# 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. 134 issues since May 2023; 6,576 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. 134, 2026-10-09](https://www.ml-quant.com/issues/2026-10-09/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): 303 papers. Crypto assets, DeFi, stablecoins and blockchain markets. - [LLMs & Text](https://www.ml-quant.com/topics/llms-text/index.md): 584 papers. Large language models, agents, sentiment and text as data in finance. - [Derivatives & Volatility](https://www.ml-quant.com/topics/derivatives-volatility/index.md): 883 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): 559 papers. Order books, market making, execution, high-frequency data and trading signals. - [Portfolio & Allocation](https://www.ml-quant.com/topics/portfolio-allocation/index.md): 628 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): 381 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): 269 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): 283 papers. Rates, the yield curve, monetary policy, inflation and macro-finance. - [Econometrics & Forecasting](https://www.ml-quant.com/topics/econometrics-forecasting/index.md): 315 papers. Forecasting, time series, econometrics and nowcasting. - [ML & AI Methods](https://www.ml-quant.com/topics/ml-ai-methods/index.md): 1135 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): 170 papers. Firms, governance, IPOs, M&A and corporate decisions. - [Other](https://www.ml-quant.com/topics/other/index.md): 1066 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. 134: October 2026, Week 2](https://www.ml-quant.com/issues/2026-10-09/index.md): 2026-10-09 - [No. 133: October 2026, Week 1](https://www.ml-quant.com/issues/2026-10-02/index.md): 2026-10-02 - [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 ## 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: October 2026, Week 2: Weekly quantitative finance newsletter *This week centers on AI systemic risk, factor discovery limits, and monetary transmission channels. Agentic AI systems create non-diversifiable contagion floors despite fleet size; automated factor mining shows no paradigm dominates; and Fed policy transmits primarily through risk appetite shifts rather than rates. See "Agentic AI and Systemic Risk," "FactorBench," and "Risk Appetite and Monetary Transmission."* ## Top picks ### 1. [Agentic AI and Systemic Financial Risk](https://arxiv.org/abs/2610.08806) · arXiv ### 2. [Quantitative Equity Evolution: Constraints and Alpha Decay](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7566701) · SSRN ### 3. [Risk Appetite and Monetary Transmission](https://econpapers.repec.org/RePEc:fip:fedfwp:103796) · RePEc ### 4. [FactorBench: Benchmarking Automated Factor Mining](https://arxiv.org/abs/2610.06947) · arXiv ### 5. [LLM Factor Discovery and Evidence Scarcity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7555699) · SSRN ## What's rising ### Housing: 36 papers this week, 1.8× the usual 20 ### ETFs and funds: 34 papers this week, 1.6× the usual 21.9 ### Volatility forecasting: 20 papers this week, 1.5× the usual 13.7 ## GitHub radar ### Quant repos rising #### [jarrodwatts/jev-trader](https://github.com/jarrodwatts/jev-trader): AI trading agent executing trades on Monad blockchain. (3 quants, 2,912 stars, +167 this week, new repo) #### [Yijia-Xiao/FinanceHarness](https://github.com/Yijia-Xiao/FinanceHarness): Autonomous financial deep research framework. (2 quants, 196 stars, +8 this week, new repo) #### [TNT-Likely/PanWatch](https://github.com/TNT-Likely/PanWatch): AI stock monitoring for A-shares, HK and US markets. (2 quants, 2,047 stars, +104 this week) ### What quants are playing with #### [morluto/rea](https://github.com/morluto/rea): Reverse engineer apps and binaries using AI agents. (35 quants, 37.0k stars, +36.6k this week) #### [storytold/photocraft](https://github.com/storytold/photocraft): Open-source Rust reimplementation of Adobe Photoshop. (22 quants, 32.1k stars, +32.0k this week, new repo) #### [openai/math](https://github.com/openai/math): Mathematics library in Lean. (21 quants, 12.8k stars, +12.8k this week, new repo) #### [Niko1221/Strata](https://github.com/Niko1221/Strata): Run Qwen3.8-Flash on consumer hardware with local APIs. (13 quants, 19.2k stars, +12.3k this week, new repo) #### [tester-army/e2e](https://github.com/tester-army/e2e): End-to-end testing framework for web and mobile apps. (7 quants, 8,332 stars, +6,418 this week, new repo) #### [boykopovar/AnyPS5](https://github.com/boykopovar/AnyPS5): Tool for porting PS5 executables to Linux and Windows. (7 quants, 19.6k stars, +16.6k this week, new repo) ## We called it ### [Dynamic resource allocation with karma: An experimental study](http://arxiv.org/abs/2404.02687v2): now published in Journal of Economic Behavior & Organization (featured 8 Jan 2025; 4 citations) ### [Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction](https://arxiv.org/abs/2402.00299): passed 50 citations (featured 7 Feb 2024, 6 days after release) ### [A comparison of cryptocurrency volatility-benchmarking new and mature asset classes](https://arxiv.org/abs/2404.04962?utm_source=dlvr.it&utm_medium=twitter): passed 25 citations (featured 10 Apr 2024, 3 days after release) ## arXiv __[Agentic AI and Systemic Financial Risk](https://arxiv.org/abs/2610.08806)__: The study shows that agentic AI systems sharing a foundation model create non-diversifiable common exposure whose systemic risk floor does not shrink as the fleet grows, shifting concern from individual model risk to population-level contagion. (2026-10-08, fanfare: 4) ![2: The set-valued risk measure \rho(X) . The acceptable set of containment configurations (green), coloured elsewhere](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-01-44530988.png) __[FactorBench: Benchmarking Automated Factor Mining](https://arxiv.org/abs/2610.06947)__: A portfolio-aware benchmark comparing five thousand factors from nine automated mining methods across five equity markets finds that no discovery paradigm consistently dominates in signal quality or portfolio performance. (2026-10-07, fanfare: 4) ![Overview of FactorBench](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-02-5c0da455.png) __[Financial World Modeling with Market-1T](https://arxiv.org/abs/2610.09048)__: The study introduces a trillion-observation U.S. equity dataset and systematically evaluates 18 representation-learning methods across nearly two decades, finding that encoders with similar predictive performance organize market state very differently. (2026-10-08, fanfare: 4) ![Paper overview](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-03-d2723b48.png) __[How LLMs Fill Missing Financial Information](https://arxiv.org/abs/2610.07798)__: When financial facts are withdrawn from prompts, identity attributes explain 96% of variation in equity allocation advice from a language model, replacing missing evidence. (2026-10-07, fanfare: 4) ![Mean identity swing in equity allocation versus evidence level with 95% confidence interval band.