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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300720


Quant 4.0: engineering quantitative investment with automated, explainable, and knowledge-driven artificial intelligence


Author(s):  Jian GUO, Saizhuo WANG, Lionel M. NI, Heung-Yeung SHUM

Affiliation(s):  IDEA Research, International Digital Economy Academy, Shenzhen 518045, China; more

Corresponding email(s):   guojian@idea.edu.cn, swangeh@connect.ust.hk, ni@ust.hk, hshum@idea.edu.cn

Key Words:  AGI, Artificial intelligence, AutoML, Causality Engineering, Deep learning, Feature engineering, Investment engineering, Knowledge graph, Knowledge reasoning, Knowledge representation, Model compression, NAS, Quant 4.0, Quantitative investment, Risk graph, XAI


Jian GUO, Saizhuo WANG, Lionel M. NI, Heung-Yeung SHUM. Quant 4.0: engineering quantitative investment with automated, explainable, and knowledge-driven artificial intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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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 the 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 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 exposures. Third, 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 investing. Putting all these together, we discuss how to build a system that practices the quant 4.0 concept. We also discuss the application of large language models in quantitative finance. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.

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