Applying long short term momory neural networks for predicting stock closing price

Abstract

The main goal of this paper is to assess the hypothesis that combining RNNs with informative input variables can provide a more effective method for predicting the next-day stock movement. Moreover, we propose using long short term memory (LSTM) aand stock basic trading data to realize the stock prediction model. For training the model, we utilize some optimization strategies, such as adaptive moment estimation (Adam) and glorot uniform initialization. We present a case study based on Standard & Poor's (S&P500) and NASDAQ. Quantities of comparison experiments were performed to evaluate this model. At last we analyze the performance of different models with a series of evaluation criteria. Stock market prediction has garnered significant interest among investment and researchers. However, accurate prediction of stock market is an extremely challenging task. Hopefully, based on the case study, we show that our forecasting system gives slightly higher prediction accuracy for the stock closing price of next day, which outperforms the comparison models.