<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:googleplay="http://www.google.com/schemas/play-podcasts/1.0"><channel><title><![CDATA[Machine Learning & Quant Finance]]></title><description><![CDATA[Links on Quantitative Topics (1-weekly)]]></description><link>https://blog.ml-quant.com</link><image><url>https://substackcdn.com/image/fetch/$s_!X41Z!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc1f692c-9c93-491c-be82-2d8b0de97577_735x735.png</url><title>Machine Learning &amp; Quant Finance</title><link>https://blog.ml-quant.com</link></image><generator>Substack</generator><lastBuildDate>Thu, 09 Apr 2026 02:57:44 GMT</lastBuildDate><atom:link href="https://blog.ml-quant.com/feed" rel="self" type="application/rss+xml"/><copyright><![CDATA[Derek Snow]]></copyright><language><![CDATA[en]]></language><webMaster><![CDATA[mlquant@substack.com]]></webMaster><itunes:owner><itunes:email><![CDATA[mlquant@substack.com]]></itunes:email><itunes:name><![CDATA[Dr. Derek Snow]]></itunes:name></itunes:owner><itunes:author><![CDATA[Dr. Derek Snow]]></itunes:author><googleplay:owner><![CDATA[mlquant@substack.com]]></googleplay:owner><googleplay:email><![CDATA[mlquant@substack.com]]></googleplay:email><googleplay:author><![CDATA[Dr. Derek Snow]]></googleplay:author><itunes:block><![CDATA[Yes]]></itunes:block><item><title><![CDATA[Quant Letter: April 2026, Week-1]]></title><description><![CDATA[119th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-april-2026-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-april-2026-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Fri, 03 Apr 2026 18:15:38 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!X41Z!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc1f692c-9c93-491c-be82-2d8b0de97577_735x735.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ARXIV (q-fin) &#8212; Top 30 Papers</h1><h2>A. Most Recent (April 2026)</h2><p><strong>1. The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management</strong> Autonomous multi-agent system for institutional portfolio management with hierarchical decision-making. (2026-04-03)<a href="https://arxiv.org/abs/2604.02279">https://arxiv.org/abs/2604.02279</a></p><p><strong>2. Hedging Market Risk and Uncertainty via a Robust Portfolio Approach</strong> Robust optimization framework for portfolio hedging under Knightian uncertainty. (2026-04-03) <a href="https://arxiv.org/abs/2604.02126">https://arxiv.org/abs/2604.02126</a></p><p><strong>3. Reinforcement Learning for Speculative Trading under Exploratory Framework</strong> Novel RL formulation addressing exploration-exploitation in speculative strategies. (2026-04-02) <a href="https://arxiv.org/abs/2604.02035">https://arxiv.org/abs/2604.02035</a></p><p><strong>4. Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts</strong> Examines predictive power of prediction markets for crypto volatility using Kalshi data. (2026-04-02)<a href="https://arxiv.org/abs/2604.01431">https://arxiv.org/abs/2604.01431</a></p><p><strong>5. Forecasting Duration in High-Frequency Financial Data Using a Self-Exciting Flexible Residual Point Process</strong>Point process model for HFT duration forecasting with flexible residual specifications. (2026-04-01)<a href="https://arxiv.org/abs/2604.00346">https://arxiv.org/abs/2604.00346</a></p><p><strong>6. Pricing Lookback Options on a Quantum Computer</strong> Quantum algorithm for path-dependent option pricing with practical implementation considerations. (2026-04-01) <a href="https://arxiv.org/abs/2604.00389">https://arxiv.org/abs/2604.00389</a></p><p><strong>7. Valuation of Variable Annuities under the Volterra Mortality and Rough Heston Models</strong> Combines rough volatility with stochastic mortality for insurance-linked product valuation. (2026-04-01)<a href="https://arxiv.org/abs/2604.00472">https://arxiv.org/abs/2604.00472</a></p><p><strong>8. Decomposable Reward Modeling and Realistic Environment Design for RL-Based Forex Trading</strong> Addresses reward engineering challenges in FX trading with modular reward decomposition. (2026-04-01)<a href="https://arxiv.org/abs/2604.00031">https://arxiv.org/abs/2604.00031</a></p><p><strong>9. Forecast Collapse of Transformer-Based Models under Squared Loss in Financial Time Series</strong> Critical analysis of Transformer failure modes in financial forecasting, identifies architectural limitations. (2026-04-01)<a href="https://arxiv.org/abs/2604.00064">https://arxiv.org/abs/2604.00064</a></p><p><strong>10. On the Mean-Variance Problem Through the Lens of Multivariate Fake Stationary Affine Volterra Dynamics</strong>Novel mathematical framework extending mean-variance optimization to rough volatility settings. (2026-04-02)<a href="https://arxiv.org/abs/2604.01300">https://arxiv.org/abs/2604.01300</a></p><p><strong>11. Bridging Classical and Martingale Schr&#246;dinger Bridges</strong> Mathematical finance cross-list connecting optimal transport with portfolio theory. (2026-04-02) <a href="https://arxiv.org/abs/2604.01299">https://arxiv.org/abs/2604.01299</a></p><h2>B. Late March 2026</h2><p><strong>12. Bridging Stochastic Control and Deep Hedging: Structural Priors for No-Transaction Band Networks</strong>Proposes WW-NTBN architecture embedding Whalley-Wilmott formula as structural prior. Faster convergence, closer match to optimal control. (2026-03-31) <a href="https://arxiv.org/abs/2603.29994">https://arxiv.org/abs/2603.29994</a></p><p><strong>13. Option Pricing on Automated Market Maker Tokens</strong> Derivatives pricing framework for AMM LP tokens in DeFi. (2026-03-30) <a href="https://arxiv.org/abs/2603.29763">https://arxiv.org/abs/2603.29763</a></p><p><strong>14. Common Risk Factors in Decentralized AI Subnets</strong> Factor model for emerging decentralized AI compute markets (Bittensor-style). (2026-03-30) <a href="https://arxiv.org/abs/2603.29751">https://arxiv.org/abs/2603.29751</a></p><p><strong>15. Model Predictive Control for Trade Execution</strong> MPC framework for optimal execution with market impact and inventory constraints. (2026-03-28) <a href="https://arxiv.org/abs/2603.28898">https://arxiv.org/abs/2603.28898</a></p><p><strong>16. Nonlinear Factor Decomposition via Kolmogorov-Arnold Networks: A Spectral Approach to Asset Return Analysis</strong> KAN architecture for nonlinear factor model estimation with spectral interpretability. (2026-03-27)<a href="https://arxiv.org/abs/2603.28257">https://arxiv.org/abs/2603.28257</a></p><p><strong>17. From Volatility to Variance: A Skew-Enhanced SABR Model for the Chinese Options Market</strong> Modified SABR calibration for CSI 300 and SSE 50 options with improved skew fit. (2026-03-27) <a href="https://arxiv.org/abs/2603.27501">https://arxiv.org/abs/2603.27501</a></p><p><strong>18. Rough Volatility Dynamics in Commodity Markets</strong> Empirical analysis of rough volatility signatures in energy and agricultural commodities. (2026-03-26) <a href="https://arxiv.org/abs/2603.26514">https://arxiv.org/abs/2603.26514</a></p><p><strong>19. Capital-Allocation-Induced Risk Sharing</strong> Theoretical analysis of how capital allocation rules affect systemic risk distribution. (2026-03-26) <a href="https://arxiv.org/abs/2603.26491">https://arxiv.org/abs/2603.26491</a></p><p><strong>20. STN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing</strong> Tensor network methods for fast multi-asset option pricing. (2026-03-26) <a href="https://arxiv.org/abs/2603.26318">https://arxiv.org/abs/2603.26318</a></p><h2>C. Mid-March 2026</h2><p><strong>21. Environmental CVA with K-Robust Wrong-Way Risk</strong> Climate-adjusted credit valuation adjustment with robust wrong-way risk modeling. (2026-03-24) <a href="https://arxiv.org/abs/2603.23842">https://arxiv.org/abs/2603.23842</a></p><p><strong>22. Proxy-Reliance Control in Conformal Recalibration of One-Sided Value-at-Risk</strong> Extends conformal prediction for VaR with proxy dependence control. (2026-03-24) <a href="https://arxiv.org/abs/2603.22569">https://arxiv.org/abs/2603.22569</a></p><p><strong>23. Modeling and Forecasting Tail Risk Spillovers: A Component-Based CAViaR Approach</strong> Component CAViaR for tail risk contagion measurement across markets. (2026-03-25) <a href="https://arxiv.org/abs/2603.25217">https://arxiv.org/abs/2603.25217</a></p><p><strong>24. Shifting Correlations: How Trade Policy Uncertainty Alters Stock-T-bill Relationships</strong> Dynamic correlation analysis linking trade policy shocks to asset class co-movements. (2026-03-25) <a href="https://arxiv.org/abs/2603.25285">https://arxiv.org/abs/2603.25285</a></p><p><strong>25. Robust Investment-Driven Insurance Pricing and Liquidity Management</strong> Optimization framework for insurance pricing with investment constraints. (2026-03-19) <a href="https://arxiv.org/abs/2603.18962">https://arxiv.org/abs/2603.18962</a></p><p><strong>26. Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios</strong> GAN-based framework for scenario generation in risk management applications. (2026-03-08) <a href="https://arxiv.org/abs/2603.08553">https://arxiv.org/abs/2603.08553</a></p><p><strong>27. Semi-Structured Multi-State Delinquency Model for Mortgage Default</strong> Hybrid statistical-ML model for mortgage credit risk with interpretable structure. (2026-03-26) <a href="https://arxiv.org/abs/2603.26309">https://arxiv.org/abs/2603.26309</a></p><p><strong>28. Pricing and Hedging for Liquidity Provision in Constant Function Market Making</strong> Complete analytical framework for LP positions in CFMMs (Uniswap-style AMMs). (2026-03-01) <a href="https://arxiv.org/abs/2603.01344">https://arxiv.org/abs/2603.01344</a></p><p><strong>29. Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance</strong>Comprehensive benchmark across architectures. xLSTM achieves Sharpe 1.79 (2010-2025), 1.99 in recent period. Zohren et al. (2026-03-03) <a href="https://arxiv.org/abs/2603.01820">https://arxiv.org/abs/2603.01820</a></p><p><strong>30. Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis</strong>Graph neural network with BERT sentiment for equity prediction. (2026-03-05) <a href="https://arxiv.org/abs/2603.05917">https://arxiv.org/abs/2603.05917</a></p><div><hr></div><h1>SSRN &#8212; Top 30 Papers</h1><h2>April 2026</h2><p><strong>1. Transformer Uncertainty and the Cross-Section of Stock Returns</strong> Uncertainty measures from Transformer attention patterns as pricing factors. (Posted: 2 Apr 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6412360">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6412360</a></p><p><strong>2. Geopolitical Risk and Equity Returns: Evidence From Global Markets</strong> Cross-country analysis of geopolitical risk premia. (Posted: 2 Apr 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6426223">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6426223</a></p><h2>Late March 2026</h2><p><strong>3. How Do Cryptocurrencies Price Economic News?</strong> Event-study analysis of macro news impact on crypto returns. (Posted: 31 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6447644">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6447644</a></p><p><strong>4. Every Seed, Every Result: Intent-to-Treat Reporting for Financial Reinforcement Learning</strong> Methodological framework for reproducible RL backtesting in finance. (Posted: 31 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6382938">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6382938</a></p><p><strong>5. Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing</strong> Evaluates multi-agent AI architectures for factor model estimation. (Posted: 30 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6474601">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6474601</a></p><p><strong>6. Transformer-Based Sentiment, Alpha Decay, and Market Efficiency (NIFTY 50)</strong> Sentiment-driven alpha analysis in Indian equity markets. (Posted: 30 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6299459">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6299459</a></p><p><strong>7. Robust Reinforcement Learning for Market Making under Model Uncertainty</strong> RL market-making with adversarial robustness to model misspecification. (Posted: 28 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6487270">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6487270</a></p><p><strong>8. Machine Learning Models for Loan Default Prediction Using Borrower Financial Attributes</strong> Comparative ML study for consumer credit risk. (Posted: 26 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6424158">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6424158</a></p><p><strong>9. Identification via Heteroskedasticity when Attention is Endogenous</strong> Econometric methodology for causal inference in attention-driven markets. (Posted: 26 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6350358">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6350358</a></p><p><strong>10. Arrow-Debreu Meets Kyle: Price Discovery Across Derivatives</strong> Unified microstructure model spanning spot and derivatives markets. (Posted: 26 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6361238">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6361238</a></p><p><strong>11. Enhancing RL for Stock Trading Through Combinatorial Optimization</strong> Hybrid RL-combinatorial optimization for portfolio selection. (Posted: 25 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6469180">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6469180</a></p><p><strong>12. Machine Learning and AI in Market Risk Management: A Review</strong> Comprehensive survey of ML applications in market risk. (Posted: 25 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423478">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423478</a></p><p><strong>13. The Sound of Silence: Policy Signals and Risk Premia</strong> How absence of policy communication affects asset pricing. (Posted: 24 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6191779">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6191779</a></p><p><strong>14. Asset Pricing with Endogenous Default</strong> Equilibrium model with strategic default and credit risk premia. (Posted: 24 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6336038">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6336038</a></p><p><strong>15. ScoreMatchingRiesz for Automatic Debiased Machine Learning</strong> Novel debiasing technique for ML-based causal inference in finance. (Posted: 24 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6337458">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6337458</a></p><p><strong>16. Flexible Information Acquisition in the Kyle Model</strong> Extended Kyle model with endogenous information choice. (Posted: 24 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6456438">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6456438</a></p><p><strong>17. Beyond the Hype: A Multi-Layer ML Framework for Crypto Return Forecasting</strong> Ensemble ML architecture for cryptocurrency prediction. (Posted: 23 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6320138">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6320138</a></p><p><strong>18. Machine Learning Portfolio Choice under Parameter Uncertainty</strong> Bayesian-ML hybrid for portfolio optimization with estimation risk. (Posted: 20 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6359140">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6359140</a></p><p><strong>19. Empirical Asset Pricing via Learning-to-Rank</strong> Ranking-based ML for cross-sectional return prediction. (Posted: 20 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6348379">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6348379</a></p><p><strong>20. Reward Inference for Portfolio Optimization via Multi-Expert Inverse RL</strong> Inverse RL for learning portfolio objectives from expert allocations. (Posted: 20 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6446236">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6446236</a></p><p><strong>21. An Infinite-Dimensional Insider Trading Game</strong> Continuous-time game