TY - JOUR
T1 - Predicting the direction of stock markets using optimized neural networks with Google Trends
AU - Hu, Hongping
AU - Tang, Li
AU - Zhang, Shuhua
AU - Wang, Haiyan
N1 - Funding Information:
The authors would like to thank the editor and referees for their helpful comments which improve the paper. The authors also would like to acknowledge the help of Taylor Meginnis for proofreading the paper. This work is in part supported by the National Natural Science Foundation of China (Grant No. 61774137 , 11061030 , 11261052 , 91430108 , 11771322 ), the Natural Science Foundation of Tianjin City of China (Grant No. 15JCYBJC16000 ), Natural Science Foundation of Shanxi Province (Grant No. 201701D22111439 and 201701D221121 ) and Shanxi Scholarship Council of China (Grant No. 2016-088 ).
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/4/12
Y1 - 2018/4/12
N2 - 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.
AB - 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.
KW - Back propagation neural network
KW - Google Trends
KW - Sine cosine algorithm
KW - Stock price
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U2 - 10.1016/j.neucom.2018.01.038
DO - 10.1016/j.neucom.2018.01.038
M3 - Article
AN - SCOPUS:85041218079
SN - 0925-2312
VL - 285
SP - 188
EP - 195
JO - Neurocomputing
JF - Neurocomputing
ER -