPT: Open-Source Financial Large Language Models](https://arxiv.org/abs/2306.06031): 492 citations (preprint), featured 2023-06-14 - [CAT3D: Create Anything in 3D with Multi-View Diffusion Models](http://arxiv.org/abs/2405.10314): 482 citations (Neural Information Processing Systems), featured 2024-05-22 - [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848): 481 citations (2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2024-10-23 - [Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model](https://arxiv.org/abs/2408.11039): 480 citations (preprint), featured 2024-08-21 - [SliceGPT: Compress Large Language Models by Deleting Rows and Columns](https://arxiv.org/abs/2401.15024): 466 citations (International Conference on Learning Representations), featured 2024-01-30 - [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://arxiv.org/abs/2406.20094): 466 citations (preprint), featured 2024-07-03 - [Training Language Models to Self-Correct via Reinforcement Learning](https://arxiv.org/abs/2409.12917): 460 citations (International Conference on Learning Representations), featured 2024-10-09 - [Capabilities of Gemini Models in Medicine](https://arxiv.org/abs/2404.18416): 446 citations (preprint), featured 2024-05-08 - [An Embodied Generalist Agent in 3D World](https://arxiv.org/abs/2311.12871): 443 citations (International Conference on Machine Learning), featured 2024-05-15 - [The Platonic Representation Hypothesis](https://arxiv.org/abs/2405.07987): 429 citations (International Conference on Machine Learning), featured 2024-05-15 - [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](http://arxiv.org/abs/2406.15349v1): 425 citations (Neural Information Processing Systems), featured 2024-11-06 - [PGSR: Planar-Based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://arxiv.org/pdf/2406.06521): 415 citations (IEEE Transactions on Visualization and Computer Graphics), featured 2024-06-12 - [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](http://dx.doi.org/10.2139/ssrn.4412788): 403 citations (preprint), featured 2023-07-05 - [RAFT: Adapting Language Model to Domain Specific RAG](https://arxiv.org/abs/2403.10131): 401 citations (preprint), featured 2024-06-12 - [Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models](https://arxiv.org/abs/2501.01423): 395 citations (2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2025-01-08 - [Data Selection for Language Models via Importance Resampling](https://arxiv.org/abs/2302.03169): 371 citations (Neural Information Processing Systems), featured 2023-10-25 - [To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning](https://arxiv.org/abs/2409.12183): 371 citations (International Conference on Learning Representations), featured 2024-09-25 - [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://arxiv.org/abs/2411.15098): 370 citations (2025 IEEE/CVF International Conference on Computer Vision (ICCV)), featured 2024-11-27 All rows: https://www.ml-quant.com/api/v1/track-record.json --- --- title: Quant Letter No. 131: May 2026, Week 3 url: https://www.ml-quant.com/issues/2026-05-20/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-09-26 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt issue_date: 2026-05-20 --- # Quant Letter No. 131: May 2026, Week 3 Sent 2026-05-20. 44 items. ## RePEc ### Historical Trending - __[Predicting VIX with ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2024.2439458%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A24%3Ay%3A2024%3Ai%3A12%3Ap%3A1857-1873)__: The study shows that machine learning can better predict the VIX by utilizing jobless claims data to enhance market volatility forecasts. (2024-01-21, shares: 12) · https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2024-i-12-p-1857-1873/ - __[Optimizing KSE-30 with ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs43069-025-00421-4%3Bh%3Drepec%3Aspr%3Asnopef%3Av%3A6%3Ay%3A2025%3Ai%3A1%3Ad%3A10.1007_s43069-025-00421-4)__: This research analyzes equity returns on the Pakistan Stock Exchange, revealing flaws in traditional methods and proposing a new strategy for efficient portfolio management. (2025-03-07, shares: 10) · https://www.ml-quant.com/papers/repec/spr-snopef-v-6-y-2025-i-1-d-10-1007-s43069-025-00421-4/ - __[Risk Parity in Fat-Tailed Markets](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Fjtsa.12792%3Bh%3Drepec%3Abla%3Ajtsera%3Av%3A46%3Ay%3A2025%3Ai%3A2%3Ap%3A353-377)__: The paper explores risk parity portfolio optimization amid volatile market conditions, indicating that advanced models can enhance performance and stability during downturns. (2025-08-11, shares: 9) · https://www.ml-quant.com/papers/repec/bla-jtsera-v-46-y-2025-i-2-p-353-377/ - __[Automated Trading in Emerging Markets](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-025-00754-3%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A11%3Ay%3A2025%3Ai%3A1%3Ad%3A10.1186_s40854-025-00754-3)__: The article discusses combining algorithmic trading with passive investing in emerging markets, advocating for an Adaptive Trading System to reduce capital losses in downturns. (2025-04-06, shares: 9) · https://www.ml-quant.com/papers/repec/spr-fininn-v-11-y-2025-i-1-d-10-1186-s40854-025-00754-3/ - __[Eurozone Banks Stock Prediction](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.icfm.ro%2FRePEc%2Fvls%2Fvls_pdf%2Fvol28i4p29-42.pdf%3Bh%3Drepec%3Avls%3Afinstu%3Av%3A28%3Ay%3A2024%3Ai%3A4%3Ap%3A29-42)__: The study finds that conventional machine learning models outperform advanced deep learning techniques in predicting stock price movements in European banks. (2024-11-01, shares: 7) · https://www.ml-quant.com/papers/repec/vls-finstu-v-28-y-2024-i-4-p-29-42/ - __[Sharpe Ratio vs. Buy-and-Hold](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Farchive.conscientiabeam.com%2Findex.php%2F29%2Farticle%2Fview%2F4102%2F8464%3Bh%3Drepec%3Apkp%3Ateafle%3Av%3A12%3Ay%3A2025%3Ai%3A1%3Ap%3A120-142%3Aid%3A4102)__: