PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractTasked 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.
Degree ProgramHonors College