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[1] Diffusion Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
压力情景下稳健投资组合优化的扩散增强强化学习
来源:ARXIV_20251009
[2] Do machine learning models improve higher-order moment forecasts? Evidence from Fama-French factors
机器学习模型能改善高阶矩预测吗？法玛法国因素的证据
来源:SSRN_20251009
[3] Tactical Asset Allocation Using Deep Reinforcement Learning And Latent Macroeconomic Conditions
基于深度强化学习和潜在宏观经济条件的战术资产配置
来源:SSRN_20251009
[4] Minimizing the Value at Risk of Loan Portfolio via Deep Neural Networks
通过深度神经网络最小化贷款组合的风险价值
来源:ARXIV_20251010
[5] Multi Agent Analysis of Off Exchange Public Information for Cryptocurrency Market Trend Prediction
加密货币市场趋势预测的场外公共信息多代理分析
来源:ARXIV_20251010
[1] Diffusion Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
标题:压力情景下稳健投资组合优化的扩散增强强化学习
作者:Himanshu Choudhary, Arishi Orra, Manoj Thakur
来源:ARXIV_20251009
Abstract : In the ever changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Conventional methods often struggle to capture the complex dynamics of market behaviour and align with diverse investor preferences. To address this, we propose an innovative framework, termed Diffusion Augmented Reinforcement Learning (DARL), which synergistically integrates Denoising Diffusion Probabilistic Models (DDPMs)......(摘要翻译及全文见知识星球)
Keywords :
[2] Do machine learning models improve higher-order moment forecasts? Evidence from Fama-French factors
标题:机器学习模型能改善高阶矩预测吗？法玛法国因素的证据
作者:Jianye Wang,Xi Peng
来源:SSRN_20251009
Abstract : This study systematically examines whether Machine Learning (ML) models improve the forecasting of higher-order moments, compared to linear factor models. Using monthly U.S. industry portfolios from January 1975 to April 2025, we implement a rolling-window forecasting framework to compare Random Forest, XGBoost, and LightGBM against the Fama-French five-factor model augmented by industry dummies. Our empirical results show that the linear specification......(摘要翻译及全文见知识星球)
Keywords : machine learning, Higher-order moments, Tail risk, Asset Pricing
[3] Tactical Asset Allocation Using Deep Reinforcement Learning And Latent Macroeconomic Conditions
标题:基于深度强化学习和潜在宏观经济条件的战术资产配置
作者:Tauseef Kazi,Ken Abbott,Joe Wayne Byers
来源:SSRN_20251009
Abstract : Traditional asset allocation techniques fail to adapt to sudden and severe economic downturns and lead to a loss of opportunities for investors. This paper seeks to address this problem by automating the Tactical Asset Allocation (TAA) framework that allocates the asset weights based on the latent macroeconomic conditions and market regimes. This project employs Deep Reinforcement Learning (DRL) to allocate funds......(摘要翻译及全文见知识星球)
Keywords : Tactical Asset Allocation, Hidden Markov Model, Deep Reinforcement Learning, Macroeconomics
[4] Minimizing the Value at Risk of Loan Portfolio via Deep Neural Networks
标题:通过深度神经网络最小化贷款组合的风险价值
作者:Albert Di Wang, Ye Du
来源:ARXIV_20251010
Abstract : Risk management is a prominent issue in peer to peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value at Risk (VaR) or Conditional Value at Risk (CVaR) of his loan portfolio. We propose a low degree of freedom......(摘要翻译及全文见知识星球)
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[5] Multi Agent Analysis of Off Exchange Public Information for Cryptocurrency Market Trend Prediction
标题:加密货币市场趋势预测的场外公共信息多代理分析
作者:Kairan Hong, Jinling Gan, Qiushi Tian, Yanglinxuan Guo, Rui Guo, Runnan Li
来源:ARXIV_20251010
Abstract : Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24 7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically grounded multi agent cryptocurrency trend prediction framework that advances the state of the art through three key innovations (1) an......(摘要翻译及全文见知识星球)
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