Long-term forecasting of stock prices using time series models: Evidence from solar industries India Ltd

Long-term stock price analysis is essential for understanding market dynamics and supporting investment decisions. Using data from the National Stock Exchange, the study focuses on Solar Industries India Limited's closing prices between January 1, 2000, and December 31, 2024. The primary objectives are to use time series analysis to model and predict closing prices, identify underlying trends, and investigate relationships with trading activity. An ARIMA model was used with Minitab software to account for data noise, trends, and seasonal fluctuations. Statistical criteria guided the model selection process, providing optimal fit and reliability. These charts helped determine trends, patterns, and seasonal elements. A total of 729 monthly observations were examined, and the best-fitting model was chosen using the Akaike Information Criterion (AICc). The Ljung-Box test verified that the ARIMA (0, 2, 1) model was the best model, as it had the lowest AICc and strong residual diagnostics (p > 0.05 for most lags). The forecasts showed anticipated pricing ranges with increasing uncertainty over time. Further statistical analysis was conducted to investigate the relationships between trading activity and stock price. While insights into the relationship between trade volume and price movement provide useful perspectives for market analysis, a strong forecasting model can help stakeholders make well-informed decisions. Overall, the integration of exploratory research with time series modeling provides a comprehensive framework for analyzing stock price behavior and forecasting future trends.