The effect of feature selection on the performance of long short-term memory neural network in stock market predictions

Abstract

Stock market predictions are a difficult and challenging task affected by numerous interrelated economic, political and social factors caused by non-linear and often unstable movements. Precisely due to the stated nature of financial time series, there is a need to develop advanced systems for stock market prediction. This research seeks to solve one of the problems of such systems, which is reflected in the selection of features to improve the performance of models that are an integral part of the system. In the paper, the wrapper method - recursive feature elimination and the filter method - feature importance, are used for feature selection. A forecasting model based on the long short-term memory (LSTM) neural network was defined to predict the movement of the stock's closing price. With this research we can conclude that for each selected stock there are certain features that have an impact on the results and that it is therefore necessary to carry out the selection of features individually.