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@article{192613,
        author = {Dr. Gorakhnath Waghmode and Sathyendar Sreepada},
        title = {Forecasting Equity Prices: An Empirical Evaluation of the ARIMA Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3310-3316},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192613},
        abstract = {Prediction of stock prices remains a critical challenge in financial markets, with accurate forecasts providing significant value to investors and policymakers. This study applies the Autoregressive Integrated Moving Average (ARIMA) model to forecast the stock prices of State Bank of India (SBI), India’s largest public sector bank. Using historical data from January 2021 to December 2023, we developed an ARIMA (1,1,1) model that demonstrates strong predictive capability for short-term forecasts. The model achieved a Root Mean Square Error (RMSE) of 4.82 and Mean Absolute Percentage Error (MAPE) of 0.78% on validation data. Our forecast for the period January 2024 to January 2026 indicates a gradual upward trend in SBI stock prices, with expected fluctuations influenced by economic conditions and banking-sector dynamics. While the ARIMA model shows excellent performance for short-term predictions, we recommend hybrid approaches combining ARIMA with machine learning models for improved long-term forecasting accuracy.},
        keywords = {Stock Price Forecasting, ARIMA Model, State Bank of India, Time Series Analysis, Financial Prediction, Banking Stocks},
        month = {February},
        }