Sharpe Ratio trading strategies consistently beat buy-and-hold, highlighting market inefficiencies and supporting the Adaptive Market Hypothesis. (2025-07-09, shares: 8) · https://www.ml-quant.com/papers/repec/pkp-teafle-v-12-y-2025-i-1-p-120-142-id-4102/ - __[Dynamic Modeling of Chinese Markets](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F01605682.2024.2329156%3Bh%3Drepec%3Ataf%3Atjorxx%3Av%3A76%3Ay%3A2025%3Ai%3A1%3Ap%3A97-110)__: A new machine learning approach using dynamic autoregressive models effectively analyzes complex time series data, offering a modern alternative in financial modeling. (2025-05-02, shares: 6) · https://www.ml-quant.com/papers/repec/taf-tjorxx-v-76-y-2025-i-1-p-97-110/ - __[News Sentiment Impact on Stock Volatility](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006086%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006086)__: Accurate news sentiment measurement significantly affects stock return volatility, with GPT-4 outperforming RavenPack in sentiment classification for Dow Jones firms. (2025-02-09, shares: 6) · https://www.ml-quant.com/papers/repec/eee-ecolet-v-247-y-2025-i-c-s0165176524006086/ - __[Machine Learning in Market Risk Management](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0305048324002135%3Bh%3Drepec%3Aeee%3Ajomega%3Av%3A133%3Ay%3A2025%3Ai%3Ac%3As0305048324002135)__: Two new deep learning models enhance the accuracy and stability of estimating Value at Risk and Expected Shortfall, improving risk management in finance. (2025-02-04, shares: 6) · https://www.ml-quant.com/papers/repec/eee-jomega-v-133-y-2025-i-c-s0305048324002135/ - __[Optimal Strategies in Pension Plans](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0167668725000137%3Bh%3Drepec%3Aeee%3Ainsuma%3Av%3A121%3Ay%3A2025%3Ai%3Ac%3Ap%3A100-110)__: The study explores optimal management strategies for target benefit pension plans, showing how fund managers can maximize expected utility through thoughtful investments and benefit adjustments. (2025-05-19, shares: 5) · https://www.ml-quant.com/papers/repec/eee-insuma-v-121-y-2025-i-c-p-100-110/ ## Papers with code ### Trending - __[SkillsVote: Governance for Agent Skills](https://github.com/MemTensor/skills-vote)__: Governance for Agent Skills: SkillsVote is a system that helps long-term AI agents manage and improve their skills over time. (2026-05-19, shares: 219) - __[MMSkills: Multimodal Skills for Agents](https://github.com/DeepExperience/MMSkills)__: Multimodal Skills for Agents: Multimodal procedural knowledge frameworks enhance visual agents by merging text and visuals for improved decision-making. (2026-05-18, shares: 101) - __[SelfDistilled RL: Enhancing Training with Self-Distillation](https://github.com/ZJU-REAL/SDAR)__: Enhancing Training with Self-Distillation: SDAR optimizes training for multiturn agents in reinforcement learning by boosting positive feedback and minimizing negative feedback. (2026-05-15, shares: 59) ### Rising - __[AI Research Trust](https://github.com/worldbench/awesome-ai-auto-research)__: AI excels in structured tasks but needs human oversight in scientific research to handle new ideas and judgment effectively. (2026-05-19, shares: 48) - __[Olympiad Reasoning Scaling](https://github.com/Simplified-Reasoning/SU-01)__: A new systematic approach boosts reasoning models, making them top contenders in math and physics competitions through advanced learning methods. (2026-05-15, shares: 42) - __[Dynamic NPC Steering](https://github.com/INV-WZQ/ReactiveGWM)__: ReactiveGWM improves game play by separating player controls from NPC actions, enhancing strategic options in various games. (2026-05-18, shares: 33) - __[DexJoCo for Manipulation](https://github.com/brave-eai/dexjoco)__: DexJoCo provides a benchmark and toolkit for evaluating dexterous manipulation skills, along with a budget-friendly data collection system. (2026-05-18, shares: 31) ## GitHub ### Finance - __[Turso: SQLite SQL Database](https://github.com/tursodatabase/turso)__: SQLite SQL Database: Turso is a SQL database that works well with SQLite. (2023-08-26, shares: 18767) - __[Optimizing LLM Inference](https://github.com/algorithmicsuperintelligence/optillm)__: The article focuses on improving inference proxies for large language models. (2024-08-22, shares: 3843) - __[Fullstack China AShare Toolkit](https://github.com/simonlin1212/a-stock-data)__: A new toolkit helps AI coding assistants analyze China's A-share market. (2026-05-11, shares: 1403) - __[Ashare Multi-Agent Framework](https://github.com/simonlin1212/TradingAgents-astock)__: A research framework uses AI agents to discuss investment strategies for the A-share market. (2026-05-13, shares: 394) - __[LLMDriven Alpha Factory](https://github.com/Miasyster/QuantGPT)__: An agent-based alpha factory enables LLMs to independently conduct backtests and analyze factors. (2026-03-20, shares: 215) ### Trending - __[AffaanmECC: AI Performance Optimization](https://github.com/affaan-m/ECC)__: AI Performance Optimization: Claude Code Codex improves AI agents' abilities by using different skills and development techniques. (2026-01-18, shares: 187081) - __[RiskLabAI: Advanced Risk Management](https://github.com/RiskLabAI/RiskLabAI.py)__: Advanced Risk Management: The article discusses how Python can be applied in financial AI applications. (2022-05-18, shares: 110) - __[ViktorAxelsenMemSkill: Evolving AI Memory Skills](https://github.com/ViktorAxelsen/MemSkill)__: Evolving AI Memory Skills: MemSkill enhances AI agents' memory skills through continuous learning. (2026-01-30, shares: 479) - __[Struktoaimirage: Unified AI Filesystem](https://github.com/strukto-ai/mirage)__: Unified AI Filesystem: The article introduces a unified virtual filesystem that boosts efficiency for AI agents. (2026-05-06, shares: 2135) - __[MVanHornCliprintingPress: AI CLI with SQLite Sync](https://github.com/mvanhorn/cli-printing-press)__: