Predicting the Direction of US Stock Prices Using Effective Transfer Entropy and Machine Learning Techniques

IEEE Access

Jae Wook Song¹  Seungmo Ku²  Woojin Chang²  Sondo Kim² 

[1] Department of Industrial Engineering, Hanyang University, Seoul, South Korea;Department of Industrial Engineering, Seoul National University, Seoul, South Korea;

关键词: Econophysics;    effective transfer entropy;    feature engineering;    information entropy;    machine learning;    prediction algorithms;   

DOI  :  10.1109/ACCESS.2020.3002174

来源: DOAJ

【 摘 要 】

This study aims to predict the direction of US stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. Then, we discover that the prediction performance on the stock price direction can be improved when the ETE driven variable is integrated as a new feature in the logistic regression, multilayer perceptron, random forest, XGBoost, and long short-term memory network. Meanwhile, we suggest utilizing the adjusted accuracy derived from the risk-adjusted return in finance as a prediction performance measure. Lastly, we confirm that the multilayer perceptron and long short-term memory network are more suitable for stock price prediction. This study is the first attempt to predict the stock price direction using ETE, which can be conveniently applied to the practical field.

【 授权许可】

Unknown