FACTOR-BASED INVESTING IN AN OVERVALUED MARKET

Abstract

Tasked with creating a market-neutral, beta-neutral, long / short, unlevered, minimal-cash portfolio for the Chicago Quantitative Alliance Challenge, our team used a factor-based methodology to develop a portfolio of securities that we believed would perform in a period of market correction from November of 2017 through March of 2018. These factors were weighted in the portfolio as size (20%), value (35%), momentum (30%), and beta (15%). We later added quality as a factor and removed size, while also adding a small sector bet on technology and healthcare. We backtested the portfolio seven times by reconstructing the factor-method with a Python algorithm that ran on data and computing power provided by the Quantopian servers. These backtests indicated that the portfolio would succeed in a period of market correction. At the end of the challenge, the portfolio returned -0.07% and had a Sharpe ratio of -0.37. Despite not generating positive returns, we believe that these results were overall positive in teaching us how to construct and manage a portfolio with an array of quantitative skills.