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文献汇总
[1] Axes that matter
重要的斧头
来源:ARXIV_20250311
[2] Assessing Uncertainty in Stock Returns
评估股票回报的不确定性
来源:ARXIV_20250311
[3] Multimodal Stock Price Prediction
多模式股票价格预测
来源:ARXIV_20250313
[4] The E Rule
E规则
来源:ARXIV_20250314
[5] A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
基于xLSTM网络的自动股票交易深度强化学习方法
来源:ARXIV_20250314
[6] Finding the Needle in (Hay)stack of Equity Return-Determinants: An Unsupervised Machine-Learning Approach
在股票回报决定因素的堆叠中找到针：一种无监督的机器学习方法
来源:SSRN_20250315
[1] Axes that matter
标题:重要的斧头
作者:Brian Huge, Antoine Savine
来源:ARXIV_20250311
Abstract : We extend the scope of differential machine learning and introduce a new breed of supervised principal component analysis to reduce dimensionality of Derivatives problems. Applications include the specification and calibration of pricing models, the identification of regression features in least square Monte Carlo, and the pre processing of simulated datasets for (differential) machine learning.......(摘要翻译及全文见知识星球)
Keywords :
[2] Assessing Uncertainty in Stock Returns
标题:评估股票回报的不确定性
作者:Yanlong Wang, Jian Xu, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang
来源:ARXIV_20250311
Abstract : This study seeks to advance the understanding and prediction of stock market return uncertainty through the application of advanced deep learning techniques. We introduce a novel deep learning model that utilizes a Gaussian mixture distribution to capture the complex, time varying nature of asset return distributions in the Chinese stock market. By incorporating the Gaussian mixture distribution, our approach effectively characterizes......(摘要翻译及全文见知识星球)
Keywords :
[3] Multimodal Stock Price Prediction
标题:多模式股票价格预测
作者:Kasymkhan Khubiev, Mikhail Semenov
来源:ARXIV_20250313
Abstract : Classical asset price forecasting methods primarily rely on numerical data, such as price time series, trading volumes, limit order book data, and technical analysis indicators. However, the news flow plays a significant role in price formation, making the development of multimodal approaches that combine textual and numerical data for improved prediction accuracy highly relevant. This paper addresses the problem of forecasting......(摘要翻译及全文见知识星球)
Keywords :
[4] The E Rule
标题:E规则
作者:Esmaeil Ebadi
来源:ARXIV_20250314
Abstract : This study develops the E Rule, a novel composite recession indicator that integrates financial market and labor market signals to improve the precision of recession forecasting. Combining the yield curve and the Sahm rule, the E Rule provides a holistic and early warning measure of economic downturns. Using historical data from 1976 onward, we empirically evaluate the E Rule s predictive......(摘要翻译及全文见知识星球)
Keywords :
[5] A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
标题:基于xLSTM网络的自动股票交易深度强化学习方法
作者:Faezeh Sarlakifar, Mohammadreza Mohammadzadeh Asl, Sajjad Rezvani Khaledi, Armin Salimi-Badr
来源:ARXIV_20250314
Abstract : Traditional Long Short Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short Term Memory (xLSTM) network in......(摘要翻译及全文见知识星球)
Keywords :
[6] Finding the Needle in (Hay)stack of Equity Return-Determinants: An Unsupervised Machine-Learning Approach
标题:在股票回报决定因素的堆叠中找到针：一种无监督的机器学习方法
作者:Srinath Mitragotri
来源:SSRN_20250315
Abstract : This paper applies unsupervised machine learning on extensive historical datasets from multiple countries to uncover a parsimonious subset of factors that have a disproportionately higher influence on equity returns in the US and Indian market. This subset of factors is different for the US and Indian markets. For the US markets, limiting the valuation risk at the time of investment is......(摘要翻译及全文见知识星球)
Keywords : Unsupervised machine learning, Association rule mining, long term equity returns, factor investing
