ARIMA-Based Forecasting of S&P BSE SENSEX Returns

Abstract

Investment in the stock market requires a delicate balance between profitability and risk management, with risk aversion playing a vital role. This study explores the ARIMA forecasting method to predict S&P BSE SENSEX returns, providing valuable insights for investors and financial experts. Using a 3-year dataset, the ARIMA (3,1,1) model was identified as the optimal choice. Diagnostic checks confirmed its reliability, ensuring unbiased and accurate forecasts. In static forecasting, the model exhibited high-quality performance with low error rates. Dynamic forecasting further revealed precision in predicting future values. While the ARIMA model aids in making informed financial decisions, it's crucial to acknowledge its limitations. This research contributes to the understanding of stock market forecasting methodologies, benefiting investors and analysts in navigating this dynamic landscape.