Market Confidence Predicts Stock Price: Beyond Supply and Demand

Xiao-Qian Sun,Hua-Wei Shen,Yuqing Zhang,Xue-Qi Cheng,Wei-Xing Zhou

doi:10.1371/journal.pone.0158742

Abstract

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

Highlights

With the increasing availability of huge databases for financial systems, financial study becomes a hot research topic
We study the problem of stock price prediction from the perspective of market confidence
Using Granger causality test, we find that stock price is strongly correlated with an index of market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day

Summary

Introduction

With the increasing availability of huge databases for financial systems, financial study becomes a hot research topic. Using Granger causality test, we find that stock price is strongly correlated with an index of market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. By combining the market confidence index together with time series of stock price, we propose a stock price prediction model based on feed forward neural network.

Results

Conclusion