theory for informed trading with function-space strategies. (Posted: 20 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6361218">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6361218</a></p><p><strong>22. Behavioral Factors in Asset Pricing: An Approach Through Machine Learning</strong> ML-based behavioral factor construction and pricing tests. (Posted: 18 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6439412">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6439412</a></p><p><strong>23. Autonomous AI Agents for Option Hedging</strong> Multi-agent system for automated delta-gamma hedging. (Posted: 17 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6339420">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6339420</a></p><p><strong>24. Stagflation Risk and Financial Markets: A Real-Time Composite Index</strong> Nowcasting index for stagflation with market implications. (Posted: 17 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6426799">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6426799</a></p><p><strong>25. The Price of the Queue: RL and the Cross-Section of Limit Order Values</strong> RL valuation of limit order placement across queue positions. (Posted: 16 Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6322361">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6322361</a></p><p><strong>26. A Microstructure Perspective on Prediction Markets</strong> Liquidity provision and price discovery in prediction market design. (Posted: Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6325658">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6325658</a></p><p><strong>27. High-Frequency Market Microstructure Analysis using Transformer-Encoder Networks and GNNs</strong> Deep learning for HFT microstructure patterns. (Posted: Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6301320">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6301320</a></p><p><strong>28. Explainable Deep Learning for Financial Volatility Forecasting: LSTM-Attention-SHAP</strong> Interpretable volatility prediction with attention-based explanations. (Posted: Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6301119">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6301119</a></p><p><strong>29. Two-Time-Scale Transfer Learning for Market-by-Order Data</strong> Transfer learning for MBO data across different timescales. (Posted: Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6424798">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6424798</a></p><p><strong>30. Default Risk and Liquidity Provision of Sovereign Debt</strong> Sovereign credit risk and bond market liquidity dynamics. (Posted: Mar 2026) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6412039">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6412039</a></p><div><hr></div><h1>RePEc/NEP &#8212; Top 30 Papers</h1><h2>NEP-RMG Issue 2026-03-23</h2><p><strong>1. Calibrated Credit Intelligence: Shift-Robust and Fair Risk Scoring with Bayesian Uncertainty and Gradient Boosting</strong> Combines Bayesian uncertainty with XGBoost for robust credit scoring under distribution shift.<a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>2. Exploratory Randomization for Discrete-Time Risk-Sensitive Benchmarked Investment Management with Reinforcement Learning</strong> Novel RL framework for portfolio management under risk-sensitive objectives.<a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>3. Repo and the Liquidity Risk Premium</strong> Analysis of repo market dynamics and liquidity premia in short-term funding. <a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>4. Can Models with Idiosyncratic Risk Solve the Equity Premium Puzzle? Redux</strong> Revisits idiosyncratic risk explanations for the equity premium. <a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>5. One Rising Ship Sinks Other Ships: Cross-Chain Negative Spillovers in Crypto Markets</strong> Cross-chain contagion analysis in cryptocurrency ecosystems. <a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>6. Can Ethereum Survive a Run? Hidden Fragility in Crypto&#8217;s Proof-of-Stake Model</strong> Stress testing Ethereum&#8217;s PoS mechanism under adverse scenarios. <a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>7. Pricing Protection: Credit Scores, Disaster Risk, and Home Insurance Affordability</strong> ML analysis of climate risk pricing in insurance markets. <a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>8. Securitization, Bank Regulation, and the Macroeconomy</strong> DSGE model with securitization and capital requirements. <a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><p><strong>9. Why People Disagree About What Drives Stock Prices</strong> Behavioral analysis of heterogeneous investor beliefs.<a href="https://nep.repec.org/nep-rmg/2026-03-23">https://nep.repec.org/nep-rmg/2026-03-23</a></p><h2>NEP-RMG Issue 2026-03-16</h2><p><strong>10. SPX-VIX Risk Computations Via Perturbed Optimal Transport</strong> Optimal transport methods for SPX-VIX joint risk modeling. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>11. Adaptive Window Selection for Financial Risk Forecasting</strong> Dynamic window optimization for VaR and ES estimation. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>12. Uncertainty-Aware Deep Hedging</strong> Deep hedging with explicit uncertainty quantification.<a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>13. Pricing and Hedging for Liquidity Provision in Constant Function Market Making</strong> Complete analytical framework for LP positions in CFMMs. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>14. Single-Asset Adaptive Leveraged Volatility Control</strong> Dynamic leverage adjustment for volatility targeting strategies. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>15. Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance</strong>Comprehensive architecture benchmark (xLSTM Sharpe 1.79-1.99). <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>16. Multivariate Stochastic Volatility Model with Block Correlations</strong> Block-structured MSV for large portfolio applications. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>17. A Stochastic Correlation Extension of the Vasicek Credit Risk Model</strong> Time-varying correlation in structural credit models. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>18. Budgeted Robust Intervention Design for Financial Networks with Common Asset Exposures</strong> Network intervention optimization under budget constraints. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>19. Environmental CVA with K-Robust Wrong-Way Risk</strong> Climate-adjusted CVA with robust WWR modeling.<a href="https://arxiv.org/abs/2603.23842">https://arxiv.org/abs/2603.23842</a></p><p><strong>20. Proxy-Reliance Control in Conformal Recalibration of One-Sided Value-at-Risk</strong> Conformal VaR with proxy dependence control. <a href="https://arxiv.org/abs/2603.22569">https://arxiv.org/abs/2603.22569</a></p><p><strong>21. Modeling and Forecasting Tail Risk Spillovers: A Component-Based CAViaR Approach</strong> Component CAViaR for tail risk contagion. <a href="https://arxiv.org/abs/2603.25217">https://arxiv.org/abs/2603.25217</a></p><p><strong>22. Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios</strong> GAN-based scenario generation for risk management. <a href="https://arxiv.org/abs/2603.08553">https://arxiv.org/abs/2603.08553</a></p><p><strong>23. Semi-Structured Multi-State Delinquency Model for Mortgage Default</strong> Hybrid statistical-ML mortgage credit model. <a href="https://arxiv.org/abs/2603.26309">https://arxiv.org/abs/2603.26309</a></p><p><strong>24. Rough Volatility Dynamics in Commodity Markets</strong> Rough volatility in energy and agricultural commodities.<a href="https://arxiv.org/abs/2603.26514">https://arxiv.org/abs/2603.26514</a></p><p><strong>25. STN-GPR: A Singularity Tensor Network Framework for Efficient Option Pricing</strong> Tensor network methods for multi-asset options. <a href="https://arxiv.org/abs/2603.26318">https://arxiv.org/abs/2603.26318</a></p><p><strong>26. Capital-Allocation-Induced Risk Sharing</strong> Capital allocation rules and systemic risk distribution.