MFM Modeling Workshop Presentations - Machine Learning in Equity Classification and Smart Beta Investing in Commodities

2019 FM Modeling Workshop Student Presentations
University of Minnesota
Friday, March 29, 2019 - 5:30pm to 6:30pm
Vincent Hall 16

Two Teams of MFM students from the 2019 MFM Modeling Workshop will present their work to the Seminar. Each group will take a half hour to cover their topics, including Q & A

Machine Learning in Equity Classification: This MFM modeling workshop team worked with various machine learning classification models with the goal of classifying equities via well-known quantitative factors such as Value and Momentum. The classification was supervised, utilizing a novel ETF dataset which was supplemented extensively. The team worked in Python, especially the Scikit Learn module. They will present their project to seminar attendees

Smart Beta Investing in Commodities: Ever since the first stocks and bonds were issued by the Dutch East India Company (VOC), investors have tried to understand what drives returns. Smart Beta strategies have gained popularity lately by offering the potential for better-than-market returns with better-defined risks, especially after the recognition in 2008 that multi-asset classes can experience severe losses at the same time despite their apparent intrinsic differences. Smart beta strategies can take many different forms, with a variety of objectives. They can simply aim at reducing risks (the “risk-based approach”) or enhancing return through exposure to systematic factors (the “factor-based” approach). In commodities investing, alternative index movement was born from frustration with the inherent biases of conventional indices. For example, negative commodity “roll yields” can erode returns by as much as 50%. This team explored the opportunities of constructing a commodity investment portfolio that uses different smart beta approaches to seek enhancing returns and risk reduction. Factors like curve, value, and momentum were examined in the back-test.