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[1] A Comparative Study of Active Algorithmic Trading and Passive Investing
主动算法交易与被动投资的比较研究
来源:SSRN_20250707
[2] skfolio
文件夹
来源:ARXIV_20250708
[3] Collective Market Dynamics Around Magnificent Seven Earnings Announcements: A Quantitative Model of Crowd Behavior and Oscillatory Response
七大盈利公告前后的集体市场动态：人群行为和振荡反应的定量模型
来源:SSRN_20250708
[4] Does Overnight News Explain Overnight Returns?
隔夜新闻能解释隔夜回报吗？
来源:SSRN_20250709
[5] Machine Learning based Enterprise Financial Audit Framework and High Risk Identification
基于机器学习的企业财务审计框架与高风险识别
来源:ARXIV_20250710
[6] Reinforcement Learning for Trade Execution with Market Impact
具有市场影响的交易执行强化学习
来源:ARXIV_20250710
[7] Machine Learning Enhanced Multi Factor Quantitative Trading
机器学习增强的多因素定量交易
来源:ARXIV_20250711
[8] Electricity Market Predictability
电力市场可预测性
来源:ARXIV_20250711
[9] Unifying Mathematical Perspectives on Reinforcement Learning: Integrating Sutton-Barto, Bertsekas and Powell
统一强化学习的数学观点：整合Sutton Barto、Bertsekas和Powell
来源:SSRN_20250711
[10] Markowitz-Informed Neural Networks (MINNs): An Interpretable Deep Learning Approach to Portfolio Optimization
Markowitz知情神经网络（MINNs）：一种可解释的投资组合优化深度学习方法
来源:SSRN_20250711
[1] A Comparative Study of Active Algorithmic Trading and Passive Investing
标题:主动算法交易与被动投资的比较研究
作者:Kris Kraack
来源:SSRN_20250707
Abstract : This study compares an active algorithmic trading strategy, a machine learning model comprising both quantitative and sentiment components, with a traditional passive buy-and-hold investment strategy through simulated trading using historical stock data under real-world market conditions. The findings demonstrate that the algorithmic strategy outperforms passive investment consistently on a set of benchmark stocks in different markets.......(摘要翻译及全文见知识星球)
Keywords : Algorithmic Trading, Active Trading, Trading Agents, Trading Strategies, Investment Strategies, Machine Learning, Passive Investing, Risk-adjusted Portfolio Management, Quantitative Finance
[2] skfolio
标题:文件夹
作者:Carlo Nicolini, Matteo Manzi, Hugo Delatte
来源:ARXIV_20250708
Abstract : Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open source Python library for portfolio construction and risk management that seamlessly integrates with the scikit learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean variance optimization to modern clustering based methods,......(摘要翻译及全文见知识星球)
Keywords :
[3] Collective Market Dynamics Around Magnificent Seven Earnings Announcements: A Quantitative Model of Crowd Behavior and Oscillatory Response
标题:七大盈利公告前后的集体市场动态：人群行为和振荡反应的定量模型
作者:Phanikrishna Jandhyala
来源:SSRN_20250708
Abstract : Stock markets often move in waves of excitement, stress, and calm when companies release their earnings. We think these swings follow a pattern like a damped oscillator-a big jump at the news and then smaller ups and downs. To test this idea, we created the Composite Behavioral Curve (CBC) by blending three measures: volume pressure, price reaction, and short term volatility.......(摘要翻译及全文见知识星球)
Keywords : Crowd behavior, market dynamics, damped oscillator, Composite Behavioral Curve, earnings announcements, time series modeling, financial markets, trader response, event-driven patterns, collective excitement
[4] Does Overnight News Explain Overnight Returns?