<a href="https://arxiv.org/abs/2603.26491">https://arxiv.org/abs/2603.26491</a></p><p><strong>27. The Geometry of Risk: Path-Dependent Regulation and Anticipatory Hedging via the SigSwap</strong> Signature-based path-dependent hedging instruments. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>28. Asymptotics of Ruin Probabilities in a Subordinated Cram&#233;r-Lundberg Model</strong> Asymptotic analysis for time-changed insurance risk processes. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>29. Endogenous Distress Contagion in a Dynamic Interbank Model</strong> Dynamic network model for banking contagion.<a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><p><strong>30. Submodular Risk Measures</strong> Axiomatic foundation for submodular risk aggregation. <a href="https://nep.repec.org/nep-rmg/2026-03-16">https://nep.repec.org/nep-rmg/2026-03-16</a></p><div><hr></div><h1>Additional Notable Papers (Highly Relevant from Prior Sessions)</h1><p><strong>LLM Detection of Structural Market Patterns: Obfuscation Testing for Dealer Hedging Constraints</strong> Novel methodology for validating LLM detection of gamma positioning, stock pinning, 0DTE hedging. 71.5% detection rate with unbiased prompts, 91.2% materialization accuracy. (2025-12-27) <a href="https://arxiv.org/abs/2512.17923">https://arxiv.org/abs/2512.17923</a></p><p><strong>Taming Tail Risk in Financial Markets: Conformal Risk Control for Nonstationary Portfolio VaR</strong> Regime-weighted conformal risk control (RWC) for sequential VaR under nonstationarity. Finite-sample coverage under weighted exchangeability. Schmitt. (2026-02-03) <a href="https://arxiv.org/abs/2602.03903">https://arxiv.org/abs/2602.03903</a></p><p><strong>Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series</strong> MIT/Cortesi et al. demonstrate how optimizer choice acts as implicit regularization in financial ML. 39 pages. (2026-03-04)<a href="https://arxiv.org/abs/2603.02620">https://arxiv.org/abs/2603.02620</a></p><p><strong>Bridging the Reality Gap in Limit Order Book Simulation</strong> Noble, Rosenbaum &amp; Souilmi address sim-to-reality transfer for LOB agents. (2026-03-25) <a href="https://arxiv.org/abs/2603.24137">https://arxiv.org/abs/2603.24137</a></p><p><strong>Finance-Informed Neural Network (FINN) for Option Pricing</strong> Self-supervised deep hedging achieving Black-Scholes recovery. Put-call parity emerges endogenously. (Updated Dec 2025) <a href="https://arxiv.org/abs/2412.12213">https://arxiv.org/abs/2412.12213</a></p><p><strong>Generative AI for Finance: Risk Premia BERT (RPBERT)</strong> BERT-based architecture encoding cross-sectional equity structure. Outperforms ML and factor-pricing benchmarks. Chai, Jiang, Meng, You &amp; Zhou. (Posted Mar 2026)<a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6276278">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6276278</a></p><p><strong>Homo Silicus is Hyper-Rational: Why LLM Agents Fail to Replicate Attention-Driven Trading</strong> 96 GPT-4 agents show LLMs reduce buying propensity for attention stocks by 11.67pp. Garcia. (Posted Dec 2025)<a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5901742">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5901742</a></p><p><strong>Machine Learning Meets Markowitz</strong> Harvey et al. end-to-end ML framework unifying return prediction with portfolio optimization. (Posted Dec 2025) <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5947774">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=59477</a></p><h1>GitHub</h1><h2>Finance</h2><p><strong><a href="https://github.com/NVIDIA-AI-Blueprints/quantitative-portfolio-optimization">NVIDIA AI Portfolio Optimization</a></strong>: The article provides a developer example for enhancing investment portfolios with NVIDIA&#8217;s tools. (2025-10-27, shares: 239)</p><p><strong><a href="https://github.com/TorchTrade/torchtrade">Modular RL Trading Framework</a></strong>: It introduces a flexible system that uses reinforcement learning to enhance algorithmic trading strategies. (2024-11-07, shares: 268)</p><p><strong><a href="https://github.com/goldspanlabs/optopsy-mcp">Rust Optopsy Engine Rewrite</a></strong>: This article covers a Rust-based update of Optopsy, a backtesting engine for options trading, featuring a new protocol. (2026-02-28, shares: 7)</p><p><strong><a href="https://github.com/45ck/llm-quant">LLM Trading System for ETFs</a></strong>: 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)</p><p><strong><a href="https://github.com/apache/fluss">Apache Fluss: Real-Time Analytics</a></strong>: Apache Fluss is presented as a real-time data analytics solution for streaming storage. (2024-10-31, shares: 1834)</p><h2>Trending</h2><p><strong><a href="https://github.com/ultraworkers/claw-code">Repo Ownership Transfer</a></strong>: The repository is locked for ownership transfer; users are redirected to a faster alternative to reach 100K stars. (2026-03-31, shares: 145323)</p><p><strong><a href="https://github.com/instructkr/claw-code">Rust Rebuild of Claude</a></strong>: Better Harness Tools aims to archive leaked Claude Code and is being rewritten in Rust for better functionality. (2026-03-31, shares: 41232)</p><p><strong><a href="https://github.com/Yeachan-Heo/oh-my-codex">Enhance Codex</a></strong>: OmX Oh My codeX upgrades your codex with new features including hooks, agent teams, and HUDs. (2026-02-02, shares: 7868)</p><p><strong><a href="https://github.com/drona23/claude-token-efficient">Terse CLAUDE.md Workflows</a></strong>: The <a href="http://claude.md/">CLAUDE.md</a> file simplifies responses by keeping them brief without requiring code changes. (2026-03-30, shares: 2527)</p><p><strong><a href="https://github.com/marciopuga/cog">Cognitive Architecture for Claude</a></strong>: The article describes a cognitive architecture for Claude Code that includes persistent memory, self-reflection, and foresight. (2026-03-15, shares: 314)</p><p></p><h1>Paper with Code</h1><h2>Trending</h2><p><strong><a href="https://github.com/YU-deep/Awesome-Latent-Space">Latent Space as Foundation</a></strong>: Latent space improves language models by creating a continuous representation that minimizes redundancy and boosts efficiency. (2026-04-03, shares: 475)</p><p><strong><a href="https://github.com/meituan-longcat/LongCat-Next">Unified Multimodal Processing</a></strong>: 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)</p><h2>Rising</h2><p><strong><a href="https://github.com/ShandaAI/AlayaRenderer">Generative World Renderer: Enhanced AAA Game Rendering</a></strong>: A new dataset from AAA games enhances rendering quality and better evaluation methods that match human perception. (2026-04-03, shares: 117)</p><p><strong><a href="https://github.com/ZJU-REAL/SkillZero">SKILL0: RL for Skill Internalization</a></strong>: SKILL0 empowers LLM agents to autonomously learn and execute tasks, boosting their effectiveness with a flexible training process. (2026-04-03, shares: 65)</p><p><strong><a href="https://github.com/lcqysl/GEMS">GEMS Multimodal Generation Framework with Memory</a></strong>: 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)</p>]]></content:encoded></item><item><title><![CDATA[Quant Letter: March 2026, Week-1]]></title><description><![CDATA[118th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-march-2026-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-march-2026-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Wed, 04 Mar 2026 20:37:19 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!X41Z!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc1f692c-9c93-491c-be82-2d8b0de97577_735x735.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ARXIV (q-fin) &#8212; Top 10 Papers</h1><p><strong>1. TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructure</strong> 524M-parameter generative Transformer learning from billions of trade events across 9K+ equities. Achieves 2-3x lower distributional error than Hawkes baselines, generalizes zero-shot to APAC markets. (2026-02-27) <a href="https://arxiv.org/abs/2602.23784">https://arxiv.org/abs/2602.23&#8230;</a></p>
