Quant 4.0:engineering quantitative investmentwith automated,explainable,and knowledge-driven artificial intelligence
作者机构：IDEA ResearchInternational Digital Economy AcademyShenzhen 518045China The Hong Kong University of Science and TechnologyHong Kong 999077China The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511453China
出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)
年 卷 期：2024年第25卷第11期
页 面：1421-1445页
核心收录：
学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学]
主 题：Artificial general intelligence Artificial intelligence Automated machine learning Causality engineering Deep learning Feature engineering Investment engineering Knowledge graph Knowledge reasoning Knowledge representation Model compression Neural architecture search Quant 4.0 Quantitative investment Risk graph Explainable artificial intelligence
摘 要：Quantitative investment(abbreviated as“quantin this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,*** 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 the quant research pipeline from small“strategy workshopsto large“alpha factories;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing *** its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-boxneural network *** address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation *** 4.0 has three key differentiating ***,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs 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 ***,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value *** all these together,we discuss how to build a system that practices the quant 4.0 *** also discuss the application of large language models in quantitative ***,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.
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