Forecasting Stock Price Movements With Deep Learning Models for time Series Data Analysis

Abstract

It is challenging to anticipate stock price fluctuations with any degree of accuracy since they are nonlinear, non-stationary, and volatile. Such challenges are addressed by developing a deep learning-based forecasting model that blends advanced preprocessing with sequential modelling. The empirical foundation with the use of S&P 500 index data, including 16,706 records (16,706 daily records) of data, is used, 1950- 2016. Data was prepared by cleaning, feature engineering, normalizing the data, and Empirical Mode Decomposition (EMD) to obtain intrinsic oscillatory modes. The nonlinear dynamics and temporal links of the time series were identified using a Gated Recurrent Unit (GRU) model. Robust error metrics, including R2, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), were used to assess the model's prediction accuracy. The findings indicate that GRU had better performance and the R2 was 97.41%, RMSE was 0.0528%, MAE was 0.0416%, and MAPE was 0.2909%. Comparative analysis also showed that GRU was better than ANN and LSTM models. These results demonstrate the ability of deep learning (DL) algorithms to accurately capture the complexity of the market, providing valuable insights that benefit forecasts for investors, portfolio managers, and policymakers. Future developments may consider high-frequency data and hybrid DL architectures to be extended to cover a wider range of applications.