Stock Price Prediction Based on Information Entropy and Artificial Neural Network

Abstract

Stock market is one of the most important components of the financial system. It directs money from investors to support the activity and development of the associated company. Therefore, understanding and modeling the stock price dynamics become critically important, in terms of financial system stability, investment strategy, and market risk control. To better model the temporal dynamics of stock price, we propose a combined machine learning framework with information theory and Artificial Neural Network (ANN). This method creatively uses information entropy to inform non-linear causality as well as stock relevance and uses it to facilitate the ANN time series modeling. Our analysis with Google, Amazon, Facebook, and Apple stock prices demonstrates the feasibility of this machine learning framework.