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-04-8b80af07.png) __[AlphaPADI: Pool-Aware Formulaic Alpha Discovery](https://arxiv.org/abs/2610.04959)__: Introduces a hierarchical discrete diffusion framework that generates pools of formulaic alphas by reconstructing complete candidate pools under current context and maximizing joint predictive performance and inner diversity. (2026-10-06, fanfare: 3) ![Cumulative returns comparison of AlphaPADI against baseline methods on CSI300 from 2023-2025.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-05-9577d8ca.png) __[VIX Decomposition: Probability and Severity](https://arxiv.org/abs/2610.03849)__: Separating risk-neutral shortfall probability from conditional severity in option prices shows that severity accounts for 91.5 percent of recession log premium movements, supplying joint empirical restrictions on uncertainty and economic activity. (2026-10-06, fanfare: 3) ![Monthly 30-day downside protection premium and VIX. Panel (a) compares the VIX with the fixed benchmark](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-06-373eddc5.png) __[Stock-JEPA: Prior-Anchored Representation Learning](https://arxiv.org/abs/2610.07006)__: A joint-embedding framework learns predictable incremental revisions from market data relative to a financial prior, combining interpretability from classic models with the pattern-capture strength of deep learning. (2026-10-07, fanfare: 3) ![Predictive utility across market states](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-07-b72a51b8.png) __[Compact Transformers for Limit Order Forecasting](https://arxiv.org/abs/2610.02917)__: MBOFormer and MBOFusion, two causal transformers with under 70 kilobyte serialized state, predict limit order book price trends with sub-millisecond latency across three markets and four prediction horizons. (2026-10-05, fanfare: 3) ![Architectures of our two L3-based models](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-08-8ad95c68.png) __[Neuroevolution for Stock Return Prediction](https://arxiv.org/abs/2610.07825)__: Tiny neuroevolved recurrent networks rank first on both forecast accuracy and daily long-short returns across four portfolios, outperforming larger transformers and proving that forecast accuracy does not guarantee trading profit. (2026-10-07, fanfare: 3) ![Rank IC as a function of scoring horizon Plot showing Rank IC as a function of scoring horizon](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-09-e68f5369.png) __[Admission Gates for Strategy Research](https://arxiv.org/abs/2610.07701)__: An injected-truth protocol reveals that statistical admission gates eliminate false discoveries in weak-signal regimes but cut adoption rates to 1–7 percent, while criteria computed on absolute rather than excess returns reject all candidates. (2026-10-07, fanfare: 3) ![Panel I (400 repetitions)](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-10-6ce485cd.png) __[Earnings Disclosure Alignment via Optimal Transport](https://arxiv.org/abs/2610.04094)__: Decomposing earnings press releases and conference calls into shared and unique textual components shows that unique call content predicts returns better than repeated material. (2026-10-06, fanfare: 3) ![Average Returns (%) Sorted by Surprise and Soft Average return heatmap, sorted by soft and surprise](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-11-63c15178.png) __[LiveMACE: Process-Aware LLM Agent Evaluation](https://arxiv.org/abs/2610.09872)__: A benchmark evaluates frontier LLMs as live trading agents and finds that realized returns often diverge from capability-specific measurements, revealing outcome-capability gaps through decision traces. (2026-10-08, fanfare: 3) ![LiveMACEBench overview](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-12-39288c40.png) __[Agentic ETFs as Emerging Asset Class](https://arxiv.org/abs/2610.06856)__: Proposes that large-language-model-driven trading agents delegated to autonomous execution define a nascent asset class, mapping infrastructure layers and projecting potential trillions in assets by 2030. (2026-10-07, fanfare: 3) ![Actively managed ETF assets under management, actuals and industry projection](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-13-94d3062c.png) __[MintEval: Behavioral Testing for Trading Code](https://arxiv.org/abs/2610.03080)__: Introduces a benchmark that compares LLM-generated trading code to reference strategies bar by bar on identical data, revealing silent failures in implementation despite passing functional tests. (2026-10-05, fanfare: 3) ![Closed setting: SpecMatch vs ActionMatch per task](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-14-4f740cdb.png) __[TradeGrad: Textual Gradient Strategy Optimization](https://arxiv.org/abs/2610.03128)__: Proposes an LLM-guided framework for trading strategy refinement using accumulated experience and cross-period robustness, achieving 27.99% annualized return on Chinese equities with 1.63 Sharpe ratio. (2026-10-05, fanfare: 3) ![Evolution of a final strategy along its ancestral lineage](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-15-9b5f31d2.png) __[Latent Continuum of Limit Order Book Regimes](https://arxiv.org/abs/2610.05740)__: Analysis of high-frequency limit-order-book geometry finds market states form a continuous structure with a dominant latent coordinate capturing between 83.66% and 85.27% of covariance variation. (2026-10-06, fanfare: 3) ![Time series of stress-dial dynamics across three covariance windows, 2022-2025.