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QuantBench: Benchmarking AI Methods for Quantitative Investment

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Listed:
  • 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.

Suggested Citation

  • Saizhuo Wang & Hao Kong & Jiadong Guo & Fengrui Hua & Yiyan Qi & Wanyun Zhou & Jiahao Zheng & Xinyu Wang & Lionel M. Ni & Jian Guo, 2025. "QuantBench: Benchmarking AI Methods for Quantitative Investment," Papers 2504.18600, arXiv.org.
    • Handle: RePEc:arx:papers:2504.18600
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    References listed on IDEAS

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