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

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 t...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.111660-111682
Main Authors: Kim, Sondo, Ku, Seungmo, Chang, Woojin, Song, Jae Wook
Format: Article
Language:English
Subjects:
Algorithms
Correlation
Econophysics
effective transfer entropy
Entropy
Exchange rates
feature engineering
Indexes
information entropy
Machine learning
Machine learning algorithms
Multilayer perceptrons
Performance prediction
prediction algorithms
Regression analysis
Short term
Stock markets
time series analysis
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3002174