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

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
Main Authors: Sondo Kim, Seungmo Ku, Woojin Chang, Jae Wook Song
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Econophysics
effective transfer entropy
feature engineering
information entropy
machine learning
prediction algorithms
Online Access:https://ieeexplore.ieee.org/document/9119388/

Internet

https://ieeexplore.ieee.org/document/9119388/

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