From curiosity to confidence: exploring investor behaviour in the age of generative AI

Introduction. The advent of generative artificial intelligence has fundamentally changed how ordinary investors get, examine, and comprehend financial data to make predictions about the stock market. Method. This study utilised a qualitative research method and snowball sampling. Semi-structured interviews with 25 investors from India, collected qualitative data which was analysed using thematic analysis in NVivo 12, accompanied by tool-assisted sentiment categorisation. Analysis. Results of the study revealed that the speed, automation, and intuitive interfaces of generative AI solutions are the main factors driving investor adoption. In addition to stock price prediction, applications include sentiment analysis, scenario modelling, risk identification, and earnings call summary. Time savings, better decision-making, and a decrease in emotional bias are among the main advantages noted. Nevertheless, issues including hallucinations, lack of context in AI outputs, verification issues, and worries about bias, ethics, and data privacy still exist. Results. Investors showed a degree of faith in generative AI technologies, frequently depending on technical indications and cross-verification from conventional financial sources. Conclusion. This study contributes to a better understanding of investor trust, verification behaviour, and human-AI interaction in generative AI-supported investment decision-making. The long-term viability of the method will rely on user trust, regulatory alignment, and responsible deployment.