title: Market Confidence Predicts Stock Price: Beyond Supply and Demand.
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.
authors.affiliation: University of Chinese Academy of Sciences, Beijing, China.|CAS Key Laboratory of Network Data Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Xiao-Qian
authors.initials: XQ
authors.lastname: Sun
authors.affiliation: CAS Key Laboratory of Network Data Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Hua-Wei
authors.initials: HW
authors.lastname: Shen
authors.affiliation: CAS Key Laboratory of Network Data Science & Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Xue-Qi
authors.initials: XQ
authors.lastname: Cheng
authors.affiliation: University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Yuqing
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D007449:Investments; D018803:Models, Economic; D016571:Neural Networks, Computer