Market Confidence Predicts Stock Price: Beyond Supply and Demand

• Published in: PloS one Vol. 11; no. 7; p. e0158742
• Main Authors: Sun, Xiao-Qian, Shen, Hua-Wei, Cheng, Xue-Qi, Zhang, Yuqing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.07.2016; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Biology and Life Sciences; Computer and Information Sciences; Confidence; Data processing; Dow Jones averages; Engineering and Technology; Hypotheses; Investments; Laboratories; Markets; Models, Economic; Neural networks; Neural Networks (Computer); People and Places; Physical Sciences; Predictions; Research and Analysis Methods; Science; Social networks; Social Sciences; Stock exchanges; Stock markets; Stock prices; Supply & demand; Supply and demand; Time series; Trends; China; Beijing China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0158742

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.