Paper: arXiv 2409.14510

Abstract

Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1,000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical techniques like quadratic optimization, shrinkage methods, and factor risk models, indicating significant mathematical complexity. It demonstrates strong empirical rigor by backtesting on 34 years of real market data (CRSP) across three major crises, evaluating multiple strategies, and presenting performance against benchmarks.

Research Flowchart

  flowchart TD
  A["Research Goal"] -->|Form Quantitative Portfolios| B["Statistical Risk Models"]
  B -->|Apply 5 Strategies| C["Backtesting Process"]
  D["CRSP Data<br>1990-2023"] --> C
  C -->|Test in 4 Periods<br>Dot Com, GFC, Covid| E["Analysis & Comparison"]
  E --> F{"Key Findings"}
  F --> G["Consistent Outperformance"]
  F --> H["Risk Management Tool"]