Artificial Intelligence-Driven Computational Financial Analytics and Algorithmic Trading: Evidence from Mumbai

Abstract

Artificial Intelligence (AI) and computational techniques have fundamentally changed the way financial analytics and algorithmic trading occur in the global capital markets. As a form of computational financial analytics, this new technology integrates machine learning, predictive models, and automatic decision making with the volume of high-volume financial data to allow investors to optimise their trading strategies. This study has established the effects of AI-driven computational financial analytics on retail investor decision-making in Mumbai, India. The research was conducted using primary data collected from 218 retail investors, which were evaluated using descriptive statistics, correlation, and regression analysis. Descriptive statistics provided insight into the level of awareness and adoption that retail investors have regarding AI-powered analytics. Correlation and simple linear regression analysis were used to assess how effective AI-powered analytics have been in influencing investor decisions. The results of the study show a very strong positive correlation (r = 0.62, p < 0.001) between the use of AI-powered analytics and the effectiveness of retail investor decision-making, and a significant linear regression model explaining 38.2% of the variance in investor decision-making effectiveness (R² = 0.382, F = 146.82, p < 0.001). These findings provide evidence that AI-powered analytics positively impact investor confidence, reduce impulsive behaviour, and enable improved performance in the financial decision-making process. Additionally, the study provides an empirical basis for understanding the impact of AI on the retail investor in an emerging market, and also provides actionable insights for fintech platforms, brokerage firms, and regulatory agencies.