论文标题
使用深度学习进行实际投资管理的横截面股票价格预测
Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management
论文作者
论文摘要
股票价格预测在学术和实际上一直是重要的研究主题。到目前为止，已经研究了各种预测股票价格的方法。通过横截面分析解释股票价格的功能称为金融领域的“因素”。许多财务实证研究已经确定了哪些股票在横截面中相对增加以及价格下降。最近，已经提出了使用机器学习，尤其是深度学习的股票价格预测方法，因为这些因素与股票价格之间的关系是复杂且非线性的。但是，没有实际的投资管理示例。因此，在本文中，我们使用深度学习进行实际投资管理提出了横断面的每日股票价格预测框架。例如，我们建立了一个投资组合，并在市场关闭时提供了信息，并在第二天开放时进行投资。我们在日本股票市场进行经验分析，并确认框架的盈利能力。
Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a "factor" in the field of finance. Many empirical studies in finance have identified which stocks having features in the cross-section relatively increase and which decrease in terms of price. Recently, stock price prediction methods using machine learning, especially deep learning, have been proposed since the relationship between these factors and stock prices is complex and non-linear. However, there are no practical examples for actual investment management. In this paper, therefore, we present a cross-sectional daily stock price prediction framework using deep learning for actual investment management. For example, we build a portfolio with information available at the time of market closing and invest at the time of market opening the next day. We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.