AI CLI with SQLite Sync: This tool picks out key features from rival APIs to build a better command-line interface for AI agents. (2026-03-23, shares: 2069) ## Podcasts ### Quantitative - __[Crisis Alpha and Portfolio Strategies](http://localhost:8888/TopTradersUnplugged/ttu-2021/homepage/yoav-git-roberto-croce)__: Yoav Git and Rob Croce from Fidelity Investments examine crisis alpha and diversification in trend following portfolio construction amid current market conditions. (2026-05-16, shares: 9) - __[Impact of Passive Investing](http://localhost:8888/TopTradersUnplugged/ttu-2021/homepage/gm101-when-passive-breaks-the-market-ft-hari-krishnan-cem-karsan)__: Hari Krishnan explores how increasing passive investment may disrupt price discovery and lead to market instability. (2026-05-20, shares: 8) - __Tom Lee on Stock Trends__: Tom Lee from Fundstrat highlights stock resilience during global tensions, shares his S&P 500 forecast, and launches the Fundstrat Granny Shots ETF. (2026-05-15, shares: 7) - __[Wealth Building for Tech Pros](https://soundcloud.com/patrick-nettlebay/financial-planning-101)__: Stan Leong and Nikhil focus on innovative financial planning and options-based income strategies tailored for engineers and executives. (2026-05-18, shares: 6) - __[Charities and Cash Risks](https://audioboom.com/posts/8894065)__: Nancy Kilpatrick and Andrzej Pioch warn UK charities about the risks and costs of holding excessive cash, advocating for more active investment strategies. (2026-05-14, shares: 6) ### Related - __[Global Futures Insights](https://atanyrate.podbean.com/e/global-rates-analyzing-eurex-and-us-futures-roll-and-cross-currency-bases/)__: Khagendra Gupta and Ipek Ozil discuss the factors influencing US and Eurex futures rollover and cross currency bases in their podcast from May 15, 2026. (2026-05-15, shares: 5) - __[Asia Equity Rally](https://traffic.megaphone.fm/GLD6472289045.mp3)__: Tim Moe from Goldman Sachs talks about how artificial intelligence and semiconductor cycles are causing differences in Asian equity markets in a May 19, 2026 podcast. (2026-05-20, shares: 3) - __[Bloomberg CTO Interview](https://macrohive.libsyn.com/ep-358-shawn-edwards-on-engineering-trust-in-ai-the-bloomberg-way-and-the-terminals-future)__: Shawn Edwards, Bloomberg's CTO, highlights technological innovations and the Bloomberg terminal, focusing on trust and process automation in a recent podcast. (2026-05-15, shares: 3) - __[EM Fixed Income Politics](https://atanyrate.podbean.com/e/em-fixed-income-inflation-pressures-and-idiosyncratic-emea-em-politics/)__: Anezka Christovova, Ben Ramsey, and Michael Harrison analyze how recent market changes are affecting the emerging market fixed income sector in their May 14, 2026 podcast. (2026-05-14, shares: 3) - __[Commodity Infrastructure Insights](https://atanyrate.podbean.com/e/global-commodities-infrastructure-101/)__: Commodity analysts discuss the effects of the Iran conflict on infrastructure recovery and supply chains in gas and metals in a podcast from May 15, 2026. (2026-05-15, shares: 3) ## Blogs ### Quantitative - __[Revisiting Low Risk Pullback Strategy](https://www.quantifiedstrategies.com/low-risk-pullback-trading-strategy/)__: The article discusses the Low Risk Pullback Strategy, a trading approach that has been effective since it was first introduced over ten years ago. (2026-05-19, shares: 1) ### Related - __[Trend Followers and Responsible Investing](https://www.man.com/insights/path-less-travelled-there-no-ri)__: Systematic macro strategies, particularly trend-following approaches, face difficulties in responsible investing. However, these challenges can be overcome with specific solutions. (2026-05-16, shares: 0) ## X / Twitter ### Quantitative - __[Crypto and Risk Parity Insights](https://x.com/quantseeker/status/2056867207326441628)__: The Research Recap discusses different financial subjects like crypto options, LEAPS (long-term equity anticipation securities), risk parity, and how to predict market volatility. (2026-05-19, shares: 3) ### Miscellaneous - __[Coding as a Moat](https://x.com/carlcarrie/status/2056780447275798837)__: Claude Code argues that focusing on taste, workflow, trust, and learning speed is now more important than just coding skills. (2026-05-19, shares: 1) - __[PostAI Record Systems Framework](https://x.com/jmelaskyriazi/status/2054704199183777801)__: The article explores how record-keeping systems are changing due to AI advancements, providing a helpful framework to understand these developments. (2026-05-14, shares: 0) ## Reddit ### Quantitative - __[Quants and Math](https://www.reddit.com/r/quantfinance/comments/1tfz30b/do_quants_produce_publishable_research_in_math/)__: (2026-05-17, shares: 40) ### Rising - __[Quant LARP Guide](https://www.reddit.com/r/quantfinance/comments/1tg5qxm/screw_quant_research_quant_trading_even_quant_dev/)__: (2026-05-17, shares: 74) --- --- title: Quant Letter No. 130: April 2026, Week 3 url: https://www.ml-quant.com/issues/2026-04-16/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-09-26 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt issue_date: 2026-04-16 --- # Quant Letter No. 130: April 2026, Week 3 Sent 2026-04-16. 