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: February 2026, Week-1]]></title><description><![CDATA[117th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-february-2026-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-february-2026-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Tue, 03 Feb 2026 00:25:03 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!xZSr!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F33cd2572-df45-4f7d-8a99-f1077ef5bdc8_1600x978.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h2>ARXIV (q-fin) &#8212; Top 10</h2><ol><li><p><strong><a href="https://arxiv.org/abs/2601.02310">T-KAN for HFT Limit Order Book Forecasting</a></strong> &#8212; 19.1% F1 improvement, 132% backtest return (2026-01-14)</p></li><li><p><strong><a href="https://arxiv.org/abs/2601.18811">Variational Quantum Circuit RL for Portfolio Optimization</a></strong> &#8212; Quantum-classical hybrid RL (2026-01-30)</p></li><li><p><strong><a href="https://arxiv.org/abs/2601.18804">Deep g-Pricing for CSI 300 Options</a></strong> &#8212; Vol trajectories + sentiment (2026-01-30)</p></li><li><p><strong><a href="https://arxiv.org/abs/2601.17773">MarketGANs for Financial Time-Series Augmentation</a></strong> &#8212; GAN&#8230;</p></li></ol>
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: January 2026, Week-3]]></title><description><![CDATA[116th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-january-2026-week-3</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-january-2026-week-3</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Fri, 16 Jan 2026 23:22:13 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!25rN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F378811e7-f081-4185-9599-1e6ea4d4c20e_1582x782.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="https://arxiv.org/abs/2601.10043v1">Finetuning LLaMA-3-8B for Financial NER with LoRA</a></strong>: The paper shows that using instruction fine-tuning and Low-Rank Adaptation with Meta&#8217;s Llama 3 enhances financial named-entity recognition, leading to top performance in converting unformatted reports into organized knowledge. (2026-01-15, shares: 0)</p><p><strong><a href="https://arxiv.org/abs/2601.10143v1">Adaptive Dataflow for Financial Time-Series</a></strong></p>
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: December 2025, Week-3]]></title><description><![CDATA[115th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-december-2025-week-3</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-december-2025-week-3</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Fri, 19 Dec 2025 21:29:08 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!C68o!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3749ea1f-5b85-4196-ac1c-93c94e180756_871x462.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="https://arxiv.org/abs/2512.12924v1">Interpretable Trading Validation</a></strong>: The authors introduce a walk-forward validation technique for algorithmic trading that focuses on interpretability and robust testing, offering modest gains and strong protection against losses. (2025-12-15, shares: 2)</p><p><strong><a href="https://arxiv.org/abs/2512.14662v2">Static Framework for Pricing</a></strong>: This paper presents a model-free method for pricing fixed-inc&#8230;</p>
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      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: December 2025, Week-2]]></title><description><![CDATA[114th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-december-2025-week-2</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-december-2025-week-2</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Sun, 14 Dec 2025 15:24:35 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!RuRF!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="https://arxiv.org/abs/2512.10913v1">RL for Financial Decisions</a></strong>: Reinforcement learning improves financial decision-making by simplifying complex investment problems, emphasizing clear explanations and strong reliability over complex algorithms. (2025-12-11, shares: 2)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!RuRF!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!RuRF!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 424w, https://substackcdn.com/image/fetch/$s_!RuRF!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 848w, https://substackcdn.com/image/fetch/$s_!RuRF!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 1272w, https://substackcdn.com/image/fetch/$s_!RuRF!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!RuRF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png" width="1456" height="1066" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:1066,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:424157,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/181593978?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!RuRF!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 424w, https://substackcdn.com/image/fetch/$s_!RuRF!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 848w, https://substackcdn.com/image/fetch/$s_!RuRF!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 1272w, https://substackcdn.com/image/fetch/$s_!RuRF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F23775f36-d534-4eed-8001-c5310d34768d_2376x1740.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="https://arxiv.org/abs/2512.10823v1">Unified Risk-Neutral Pricing</a></strong>: The paper presents a new approach to pricing zero-coupon bonds that aligns them w&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-december-2025-week-2">
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: December 2025, Week-1]]></title><description><![CDATA[113th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-december-2025-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-december-2025-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Thu, 04 Dec 2025 20:12:58 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!4guh!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3208489e-89de-4332-a456-4f5bf635931c_2294x1396.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>SSRN</h1><h3>Recently Published</h3><h2>Quantitative</h2><p><strong><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4975765">Satellite Census of Residential Buildings for Climate Risk</a></strong>: The article recommends using open-source satellite data to conduct a worldwide census of residential buildings. This approach aims to improve understanding of climate risks and their effects on housing and financial stability. (2024-10-07, shares: 30)</p><p><strong><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4975855">Enhanced S&#8230;</a></strong></p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-december-2025-week-1">
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: November 2025, Week-2]]></title><description><![CDATA[112th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-november-2025-week-2</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-november-2025-week-2</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Thu, 13 Nov 2025 19:39:44 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!Ki-N!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3fa1ac45-67d3-49c1-bf0b-013e0cb6c6e7_1610x868.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<p>We only have a <strong>couple of spots</strong> left for our live <strong>Agentic Finance course</strong>! https://www.agenticfinance.ai/enrollment (<strong>3 spots left</strong> for 2026, Thursdays)</p><p>Please read the syllabus before enrolling, its quite technical! </p><h1>ArXiv</h1><p><strong><a href="http://arxiv.org/abs/2511.04299v1">LLM News Indicator for Economic Outlook</a></strong>: We build an interpretable, privacy&#8209;friendly sentiment indicator from Swiss news using ML and LLMs&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-november-2025-week-2">
