International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Driven Asset Management with Behavioral Profiling: A Dual-Strategy Prototype

Author(s) Abhijeet More
Country India
Abstract Conventional portfolio strategies often over-rely on static risk measures or neglect the behavioral tendencies of both clients and markets. While AI-driven systems already exist, their role in strategy allocation remains limited, particularly when psychological dynamics and risk capacity are not explicitly integrated. This paper addresses that gap by introducing a dual-strategy asset management prototype designed to adapt allocations dynamically to client profiles and evolving market conditions. The framework combines two modules: a safe-risk strategy that emphasizes structural reliability, and a high-risk strategy that embraces volatility through liquidity-trap identification. At its core, allocation decisions are guided by an AI-driven profiling system that balances quantitative metrics with behavioral scoring, ensuring that discipline and psychology jointly inform risk exposure. A central innovation is the formalization of the Bi-Directional Liquidity Trap (BDLT), a concept developed in this work to identify and exploit dual-trap market structures. This is integrated into the allocation process alongside a behavioral weighting scheme, where 60% of the decision score derives from client behavior and 40% from numerical portfolio metrics. The prototype was tested in live market conditions, with equity curve results illustrating controlled drawdowns, adaptability in stagnant phases, and scalability across client profiles. By merging client psychology with algorithmic structure, this work outlines a model for asset management that is not only risk-aware but also adaptive. This framework sets the stage for future research into AI-assisted portfolio design that has potential that bridges market mechanics with human behavior.
Keywords Artificial Intelligence, Behavioral Science, Portfolio Allocation
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-11
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.66176
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E-ISSN 2582-2160
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