- The report systematically reviews 80 new quantitative finance-related research papers in May 2025, covering areas such as equity research, fixed income, fund studies, asset allocation, machine learning applications, and ESG-related studies [1][2][3] - Equity research includes studies on fundamental factors, price-volume and alternative factors, factor research, active quantitative strategies, and other categories, exploring investor behavior biases, asset pricing models, market structure distortions, prediction model innovations, and corporate resilience mechanisms [2][10] - Fixed income research focuses on high-frequency inflation forecasting, sovereign risk premium decomposition, and stochastic interest rate model innovations, with findings such as weekly online inflation rates predicting yield curve slope factors and semi-Markov-modulated Hull-White/CIR models achieving semi-analytical pricing for zero-coupon bonds [22][23] - Fund studies investigate fund selection factors, fund style evaluation, and behavioral biases, revealing strategies like liquidity picking driving excess returns and public pension funds underperforming benchmarks due to alternative investment errors post-2008 [28][30] - Asset allocation research explores multi-asset portfolio management paradigm shifts, systematic currency management, and volatility connectedness constraints, demonstrating dynamic adaptation mechanisms and enhanced performance during crises [32][33][35] - Machine learning applications in finance include innovations in volatility forecasting, credit risk prediction using GraphSAGE models, and long-memory stochastic interval models, significantly improving prediction accuracy and economic value [36][38][40] - ESG-related studies analyze green innovation drivers, ESG evaluation distortions, and corporate environmental response strategies, highlighting mechanisms like family business constraints on green innovation and AI-driven manufacturing green transformation [42][43][45]

Group 1: Quantitative Finance Research Overview - A total of 80 new quantitative finance-related research papers were added this month, with the following distribution: 31 on equity research, 4 on fund research, 8 on bond research, 9 on asset allocation, 3 on machine learning applications in finance, and 22 on ESG-related research[1] - Equity research covers various topics including investor behavior biases, asset pricing models, and market structure distortions, impacting capital markets[2] - Bond research focuses on interest rate bonds, credit bonds, and other bond markets, analyzing high-frequency inflation forecasting and pricing distortion mechanisms[2] Group 2: Specific Findings in Research - High-frequency online inflation rates predict yield curve slope factors with a contribution rate of 61%[22] - The sovereign risk premium in the Eurozone is primarily driven by credit risk premiums, with Italy accounting for 78% of this effect[22] - Climate disasters lead to a temporary premium for green bonds over brown bonds, which diminishes within five months due to behavioral overreaction[24] Group 3: Machine Learning and Risk Management - Machine learning models significantly improve the prediction of implied volatility, showing economic value superior to traditional models[38] - The GraphSAGE model enhances credit risk prediction accuracy by 19% through integrating stock returns, risk spillovers, and trading networks[38] - Long Memory Stochastic Interval Models (LMSR) capture persistent characteristics in volatility, reducing out-of-sample prediction loss by 38%[38]