Backtesting Investment Strategies with MATLAB
Michael Robbins, Columbia University
Malin Ortenblad, Columbia University
Yao Shang, Columbia University
Learn how to define investment strategies and leverage the Backtesting framework to run backtests, analyze and compare results, and generate performance metrics for your strategies from historical or simulated data.
- Integration of other technologies seamlessly with the Backtesting framework
- Integration of ESG factors into an investment strategy
- Integration of GTAA factors into an investment strategy
- Integration of Brinson attribution into an investment strategy
- Complex path-dependent costs and fees and their effect on incentives and performance
- Agent-based borrowing and lending and the effect on cash drag and forced liquidations
- Significant modifications to the backtesting framework code
- Backtest and compare multiple investment strategies
- Visualize the results and leverage performance metrics to pick a strategy
About the Presenters
Alex Roumi joined MathWorks in February 2020. His focus is on computational finance projects. Prior to MathWorks, Alex worked on the electrical design of airport buildings and airfield lighting of runways taxiways and aprons, including Dubai airport.
Michael Robbins has been the Chief Investment Officer of five large investment firms, including a bank with 8½ million clients. He has managed pensions, endowments, family offices and was the Chief Risk Officer for the State of Utah’s systems. He is a professor at Columbia University where he teaches quantitative investing, including graduate classes in Global Macroeconomic Tactical Asset Allocation (GTAA) and Environmental, Social, and Governance (ESG) investing. Michael specializes in governance, asset allocation, and manager search and selection. Look for Michael’s new book, Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing, in January of 2022. Published by McGraw-Hill.
Malin Ortenblad led a team of graduate students that used machine learning to study a European hedge fund in Michael’s Environmental, Social, and Governance (ESG) course at Columbia University. She then helped manage dozens of students for Michael’s Global Tactical Asset Allocation (GTAA) course as a CA. In December of 2020, she graduated from Columbia University with a Master’s in Business Analytics. Malin has expertise in healthcare strategy consulting including machine learning during a cancer research project for Frederick National Laboratories, a hospital in the National Institutes of Health and Cancer Research Institutes systems. She is now working as a consultant in healthcare.
Yao Shang is a CA in Michael’s external mandates course where she has researched performance attribution, including the modeling of complex path-dependent cost and fee structures and their effect on manager incentivization and performance, and highly realistic backtesting, including the implementation of agent-based borrowing and lending to simulate the effects of cash drag and forced liquidations. She has worked with wealth managers at Morgan Stanley using deep learning techniques and holds several degrees from Rensselaer Polytechnic Institute. She will receive a Master’s in Operations Research from Columbia University in May of 2021.
Recorded: 16 Mar 2021
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