标题:隔夜新闻能解释隔夜回报吗？
作者:Paul Glasserman,Kriste Krstovski,Paul-Robert Laliberte,Harry Mamaysky
来源:SSRN_20250709
Abstract : Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.4 million news articles. We apply a novel technique......(摘要翻译及全文见知识星球)
Keywords : intraday and overnight returns, asset pricing, NLP, machine learning JEL Codes: G10, G12, G14
[5] Machine Learning based Enterprise Financial Audit Framework and High Risk Identification
标题:基于机器学习的企业财务审计框架与高风险识别
作者:Tingyu Yuan, Xi Zhang, Xuanjing Chen
来源:ARXIV_20250710
Abstract : In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, complex business structures, and evolving fraud tactics. This study proposes an AI driven framework for enterprise financial audits and high risk identification, leveraging machine learning to improve efficiency and accuracy. Using a......(摘要翻译及全文见知识星球)
Keywords :
[6] Reinforcement Learning for Trade Execution with Market Impact
标题:具有市场影响的交易执行强化学习
作者:Patrick Cheridito, Moritz Weiss
来源:ARXIV_20250710
Abstract : In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic normal distributions to model random allocations, the framework enables efficient training of......(摘要翻译及全文见知识星球)
Keywords :
[7] Machine Learning Enhanced Multi Factor Quantitative Trading
标题:机器学习增强的多因素定量交易
作者:Yimin Du
来源:ARXIV_20250711
Abstract : This paper presents a comprehensive machine learning framework for quantitative trading that achieves superior risk adjusted returns through systematic factor engineering, real time computation optimization, and cross sectional portfolio construction. Our approach integrates multi factor alpha discovery with bias correction techniques, leveraging PyTorch accelerated factor computation and advanced portfolio optimization. The system processes 500 1000 factors derived from open source alpha101......(摘要翻译及全文见知识星球)
Keywords :
[8] Electricity Market Predictability
标题:电力市场可预测性
作者:Jinbo Cai, Wenze Li, Wenjie Wang
来源:ARXIV_20250711
Abstract : With stakeholder level in market data, we conduct a comparative analysis of machine learning (ML) for forecasting electricity prices in Singapore, spanning 15 individual models and 4 ensemble approaches. Our empirical findings justify the three virtues of ML models (1) the virtue of capturing non linearity, (2) the complexity (Kelly et al., 2024) and (3) the l2 norm and bagging......(摘要翻译及全文见知识星球)
Keywords :
[9] Unifying Mathematical Perspectives on Reinforcement Learning: Integrating Sutton-Barto, Bertsekas and Powell
标题:统一强化学习的数学观点：整合Sutton Barto、Bertsekas和Powell
作者:Miquel Noguer I Alonso
来源:SSRN_20250711
Abstract : Reinforcement Learning (RL) has emerged as a fundamental paradigm for sequential decision-making under uncertainty. This paper presents a comprehensive unifying framework that integrates three influential mathematical approaches to RL: the approximate dynamic programming perspective of Dimitri Bertsekas, the operations research approach of Warren Powell, and the agent-environment interaction framework of Richard Sutton and Andrew Barto. We first examine the mathematical foundations,......(摘要翻译及全文见知识星球)
Keywords : Reinforcement Learning, Dynamic Programming, Approximate Dynamic Programming, Bellman Operators, Policy Approximation, Temporal Difference Learning, Value Function Approximation
[10] Markowitz-Informed Neural Networks (MINNs): An Interpretable Deep Learning Approach to Portfolio Optimization
标题:Markowitz知情神经网络（MINNs）：一种可解释的投资组合优化深度学习方法
作者:William Smyth,Philip Ernst,Yinsen Miao
来源:SSRN_20250711
Abstract : This paper introduces Markowitz-Informed Neural Networks (MINNs), a novel framework that unites the principles of mean-variance optimization (MVO) with the adaptability and power of modern neural networks. By learning portfolio weights and interpretable covariance structure simultaneously, without matrix inversion, MINNs offer a transparent and more stable alternative to traditional MVO. Embedding financial structure within the learning process, they support practical, robust,......(摘要翻译及全文见知识星球)
Keywords : Markowitz-Informed Neural Networks (Minns), Interpretable Deep Learning, Portfolio Optimization, Mean-Variance Optimization, Explainable AI, Responsible AI, Covariance Estimation, Whitebox Models
