Paper: arXiv 2301.04020

Authors: Jian Guo, Saizhuo Wang, Lionel M. Ni, Heung-Yeung Shum

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.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: This paper presents a strong conceptual paradigm shift and vision for the next generation of quantitative investing (Quant 4.0), detailing AutoML, XAI, and Knowledge-driven AI frameworks. However, as a vision and survey paper, it lacks heavy mathematical derivations and empirical backtests, focusing instead on system architecture and future research challenges.

Research Flowchart

  flowchart TD
    A[Research Goal: Define Quant 4.0 Engineering Perspective] --> B[Inputs: Evolution of Quant 1.0-3.0 & Deep Learning Bottlenecks]
    B --> C[Core Methodology: Integration of 3 AI Paradigms]
    C --> D1[Automated AI: Auto-modeling]
    C --> D2[Explainable AI: Risk Interpretation]
    C --> D3[Knowledge-driven AI: Prior Knowledge]
    D1 & D2 & D3 --> E[System Architecture & Solutions for 10 Key Challenges]
    E --> F[Outcome: Next-Gen Quant Investment Framework]