mlsanityData to 7 Oct 2026

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

arXiv 2301.04020 ↗First posted 13 Dec 2022q-fin.CP cs.AIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 116 of its 372 references that are in the map. First posted on arXiv 13 Dec 2022.

Builds on

116 of 372 references.

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 2.

5 papers.

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  4. 2023Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Surveycited by 3
  5. 2024Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learningcited by 0