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ADAPT: Adaptive Decision Analysis for Portfolio Trading: A GenAI Driven Approach

Author

Listed:
  • Mohammad Dehghani

    (Northeastern University Bosom)

  • Violeta Cvetkoska

    (Ss. Cyril and Methodius University in Skopje, Faculty of Economics-Skopje)

Abstract

This paper presents ADAPT, Adaptive Decision Analysis for Portfolio Trading, a novel framework that leverages Generative AI to create diverse virtual expert panels for investment decision-making. Traditional portfolio optimization faces a critical bottleneck as obtaining diverse expert opinions is expensive, time-consuming, and prone to inconsistency. ADAPT addresses this by using Large Language Models to generate virtual financial experts with distinct investment philosophies who collaborate through established multi-criteria decision-making methods, specifically AHP for criteria weighting and TOPSIS for stock ranking. The framework integrates quantitative market indicators with GenAI-powered sentiment analysis to capture both numerical and qualitative market signals. Experimental results demonstrate that virtual experts maintain consistent reasoning, achieving AHP consistency ratios, while providing diverse perspectives that enable scalable, transparent, and consensus-driven portfolio recommendations without reliance on human expert panels.

Suggested Citation

  • Mohammad Dehghani & Violeta Cvetkoska, 2026. "ADAPT: Adaptive Decision Analysis for Portfolio Trading: A GenAI Driven Approach," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-23493-3_35
    DOI: 10.1007/978-3-032-23493-3_35
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