The GW Quant Student Investment Fund is the newest fund of the GW Investment Institute (GWII) launched in December of 2021.
The finance industry has experienced large changes due to technological innovations, for example the increase in quantitative strategies. This course provides an overview of some common quant investing strategies and focuses on data-driven models, which is different from the fundamental analysis and subjective assessments which are the focus of the other GWII student funds. Students learn how to analyze time-series data and work in groups to find and analyze real data and build and test a predictive model.
Below is a short summary of the 8 final projects for the GW Quant Student Investment Fund for Spring 2022.
Foggy Fund: Used a three-factor model and tracked its momentum via an RSI indicator to understand which areas of the tech sector are performing best and tailor an investment strategy accordingly.
Quant Queens: Investigated the perceived information advantage of members of Congress to see if using their stock trades and other criteria about the members could outperform the market.
Steeling Alpha: Developed a model to predict the price of American steel producing companies.
Dividend Factor Model: Developed a multi-factor model to see whether dividend-paying stocks could outperform with lower volatility.
Quantify the Fundamental Elements: Used an F-score model for the company fundamentals and then selected companies with highest momentum
Quant Squad Capital: Investigated event-driven trading for sports betting companies around big sporting events.
Axe Capital: Focused on momentum trading and developed a model based on exponential moving averages.
The E Screen: Interested in whether environmental performance (operationalized as a company’s reduction in greenhouse gas emissions) helped predict stock price.
Given that the teams only had one semester to learn about quant investing and develop models they all had recommendations for future research, such as: including other variables, examining other time frames, gaining a better understanding of entry and exit points or position sizing, and examining the effect of more frequent trading.
Thanks to all the students for their hard work during the semester.
Rodney Lake