The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents

ArXiv ID: 2505.14727 “View on arXiv”

Authors: Mohammad Rubyet Islam

Abstract

The pursuit of alpha returns that exceed market benchmarks has undergone a profound transformation, evolving from intuition-driven investing to autonomous, AI powered systems. This paper introduces a comprehensive five stage taxonomy that traces this progression across manual strategies, statistical models, classical machine learning, deep learning, and agentic architectures powered by large language models (LLMs). Unlike prior surveys focused narrowly on modeling techniques, this review adopts a system level lens, integrating advances in representation learning, multimodal data fusion, and tool augmented LLM agents. The strategic shift from static predictors to contextaware financial agents capable of real time reasoning, scenario simulation, and cross modal decision making is emphasized. Key challenges in interpretability, data fragility, governance, and regulatory compliance areas critical to production deployment are examined. The proposed taxonomy offers a unified framework for evaluating maturity, aligning infrastructure, and guiding the responsible development of next generation alpha systems.

Keywords: Large Language Models (LLMs), Agentic Systems, Representation Learning, Multimodal Data Fusion, Alpha Generation, Equities

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats
  • Why: The paper presents a high-level taxonomy and reviews concepts like Jensen’s Alpha and CAPM equations, but lacks code, backtests, or specific implementation details. It focuses on theoretical evolution and future directions rather than reproducible empirical methods.
  flowchart TD
    A["Research Goal<br>Evolution of Alpha in Finance"] --> B["Key Methodology<br>Five-Stage Taxonomy Framework"]
    B --> C["Computational Process<br>System-Level Review Lens"]
    C --> D{"Data Inputs<br>Manual to Agentic Systems"}
    D --> E["Computational Process<br>Representation Learning<br>Multimodal Fusion"]
    E --> F["Key Findings<br>Shift to Context-Aware Agents"]
    F --> G["Outcomes<br>Unified Framework for Next-Gen Alpha"]