Crisis Alpha: A High-Performance Trading Algorithm Tested in Market Downturns

TL;DR

This paper introduces high-performance trading algorithms tested during major market downturns, demonstrating their robustness and effectiveness in managing risk and outperforming market benchmarks across different crises.

Contribution

It develops and evaluates three statistical risk models for portfolio construction, showing their consistent outperformance during major financial crises.

Findings

Models outperform market excess returns in all tested periods

Risk models are effective in diverse economic downturns

Backtested on extensive historical data from 1990 to 2023

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.