53 items. ## arXiv ### Finance - __[Lambda Rényi Value-at-Risk: A New Measure](https://arxiv.org/abs/2604.10657v1)__: A New Measure: The article introduces the Lambda extension of Rényi entropic value-at-risk (Λ-EVaR), a new risk measure designed for better risk management by allowing adjustable confidence levels and sensitivity to higher moments. (2026-04-12, shares: 0) · https://www.ml-quant.com/papers/arxiv/2604.10657/ ### Historical Trending - __[AI Agents in Finance](https://arxiv.org/abs/2603.13942v2)__: Recent AI advancements are enhancing financial automation by creating integrated systems that use autonomous agents for better decision-making and processing, highlighting the need for effective agent governance. (2026-03-14, shares: 1) · https://www.ml-quant.com/papers/arxiv/2603.13942/ - __[Causal PDE-Control for Portfolio Optimization](https://arxiv.org/abs/2509.09585v3)__: Causal PDE-Control Models (CPCMs) offer a strong and clear framework for portfolio allocation that combines causal factors and complex filtering, outperforming standard econometric and machine-learning techniques. (2025-09-11, shares: 1) · https://www.ml-quant.com/papers/arxiv/2509.09585/ - __[Meanfield Models in Insurance](https://arxiv.org/abs/2511.04198v2)__: A mean-field model simplifies complex insurance liabilities into manageable solutions, showing that large groups of interdependent individuals can be effectively analyzed in both life and non-life insurance scenarios. (2025-11-06, shares: 0) · https://www.ml-quant.com/papers/arxiv/2511.04198/ - __[Software Skills through Digital Traces](https://arxiv.org/abs/2504.03581v2)__: Python is helping software programmers develop important skills through a more structured learning process due to recent tech changes. (2025-04-04, shares: 0) · https://www.ml-quant.com/papers/arxiv/2504.03581/ - __[Automating Customer Needs with LLMs](https://arxiv.org/abs/2503.01870v2)__: Large Language Models are streamlining the process of identifying customer needs, letting analysts concentrate on more valuable work while still delivering precise insights. (2025-02-25, shares: 0) · https://www.ml-quant.com/papers/arxiv/2503.01870/ - __[Impact of Remote Work on EU Development](https://arxiv.org/abs/2604.08252v1)__: Remote work after the pandemic is causing people to move within cities for better quality of life, rather than relocating to rural areas. (2026-04-09, shares: 0) · https://www.ml-quant.com/papers/arxiv/2604.08252/ ## RePEc ### Historical Trending - __[VIX Prediction with ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2024.2439458%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A24%3Ay%3A2024%3Ai%3A12%3Ap%3A1857-1873)__: Machine learning improves predictions of the VIX by highlighting the impact of weekly jobless claims on market volatility. (2024-09-05, shares: 12) · https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2024-i-12-p-1857-1873/ - __[KSE0 Portfolio Optimization](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs43069-025-00421-4%3Bh%3Drepec%3Aspr%3Asnopef%3Av%3A6%3Ay%3A2025%3Ai%3A1%3Ad%3A10.1007_s43069-025-00421-4)__: The study analyzes asset effects on downturns in the Pakistan Stock Exchange and suggests a portfolio optimization strategy. (2025-07-19, shares: 10) · https://www.ml-quant.com/papers/repec/spr-snopef-v-6-y-2025-i-1-d-10-1007-s43069-025-00421-4/ - __[Automated Trading in Emerging Markets](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-025-00754-3%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A11%3Ay%3A2025%3Ai%3A1%3Ad%3A10.1186_s40854-025-00754-3)__: It addresses challenges for emerging market investors during downturns and presents a new trading system to stabilize portfolios. (2025-02-11, shares: 9) · https://www.ml-quant.com/papers/repec/spr-fininn-v-11-y-2025-i-1-d-10-1186-s40854-025-00754-3/ - __[Risk Parity with Tail Risk](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Fjtsa.12792%3Bh%3Drepec%3Abla%3Ajtsera%3Av%3A46%3Ay%3A2025%3Ai%3A2%3Ap%3A353-377)__: The research explores a risk parity portfolio optimization method, showing enhanced performance during market stress using a non-Gaussian approach. (2025-01-07, shares: 9) · https://www.ml-quant.com/papers/repec/bla-jtsera-v-46-y-2025-i-2-p-353-377/ - __[Eurozone Bank Stock Prediction](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.icfm.ro%2FRePEc%2Fvls%2Fvls_pdf%2Fvol28i4p29-42.pdf%3Bh%3Drepec%3Avls%3Afinstu%3Av%3A28%3Ay%3A2024%3Ai%3A4%3Ap%3A29-42)__: Findings reveal that traditional machine learning models better predict stock price direction than deep learning models in the Eurozone banking sector. (2024-10-07, shares: 7) · https://www.ml-quant.com/papers/repec/vls-finstu-v-28-y-2024-i-4-p-29-42/ - __[Sharpe Ratio and Market Efficiency](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Farchive.conscientiabeam.com%2Findex.php%2F29%2Farticle%2Fview%2F4102%2F8464%3Bh%3Drepec%3Apkp%3Ateafle%3Av%3A12%3Ay%3A2025%3Ai%3A1%3Ap%3A120-142%3Aid%3A4102)__: Sharpe Ratio Minimae and Maximae strategies outperform buy-and-hold investments, confirming the Adaptive Market Hypothesis in global stock indices from 1998 to 2023. (2025-06-24, shares: 8) · https://www.ml-quant.com/papers/repec/pkp-teafle-v-12-y-2025-i-1-p-120-142-id-4102/ - __[News Sentiment in Stock Volatility](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006086%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006086)__: Accurate news sentiment measurement improves understanding of stock return volatility, with GPT-4 outperforming RavenPack in Dow Jones firms from 2019 to 2023. (2025-02-13, shares: 6) · https://www.ml-quant.com/papers/repec/eee-ecolet-v-247-y-2025-i-c-s0165176524006086/ - __[Finite Mixture Models in Finance](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F01605682.2024.2329156%3Bh%3Drepec%3Ataf%3Atjorxx%3Av%3A76%3Ay%3A2025%3Ai%3A1%3Ap%3A97-110)__: A new machine learning method for analyzing complex time series proves flexible and accurate for financial data, offering an alternative to traditional models during the COVID-19 pandemic. (2025-03-02, shares: 6) · https://www.ml-quant.com/papers/repec/taf-tjorxx-v-76-y-2025-i-1-p-97-110/ - __[ML Approaches to Tail Risk](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0305048324002135%3Bh%3Drepec%3Aeee%3Ajomega%3Av%3A133%3Ay%3A2025%3Ai%3Ac%3As0305048324002135)__: The paper introduces two deep learning frameworks for better estimating Value at Risk and Expected Shortfall, surpassing traditional models and enhancing financial institutions' capital allocation under Basel regulations. (2025-05-19, shares: 6) · https://www.ml-quant.com/papers/repec/eee-jomega-v-133-y-2025-i-c-s0305048324002135/ - __[Consumption Expectations and Risk Premia](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006037%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006037)__: Disagreements in macroeconomic expectations influence financial risk premia and stock market returns, as evidenced in a dynamic version of the Fama-French 5 factor model. (2025-09-22, shares: 5) · https://www.ml-quant.com/papers/repec/eee-ecolet-v-247-y-2025-i-c-s0165176524006037/ ## Papers with code ### Trending - __[SkillClaw: Skill Evolution](https://github.com/AMAP-ML/SkillClaw)__: Skill Evolution: SkillClaw enhances multiuser AI systems by leveraging group interactions to strengthen shared abilities. (2026-04-12, shares: 376) - __[ClawGUI: Unified GUI](https://github.com/ZJU-REAL/ClawGUI)__: Unified GUI: ClawGUI is an open-source tool that streamlines the development of GUI agents using unified reinforcement learning across different platforms. (2026-04-15, shares: 367) - __[DDTree: Speculative Decoding](https://github.com/liranringel/ddtree)__: Speculative Decoding: DDTree improves speculative decoding by generating draft trees from data distributions and validating multiple paths at once. (2026-04-15, shares: 200) - __[Strips as Tokens](https://github.com/Xrvitd/SATO)__: SATO introduces a new way to order tokens in transformers that improves mesh generation by preserving edge flow using triangle strip sequences. (2026-04-14, shares: 55) - __[Introspective Consistency](https://github.com/Introspective-Diffusion/I-DLM)__: Introspective Diffusion Language Models enhance autoregressive models by refining their output consistency through advanced decoding and optimized methods. (2026-04-14, shares: 53) - __[HabitatGS Navigation](https://github.com/zju3dv/habitat-gs)__: HabitatGS enhances HabitatSim with 3D Gaussian Splatting for realistic visuals and dynamic avatars, improving AI agent navigation and generalization. (2026-04-15, shares: 46) ### Rising - __[KnowUBench: Mobile Agent Evaluation](https://github.com/ZJU-REAL/KnowU-Bench)__: Mobile Agent Evaluation: KnowUBench assesses how well personalized mobile agents can understand user preferences and provide helpful assistance in real-world graphical user interfaces. (2026-04-10, shares: 46) - __[KnowRL: LLM Reasoning Enhancement](https://github.com/Hasuer/KnowRL)__: LLM Reasoning Enhancement: KnowRL improves the reasoning abilities of language models through a framework that uses reinforcement learning to provide better, guided interactions. (2026-04-15, shares: 41) - __[OnPolicy Distillation in Language Models](https://github.com/thunlp/OPD)__: Effective distillation in large language models depends on matching thought processes between teacher and student models, with teachers needing to impart new skills. (2026-04-15, shares: 35) - __[Parallel Decoding for Diffusion Models](https://github.com/czg1225/DMax)__: DMax introduces a new technique for diffusion language models that reduces mistakes in parallel decoding. (2026-04-10, shares: 34) - __[Autonomous ML with AiScientist](https://github.com/AweAI-Team/AiScientist)__: AiScientist develops a system that boosts long-term machine learning research by improving coordination and project management. (2026-04-15, shares: 33) - __[Benchmarking LLMs for Human Behavior Simulation](https://github.com/icip-cas/OmniBehavior)__: The OmniBehavior benchmark reveals that large language models have difficulty mimicking complex human behaviors due to biases and limited diversity. (2026-04-10, shares: 22) ## GitHub ### Finance - __[Alpha Stock Factors via RL](https://github.com/ICT-FinD-Lab/alphagen)__: The article explores how reinforcement learning can be applied to develop stock prediction factors. (2022-07-05, shares: 1069) - __[Financial Features](https://github.com/YuxinSUN89/quant-ohlcv-feature)__: It compiles 300 features and factors drawn from both research and industry perspectives. (2026-04-09, shares: 72) - __[Feature Engineering for Quant](https://github.com/lucasinglese/oryon)__: The paper outlines effective methods for feature and target engineering, utilizing a Rust core with a Python interface. (2026-03-23, shares: 14) - __[Science Agent Skills](https://github.com/K-Dense-AI/scientific-agent-skills)__: It presents practical skills for agents to improve tasks in research, engineering, finance, and writing. (2025-10-19, shares: 18128) - __[Open Source AI Trading Agent](https://github.com/alsk1992/CloddsBot)__: The article introduces an autonomous open-source AI trading agent capable of trading in various markets and managing risk. (2026-01-26, shares: 159) ### Trending - __[MaalSalan Tool](https://github.com/maaslalani/sheets)__: A terminal tool enables users to manage spreadsheets directly from the command line. (2026-04-01, shares: 1796) - __[JackWener OpenCLI](https://github.com/jackwener/OpenCLI)__: A platform transforms websites or apps into command-line interfaces for easy AI tool integration. (2026-03-14, shares: 14939) - __[MemPalace AI System](https://github.com/MemPalace/mempalace)__: A free, highly effective AI memory system has been tested successfully. (2026-04-05, shares: 42961) - __[Caveman Token Tech](https://github.com/JuliusBrussee/caveman)__: Claude Code is a new skill that minimizes token usage by using simpler language. (2026-04-04, shares: 10369) - __[Small Fish 9M LLM](https://github.com/arman-bd/guppylm)__: A lightweight language model, with 9 million parameters, imitates the speech of a small fish. (2026-03-29, shares: 2145) ## Podcasts ### Quantitative - __[Navigating Chaos](http://localhost:8888/TopTradersUnplugged/ttu-2021/homepage/si395-the-hidden-truth-about-cta-alpha-ft-andrew-beer)__: Andrew and Niels examine how global uncertainties and tech advancements are changing systematic investing and trend following methods. (2026-04-11, shares: 9) - __[Kaplan's