The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908373296873472 |
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| author | Islam, Mohammad Rubyet |
| author_facet | Islam, Mohammad Rubyet |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14727 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents Islam, Mohammad Rubyet Machine Learning Computational Finance 91G70 Statistical methods, risk measures 91B84 Economic models (financial models, industrial models, growth models) I.2.6; I.5.1; I.2.7 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. |
| title | The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents |
| topic | Machine Learning Computational Finance 91G70 Statistical methods, risk measures 91B84 Economic models (financial models, industrial models, growth models) I.2.6; I.5.1; I.2.7 |
| url | https://arxiv.org/abs/2505.14727 |