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-16-b7432ccc.png) __[Algorithmic Greenwashing Detection in ESG](https://arxiv.org/abs/2610.02225)__: Fuses SEC financials with EPA emissions data and conformal machine learning to quantify divergence between self-reported and physical emissions, finding algorithmic divergence predicts lower valuation and profitability. (2026-10-05, fanfare: 3) ![Uncertainty Quantification via Mondrian Conformal Prediction (Discrete Bounds)](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-17-fb889fb1.png) __[Expected Utility Regret Rule for Portfolio Choice](https://arxiv.org/abs/2610.02290)__: Proposes a portfolio rule that simultaneously selects class and estimates weights to minimize expected utility regret, attaining both minimax and Bayes lower bounds without requiring prior information. (2026-10-05, fanfare: 3) ![Moment separation: worst-case expected utility regret over the two orderings of return distributions with identical](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-18-48b891ea.png) __[Adversarial Training for Deep Hedging](https://arxiv.org/abs/2610.07162)__: WRAP combines Wasserstein reweighting and optimal-transport perturbations in a distributionally robust framework to make deep hedging policies robust to nonstationarity and distributional drift in market conditions. (2026-10-07, fanfare: 3) ![Loss landscapes across deep-hedging training schemes from Table 1 : (A) ERM-W , (B) \phi -W , (C) OT-W , and (D) WRAP-W](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-19-5c03f47c.png) __[When LLM Agents Should Trust Memory](https://arxiv.org/abs/2610.11732)__: MemTrial uses factorial design to isolate what each memory contributes to portfolio decisions by crediting experiences with their marginal effect rather than shared market moves. (2026-10-09, fanfare: 3) ![Comparison of existing agent approach versus MemTrial's memory trust mechanism with factorial design](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-20-e6c9f2c4.png) __[Ranking Prior Alignment for Credit Risk](https://arxiv.org/abs/2610.11146)__: A model-agnostic framework distills ranking priors from experts or teacher models into credit scorers via KL divergence, improving cold-start performance with scarce labeled data. (2026-10-09, fanfare: 3) ![Robustness checks and ablation studies](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-21-115679c0.png) __[Execution Assumptions in LLM Trading Benchmarks](https://arxiv.org/abs/2610.05077)__: Varying execution realism from ideal fills to latency and impact reshuffles rankings of LLM and classical trading policies, showing how backtest conventions affect headline results. (2026-10-06, fanfare: 3) ![Kendall \tau\_{b} between the E0 ranking and each stressed ranking](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-22-92c85ddc.png) __[Credal Machine Learning for Risk-Averse Decisions](https://arxiv.org/abs/2610.12115)__: A method represents epistemic uncertainty with credal sets and combines them with a decision rule that avoids catastrophic predictions under conditional value-at-risk minimization. (2026-10-09, fanfare: 3) ![Set size and accuracy versus corruption severity, showing CreWare adapting set size with degraded accuracy.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-23-8c4c6f36.png) __[Robust CVaR Portfolio Selection with Penalties](https://arxiv.org/abs/2610.09246)__: Derives a closed-form expression for worst-case conditional value-at-risk under reward-penalty mechanisms and distribution uncertainty, enabling optimal portfolio allocations that balance losses with downside risk. (2026-10-08, fanfare: 2) ![Cumulative wealth comparison of six portfolio models from 2010-2025 with clear legend and time series.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-24-23778d6b.png) __[HAN-Mamba: Multi-Scale Volatility Forecasting](https://arxiv.org/abs/2610.10323)__: Replaces transformers with selective state space encoders in a hierarchical architecture for realized volatility forecasting, reducing error to 0.1927 on the Optiver benchmark with 33 percent fewer parameters than the attention variant. (2026-10-08, fanfare: 2) ![Context-length scaling](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-25-49b3c3ef.png) __[StaFIR: Learned Stationarity-Aware Filters](https://arxiv.org/abs/2610.07430)__: A convex method learns causal filters that balance stationarity and input preservation for financial time series, adapting filtering strength to persistence while preserving signal similarity. (2026-10-07, fanfare: 2) ![Per-series forecast performance on the log five-day realized-variance series](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-26-4cf8b5c3.png) __[Regime-Aware Equity Correlation Matrices](https://arxiv.org/abs/2610.12166)__: Fitting separate dependence matrices to lower-tail, central, and upper-tail regimes reveals tail-driven co-movements that single Gaussian-copula structures systematically miss. (2026-10-09, fanfare: 2) ![Three regime-specific correlation matrices: lower-tail, middle-regime, and upper-tail heatmaps.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-27-6f3b5c5c.png) __[State-Dependent Hawkes Models in Electricity](https://arxiv.org/abs/2610.08169)__: A regime-switching Hawkes process models intraday electricity order flow across liquidity conditions, showing that stress mainly strengthens self-excitation rather than reshaping cross-side dynamics. (2026-10-07, fanfare: 2) ![Intraday electricity market evolution: mid-price, bid-ask spread, and trading volume over time.](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-28-5ac1b503.png) __[Stochastic Control for Goal-Based Investing](https://arxiv.org/abs/2610.10046)__: A framework characterizes optimal investment policies for reaching financial goals by deadline, revealing that policies can decrease in asset drift and need not converge to risk-free allocation. (2026-10-08, fanfare: 2) ![Goal reaching time distributions comparing hyperbolic and exponential discounting with different parameters](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-29-9b583a75.png) __[Robust Enhanced Index Tracking Under Uncertainty](https://arxiv.org/abs/2610.04221)__: Robust optimization models minimize worst-case loss and downside risk for enhanced indexing under distributional uncertainty, beating benchmarks and standard robust approaches in out-of-sample wealth. (2026-10-06, fanfare: 2) ![Cumulative wealth and Sharpe ratios of portfolios based on the M-ER, M-TSV, and M-V models under the uncertainty set](https://www.ml-quant.com/issues/2026-10-09/figures/arxiv-30-b31b0dba.png) ## SSRN __[Quantitative Equity Evolution: Constraints and Alpha Decay](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7566701)__: Surveys fifty years of quantitative investing as a history of changing implementation boundaries from diversification through machine learning, emphasizing alpha decay and the gap between research and production systems. (2026-10-08, fanfare: 4) __[LLM Factor Discovery and Evidence Scarcity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7555699)__: An autonomous agent evolved 940 factors over 17 days and finds that reusing backtest data inflates edge by a quarter to a third and in-sample improvement predicts worse performance. (2026-10-05, fanfare: 4) __[Fed Surprises: Rate-Path, Information, and Tone](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7560218)__: