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ExpectedReturns Factor Creation and Feature Engineering
In this project, you shall reproduce work related to concepts referenced in Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.
From the Description;
This comprehensive reference delivers a toolkit for harvesting market rewards from a wide range of investments. Written by a world-renowned industry expert, the reference discusses how to forecast returns under different parameters. Expected returns of major asset classes, investment strategies, and the effects of underlying risk factors such as growth, inflation, liquidity, and different risk perspectives, are also explained. Judging expected returns requires balancing historical returns with both theoretical considerations and current market conditions. Expected Returns provides extensive empirical evidence, surveys of risk-based and behavioral theories, and practical insights.
You will use some functions found in popular R in finance packages such as FactorAnalytics and
PerformanceAnalytics. While extremely helpful, these packages do not completely construct factors, variables, or features
which is the aim of this project. Making this work open source stands to help a large cross section of those working in the industry as well as hobbyists who want to gain a deeper understanding of these methods.
This will be a series of functions to create a factor analysis framework researchers can use to input data, and construct any factor they wish.
Mentors will guide your understanding of the topic, support the learning of good
practices in software development for quantitative finance using R, and
provide quality market data for testing & validating these approaches.
Students engaged in this project will obtain a deeper understanding of:
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- Developing R packages
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- Creating variables for statistical or machine learning analysis
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- Factor Analysis & active portfolio management
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- How researchers can use a factor analysis framework to input data, and construct any factor they wish.
Open source cross-sectional asset pricing repository
Replication Process in Finance
Hou, Kewei and Mo, Haitao and Xue, Chen and Zhang, Lu (2016). Which Factors?
Value-oriented equity selection, chapter 12.
Asness, Clifford and Frazzini, Andrea (2012). The devil in HML's details
Asness, Clifford S. and Moskowitz, Tobias J. and Pedersen, Lasse Heje (2013). Value and momentum everywhere
Commodity Momentum and trend following, Chapter 14.
Moskowitz, Tobias J and Ooi, Yao Hua and Pedersen, Lasse Heje (2012). Time Series Momentum
Balts, Kosowski (2012). Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations
Balts, Kosowski (2013). Momentum Strategies in Futures Marketsand Trend-Following Funds
Ari Levine, Yao Hua Ooi, Matthew P. Richardson, Caroline Sasseville (2016). Commodities for the Long Run
...and more!
- Read the texts referenced above
- Get familiar with the ExpectedReturns project.
- Create factor constructor functions for all papers listed above, ie feature engineering in ML parlance.
- Add Unit tests using the
tinytestR package, throughout the course of creating and testing your functions.
- EVALUATING MENTOR Justin M. Shea, Executive Director Finance Honors track & Assistant Prof
author of
neverhpfilter,wooldridge, andphoenixdownR packages. Contributor toPerformanceAnalyticsandFactorAnalyticspackages. [email protected] - Brian Peterson has developed some of the most popular R packages for quantitative finance, and has been a GSOC administrator from 2008-2022.
- Bryan Rodriguez, Quantitative Analyst - WF
- Erol Biceroglu, Manager Investment Policy - RBC Global Asset Management
Firstly, please reach out to mentors directly with questions. We would love to chat with you and gauge your interest in the project.
Next, please do one or more of the following tests before contacting the mentors above. We encourage work on Linux Debian-based distributions.
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Pre-req: Please show show us a Github link, .R, .Rmd, or similar files which demonstrate an R project you've completed.
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Easy: Begin by downloading and building the
ExpectedReturnsandFactorAnalyticspackages locally. List any build errors or issues you encounter on install, and see if you can work through those and get the package to build.
library(remotes)
install_github("JustinMShea/ExpectedReturns")
install_github("braverock/FactorAnalytics")
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Intermediate: Check the files in the
vignettesdirectory and find one that doesn't build and identify bugs. Message the authors privately with issues you would open (don't post this in public). -
Harder: Reflect on the steps above. How do you interpret the statistical estimates of the vignettes that are working for you? In addition, was there any repetitious code in the vignette that may be written as a function for future use? If so please include it as an example.
Students, please post a link to your test results here.