Predicting the direction of stock markets using optimized neural networks with Google Trends

Abstract

Abstract The stock market is affected by many factors, such as political events, general economic conditions, and traders’ expectations. Predicting the direction of stock markets movement has been one of the most widely investigated and challenging problems for investors and researchers as well. Many researchers focus on stock market analysis using advanced knowledge of mathematics, computer sciences, economics and many other disciplines. In this paper, we present an improved sine cosine algorithm (ISCA), which introduces an additional parameter into the sine cosine algorithm (SCA), to optimize the weights and basis of back propagation neural networks (BPNN). Thus, ISCA and BPNN are combined to create a new network, ISCA-BPNN, for predicting the directions of the opening stock prices for the S&P 500 and Dow Jones Industrial Average Indices, respectively. In addition, Google Trends data are taken into consideration for improving stock prediction. We analyze two types of prediction: Type I is the prediction without Google Trends and Type II is the prediction with Google Trends. The predictability of stock price direction is verified by using the hybrid ISCA-BPNN model. The experimental results indicate that ISCA–BPNN outperforms BPNN, GWO-BPNN, PSO-BPNN, WOA-BPNN and SCA-BPNN in terms of predicting the direction of the opening price for both types and significantly for Type II. The hit ratios for ISCA-BPNN with Google Trends reach 86.81% for the S&P 500 Index, and 88.98% for the Dow Jones Industrial Average Index. Our results show that Google Trends can help in predicting the direction of the stock market index.