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

Mohammad Dehghani () and Violeta Cvetkoska ()
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Mohammad Dehghani: Northeastern University Bosom
Violeta Cvetkoska: Ss. Cyril and Methodius University in Skopje, Faculty of Economics-Skopje

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 583-598 from Springer

Abstract: 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.

Keywords: GenAI; Portfolio Optimization; Multi-Criteria Decision Analysis; AHP; TOPSIS; Virtual Experts; Financial Decision Support; Agentic AI (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-23493-3_35

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DOI: 10.1007/978-3-032-23493-3_35

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