An Application of Artificial Intelligence for Fundamental Analysis of Stock Selection During Inflationary Phases in the Indian Economy

Abstract

Inflation is a major macroeconomic factor influencing investment strategies, particularly in the Indian market, where stock market volatility is strongly affected by broader economic conditions. In such periods, stock selection becomes more complex and requires analytical approaches capable of capturing both firm-level financial strength and changing market dynamics. This study aims to explore the integration of artificial intelligence with traditional fundamental analysis techniques in order to improve stock market decision-making during inflationary phases in the Indian economy. The study is based on the financial analysis of five NSE-listed companies, namely Reliance Industries Ltd, Tata Consultancy Services Ltd, Infosys Ltd, HDFC Bank Ltd, and ICICI Bank Ltd. Traditional tools of fundamental analysis, including ratio analysis, trend analysis, and SWOT analysis, are used to assess financial stability, while artificial intelligence is incorporated conceptually through predictive analytics to strengthen investment evaluation. The findings show that companies with stronger financial stability, lower debt exposure, and better profitability indicators perform more favorably during inflationary periods. The study also indicates that artificial intelligence improves the efficiency and accuracy of stock market decision-making by enhancing predictive interpretation and identifying patterns beyond traditional historical analysis. The integration of artificial intelligence and fundamental analysis provides a more effective framework for stock selection during inflation in the Indian market. Future studies should empirically test AI-based stock selection models, extend the sample to additional sectors and firms, and incorporate other macroeconomic variables alongside inflation.