The study decomposes Federal Reserve announcements into three orthogonal shocks and traces each through the equity cross-section, finding rate-path surprises dominate while standard balance-sheet characteristics carry no additional power beyond beta and size. (2026-10-06, fanfare: 4) __[Firm-Level AI Exposure Predicts Returns Post-Launch](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7575518)__: A natural-language analysis of 13,757 firms finds AI exposure surged after ChatGPT and predicts 1.37 percent higher annual returns, though the effect is concentrated after the launch and driven by firm-specific factors. (2026-10-08, fanfare: 4) __[Monetary Policy Risk and Asset Prices](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7585438)__: Asset prices respond to monetary policy uncertainty around FOMC announcements; a New Keynesian model shows policy risk accounts for much of equity and inflation-bond risk premia and weakens activity when elevated. (2026-10-08, fanfare: 4) __[Volatility Targeting Over a Century](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7585278)__: Analyzing 100 years of daily data, the study finds volatility scaling harmed returns before 1985 but improved them after, a shift attributed to derivatives markets and deregulation, with walk-forward Sharpe ratio of 1.01. (2026-10-08, fanfare: 4) __[Multi-Strategy Quantitative Portfolio](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7561158)__: Eleven systematic strategies across equities, futures, and currencies achieve a 1.91 Sharpe ratio net of costs from January 2012 to June 2026, with 23.6% compound annual return. (2026-10-07, fanfare: 4) __[Demographics, Debt, and Equity Risk Premia](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7570178)__: A two-country model attributes 251 basis points of U.S. equity premium gains and 71 basis points of global rate decline since 1980 to demographic aging. (2026-10-07, fanfare: 4) __[Pre-registered Testing of Trading Signals](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7553278)__: Applies a strict pre-registered protocol to eleven trading signals across equity and futures markets, finding only trend survives development and hold-out periods with borderline significance after multiple-testing adjustment. (2026-10-03, fanfare: 3) __[Cross-Sectional Risk and Corporate Bond Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7566162)__: Constructs a characteristics-based systematic risk measure using rate jumps that predicts out-of-sample corporate bond returns, with high-minus-low decile portfolio earning 9.12% annually and 7.80% alpha. (2026-10-05, fanfare: 3) __[The Price of Conviction in Asset Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7571098)__: The study shows that high-conviction beliefs exert outsized influence on prices, with a long-short portfolio exploiting conviction-weighted disagreement earning 13.2 percent annualized six-factor alpha. (2026-10-07, fanfare: 3) __[LLM Agents Build Auditable Equity Alpha Models](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7568638)__: An autonomous research loop in which agents propose factor candidates and code verifies them builds multi-factor alpha models, screening 212 predictors and admitting 14 that raise baseline information coefficient out-of-sample. (2026-10-07, fanfare: 3) __[White-Box Alpha Mining with Quality-Control Loops](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7576913)__: GFlowAlpha uses neuro-symbolic search and internalizes econometric quality control as online rewards to mine white-box factors, delivering mean out-of-sample daily ICIR of 0.485 with Fama-MacBeth t-statistics of 4.99 on Chinese stocks. (2026-10-07, fanfare: 3) __[Deflated Sharpe Ratios Fail Under Adaptive Search](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7557458)__: The research proves that query-counted Deflated Sharpe Ratios certify noise in adaptive research and proposes sealed-holdout verification, where validity depends on bits revealed rather than strategy count. (2026-10-05, fanfare: 3) __[Long-Horizon Credit Spreads Test Pricing Kernels](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7570200)__: The study shows that Baa-Aaa spreads at 27-year maturity test pricing kernels better than shorter maturities, with a structural refinancing model matching observed spreads while prior kernels overpredict by 27 to 68 basis points. (2026-10-07, fanfare: 3) __[LLMs Reduce Information Frictions in Bonds](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7577906)__: Shows that large-language-model scores of default risk from earnings calls predict bond rating migration one year ahead where analysts disagreed, capturing information not yet priced in spreads. (2026-10-07, fanfare: 2) __[DeFi Token Revenue Surprises Predict Short-Term Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7566845)__: Revenue surprises on 95 DeFi protocols predict abnormal token returns, with one-standard-deviation surprises associated with 28.8 basis points higher returns over three days when tokens have direct revenue claims. (2026-10-06, fanfare: 3) __[Machine Learning Predicts Cryptocurrency Contagion](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7578304)__: The study forecasts time-varying return spillovers among cryptocurrencies using machine learning, finding monetary policy and funding rates drive connectedness and classifying events that shift spillover composition into contagion types. (2026-10-07, fanfare: 3) __[Bitcoin ETFs Tighten Spillovers from Equities](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7566372)__: The research shows that after U.S. Bitcoin ETF approval in January 2024, return and volatility linkages between Bitcoin and equities strengthened substantially, with volatility spillovers becoming markedly asymmetric from stocks to crypto. (2026-10-05, fanfare: 3) __[AI Exposure and Financial Stability Risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7577948)__: The study measures AI exposure across 284 U.S. firms and finds that an AI-factor explains only 18% variance alongside conventional factors, suggesting AI is a distinct cross-industry risk lens separate from traditional spillovers. (2026-10-07, fanfare: 3) __[Causal Uplift Modeling for Credit Restructuring](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7556744)__: A causal framework compares six estimators of treatment effect to optimally assign debt restructuring interventions, revealing that targeting borrowers by predicted effect generates substantially positive value where blanket treatment fails. (2026-10-03, fanfare: 3) __[Exchange Rate Regimes and Term Premium