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A Systematic Literature Review of Asset Pricing: Insights from AI and Big Data

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Abstract

This paper systematically reviews the role of big data and artificial intelligence (AI) in asset pricing, analysing 130 journal articles published between 1994 and 2024. We categorise the literature into three themes: AI in asset pricing, big data in asset pricing, and their integration. Publications have grown exponentially since 2019 suggesting structural changes in asset pricing research which we highlight using thematic analysis. The bibliometric analysis shows key trends in AI models for predictive analytics and factor analysis. We identify research gaps and call for adaptive regulatory frameworks to support the ethical use of AI and big data in financial modelling.

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  • Barson, Zynobia & Ahadzie, Richard Mawulawoe & Daugaard, Dan & Vespignani, Joaquin, 2025. "A Systematic Literature Review of Asset Pricing: Insights from AI and Big Data," Working Papers 2025-03, University of Tasmania, Tasmanian School of Business and Economics.
    • Handle: RePEc:tas:wpaper:29456384
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    References listed on IDEAS

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    9. Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2020. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-52, December.
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    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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