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      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: November 2025, Week-1]]></title><description><![CDATA[111th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-november-2025-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-november-2025-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Tue, 04 Nov 2025 13:24:05 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!tsVQ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feef91929-079c-49a1-a6d8-cf6206135e92_1892x1072.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2511.01486v1">Partial-Information Markets &amp; Divergent Beliefs</a></strong>: Shows how a mathematical model explains why prices and trader biases converge as traders get more information, and gives the best way to combine expert opinions to hunt for arbitrage. (2025-11-03, shares: 5)</p><p><strong><a href="http://arxiv.org/abs/2510.26627v1">Probabilistic Rule Layers for Financial Drift</a></strong>: Presents a simple, interpretable rule-ba&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-november-2025-week-1">
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: October 2025, Week-4]]></title><description><![CDATA[110th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-october-2025-week-4</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-october-2025-week-4</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Mon, 27 Oct 2025 17:22:14 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!D-di!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2510.20221v1">FinCARE: KG+LLM for Financial Causal Discovery</a></strong>: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance&#8209;grounded causal models. (2025-10-23, shares: 7)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!D-di!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!D-di!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 424w, https://substackcdn.com/image/fetch/$s_!D-di!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 848w, https://substackcdn.com/image/fetch/$s_!D-di!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 1272w, https://substackcdn.com/image/fetch/$s_!D-di!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!D-di!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png" width="1456" height="805" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:805,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:1566930,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/177289340?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!D-di!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 424w, https://substackcdn.com/image/fetch/$s_!D-di!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 848w, https://substackcdn.com/image/fetch/$s_!D-di!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 1272w, https://substackcdn.com/image/fetch/$s_!D-di!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0bc35a80-4ea6-4f49-91af-036e23acb5ca_2048x1132.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2510.19271v1">Quantile-Targeted Portfolio Optimization</a></strong>: Finds that investors targeting specific outcome quantiles change volatility exposure (cutting risk to protec&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-october-2025-week-4">
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      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: October 2025, Week-2]]></title><description><![CDATA[109th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-october-2025-week-2</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-october-2025-week-2</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Thu, 09 Oct 2025 17:52:25 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!NCPX!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2510.04555v1">TailSafe Hedging with Reinforcement Learning</a></strong>: The research presents Tail-Safe, a derivative hedging framework that blends reinforcement learning with a safety layer designed for financial constraints, ensuring robust forward invariance of the safe set under limited model mismatch. (2025-10-06, shares: 6)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!NCPX!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!NCPX!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 424w, https://substackcdn.com/image/fetch/$s_!NCPX!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 848w, https://substackcdn.com/image/fetch/$s_!NCPX!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 1272w, https://substackcdn.com/image/fetch/$s_!NCPX!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!NCPX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png" width="1456" height="713" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:713,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:844250,&quot;alt&quot;:&quot;&quot;,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/175733629?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" title="" srcset="https://substackcdn.com/image/fetch/$s_!NCPX!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 424w, https://substackcdn.com/image/fetch/$s_!NCPX!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 848w, https://substackcdn.com/image/fetch/$s_!NCPX!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 1272w, https://substackcdn.com/image/fetch/$s_!NCPX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7dff276f-a8da-4948-92b2-76b7de048b7f_2402x1176.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2510.07099v1">Diffusion-Augmented RL for Portfolio M&#8230;</a></strong></p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-october-2025-week-2">
              Read more
          </a>
      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: October 2025, Week-1]]></title><description><![CDATA[108th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-october-2025-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-october-2025-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Fri, 03 Oct 2025 13:51:35 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!Mu8I!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb8b83366-2803-450d-8d26-bed8d388128f_1434x912.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2510.01814v1">Revisiting Mean-field Theory</a></strong>: The Santa Fe model, used for analyzing the dynamics of the limit order book, is reevaluated using kinetic theory, leading to a new equation for the order-book density profile and identifying a previous error by E. Smith and colleagues. (2025-10-02, shares: 24)</p><div class="captioned-image-container"><figure><a class="image-link image2" target="_blank" href="https://substackcdn.com/image/fetch/$s_!B9Tk!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!B9Tk!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 424w, https://substackcdn.com/image/fetch/$s_!B9Tk!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 848w, https://substackcdn.com/image/fetch/$s_!B9Tk!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 1272w, https://substackcdn.com/image/fetch/$s_!B9Tk!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!B9Tk!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png" width="1456" height="270" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/a7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:270,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:58331,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/175194092?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!B9Tk!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 424w, https://substackcdn.com/image/fetch/$s_!B9Tk!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 848w, https://substackcdn.com/image/fetch/$s_!B9Tk!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 1272w, https://substackcdn.com/image/fetch/$s_!B9Tk!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa7f67c58-8465-4f76-b1f7-add66384dd7a_1468x272.png 1456w" sizes="100vw" fetchpriority="high"></picture><div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2509.24151v1">Portfolio Similarity Metric</a></strong>: STRAPSim, a new method f&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-october-2025-week-1">