Insights](https://alphaexchange.simplecast.com/episodes/vice-chairman-of-goldman-sachs-and-former-president-of-the-dallas-fed-YYGL4v7j)__: Rob Kaplan shares lessons from his time at the Dallas Fed, noting economic changes driven by fiscal policy and larger forces beyond the Fed's reach. (2026-04-13, shares: 6) - __AI in Software Investing__: Alex Rubalcava and Paul Bricault discuss the benefits and hurdles of AI for startups and investors, stressing the importance of quick decision-making in early-stage investments. (2026-04-10, shares: 6) - __[Adapting to Change](http://localhost:8888/TopTradersUnplugged/ttu-2021/homepage/ttu152-where-is-the-open-ft-toby-crabel)__: Toby Crabel reflects on his trading journey, discussing market evolution, shifting momentum trends, and the critical role of execution in trading success. (2026-04-15, shares: 5) - __[Improving DC Outcomes](https://audioboom.com/posts/8890720)__: Lesley-Ann Morgan and Jenny Hazan address the challenge of providing sufficient retirement income in the DC sector, highlighting the role of behavioral science and technology in enhancing outcomes for members. (2026-04-16, shares: 5) ### Related - __[Jim Zelter on AI and Investments](https://traffic.megaphone.fm/GLD7856519704.mp3)__: Jim Zelter highlights global investment prospects and increasing capital spending in his recent interview. (2026-04-16, shares: 4) - __[Wheel Next: Upgrading Python Installs](https://talkpython.fm/episodes/show/544/wheel-next-packaging-peps)__: Upgrading Python Installs: A coalition is creating Wheel Next to enhance Python package installations with hardware-specific builds for improved performance. (2026-04-10, shares: 4) - __[Iran Conflict's Impact on Global Markets](https://traffic.megaphone.fm/GLD6352342939.mp3)__: Dominic Wilson examines how the Iran conflict and US sanctions affect global markets and investment approaches. (2026-04-14, shares: 3) - __[Domer: 18 Years in Political Betting](https://rss.com/podcasts/confessionsmm/2741086)__: 18 Years in Political Betting: Political bettor Domer shares experiences and strategies from his successful career in prediction markets. (2026-04-16, shares: 3) - __[Stock Market Rally: Genuine or Temporary?](https://interactive-brokers-podcast.podbean.com/e/rally-or-mirage-what-s-really-driving-stocks/)__: Genuine or Temporary?: Kevin Davitt discusses the recent equity market rally and its effects on volatility trends and stock performance. (2026-04-15, shares: 3) ## Blogs ### Related - __[Hedging in Strong Markets](https://stockviz.substack.com/p/stay-calm-and-hedge-along)__: Hedging can be expensive and less effective in strong markets, but using a market-neutral strategy has provided much better risk-adjusted returns than not hedging over the past five years. (2026-04-11, shares: 3) ## X / Twitter ### Miscellaneous - __[Investing in Autonomous Driving](https://x.com/carlcarrie/status/2042724714087412097)__: A recent article highlights a link between autonomous driving and autonomous investing, noting that although many asset managers expect generative AI to transform the finance industry, very few have a clear strategy for implementing it. (2026-04-10, shares: 2) ## Reddit ### Quantitative - __[Crypto Quants' Beliefs](https://www.reddit.com/r/quantfinance/comments/1shn7df/do_quants_on_crypto_desks_at_large_firms_actually/)__: (2026-04-10, shares: 26) ### Rising - __[Career Advice](https://www.reddit.com/r/quant/comments/1shslq1/confused_about_my_career_prospects_in_current/)__: (2026-04-10, shares: 29) --- --- title: Quant Letter No. 129: April 2026, Week 1 url: https://www.ml-quant.com/issues/2026-04-03/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-09-26 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt issue_date: 2026-04-03 --- # Quant Letter No. 129: April 2026, Week 1 Sent 2026-04-03. 43 items. ## arXiv ### Finance - __[Valuing Variable Annuities with Non-Markovian Models](https://arxiv.org/abs/2604.00472v2)__: The paper discusses how to value variable annuity contracts that offer early surrender options. It uses advanced models and deep learning to find the best strategies for surrendering the contracts, while also providing protection against losses through minimum benefits. (2026-04-01, shares: 0) · https://www.ml-quant.com/papers/arxiv/2604.00472/ ### Economics - __[Partial Automation in Human-AI Collaboration](https://arxiv.org/abs/2603.29121v1)__: The paper discusses a model that suggests combining human effort with partial automation is usually cheaper and more effective than fully automating complex tasks. (2026-03-31, shares: 0) · https://www.ml-quant.com/papers/arxiv/2603.29121/ ### Historical Trending - __[Valuing European Options with Two-Asset Lévy Models](https://arxiv.org/abs/2511.02700v2)__: The article introduces a better method for pricing European-style options using two-asset exponential Lévy models. It focuses on faster calculations by employing fast Fourier transforms and a semi-Lagrangian approach, surpassing older techniques. (2025-11-04, shares: 0) · https://www.ml-quant.com/papers/arxiv/2511.02700/ ## RePEc ### Historical Trending - __[VIX Prediction with ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2024.2439458%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A24%3Ay%3A2024%3Ai%3A12%3Ap%3A1857-1873)__: The study shows that machine learning can better predict the CBOE Volatility Index (VIX) by using jobless claims data for market volatility forecasts. (2024-03-01, shares: 12) · https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2024-i-12-p-1857-1873/ - __[Volatile KSE-30 Stocks](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs43069-025-00421-4%3Bh%3Drepec%3Aspr%3Asnopef%3Av%3A6%3Ay%3A2025%3Ai%3A1%3Ad%3A10.1007_s43069-025-00421-4)__: This research examines equity returns on the Pakistan Stock Exchange, identifying trends and suggesting a new portfolio optimization strategy for asset management. (2025-01-08, shares: 