Forecasting the Performance of AI-Driven Stocks : A Time Series Analysis - L. L., Amirtha; S., Yazhini; R., Ramalakshmi; Duraisamy, A.; Selvasundaram, K.; Sridharan, R. (Indian Journal of Finance, 2026)
Purpose : This study investigated the impact of artificial intelligence (AI) adoption on the price dynamics of AI-driven stocks. Within the Nifty India Digital Index, this study focused on stock prices of Tata Elxsi, Infosys, and Persistent Systems owing to their strategic commitment to AI through considerable investments in proprietary platforms, intellectual platforms, and other AI-driven revenue streams. Methodology : Employing daily closing price data from April 1, 2022, to March 31, 2025, this study applied a suitable econometric time-series model to capture the heterogeneous statistical properties of stock prices. The best model was selected based on rigorous diagnostic testing and forecast performance evaluation. Findings : The study found that AI-driven stocks exhibited different statistical properties, requiring customized forecasting models. The EGARCH (1,1) model best predicted Tata Elxsi stock prices, while ARFIMA models provided the best forecasts for Infosys and Persistent Systems stock prices. Implications : These findings offered different perspectives to investors in framing stock-specific forecasting strategies while analyzing AI-driven stocks. Originality : This study is the first of its kind, as it emphasizes the role of tailored econometric modeling in prediction accuracy while amalgamating AI-driven market context with time series forecasting in the Indian digital economy.