Spillovers](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7588778)__: U.S. monetary tightening raises term premia sharply in pegged economies but not floating ones; a DSGE model shows pegs force adjustment through domestic rates, tightening leverage constraints and amplifying spillovers. (2026-10-09, fanfare: 3) __[Geopolitical Skewness Risk and Bond Premia](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7570538)__: A measure of cross-country geopolitical risk skewness predicts U.S. Treasury risk premia out-of-sample and forecasts weaker activity, adding incremental value beyond yield curves and standard macroeconomic predictors. (2026-10-07, fanfare: 3) __[Geoeconomic Fear and Sovereign Spreads](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7580558)__: The study constructs a geoeconomic fear index from news articles and finds that higher fear predicts wider emerging-market sovereign spreads, especially for speculative-grade issuers. (2026-10-09, fanfare: 3) __[Debt Maturity and Corporate Investment](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7589333)__: A global study of 357,630 firm-years shows firms with more short-term debt cut capital expenditure by 2.156 percentage points, mainly due to refinancing availability constraints. (2026-10-09, fanfare: 3) __[Common Banks Limit Supply-Chain Shocks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7588198)__: Using Portuguese wildfire data, the research finds firms whose banks also lend to suppliers experience 10 percentage points smaller credit declines and face attenuated real losses. (2026-10-09, fanfare: 3) __[Delta-Guided RL for Derivative Hedging](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7585078)__: A reinforcement learning method embedding Black-Scholes delta guidance cuts hedging error by 19-38% across simulated and real options data with robust statistical significance. (2026-10-08, fanfare: 3) __[Hybrid Graph Neural Network for RV Forecasting](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7576359)__: A graph neural network combining temporal and cross-asset volatility patterns reduces mean squared error and quasi-likelihood loss by approximately 9.9% and 3.4% on Dow stocks. (2026-10-07, fanfare: 3) __[Expectations and Monetary Policy Transmission](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7588262)__: Optimal rate-setting creates time-inconsistency: market expectations become too sensitive to policy surprises, amplifying real effects of central bank forecast errors; less aggressive rules cut output volatility. (2026-10-09, fanfare: 3) __[Anomalies in Indian Equities and Real Costs](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7551520)__: Well-documented Indian equity anomalies replicate strongly in-sample but all fail to survive 12 to 32 basis point retail transaction costs; required directional accuracy reaches 72% at hourly horizons. (2026-10-03, fanfare: 3) ## RePEc __[Risk Appetite and Monetary Transmission](https://econpapers.repec.org/RePEc:fip:fedfwp:103796)__: The research shows Fed policy affects risk asset prices primarily through risk appetite shifts, not interest rates; transmission via risk appetite dwarfs rate-channel effects. (2026-10-05, fanfare: 4) ![Rolling regression of RISK on MPS](https://www.ml-quant.com/issues/2026-10-09/figures/repec-01-7c1065f0.png) __[Treasury Yield Effects of Supply Demand](https://econpapers.repec.org/RePEc:fip:fedgif:103792)__: A demand-system framework quantifies that $100 billion in Treasury supply raises five-year yields by roughly 3 basis points, with effects driven by changing foreign and hedge-fund participation. (2026-10-05, fanfare: 3) ![Stacked area chart decomposing five-year yield changes into standard factors, market multiplier, and investor purchases](https://www.ml-quant.com/issues/2026-10-09/figures/repec-02-e09dc4cc.png) __[Dollar Collateral and Global Capital Flows](https://econpapers.repec.org/RePEc:ces:ceswps:_13008)__: A model shows U.S. Treasury collateral advantage drives dollar appreciation during stress and reconciles exorbitant privilege, financial intermediation, and convenience yield perspectives. (2026-10-05, fanfare: 3) ![US external dynamics during crises: NFA trends, exchange rates, capital inflows, and regression relationships.](https://www.ml-quant.com/issues/2026-10-09/figures/repec-03-34444440.png) __[Monetary Policy and Wealth Distribution](https://econpapers.repec.org/RePEc:bfr:banfra:1064)__: Rate cuts initially reduce wealth inequality but increase it medium-term; housing drives effects at the bottom while equities drive them at the top. (2026-10-05, fanfare: 3) ![Wealth share changes across distribution after interest rate shock over time](https://www.ml-quant.com/issues/2026-10-09/figures/repec-04-ddce3881.png) __[Financial Vulnerability Index for U.S. System](https://econpapers.repec.org/RePEc:fip:fedgfe:103791)__: An index capturing structural financial weaknesses displays gradual buildup before crises, predicts shock amplification, and reveals delayed monetary policy transmission effects. (2026-10-05, fanfare: 3) ![Fitted skewed-t distributional forecasts of GDP growth conditional on lagged GDP growth and FVI or NFCI. The fitted](https://www.ml-quant.com/issues/2026-10-09/figures/repec-05-1a035ab3.png) __[Credit Frictions and Aggregate Economic Output](https://econpapers.repec.org/RePEc:nbr:nberwo:35552)__: UK firm-level data shows relaxing credit frictions raises output by 25% and wages by 23%, with most gains from capital accumulation rather than reallocation. (2026-10-07, fanfare: 3) ![Output change comparing SME and large firms under low friction scenario, 2004 vs 2019.](https://www.ml-quant.com/issues/2026-10-09/figures/repec-06-cdd4f487.png) __[AI Intensity and Economic Shock Responses](https://econpapers.repec.org/RePEc:ces:ceswps:_13000)__: Industries with higher AI intensity show significantly higher returns and valuations when hit by supply and technology shocks but not demand shocks. (2026-10-08, fanfare: 3) ![Returns, valuations and volatility following a technology shock, excluding 2020](https://www.ml-quant.com/issues/2026-10-09/figures/repec-07-850b0734.png) __[A Currency Premium Puzzle](https://econpapers.repec.org/RePEc:nbr:nberwo:35572)__: The paper proves that asset pricing models solving equity and risk-free rate puzzles fail to generate observed interest-rate differentials between risky and safe currencies. (2026-09-26, fanfare: 3) ![Currency Premium in SDF Space](https://www.ml-quant.com/issues/2026-10-09/figures/repec-08-c0dad59b.png) __[The Implied Equity Term Structure](https://econpapers.repec.org/RePEc:fip:fednsr:103730)__: Inferring expected returns