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QuantBench: Benchmarking AI Methods for Quantitative Investment

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

TL;DR

QuantBench introduces an industrial-grade benchmark platform for AI in quantitative investment, delivering standardized datasets, flexible model integration, and full-pipeline evaluation from data ingestion to execution. It emphasizes a layered, data-to-learning-objective architecture and a market-simulation environment, enabling task-specific and task-agnostic assessments. Through comprehensive empirical studies, the work reveals key directions such as continual learning for distribution shifts, more effective relational-data modeling, and ensemble-based robustness against overfitting in low-SNR financial data. By providing an open, unified framework, QuantBench aims to accelerate translation of academic advances into practice and foster collaboration between researchers and industry practitioners.

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.

QuantBench: Benchmarking AI Methods for Quantitative Investment

TL;DR

QuantBench introduces an industrial-grade benchmark platform for AI in quantitative investment, delivering standardized datasets, flexible model integration, and full-pipeline evaluation from data ingestion to execution. It emphasizes a layered, data-to-learning-objective architecture and a market-simulation environment, enabling task-specific and task-agnostic assessments. Through comprehensive empirical studies, the work reveals key directions such as continual learning for distribution shifts, more effective relational-data modeling, and ensemble-based robustness against overfitting in low-SNR financial data. By providing an open, unified framework, QuantBench aims to accelerate translation of academic advances into practice and foster collaboration between researchers and industry practitioners.

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.

Paper Structure

This paper contains 27 sections, 6 figures, 9 tables.

Table of Contents

  1. Introduction
  2. The Quant Pipeline
  3. Design of QuantBench
  4. Data
  5. Increasing Data Breadth
  6. Going Deep
  7. Models
  8. Architectural Design
  9. Temporal Models
  10. Spatial Models
  11. Training Objective
  12. Evaluation
  13. Task-specific Metrics
  14. Task-agnostic Metrics
  15. Empirical Study
  16. ...and 12 more sections

Figures (6)

  • Figure 1: Overview of QuantBench. Upper: Quant research pipeline covered in QuantBench. Lower: The layered design of QuantBench.
  • Figure 2: Data processing pipeline of QuantBench. Blocks with green background are already supported in QuantBench, and blocks with blue background are planned to be supported in the future.
  • Figure 3: A non-exhaustive illustration of models covered in QuantBench and their evolution
  • Figure 4: Comparison of different rolling schemes
  • Figure 5: Ensemble curve with variance illustrated. Shaded area indicates different rolling periods.
  • ...and 1 more figures