Systematic Comparison, Evaluation and Identification of Robust Model to Forecast the Closing Price of S&P 500 Financial Sector through Classical and AI-Based Approaches

Abstract

S&P 500 is the largest and state-of-the-art stock market index in North America, which attracted a wide range of audience. The primary objective of this study is to compare the widely used four stock forecasting approaches: Long Term- Short Term Memory (LSTM), Gated Recurrent Unit, (GRU), Convolutional Neural Network, (CNN) and traditional forecasting approach: Auto- Regressive Integrated Moving Average (ARIMA) to identify the best and more robust forecasting model for daily and weekly closing price on the S&P 500 financial sector. Thus, we developed and compared the performance and quality of these AIbased approaches with baseline traditional ARIMA model using well- defined two statistical metrics, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as the evaluation criterion. In the scope of our study, we have found that the LSTM outperforms (with more than 15% improvement in RMSE and with more than 30% improvement in MAE compared to ARIMA) 2 out of 3 train/test data splits compared to other proposed deep learning approaches including GRU and traditional ARIMA models with respect to two widely used RMSE and MAE evaluation metrics for daily closing price forecasting in the S&P 500 Financial Sector. Additionally, in the weekly closing price forecasting models, the traditional ARIMA model outperforms all deep learning models on 2 out of 3 train/test data splits with respect to the statistical metric RMSE.