from stock prices and cash flows reveals an upward-sloping equity term structure, but value and speculative-grade firms show flat or downward slopes. (2026-10-09, fanfare: 3) ![Implied equity term structure showing return premia across maturity horizons in good and bad times.](https://www.ml-quant.com/issues/2026-10-09/figures/repec-09-71e432d8.png) __[Volatility Disagreement in Options Market](https://econpapers.repec.org/RePEc:nbr:nberwo:35500)__: Cross-sectional dispersion in volatility forecasts predicts delta-hedged straddle losses of 5.14% per month, consistent with mispricing rather than risk compensation. (2026-10-07, fanfare: 3) ![High-Minus-Low Decile Portfolios Based on VDIS conditional on Stock and Option Characteristics](https://www.ml-quant.com/issues/2026-10-09/figures/repec-10-0342c16d.png) __[ECB's Whatever It Takes and Bank Risk-Taking](https://econpapers.repec.org/RePEc:bdm:wpaper:2026-01)__: The ECB's "whatever it takes" announcement reversed euro area banks' risk appetite, reducing loan growth and credit risk, showing capitalization can curb lending during crises. (2026-10-05, fanfare: 3) ![Coefficient trend showing reversal in euro bank risk-taking post-announcement](https://www.ml-quant.com/issues/2026-10-09/figures/repec-11-af128d56.png) __[Global Firms and Capital Allocation](https://econpapers.repec.org/RePEc:nbr:nberwo:35652)__: A quantified general-equilibrium model of 23,000 firms across 48 countries shows financial and trade liberalization concentrate activity in largest firms with larger gains for emerging economies. (2026-09-26, fanfare: 3) __[Banker Outside Options and Credit Risk](https://econpapers.repec.org/RePEc:ajk:ajkdps:429)__: Improved job opportunities for bankers increase non-investment-grade lending and borrower risk without compensating spreads, raising systemic risk through weakened workplace discipline. (2026-10-09, fanfare: 3) ![Dynamic effects of risky loan volume growth and financial risk with confidence bands](https://www.ml-quant.com/issues/2026-10-09/figures/repec-13-8a4e0789.png) __[Policy Beliefs Drive Returns in Chinese Funds](https://econpapers.repec.org/RePEc:nbr:nberwo:35528)__: The study shows that mutual fund managers' policy beliefs predict market returns and explain positive alphas, while sentiment beliefs attract flows but lack predictive power. (2026-10-09, fanfare: 3) __[Distinguishing FX Shocks from Fundamentals in EMDEs](https://econpapers.repec.org/RePEc:imf:imfsdn:2026/003)__: A framework using deviations from interest-rate parity identifies when exchange-rate movements stem from financial shocks rather than fundamentals, aiding intervention decisions. (2026-10-05, fanfare: 3) __[Sentiment Arc Shape in Monetary Policy Communication](https://econpapers.repec.org/RePEc:onb:oenbwp:281)__: The sequencing and emphasis of sentiment within central bank statements predict rate decisions and inflation expectations beyond average tone alone. (2026-10-05, fanfare: 3) __[The Terra Luna Blockchain Run Dynamics](https://econpapers.repec.org/RePEc:ehl:lserod:137575)__: Granular blockchain data reveals the Terra crash resulted from subsidized money creation, real-time transaction visibility, and investor concentration amplifying financial fragility. (2026-10-07, fanfare: 3) ![Anchor's daily cash flow decomposition and balance decline through Terra collapse](https://www.ml-quant.com/issues/2026-10-09/figures/repec-17-d961ee1a.png) __[Continuous Time Models Beat HAR for Volatility](https://econpapers.repec.org/RePEc:boa:wpaper:202648)__: Fractional processes outperform discrete models like HAR for forecasting realized volatility at longer horizons by better balancing recent levels against long-run persistence. (2026-10-05, fanfare: 2) __[Asymmetric Inflation Risk and Monetary Policy](https://econpapers.repec.org/RePEc:bde:wpaper:2626)__: A model with time-varying skewness in cost-push shocks generates persistent stagflationary effects; optimal policy leans against the balance of inflation risks. (2026-10-02, fanfare: 2) ![Distribution shifts and responses to one-month shocks with asymmetric skewness distributions](https://www.ml-quant.com/issues/2026-10-09/figures/repec-19-bfb8d6fa.png) __[Energy Shocks and Euro Area Monetary Policy](https://econpapers.repec.org/RePEc:adl:wpaper:2026-07)__: A DSGE model reveals energy and exchange-rate shocks drive euro inflation volatility, and optimal policy can exploit the exchange-rate channel rather than looking through energy shocks. (2026-10-05, fanfare: 2) ![Drivers of headline inflation and GDP growth: a historical decomposition](https://www.ml-quant.com/issues/2026-10-09/figures/repec-20-f5678d00.png) __[Classifying Macro News from Asset Co-movements](https://econpapers.repec.org/RePEc:bca:bocsap:26-7)__: CLONE decomposes daily price moves into demand, productivity, inflation, and policy news using stocks, bonds, and inflation swaps; aggregate demand dominated until 2021. (2026-09-30, fanfare: 2) ![Variance Ratios The figure shows the variance ratios from equation 5 for the S&P 500 Index, 2-year U.S. Treasury yield,](https://www.ml-quant.com/issues/2026-10-09/figures/repec-21-d6c6a815.png) __[Term Spread Volatility Predicts Economic Activity](https://econpapers.repec.org/RePEc:hal:journl:hal-05740995)__: Treasury yield curve slope volatility forecasts industrial production and employment growth at medium and long horizons, outperforming term spread alone after the 2008 crisis. (2026-10-05, fanfare: 2) ![Estimated R2 when forecasting economic activity 6 months ahead using TS](https://www.ml-quant.com/issues/2026-10-09/figures/repec-22-4929ec80.png) __[Bayesian Shrinkage in High-Dimensional Panel VARs](https://econpapers.repec.org/RePEc:adl:wpaper:2026-05)__: A Bayesian spike-and-slab method improves high-dimensional panel VAR forecasting and reveals heterogeneous spillover structures in sovereign bonds and macro data. (2026-10-05, fanfare: 2) ![Posterior Dynamic Interdependencies Across the Euro Area](https://www.ml-quant.com/issues/2026-10-09/figures/repec-23-001f422a.png) __[Eurodollars and Bretton Woods Monetary Transmission](https://econpapers.repec.org/RePEc:aim:wpaimx:2625)__: US monetary tightening attracted capital through Eurodollar borrowing but did not transmit to foreign output, revealing continued financial segmentation despite offshore growth. (2026-10-05, fanfare: 2) ![Fed discount rate, fed funds rate, T-Bills rate, and Eurodollar rate from 1947-1973.](https://www.ml-quant.com/issues/2026-10-09/figures/repec-24-f7501234.png) __[Interest Rate Pass-Through in DeFi](https://econpapers.repec.org/RePEc:abo:neswpt:sp0003)__: The study finds that US risk-free rates predict stablecoin