Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

By Mingyue Qiu, Yu Song. 2016

264 citations recorded by the source corpus.

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

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Citations in the corpus

CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “In (Qiu et al., 2016), authors used GA and SA to ﬁnd initial weights of an ANN, and then back-propagation algorithm is used to train the network.”