ICREST: International Cross-Reference to Exchange-Based Stock Trend Prediction Using Long Short-Term Memory

Abstract

AbstractStock market investments have been primarily aimed at gaining higher profits from the investment; a large number of companies get listed on various stock exchanges to initiate trading through the stock market. For the potential expansion of market tradings, several companies may choose to get listed on multiple exchanges which may be domestic and/or international. In this article, we propose an international cross-reference to exchange-based stock trend (iCREST) prediction approach to study how historical stock market data of a company listed on internationally-located stock exchanges can be integrated. We consider the timezone and currency variations in order to unify the data; we also incorporate data integration-based pre-processing to eliminate loss of useful stock price information. We calculate the difference between exchange prices of a company and adopt long short-term memory (LSTM) models to predict one-day-ahead stock trend on respective exchanges. Our work can be considered as one of the novel approaches that integrate the international stock exchanges to predict the stock trend of corresponding markets. For the experiment, we take datasets of five companies listed on National Stock Exchange (NSE), Bombay Stock Exchange (BSE), as well as New York Stock Exchange (NYSE); the prediction performance is evaluated using directional accuracy (DA), precision, recall, and F-measure metrics. The results using these metrics indicate performance improvement with international exchanges, and hence the potential adaptability of the proposed approach.