lending rates in decentralized finance, though transmission operates mainly through crypto-native factors like utilization. (2026-10-05, fanfare: 2) __[Optimal Currency Strategies Under CIP Violations](https://econpapers.repec.org/RePEc:nbr:nberwo:35498)__: Extending Campbell et al., the analysis shows empirically measured deviations from interest parity reverse optimal currency demands for emerging-market investors. (2026-09-26, fanfare: 2) __[Dollar Safe Asset Convenience Yields Persist](https://econpapers.repec.org/RePEc:nbr:nberwo:35742)__: Dollar safe asset convenience yields remain stable domestically since 2019 but decline globally relative to other currencies, with repo markets leading. (2026-09-28, fanfare: 2) __[Housing Volatility Spillovers and Policy Networks](https://econpapers.repec.org/RePEc:pre:wpaper:202630)__: A time-varying VAR network reveals supply-inelastic coastal markets absorb largest regulatory shocks and spillovers persist to monetary conditions and inflation expectations. (2026-10-05, fanfare: 2) ![Macroeconomic Policy and Financial Indicators](https://www.ml-quant.com/issues/2026-10-09/figures/repec-28-6f1edacb.png) __[Volatility-Based ID of April 2025 Tariff Shocks](https://econpapers.repec.org/RePEc:bde:wpaper:2629e)__: Volatility and kurtosis reveal that dollar depreciation and rising Treasury yields reflect multiple distinct shocks rather than a single tariff announcement shock. (2026-10-05, fanfare: 2) ![Impulse response functions](https://www.ml-quant.com/issues/2026-10-09/figures/repec-29-fe0ebc16.png) __[Professional Forecasters' Perceived Policy Rules](https://econpapers.repec.org/RePEc:adl:wpaper:2026-02)__: Time-varying beliefs about the central bank's reaction function collapsed at the zero bound and rose only after observed tightening, not from guidance alone. (2026-10-05, fanfare: 2) ![Cash rate and headline CPI forecasts](https://www.ml-quant.com/issues/2026-10-09/figures/repec-30-efde0a4b.png) --- --- title: Track record url: https://www.ml-quant.com/track-record/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-10-09 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: 2426 - Now published (of those featured at least a year ago): 26% - Cited 100+ times: 12 - 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): 509 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): 266 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): 237 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): 234 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): 191 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): 143 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): 141 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): 126 citations; featured 2023-07-26, 7 days after release; not yet published - [ESG Reputation Risk Matters: An Event Study Based on Social Media Data](https://arxiv.org/abs/2307.11571): 111 citations; featured 2023-07-26, 5 days after release; not yet published - [Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis](https://arxiv.org/abs/2305.07972): 110 citations; featured 2023-05-18, 5 days after release; Annual Meeting of the Association for Computational Linguistics - [A Scoping Review of ChatGPT Research in Accounting and Finance](http://dx.doi.org/10.1016/j.accinf.2024.100715): 103 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): 101 citations; featured 2023-05-24, 5 days after release; not yet published ## Now out in journals - [Dynamic resource allocation with karma: An experimental study](http://arxiv.org/abs/2404.02687v2): Journal of Economic Behavior & Organization, 4 citations - [PPI++: Efficient Prediction-Powered Inference](https://arxiv.org/abs/2311.01453): Annals of Applied Statistics, 140 citations - [Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation](https://arxiv.org/abs/2502.02464): Annual Meeting of the Association for Computational Linguistics, 13 citations - [Optimal automation under overdispersed discrete risk: thresholds and hysteresis in a Negative Binomial model](http://arxiv.org/abs/2510.05487v1): Journal of Industrial and Management Optimization, 1 citations - [Market-driven equilibria for distributed photovoltaic panel investment](http://arxiv.org/abs/2509.07203v1): Applied Energy, 0 citations - [Impacts of large-scale food fortification on the cost of nutrient-adequate diets: a modelling study in 89 countries](http://arxiv.org/abs/2511.05438v1): BMJ Public Health, 0 citations ## Most cited featured papers - [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://arxiv.org/abs/2402.03300): 9718 citations (preprint), featured 2024-02-07 - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/pdf/2312.00752.pdf): 9474 citations (preprint), featured 2024-06-05 - [Mistral 7B](https://arxiv.org/abs/2310.06825): 4011 citations (preprint), featured 2023-10-16 - [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://arxiv.org/abs/2406.01574): 2343 citations (Neural Information Processing Systems), featured 2024-10-09 - [Depth Anything V2](https://arxiv.org/abs/2406.09414): 2329 citations (Neural Information Processing Systems), featured 2024-06-20 - [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://arxiv.org/abs/2408.03314): 2266 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): 2044 citations (International Conference on Machine Learning), featured 2024-06-05 - [Octo: An Open-Source Generalist Robot Policy](https://arxiv.org/abs/2405.12213): 2026 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): 1914 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): 1802 citations (preprint), featured 2024-12-12 - [Qwen2.5-Coder Technical Report](https://arxiv.org/abs/2409.12186): 1628 citations (preprint), featured 2024-09-25 - [MemGPT: Towards LLMs as Operating Systems](https://arxiv.org/abs/2310.08560): 1558 citations (preprint), featured 2023-10-16 - [s1: Simple test-time scaling](https://arxiv.org/pdf/2501.19393): 1521 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): 1507 citations (preprint), featured 2024-05-22 - [Mastering Diverse Domains through World Models](https://arxiv.org/abs/2301.04104): 1485 citations (preprint), featured 2024-04-24 - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](http://dx.doi.org/10.48550/arxiv.2303.08896): 1302 citations (Conference on Empirical Methods in Natural Language Processing), featured 2023-10-16 - [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://arxiv.org/abs/2404.02905): 1209 citations (Neural Information Processing Systems), featured 2024-06-12 - [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734): 1206 citations (Neural Information Processing Systems), featured 2024-07-10 - [A