Unraveling asset pricing with AI: A systematic literature review

Abstract

Asset pricing, long recognized as a cornerstone of financial studies with multiple Nobel Prizes in Economics, is experiencing a profound transformation through the integration of artificial intelligence (AI). This study highlights the convergence of finance and computer science in asset pricing, offering novel insights into AI-driven approaches through an in-depth analysis of hundreds of research papers. The study begins by examining the key factors influencing asset pricing, highlighting the significance of factor interactions in AI-driven asset pricing models. It then systematically reviews various econometric and machine learning models from both financial and computational perspectives, underscoring the importance of designing predictive asset pricing models based on financial assumptions and principles. This reflects the inevitable convergence of finance and computer science in the field of asset pricing. Finally, the study outlines three research directions, providing actionable guidance for future exploration: (1) the development of large-scale multimodal datasets to equip advanced models with the breadth of information required to enhance foresight, (2) the integration of fundamental economic theories into model design to enhance relevance and resilience, emulating the nuanced decision-making processes of experienced traders, and (3) improving the interpretability of deep learning models to bridge the gap between their outputs and actionable insights. In addition, this study introduces the QuantPlus project, an initiative designed to provide large-scale datasets that empower researchers to evaluate and advance innovative models.