Artificial Intelligence Based Behavioural Finance in Shaping Investment Strategies to Analysis of Key Biases and Heuristics

Dr.  Venkateswara Rao Podile; M. Adithya Varun; Satapati Dhanurmika; Gokaraju Harsha; Gnanada Gnana Sai Abhijit

doi:10.69996/vgnnc335

Authors

Dr. Venkateswara Rao Podile Author
M. Adithya Varun Author
Satapati Dhanurmika Author
Gokaraju Harsha Author
Gnanada Gnana Sai Abhijit Author

DOI:

https://doi.org/10.69996/vgnnc335

Keywords:

Artificial; Intelligence, Probabilistic Modeling, Behavioural Finance, Prospect Theory, Irrational Exuberance

Abstract

This paper explores the job of behavioural finance in forming investment strategies zeroing in on key predispositions and heuristics. behavioural finance looks at what mental elements and inclinations mean for monetary choices, frequently wandering from level-headed assumptions. Conventional money expects objective way of behaving pointed toward augmenting returns while limiting dangers; in any case, genuine financial backer way of behaving habitually strays from this ideal because of mental easy routes and predispositions. This study examines critical predispositions like overconfidence, loss aversion, mental accounting, and herd behaviour, exploring their effect on market elements and individual financial backer results. By understanding these predispositions, financial backers and monetary consultants can foster techniques that adjust better to financial backer brain science, possibly prompting more predictable investment results and further developed portfolio the executives. This study explores the influence of behavioral biases on investment decision-making using the PMBAC (Probabilistic Modeling and Behavioral Analysis for Cognitive biases) framework. Key biases, such as overconfidence, loss aversion, and herding behavior, are examined to understand their impact on market performance and investor returns. The analysis reveals that overconfidence occurred in 78% of the cases, resulting in an average return impact of -25%, while loss aversion occurred in 85% of instances, leading to a -5% return impact. Herding behavior, observed in 65% of the cases, was associated with a 15% return impact, showing that following the crowd can lead to short-term gains but greater long-term risks. The study also identifies the role of other biases such as mental accounting (55% occurrence, -3% return impact), anchoring (72% occurrence, -10% return impact), and status quo bias (63% occurrence, -2% return impact). In terms of market performance, herding behavior was linked to a 40% overvaluation, while loss aversion contributed to a 10% market overvaluation. The findings highlight the pervasive nature of these biases in financial decision-making and their significant consequences on risk-adjusted returns, with some biases leading to a negative impact on returns and others fueling market bubbles.

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