Paper: arXiv 2504.18600

Authors: Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, Jian Guo

Abstract

The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This gap hinders research progress and limits the practical application of academic innovations. We present QuantBench, an industrial-grade benchmark platform designed to address this critical need. QuantBench offers three key strengths: (1) standardization that aligns with quantitative investment industry practices, (2) flexibility to integrate various AI algorithms, and (3) full-pipeline coverage of the entire quantitative investment process. Our empirical studies using QuantBench reveal some critical research directions, including the need for continual learning to address distribution shifts, improved methods for modeling relational financial data, and more robust approaches to mitigate overfitting in low signal-to-noise environments. By providing a common ground for evaluation and fostering collaboration between researchers and practitioners, QuantBench aims to accelerate progress in AI for quantitative investment, similar to the impact of benchmark platforms in computer vision and natural language processing.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on constructing a standardized benchmark platform (QuantBench) and discusses empirical findings from using it, which is highly implementation and data-heavy. While it references mathematical concepts like optimization and reinforcement learning, the paper itself is more about the system architecture and empirical results rather than deriving new mathematical theory.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop<br>Industrial-Grade AI Benchmark<br>for Quantitative Investment"] --> B["QuantBench Platform<br>Design & Implementation"]
  B --> C["Key Data & Methodology<br>Multi-Asset Financial Data<br>Full Pipeline Coverage"]
  C --> D{"Computational Process:<br>Empirical Evaluation<br>on Diverse AI Algorithms"}
  D --> E["Key Findings & Outcomes<br>Continual Learning Needs<br>Relational Data Modeling<br>Robust Overfitting Mitigation<br>Standardized Research Platform"]

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3 of 110 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.