构建量化投资AI的工业级评测平台，推动学术与实践融合。
QuantBench: Benchmarking AI Methods for Quantitative Investment
- 基于产业实践设计标准化评测框架，覆盖完整投研流程。
- 实证发现需强化持续学习、关系建模与低信噪比下的抗过拟合能力。
- 适合金融AI研究者与量化从业者共同验证算法有效性。
人工智能在量化投资领域取得显著进展，但缺乏与行业实践对齐的标准化评测基准，制约了研究推进与学术创新的落地应用。本文提出QuantBench——一个面向工业场景的评测平台，具备三大优势：（1）与量化投资行业实践高度一致的标准化设计；（2）支持多种AI算法灵活集成；（3）覆盖从数据到策略的全链条投研流程。基于该平台的实证研究揭示关键方向：需加强持续学习以应对分布漂移，改进金融关系数据建模方法，以及提升在低信噪比环境下的鲁棒性。QuantBench旨在为研究者与从业者提供统一评估基础，促进协作，推动AI在量化投资领域的进展，类比计算机视觉与自然语言处理中的标杆平台影响力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。