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[1] Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
基于CNN和GRU的金融市场情绪综合分析，用于风险预测和预警系统
来源:ARXIV_20241216
[2] Continuous time optimal investment with portfolio constraints
具有投资组合约束的连续时间最优投资
来源:ARXIV_20241217
[3] Auto Regressive Control of Execution Costs
执行成本的自回归控制
来源:ARXIV_20241217
[4] PolyModel for Hedge Funds Portfolio Construction Using Machine Learning
利用机器学习构建对冲基金投资组合的多模型
来源:ARXIV_20241217
[5] From Votes to Volatility Predicting the Stock Market on Election Day
从投票到波动预测选举日股市
来源:ARXIV_20241217
[6] A Deep Learning Approach for Trading Factor Residuals
交易因子残差的深度学习方法
来源:ARXIV_20241217
[7] S&P 500 Trend Prediction
标准普尔500指数趋势预测
来源:ARXIV_20241217
[8] Prediction Enhanced Monte Carlo
增强蒙特卡洛预测
来源:ARXIV_20241217
[9] Stochastic Gradient Descent in the Optimal Control of Execution Costs
执行成本最优控制中的随机梯度下降
来源:ARXIV_20241218
[10] AI Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics
人工智能增强因子分析预测标普500指数股票动态
来源:ARXIV_20241218
[11] Hunting Tomorrow s Leaders
狩猎明日领袖
来源:ARXIV_20241218
[12] A Pontryagin Guided Neural Policy Optimization Framework for Merton s Portfolio Problem
Merton投资组合问题的Pontryagin引导神经策略优化框架
来源:ARXIV_20241218
[13] Does One Pattern Fit All? Image Analysis for Different Equity Styles
一种模式适合所有人吗？不同股权风格的图像分析
来源:SSRN_20241218
[14] Uncertainty Quantification in Portfolio Temperature Alignment
投资组合温度校准中的不确定性量化
来源:ARXIV_20241220
[1] Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
标题:基于CNN和GRU的金融市场情绪综合分析，用于风险预测和预警系统
作者:You Wu, Mengfang Sun, Hongye Zheng, Jinxin Hu, Yingbin Liang, Zhenghao Lin
来源:ARXIV_20241216
Abstract : This document presents an in depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is utilized to preprocess and analyze extensive network text data, identifying local features and patterns. The extracted feature sequences are then input into the GRU model......(摘要翻译及全文见知识星球)
Keywords :
[2] Continuous time optimal investment with portfolio constraints
标题:具有投资组合约束的连续时间最优投资
作者:Huy Chau, Duy Nguyen, Thai Nguyen
来源:ARXIV_20241217
Abstract : In a reinforcement learning (RL) framework, we study the exploratory version of the continuous time expected utility (EU) maximization problem with a portfolio constraint that includes widely used financial regulations such as short selling constraints and borrowing prohibition. The optimal feedback policy of the exploratory unconstrained classical EU problem is shown to be Gaussian. In the case where the portfolio weight......(摘要翻译及全文见知识星球)
Keywords :
[3] Auto Regressive Control of Execution Costs
标题:执行成本的自回归控制
作者:Simeon Kolev
来源:ARXIV_20241217
Abstract : Bertsimas and Lo s seminal work established a foundational framework for addressing the implementation shortfall dilemma faced by large institutional investors. Their models emphasized the critical role of accurate knowledge of market microstructure and price information dynamics in optimizing trades to minimize execution costs. However, this paper recognizes that perfect initial knowledge may not be a realistic assumption for new investors......(摘要翻译及全文见知识星球)
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[4] PolyModel for Hedge Funds Portfolio Construction Using Machine Learning
标题:利用机器学习构建对冲基金投资组合的多模型
作者:Siqiao Zhao, Dan Wang, Raphael Douady
来源:ARXIV_20241217
Abstract : The domain of hedge fund investments is undergoing significant transformation, influenced by the rapid expansion of data availability and the advancement of analytical technologies. This study explores the enhancement of hedge fund investment performance through the integration of machine learning techniques, the application of PolyModel feature selection, and the analysis of fund size. We address three critical questions (1) the......(摘要翻译及全文见知识星球)
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[5] From Votes to Volatility Predicting the Stock Market on Election Day
标题:从投票到波动预测选举日股市
作者:Igor L.R. Azevedo, Toyotaro Suzumura
来源:ARXIV_20241217
Abstract : Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them to specific scenarios has become increasingly important. Election......(摘要翻译及全文见知识星球)
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[6] A Deep Learning Approach for Trading Factor Residuals
标题:交易因子残差的深度学习方法
作者:Wo Long, Victor Xiao
来源:ARXIV_20241217
Abstract : The residuals in factor models prevalent in asset pricing presents opportunities to exploit the mis pricing from unexplained cross sectional variation for arbitrage. We performed a replication of the methodology of Guijarro Ordonez et al. (2019) (G P Z) on Deep Learning Statistical Arbitrage (DLSA), originally applied to U.S. equity data from 1998 to 2016, using a more recent out of......(摘要翻译及全文见知识星球)
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[7] S&P 500 Trend Prediction
标题:标准普尔500指数趋势预测
作者:Shasha Yu, Qinchen Zhang, Yuwei Zhao
来源:ARXIV_20241217
