Recurrent Neural Networks in Time-Series Forecasting: A Deep Learning Approach to Stock Market Prediction

Abstract

Stock market prediction has been a grand challenge due to dynamic nature, non-linearity and volatility of the financial markets. Traditional statistical models have proved useful historically, but are less likely to successfully model the complex temporal dependencies in stock price data. In recent years there was a breakthrough in deep learning, namely Recurrent Neural Networks (RNNs), which opens up new opportunities in time-series forecasting. The purpose of the work is to investigate how three variations of RNN-based models, such as Simple RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), perform on the task of predicting the direction of the stock market using the historical data of the S&P 500 index. The models were trained and tested through a pure experimental design that factored in a 60-day look-back window, normalization, and sequence modeling across diverse performance measures that include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Directional Accuracy (DA). This is rather clear in the findings that the LSTM and GRU networks are significantly superior to the Simple RNN in predictive power and robustness to varying market environments. LSTM in particular generalized the most, over 81 percent of its predictions fell within 2 percent error margin and directional accuracy of 72.4 percent on the test set. In addition to enhancing the applicability of deep RNN architectures in financial prediction, the results also imply that they can be applicable to algorithmic trading and investment decisions systems. Future research directions might be observed in the sphere of multi-modal data source integration and model interpretability, which would allow to advance the domain of deep learning applicability in finance further.

Keywords:  Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Unit, Stock Market Forecasting, Deep Learning, Time-Series Prediction, Financial Markets, Algorithmic Trading, Directional Accuracy, Predictive Modeling