Impact of Time Steps on Stock Market Prediction with LSTM

Abstract: Machine learning models as tools for predicting time series have in recent years proven to perform exceptionally well. With financial time series in the form of stock indices being inherently complex and subject to noise and volatility, the prediction of stock market movements has proven to be especially difficult throughout extensive research. The objective of this study is to thoroughly analyze the LSTM architecture for neural networks and its performance when applied to the S&P 500 stock index. The main research question revolves around quantifying the impact of varying the number of time steps in the LSTM model on predictive performance when applied to the S&P 500 index. The data used in the model is of high reliability downloaded from the Bloomberg Terminal, where the closing price has been used as feature in the model. Other constituents of the model have been based in previous research, where satisfactory results have been reached. The results indicate that among the evaluated time steps, ten steps provided the superior performance. However, the impact of varying time steps is not all too significant for the overall performance of the model. Finally, the implications of the results for the field of research present themselves as good basis for future research, where parameters are varied and fine-tuned in pursuit of optimal performance.