mlsanityData to 14 Oct 2026

QuantBench: Benchmarking AI Methods for Quantitative Investment

arXiv 2504.18600 ↗First posted 24 Apr 2025q-fin.CP cs.AI cs.CEcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 76 references that are in the map. First posted on arXiv 24 Apr 2025.

Builds on

35 of 76 references.

References · 35

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarkingcited by 21
  2. 2026FactorBench: A Portfolio-Aware Benchmark for Automated Factor Miningcited by 0