Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence

• URL: http://arxiv.org/abs/2301.04020v1
• Date: Tue, 13 Dec 2022 11:53:48 GMT
• Title: Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence
• Authors: Jian Guo, Saizhuo Wang, Lionel M. Ni, Heung-Yeung Shum
• Abstract summary: We introduce Quant 4.0 and provide an engineering perspective for next-generation quant. automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes. Third, knowledge-driven AI is a supplement to data-driven AI and it incorporates prior knowledge into modeling to improve investment decision.
• Score: 9.026263077693077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Quant 4.0 has three key differentiating components. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.

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