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QuantBench: Benchmarking AI Methods for Quantitative Investment

Author

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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    1. Thanh Trung Huynh & Minh Hieu Nguyen & Thanh Tam Nguyen & Phi Le Nguyen & Matthias Weidlich & Quoc Viet Hung Nguyen & Karl Aberer, 2022. "Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction," Papers 2211.07400, arXiv.org, revised Nov 2022.
    2. Tianping Zhang & Yuanqi Li & Yifei Jin & Jian Li, 2020. "AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment," Papers 2002.08245, arXiv.org, revised Apr 2020.
    3. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    4. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Ernst Schaumburg, 2020. "Characteristic-Sorted Portfolios: Estimation and Inference," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 531-551, July.
    5. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    6. Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
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    Cited by:

    1. Hongjun Ding & Binqi Chen & Jinsheng Huang & Taian Guo & Zhengyang Mao & Guoyi Shao & Lutong Zou & Luchen Liu & Ming Zhang, 2025. "AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining," Papers 2508.13174, arXiv.org.
    2. Weixian Waylon Li & Hyeonjun Kim & Mihai Cucuringu & Tiejun Ma, 2025. "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?," Papers 2505.07078, arXiv.org, revised Feb 2026.

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