Experiential Learning Project (ELP): Building Tools of Portfolio Monitoring, Performance Attribution and Risk Management
Client: Smith Investment Fund (SIF) at the University of Maryland
Course Prerequisites:
- Investment or Capital Market (with good academic performance)
- Financial data analytics (such as regression, cluster analysis, time series analysis)
- Good skills on programming in R or Python
- Excellent communication skills
Process to Select Students:
- Open to qualified MF and MQF students
- A short cover letter explains your background and why you are interested in this project
- Most recent CV, transcript, and relevant course grades
Project Overview:
Students taking this ELP will have hands-on experience to build equity market monitoring and portfolio risk management tools for the University of Maryland’s Premier Investment Group. Skills learned in this project are exactly what have been used daily in money management industry (such as mutual funds, hedge funds and pension funds). These skills will help students typically land on portfolio risk management and financial analytics positions in the financial services industry.
The UMD SIF is supported by the Robert H. Smith School of Business Foundation, Inc. Currently, there are three student investment funds: Mayer Fund, Senbet Fund, and Global Equity Fund. The SIF investment philosophy builds on a top-down strategy to evaluate a company’s securities for potential investment by conducting economic, financial, industry and company analyses. Building portfolio monitoring and risk analysis tools are important for the success and sustainability of the student investment funds.
The focus of this project is to get students familiar with scientific processes used in portfolio management industry, including factor portfolio monitoring, portfolio performance attribution, and portfolio risk analysis. We will construct equity quant monitoring tools such as market statistics of cross-sectional dispersion and pairwise correlation, factor performance and trend, factor valuation spreads, cross-sectional rank correlations among factors, and factor macro risk profiles. By using the SIF portfolio historical return and holdings data (along with the benchmark S&P 500 index and constituent data), students can perform portfolio attribution analysis to decompose portfolio returns into allocations (factor bets) and selections (stock picking). Students will also learn how to use industry-standard Barra or Axioma risk models to understand portfolio risk and present analyses to clients or investment consultants.
Skills Learned from this ELP:
- Master the holistic process of analyzing equity portfolio in practice from data sources, data inputs of firm-level fundamentals, sectors/industries and macro factors, iterations in statistical analyses, and dynamic visualization in results.
- Extract, manage and manipulate a large cross-section of equity market data.
- Demonstrate ability to perform data analysis and visualize results with R or Python language.
- Ability to understand portfolio positions, performance and risk bets. Opportunities to communicate, interpret, and present results clearly to clients (such as portfolio managers, analysts, investment consultants).
Week 1 will be introduction of the portfolio management process used in industry. Weeks 2-3 will be used to develop state-of-the-art market monitoring tools. Week 4-5 will be used to develop portfolio attribution system to examine portfolio performance. Week 6-7 will be used to develop a min version of Barra or Axioma risk analytics. Week 8 (during finals week) will be the presentation to our client (SIF representatives).
This ELP is not like a standard MFin/MQF course where each week a lecture is made, but rather the role of the faculty advisor is to provide guidance to the team and to solve problems that may arise throughout the project. The students “own” the project including project management, making team assignments and adhering to scheduled deliverables. Elevating issues early to the faculty advisor will ensure timely attention to resolving any issues that arise. Students will be expected to speak and interact during our sessions.