Simple and Effective Pruning Approach for Large Language Models](https://arxiv.org/pdf/2306.11695.pdf): 1012 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): 993 citations (International Conference on Learning Representations), featured 2024-01-03 - [TÜLU 3: Pushing Frontiers in Open Language Model Post-Training](https://arxiv.org/abs/2411.15124): 975 citations (preprint), featured 2025-02-05 - [Simple and Effective Masked Diffusion Language Models](https://arxiv.org/abs/2406.07524): 947 citations (Neural Information Processing Systems), featured 2024-06-12 - [TinyLlama: An Open-Source Small Language Model](https://arxiv.org/abs/2401.02385): 934 citations (preprint), featured 2024-01-09 - [Training Large Language Models to Reason in a Continuous Latent Space](https://arxiv.org/abs/2412.06769): 811 citations (preprint), featured 2024-12-12 - [Fourier Neural Operator with Learned Deformations for PDEs on General Geometries](https://arxiv.org/abs/2207.05209): 765 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): 745 citations (Neural Information Processing Systems), featured 2024-04-10 - [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://arxiv.org/abs/2501.09747): 741 citations (Robotics), featured 2025-01-23 - [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://arxiv.org/abs/2311.17245): 685 citations (Neural Information Processing Systems), featured 2024-04-03 - [BLINK: Multimodal Large Language Models Can See but Not Perceive](https://arxiv.org/pdf/2404.12390.pdf): 644 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): 637 citations (2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2024-05-01 - [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://arxiv.org/pdf/2310.10642.pdf): 635 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): 629 citations (preprint), featured 2023-12-06 - [Continuous 3D Perception Model with Persistent State](https://arxiv.org/abs/2501.12387): 621 citations (2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2025-01-23 - [Ferret: Refer and Ground Anything Anywhere at Any Granularity](https://arxiv.org/abs/2310.07704): 618 citations (International Conference on Learning Representations), featured 2023-10-16 - [TOFU: A Task of Fictitious Unlearning for LLMs](https://arxiv.org/abs/2401.06121): 612 citations (preprint), featured 2024-01-17 - [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://arxiv.org/abs/2303.09535): 589 citations (2023 IEEE/CVF International Conference on Computer Vision (ICCV)), featured 2023-10-16 - [Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks](https://arxiv.org/abs/2404.02151): 587 citations (International Conference on Learning Representations), featured 2024-06-20 - [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://arxiv.org/abs/2305.00050): 587 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): 579 citations (2025 IEEE/CVF International Conference on Computer Vision (ICCV)), featured 2024-11-20 - [Gated Linear Attention Transformers with Hardware-Efficient Training](https://arxiv.org/abs/2312.06635): 578 citations (International Conference on Machine Learning), featured 2023-12-13 - [Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models](https://arxiv.org/abs/2405.01535): 564 citations (Conference on Empirical Methods in Natural Language Processing), featured 2024-05-08 - [FinGPT: Open-Source Financial Large Language Models](https://arxiv.org/abs/2306.06031): 509 citations (preprint), featured 2023-06-14 - [Llemma: An Open Language Model For Mathematics](https://arxiv.org/abs/2310.10631): 506 citations (International Conference on Learning Representations), featured 2023-10-18 - [Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model](https://arxiv.org/abs/2408.11039): 500 citations (preprint), featured 2024-08-21 - [Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2410.13848): 498 citations (2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2024-10-23 - [SliceGPT: Compress Large Language Models by Deleting Rows and Columns](https://arxiv.org/abs/2401.15024): 490 citations (International Conference on Learning Representations), featured 2024-01-30 - [CAT3D: Create Anything in 3D with Multi-View Diffusion Models](http://arxiv.org/abs/2405.10314): 490 citations (Neural Information Processing Systems), featured 2024-05-22 - [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://arxiv.org/abs/2406.20094): 486 citations (preprint), featured 2024-07-03 - [Training Language Models to Self-Correct via Reinforcement Learning](https://arxiv.org/abs/2409.12917): 481 citations (International Conference on Learning Representations), featured 2024-10-09 - [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](http://arxiv.org/abs/2406.15349v1): 458 citations (Neural Information Processing Systems), featured 2024-11-06 - [An Embodied Generalist Agent in 3D World](https://arxiv.org/abs/2311.12871): 453 citations (International Conference on Machine Learning), featured 2024-05-15 - [Capabilities of Gemini Models in Medicine](https://arxiv.org/abs/2404.18416): 451 citations (preprint), featured 2024-05-08 - [The Platonic Representation Hypothesis](https://arxiv.org/abs/2405.07987): 438 citations (International Conference on Machine Learning), featured 2024-05-15 - [PGSR: Planar-Based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://arxiv.org/pdf/2406.06521): 436 citations (IEEE Transactions on Visualization and Computer Graphics), featured 2024-06-12 - [Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models](https://arxiv.org/abs/2501.01423): 428 citations (2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)), featured 2025-01-08 - [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](http://dx.doi.org/10.2139/ssrn.4412788): 416 citations (preprint), featured 2023-07-05 - [RAFT: Adapting Language Model to Domain Specific RAG](https://arxiv.org/abs/2403.10131): 411 citations (preprint), featured 2024-06-12 - [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://arxiv.org/abs/2411.15098): 385 citations (2025 IEEE/CVF International Conference on Computer Vision (ICCV)), featured 2024-11-27 - [Data Selection for Language Models via Importance Resampling](https://arxiv.org/abs/2302.03169): 383 citations (Neural Information Processing Systems), featured 2023-10-25 - [Agent Workflow Memory](https://arxiv.org/abs/2409.07429): 378 citations (International Conference on Machine Learning), featured 2024-09-18 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: 2026-10-09 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: 2026-10-09 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: 2026-10-09 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)