Abstract : This project aims to predict short term and long term upward trends in the S&P 500 index using machine learning models and feature engineering based on the 101 Formulaic Alphas methodology. The study employed multiple models, including Logistic Regression, Decision Trees, Random Forests, Neural Networks, K Nearest Neighbors (KNN), and XGBoost, to identify market trends from historical stock data......(摘要翻译及全文见知识星球)
Keywords :
[8] Prediction Enhanced Monte Carlo
标题:增强蒙特卡洛预测
作者:Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, Henry Lam
来源:ARXIV_20241217
Abstract : Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large scale, path dependent problems that hinder straightforward parallelization. A natural alternative is to replace simulation with machine learning or surrogate prediction, though this introduces challenges in understanding the resulting this http URL introduce a Prediction Enhanced Monte Carlo (PEMC) framework where we......(摘要翻译及全文见知识星球)
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[9] Stochastic Gradient Descent in the Optimal Control of Execution Costs
标题:执行成本最优控制中的随机梯度下降
作者:Simeon Kolev
来源:ARXIV_20241218
Abstract : Bertsimas and Lo s seminal work laid the groundwork for addressing the implementation shortfall dilemma in institutional investing, emphasizing the significance of market microstructure and price dynamics in minimizing execution costs. However, the ability to derive a theoretical Optimum market order policy is an unrealistic assumption for many investors. This study aims to bridge this gap by proposing an approach that......(摘要翻译及全文见知识星球)
Keywords :
[10] AI Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics
标题:人工智能增强因子分析预测标普500指数股票动态
作者:Jiajun Gu, Zichen Yang, Xintong Lin, Sixun Chen, YuTing Lu
来源:ARXIV_20241218
Abstract : This project investigates the interplay of technical, market, and statistical factors in predicting stock market performance, with a primary focus on S&P 500 companies. Utilizing a comprehensive dataset spanning multiple years, the analysis constructs advanced financial metrics, such as momentum indicators, volatility measures, and liquidity adjustments. The machine learning framework is employed to identify patterns, relationships, and predictive capabilities of these......(摘要翻译及全文见知识星球)
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[11] Hunting Tomorrow s Leaders
标题:狩猎明日领袖
作者:Vidhi Agrawal, Eesha Khalid, Tianyu Tan, Doris Xu
来源:ARXIV_20241218
Abstract : This study applies machine learning to predict S&P 500 membership changes key events that profoundly impact investor behavior and market dynamics. Quarterly data from WRDS datasets (2013 onwards) was used, incorporating features such as industry classification, financial data, market data, and corporate governance indicators. Using a Random Forest model, we achieved a test F1 score of 0.85, outperforming logistic regression......(摘要翻译及全文见知识星球)
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[12] A Pontryagin Guided Neural Policy Optimization Framework for Merton s Portfolio Problem
标题:Merton投资组合问题的Pontryagin引导神经策略优化框架
作者:Jeonggyu Huh
来源:ARXIV_20241218
Abstract : We present a neural policy optimization framework for Merton s portfolio optimization problem that is rigorously aligned with Pontryagin s Maximum Principle (PMP). Our approach employs a discrete time, backpropagation through time (BPTT) based gradient method, but unlike conventional data driven methods, we establish a direct connection to the underlying continuous time optimality conditions. By approximating adjoint variables from a policy......(摘要翻译及全文见知识星球)
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[13] Does One Pattern Fit All? Image Analysis for Different Equity Styles
标题:一种模式适合所有人吗？不同股权风格的图像分析
作者:YuChen Den,Kendro Vincent
来源:SSRN_20241218
Abstract : This paper investigates the predictability of stock returns using Convolutional Neural Networks (CNNs) applied to images generated from stock prices and volumes. We employ multi-class classification models to predict stock returns which outperform binary classification models. Additionally, our study examines the predictability of different stock styles, revealing that small-capital stocks generally exhibit higher annual Sharpe ratios and more pronounced monthly excess......(摘要翻译及全文见知识星球)
Keywords : technical analysis, cross-sectional return predictability, stock chart pattern recognition, Convolutional Neural Network
[14] Uncertainty Quantification in Portfolio Temperature Alignment
标题:投资组合温度校准中的不确定性量化
作者:Hendrik Weichel, Aleksandr Zinovev, Heikki Haario, Martin Simon
来源:ARXIV_20241220
Abstract : We present a novel Bayesian framework for quantifying uncertainty in portfolio temperature alignment models, leveraging the X Degree Compatibility (XDC) approach with the scientifically validated Finite Amplitude Impulse Response (FaIR) climate model. This framework significantly advances the widely adopted linear approaches that use the Transient Climate Response to Cumulative CO2 Emissions (TCRE). Developed in collaboration with right , one of the......(摘要翻译及全文见知识星球)
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