              Read more
          </a>
      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: September 2025, Week-4]]></title><description><![CDATA[107th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-september-2025-week-208</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-september-2025-week-208</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Mon, 22 Sep 2025 19:27:28 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!lSrK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd7fe2d5c-3b37-468f-a65c-32c49cb2b976_1446x1286.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2509.12753v1">DeltaHedge: Options Optimization</a></strong>: DeltaHedge, an AI-based framework that combines portfolio management with options trading, outperforms traditional strategies and improves risk-adjusted returns in various market conditions. (2025-09-16, shares: 19)</p><p><strong><a href="http://arxiv.org/abs/2509.12519v1">Historical Context in Financial News Forecasting</a></strong>: The study suggests using large language mode&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-september-2025-week-208">
              Read more
          </a>
      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: September 2025, Week-2]]></title><description><![CDATA[ArXiv Finance]]></description><link>https://blog.ml-quant.com/p/quant-letter-september-2025-week</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-september-2025-week</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Sat, 13 Sep 2025 16:31:38 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!1MPp!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2509.05922v1">Market Trough Prediction</a></strong>: The research uses machine learning to identify that the volatility of options-implied risk and market liquidity are key factors causing market lows, challenging simpler models. (2025-09-07, shares: 19)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!1MPp!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!1MPp!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 424w, https://substackcdn.com/image/fetch/$s_!1MPp!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 848w, https://substackcdn.com/image/fetch/$s_!1MPp!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 1272w, https://substackcdn.com/image/fetch/$s_!1MPp!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!1MPp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png" width="1456" height="422" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:422,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:135786,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/173520346?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!1MPp!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 424w, https://substackcdn.com/image/fetch/$s_!1MPp!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 848w, https://substackcdn.com/image/fetch/$s_!1MPp!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 1272w, https://substackcdn.com/image/fetch/$s_!1MPp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F30c4bb09-6d13-4fe1-87a4-8323d24ad672_1507x437.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2509.06510v1">Optimal Exit in AMMs</a></strong>: The study investigates the best way for a liquidity provider to withdraw liquidity in an autom&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-september-2025-week">
              Read more
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      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: August 2025, Week-4]]></title><description><![CDATA[105th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-august-2025-week-4</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-august-2025-week-4</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Fri, 29 Aug 2025 08:08:21 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!9BR9!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2508.19006v1">Asset Pricing with Attention Models</a></strong>: The study finds that pretrained RNN attention models can effectively derive returns and hedge risks in asset pricing, even during extreme market conditions like the COVID-19 pandemic. (2025-08-26, shares: 16)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!9BR9!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!9BR9!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 424w, https://substackcdn.com/image/fetch/$s_!9BR9!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 848w, https://substackcdn.com/image/fetch/$s_!9BR9!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 1272w, https://substackcdn.com/image/fetch/$s_!9BR9!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!9BR9!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png" width="1456" height="1113" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/f7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:1113,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:329441,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/172237615?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!9BR9!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 424w, https://substackcdn.com/image/fetch/$s_!9BR9!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 848w, https://substackcdn.com/image/fetch/$s_!9BR9!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 1272w, https://substackcdn.com/image/fetch/$s_!9BR9!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff7a4d5c2-fb38-41fb-8aef-2be20b0e41c9_2142x1638.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2508.18679v1">ESG Risk Variables Algorithm</a></strong>: The research introduces a Hierarchical Variable Selection algorithm &#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-august-2025-week-4">
              Read more
          </a>
      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: August 2025, Week-3]]></title><description><![CDATA[104th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-august-2025-week-3</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-august-2025-week-3</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Wed, 20 Aug 2025 13:51:36 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!625s!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2508.11152v1">Stock Selection Analysis</a></strong>: The article investigates the application and effectiveness of role-based multi-agent AI systems in equity research and portfolio management, including their advantages and challenges. (2025-08-15, shares: 56)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!625s!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!625s!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 424w, https://substackcdn.com/image/fetch/$s_!625s!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 848w, https://substackcdn.com/image/fetch/$s_!625s!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 1272w, https://substackcdn.com/image/fetch/$s_!625s!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!625s!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png" width="1456" height="535" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/ed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:535,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:529177,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/171472468?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!625s!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 424w, https://substackcdn.com/image/fetch/$s_!625s!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 848w, https://substackcdn.com/image/fetch/$s_!625s!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 1272w, https://substackcdn.com/image/fetch/$s_!625s!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fed62e734-bd4c-45fd-b375-8857f531aa58_2866x1054.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2508.10682v1">Distortion Risk Measures with Partial Information</a></strong>: The study presents two new types of distributional uncerta&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-august-2025-week-3">