10) · https://www.ml-quant.com/papers/repec/spr-snopef-v-6-y-2025-i-1-d-10-1007-s43069-025-00421-4/ - __[Automated Trading in Emerging Markets](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-025-00754-3%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A11%3Ay%3A2025%3Ai%3A1%3Ad%3A10.1186_s40854-025-00754-3)__: The paper highlights issues with algorithmic trading and passive investing in downturns, proposing a new trading system to help stabilize emerging market portfolios in crises. (2025-03-18, shares: 9) · https://www.ml-quant.com/papers/repec/spr-fininn-v-11-y-2025-i-1-d-10-1186-s40854-025-00754-3/ - __[Risk Parity with Heavy Tails](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Fjtsa.12792%3Bh%3Drepec%3Abla%3Ajtsera%3Av%3A46%3Ay%3A2025%3Ai%3A2%3Ap%3A353-377)__: A new portfolio optimization method using expected shortfall offers better stability and lower turnover during market turbulence by considering extreme asset returns. (2025-12-21, shares: 9) · https://www.ml-quant.com/papers/repec/bla-jtsera-v-46-y-2025-i-2-p-353-377/ - __[Deep Learning vs. Traditional Models](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.icfm.ro%2FRePEc%2Fvls%2Fvls_pdf%2Fvol28i4p29-42.pdf%3Bh%3Drepec%3Avls%3Afinstu%3Av%3A28%3Ay%3A2024%3Ai%3A4%3Ap%3A29-42)__: The study finds that traditional machine learning approaches outperform deep learning in predicting daily stock price movements for major Eurozone banks during volatile markets. (2024-07-05, shares: 7) · https://www.ml-quant.com/papers/repec/vls-finstu-v-28-y-2024-i-4-p-29-42/ - __[Sharpe Ratio vs. Buy-and-Hold in Markets](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Farchive.conscientiabeam.com%2Findex.php%2F29%2Farticle%2Fview%2F4102%2F8464%3Bh%3Drepec%3Apkp%3Ateafle%3Av%3A12%3Ay%3A2025%3Ai%3A1%3Ap%3A120-142%3Aid%3A4102)__: The study shows that Sharpe Ratio trading strategies work better than buy-and-hold strategies in global stock markets, aligning with the Adaptive Market Hypothesis due to market inefficiencies. (2025-03-27, shares: 8) · https://www.ml-quant.com/papers/repec/pkp-teafle-v-12-y-2025-i-1-p-120-142-id-4102/ - __[Role of News Sentiment in Stock Volatility](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006086%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006086)__: A simulation finds that accurately gauged news sentiment, especially from GPT-4, significantly affects stock return volatility, outperforming RavenPack from 2019 to 2023. (2025-07-28, shares: 6) · https://www.ml-quant.com/papers/repec/eee-ecolet-v-247-y-2025-i-c-s0165176524006086/ - __[Mixture Models in Chinese Financial Markets](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F01605682.2024.2329156%3Bh%3Drepec%3Ataf%3Atjorxx%3Av%3A76%3Ay%3A2025%3Ai%3A1%3Ap%3A97-110)__: A new machine learning technique improves time series analysis by using unsupervised classification for autoregressive models, proving effective with financial data during COVID-19. (2025-03-18, shares: 6) · https://www.ml-quant.com/papers/repec/taf-tjorxx-v-76-y-2025-i-1-p-97-110/ - __[Machine Learning for Market Risk Management](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0305048324002135%3Bh%3Drepec%3Aeee%3Ajomega%3Av%3A133%3Ay%3A2025%3Ai%3Ac%3As0305048324002135)__: Two new deep learning frameworks enhance the estimation of Value at Risk (VaR) and Expected Shortfall (ES), providing better risk management for financial institutions compared to traditional methods. (2025-12-05, shares: 6) · https://www.ml-quant.com/papers/repec/eee-jomega-v-133-y-2025-i-c-s0305048324002135/ - __[Macroeconomic Expectations and Financial Risk Premia](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0165176524006037%3Bh%3Drepec%3Aeee%3Aecolet%3Av%3A247%3Ay%3A2025%3Ai%3Ac%3As0165176524006037)__: Evidence indicates that differing macroeconomic expectations influence financial risk premiums, with varying impacts on stock returns based on disagreement over consumption and productivity. (2025-11-21, shares: 5) · https://www.ml-quant.com/papers/repec/eee-ecolet-v-247-y-2025-i-c-s0165176524006037/ ## Papers with code ### Trending - __[Latent Space as Foundation](https://github.com/YU-deep/Awesome-Latent-Space)__: Latent space improves language models by creating a continuous representation that minimizes redundancy and boosts efficiency. (2026-04-03, shares: 475) - __[Unified Multimodal Processing](https://github.com/meituan-longcat/LongCat-Next)__: The Discrete Native Autoregressive framework enables integrated handling of various data types through a common discrete space and innovative visual transformer design. (2026-04-01, shares: 280) ### Rising - __[Generative World Renderer: Enhanced AAA Game Rendering](https://github.com/ShandaAI/AlayaRenderer)__: Enhanced AAA Game Rendering: A new dataset from AAA games enhances rendering quality and better evaluation methods that match human perception. (2026-04-03, shares: 117) - __[SKILL0: RL for Skill Internalization](https://github.com/ZJU-REAL/SkillZero)__: RL for Skill Internalization: SKILL0 empowers LLM agents to autonomously learn and execute tasks, boosting their effectiveness with a flexible training process. (2026-04-03, shares: 65) - __[GEMS Multimodal Generation Framework with Memory](https://github.com/lcqysl/GEMS)__: GEMS introduces a multimodal framework that helps agents refine their skills and memory, leading to improved performance across different tasks. (2026-04-01, shares: 30) ## GitHub ### Finance - __[NVIDIA AI Portfolio Optimization](https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization)__: The article provides a developer example for enhancing investment portfolios with NVIDIA's tools. (2025-10-27, shares: 239) - __[Modular RL Trading Framework](https://github.com/TorchTrade/torchtrade)__: It introduces a flexible system that uses reinforcement learning to enhance algorithmic trading strategies. (2024-11-07, shares: 268) - __[Rust Optopsy Engine Rewrite](https://github.com/goldspanlabs/optopsy-mcp)__: