IMPACT OF GENERATIVE AI ON EQUITY RESEARCH AND INVESTMENT DECISION-MAKING IN INDIAN FINANCIAL INSTITUTIONS

Abstract

The current research problem is the study titled "Impact of Generative AI on Equity Research and Investment Decision-Making in Indian Financial Institutions the impact that the use of Generative Artificial Intelligence tools have on the essence of equity research, financial data analysis, and investment decision-making within the rapidly developing financial sector in India. With the recent spread of AI-enabled platforms like ChatGPT, Google Gemini, Microsoft Copilot, and AI applications in Bloomberg, the details of their relevance to the Indian context, with its rising capital market, growing institutional investment foundation, and increased technological use by financial practitioners, are a focus of significant and under-researched scholarly study. The main aim of this study is to evaluate the impact of Generative AI on the quality, speed, accuracy and effectiveness of equity research and investment decision-making. The secondary aims are to learn about the existing levels of awareness and usage of AI tools amongst Indian professionals in the financial sector, to understand what perceived benefits and risks of AI adoption the professionals have, and whether the demographic characteristics like gender and work experience impact the attitudes and the use behaviour towards AI. The research design is based on quantitative research design and descriptive approach is utilized. A structured questionnaire using Google Forms as the data collection method provided 103 usable responses and were obtained as a cross-section of equity research analysts, portfolio managers, investment advisors, bank/financial service employees, and finance students. Categorical items, as well as Likert-scale items (scales of 1 (Strongly Disagree) to 5 (Strongly Agree)) were also included in the questionnaire. The secondary data have been sourced in the published academic journals documents and research papers in the financial sector and pertinent websites. The program was analyzed utilizing the tools of Statistical Package of Social Sciences (SPSS) which comprised the frequency distribution of the data (demographic), descriptive statistics (mean, standard deviation, variance), relativistic study of the correlations with other variables via Pearson correlation, Chi- Square test of Independence and multiple linear tests.

6 regression analysis. The level of statistical significance was rated by measuring it at the 0.05 level in all of the inferential tests. The main conclusions drawn during the investigation imply that the knowledge of Generative AI tools among the participants of the study is almost universal since, 91.3% of the respondents admit to being familiar with these tools when it comes to the financial issues. The most common advantage of AI was that it could improve the speed of equity research and the improvement of financial data accuracy and the decrease of manual work were rated in positive average. The mean score of institutional integration of AI in investment research (M = 3.573) was the highest and portrayed a high level of aspirational support of AI embedding in the processes of professional research. The Alpha Cronach was 0.841, which affirmed the high level of internal consistency of the measurement scale. The Pearson correlation analysis has shown the accuracy of financial analysis to be the central mediating variable and exhibits the strongest and most prevalent relationships among all the performance related AI perception variables. Multiple linear regression ensured that accuracy of analysis (β = 0.321, p = 0.004) and lessening manual effort ( 0.227, p = 0.028) are the two statistically significant individual predictors of enhancing the investment decision-making. Chi-Square results showed that there are no significant relations between gender and AI awareness, experience level and AI usage frequency which once again confirm that AI adoption is influenced by a contextual and institutional factor but not the conventional traits of the population. The paper draws the conclusion that Generative AI has the significant transformative potential in the equity research and investment decision-making in Indian financial institutions. To make this potential come to fruition in a sustainable manner, financial institutions will be required to pour investments into AI governance systems, organized training curricula and quality assurance systems that accommodate concerns with data reliability, ethical utilisation and compliance with regulations. The results can be applied by the researchers, practitioners, and policymakers operating in the interface between artificial intelligence and financial services within India.