Study of Behavioural Investment in India - Sood, Tushar (Digital Repository of Theses, 2025)
The rapid evolution of investment markets and it’s increasing complexity especially with decision-making requires innovative approaches to optimise investment outcomes. While traditional investment systems rely on centralised data repositories and decisionmaking processes, these approaches often suffer from data silos, privacy concerns, and information asymmetry. Federated investment, a decentralised & distributed framework that leverages modern AI technologies, offers a promising solution to the challenge of privacy-preserving investment decision-making. Federated Investments can enhance investment decisionmaking by implementing the concept of collaborative knowledge sharing. In my research, I investigate the potential of Federated Machine Learning to enhance investment decision-making. I intend to explore the need and advantages or disadvantages of implementing Federated Investments Decision Making (FIDM), a framework where collaborative learning occurs without sending investors behavioural data. In my research I also surface the urgent need for data security, privacy, and fairness in predictions of financial news distribution, aiming to contribute to a more efficient, equitable, and secure investment ecosystem. In my research, I study the impact of finfluencers on investors’ behaviour in INDIA. The massive adoption of digital platforms and Generative Video Content motivates financial gurus to spread asymmetric market news, leading to wrong and incorrect perceptions about market conditions. This causes investors to make unjustified investment decisions leading to losses and, hence averting future investments. In conclusion, we can see how FIDM can preserve the privacy of investment portfolios at the same time implement market news filtering related to the behaviour of investors.