This article covers a Rust-based update of Optopsy, a backtesting engine for options trading, featuring a new protocol. (2026-02-28, shares: 7) - __[LLM Trading System for ETFs](https://github.com/45ck/llm-quant)__: It highlights a paper trading system using LLM and Claude for macro ETF strategies, equipped with a secure trade ledger. (2026-03-24, shares: 19) - __[Apache Fluss: Real-Time Analytics](https://github.com/apache/fluss)__: Real-Time Analytics: Apache Fluss is presented as a real-time data analytics solution for streaming storage. (2024-10-31, shares: 1834) ### Trending - __[Repo Ownership Transfer](https://github.com/ultraworkers/claw-code)__: The repository is locked for ownership transfer; users are redirected to a faster alternative to reach 100K stars. (2026-03-31, shares: 145323) - __[Rust Rebuild of Claude](https://github.com/instructkr/claw-code)__: Better Harness Tools aims to archive leaked Claude Code and is being rewritten in Rust for better functionality. (2026-03-31, shares: 41232) - __[Enhance Codex](https://github.com/Yeachan-Heo/oh-my-codex)__: OmX Oh My codeX upgrades your codex with new features including hooks, agent teams, and HUDs. (2026-02-02, shares: 7868) - __[Terse CLAUDE.md Workflows](https://github.com/drona23/claude-token-efficient)__: The CLAUDE.md file simplifies responses by keeping them brief without requiring code changes. (2026-03-30, shares: 2527) - __[Cognitive Architecture for Claude](https://github.com/marciopuga/cog)__: The article describes a cognitive architecture for Claude Code that includes persistent memory, self-reflection, and foresight. (2026-03-15, shares: 314) ## Podcasts ### Quantitative - __Liquid Equity Insights__: Owen Lamont and Randy Cohen discuss private equity's impact on public markets and the changing investment landscape, highlighting the trend of the US stock market becoming increasingly influenced by Korean market dynamics. (2026-04-03, shares: 9) - __[Limitations of Market Control](http://localhost:8888/TopTradersUnplugged/ttu-2021/homepage/si393-the-illusion-of-control-in-modern-markets-ft-yoav-git)__: Niels and Yoav analyze traditional market responses and risk management issues during geopolitical tensions and shifting narratives. (2026-03-28, shares: 7) - __[Alt Data Salesman's Confessions](https://shows.acast.com/the-alternative-data-podcast/episodes/the-zach-zboralske-episode)__: Zach Zboralske shares his experiences in sales, focusing on pricing transaction data and the growing significance of alternative data in finance. (2026-03-30, shares: 6) - __[US Government Bonds' Shifting Role](https://alphaexchange.simplecast.com/episodes/the-shock-heard-round-the-world-us-government-bonds-45iE6u_0)__: The article suggests that the US Treasury market is losing its status as risk-free due to rising uncertainties linked to the US government. (2026-03-31, shares: 5) - __[Building Businesses in PE](https://traffic.megaphone.fm/GLD2255196196.mp3)__: Steve Klinsky reflects on the evolution of the private equity industry, his strategies for building businesses, and the current macroeconomic challenges in a discussion with Goldman Sachs' Alison Mass. (2026-03-31, shares: 5) ### Related - __[Banking Hype vs. Reality](https://www.buzzsprout.com/803279/episodes/18933442-banking-s-hype-history-and-balancing-reality.mp3)__: The article explores how programming in banks has shifted from SAS to Python, addressing challenges from AI trends and regulations while prioritizing genuine education. (2026-03-31, shares: 5) - __[Power Imbalances and Consequences](http://localhost:8888/TopTradersUnplugged/ttu-2021/homepage/peter-atwater)__: Peter Atwater discusses societal power imbalances, connecting the behavior of investors with a decline in confidence among the less powerful. (2026-04-01, shares: 4) - __[AI Data Center Power Challenges](https://traffic.megaphone.fm/GLD3928326355.mp3)__: Goldman Sachs Research highlights the rising demand for AI-powered data centers and factors that may influence future energy needs, noting that views can evolve. (2026-04-02, shares: 3) - __[EM Fixed Income Stability Wait](https://atanyrate.podbean.com/e/em-fixed-income-still-waiting-for-the-conflict-to-pass-over/)__: Jonny Goulden, Anezka Christovova, and Ben Ramsey analyze recent changes in the fixed income market for emerging markets in a JPMorgan podcast. (2026-03-31, shares: 3) - __[Corporate Data Breach Risks](https://datascienceathome.podbean.com/e/productivity-is-the-new-data-breach-ep-301/)__: The article cautions that employees might jeopardize corporate security by misusing AI tools like ChatGPT, stressing the importance for firms to tackle this issue. (2026-03-31, shares: 2) ## Blogs ### Quantitative - __[Market Response to Trump's Social Media Announcement](https://rajivsethi.substack.com/p/information-contagion)__: On March 24, oil and stock trading surged right before Donald Trump reported positive talks with Iran, causing oil prices to drop and stock indexes to rise sharply. (2026-03-31, shares: 4) ### Related - __[Trump's Announcement Impact](https://rajivsethi.substack.com/p/information-contagion)__: Trump's announcement about negotiations with Iran led to increased trading activity, causing oil prices to drop and stock prices to rise. (2026-03-31, shares: 4) - __[Commodities: Contango to Backwardation](https://stockviz.substack.com/p/the-shape-of-futures)__: Contango to Backwardation: The US-Iran War has changed oil prices from contango to backwardation, indicating a higher demand for immediate oil delivery than for future delivery. (2026-03-28, shares: 2) ## Reddit ### Quantitative - __[Interview Prep](https://www.reddit.com/r/quantfinance/comments/1s5agw2/interviewers_when_did_you_know_you_were_ready_to/)__: (2026-03-27, shares: 14) ### Rising - __[Model Validation](https://www.reddit.com/r/quant/comments/1s5hnla/i_kinda_love_working_in_model_validation_tbh/)__: (2026-03-27, shares: 145)