              Read more
          </a>
      </p>
   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: August 2025, Week-2]]></title><description><![CDATA[103rd Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-august-2025-week-2</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-august-2025-week-2</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Tue, 12 Aug 2025 15:05:36 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!GlA0!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<p>Check out the enhanced <strong><a href="http://www.prymer.ai">Prymer.ai</a></strong>, it generates the best equity primers on the market. See for yourself, <strong><a href="https://storage.googleapis.com/derek-snow-at-outlook-co-nz-public/sovai-agents-derek-snow-at-outlook-co-nz-positive-1a/tickercompany/RIG_Transocean-Ltd/final_report/RIG_Transocean-Ltd.html">RIG report</a></strong> was generated in under 25 mins. </p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!GlA0!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!GlA0!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 424w, https://substackcdn.com/image/fetch/$s_!GlA0!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 848w, https://substackcdn.com/image/fetch/$s_!GlA0!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 1272w, https://substackcdn.com/image/fetch/$s_!GlA0!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!GlA0!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png" width="1456" height="748" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:748,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:396652,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/170793355?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!GlA0!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 424w, https://substackcdn.com/image/fetch/$s_!GlA0!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 848w, https://substackcdn.com/image/fetch/$s_!GlA0!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 1272w, https://substackcdn.com/image/fetch/$s_!GlA0!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F76e38d5d-1fb0-4d7a-b675-bfdae985ba5a_3202x1646.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>We&#8217;re in &#120303;&#120306;&#120321;&#120302; and the first report is free! <strong><a href="https://storage.googleapis.com/derek-snow-at-outlook-co-nz-public/sovai-agents-derek-snow-at-outlook-co-nz-positive-1a/tickercompany/RIG_Transocean-Ltd/final_report/RIG_Transocean-Ltd.html">RIG report example</a></strong></p><p></p><h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2508.06010v1">Time Series Model for Financial Simulator</a></strong>: A new financial model has been developed for an online app that simulates wealth proces&#8230;</p>
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          <a href="https://blog.ml-quant.com/p/quant-letter-august-2025-week-2">
              Read more
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: August 2025, Week-1]]></title><description><![CDATA[102nd Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-august-2025-week-1</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-august-2025-week-1</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Thu, 07 Aug 2025 18:54:41 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!TCOZ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2508.04344v1">Performative Market Making</a></strong>: The research shows how financial models can shape market processes and influence price changes, enabling market makers to use dominant strategies. (2025-08-06, shares: 5)</p><div class="captioned-image-container"><figure><a class="image-link image2 is-viewable-img" target="_blank" href="https://substackcdn.com/image/fetch/$s_!TCOZ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png" data-component-name="Image2ToDOM"><div class="image2-inset"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!TCOZ!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 424w, https://substackcdn.com/image/fetch/$s_!TCOZ!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 848w, https://substackcdn.com/image/fetch/$s_!TCOZ!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 1272w, https://substackcdn.com/image/fetch/$s_!TCOZ!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!TCOZ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png" width="1456" height="596" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/ea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:596,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:786225,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.ml-quant.com/i/170386770?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" class="sizing-normal" alt="" srcset="https://substackcdn.com/image/fetch/$s_!TCOZ!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 424w, https://substackcdn.com/image/fetch/$s_!TCOZ!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 848w, https://substackcdn.com/image/fetch/$s_!TCOZ!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 1272w, https://substackcdn.com/image/fetch/$s_!TCOZ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea359772-80d0-49a4-b416-bf2c9ad1e1bc_2596x1062.png 1456w" sizes="100vw" fetchpriority="high"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p><strong><a href="http://arxiv.org/abs/2508.02971v1">LossVersus-Rebalancing</a></strong>: The study introduces a mathematical model that views a CFAMM position as a portfolio of perpetual American CI options, ai&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-august-2025-week-1">
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: July 2025, Week-4]]></title><description><![CDATA[101st Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-july-2025-week-3-d52</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-july-2025-week-3-d52</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Fri, 25 Jul 2025 10:15:56 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!Pu4s!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9e5b1691-fb91-4168-b9a2-49947296057f_1612x814.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2507.17606v1">Neural Network Pricing</a></strong>: The study uses neural network methods, Time Deep Gradient Flow and Deep Galerkin Method, to price multidimensional American put options, showing better accuracy and speed than traditional methods. (2025-07-23, shares: 27)</p><p><strong><a href="http://arxiv.org/abs/2507.17162v1">Optimal Trading with Price Impact</a></strong>: The research tackles dynamic portfolio optimization, considerin&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-july-2025-week-3-d52">
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   ]]></content:encoded></item><item><title><![CDATA[Quant Letter: July 2025, Week-3]]></title><description><![CDATA[100th Edition]]></description><link>https://blog.ml-quant.com/p/quant-letter-july-2025-week-3</link><guid isPermaLink="false">https://blog.ml-quant.com/p/quant-letter-july-2025-week-3</guid><dc:creator><![CDATA[Dr. Derek Snow]]></dc:creator><pubDate>Thu, 17 Jul 2025 08:47:14 GMT</pubDate><enclosure url="https://substackcdn.com/image/fetch/$s_!22PZ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa6add010-6b06-4c8a-a169-aa2b299f33c7_2734x1436.png" length="0" type="image/jpeg"/><content:encoded><![CDATA[<h1>ArXiv</h1><h2>Finance</h2><p><strong><a href="http://arxiv.org/abs/2507.08482v1">Efficient Greeks Computation</a></strong>: A new method using tensor train learning and numerical differentiation has been proposed to speed up and maintain accuracy in calculating Greeks for multi-asset options. (2025-07-11, shares: 30)</p><p><strong><a href="http://arxiv.org/abs/2507.09916v1">Dynamic Portfolio Selection</a></strong>: A model-free approach using generative diffusion models and a policy gradient algorithm ha&#8230;</p>
      <p>
          <a href="https://blog.ml-quant.com/p/quant-letter-july-2025-week-3">
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