As witnessed by recent events, the lack of portfolio diversification and risk control can severely impact the financial goals and long term plans for individual investment accounts, retirement accounts, University Endowment funds, and Municipal Pension funds alike. And as recent examples of speculative booms and busts have revealed (GameStop), investors sometime exhibit a lack of diversification among their securities holdings, and investments are not as safe as they initially seem.
In this project, you shall explore and implement several investment strategies in R
, as inspired by one of the most interesting investment references on the topic
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.
Your objective will be to reproduce key approaches suggested by the text and test
performance on current market conditions with R
. You will use functions found
in popular R
in finance packages such as FactorAnalytics
,
PerformanceAnalytics
and PortfolioAnalytics
. But you will also need to write
functions of your own to streamline workflows and implement solutions. While
these packages are excellent and widely used, there are gaps in the workflows
involved in constructing portfolio management strategies we'd like to fill.
Expected Returns: FactorAnalyticss
project & ImpactMentors will guide your understanding of the topic, support your use of best
practices in software development for quantitative finance using R
, and
provide market data for validating these approaches.
Ultimately, this work will be organized into an open source R
package. It will
complement the text and provide data, functions, and reproducible examples to
guide academics, practitioners, and hobbyists in the R
community in applying
the work to their own research or portfolio management endeavors.
Students engaged in this project will obtain a deeper understanding of:
i) Data Science applications in finance
ii) Quantitative Analysis of active portfolio management
We'll focus on these specific subsections to explore
Fama, E. F.; French, K. R. (1993). Common risk factors in the returns on stocks and bonds
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
FactorAnalytics
R package functions.tinytest
R packageneverhpfilter
, wooldridge
, and phoenixdown
R packages. Contributor
to PerformanceAnalytics
and FactorAnalytics
packages. This will be his 3rd
year mentoring at GSoC.Students, please do one or more of the following tests before contacting the mentors above. We encourage work on Linux Debian-based distributions.
ExpectedReturns
and
FactorAnalytics
packages locally. Work through, and list any build errors or issues you encounter on install.library(remotes) install_github("JustinMShea/ExpectedReturns") install_github("braverock/FactorAnalytics")
Intermediate: Locate the expected-returns-replications.Rmd
file in the vignettes
directory. Refactor sections of this vignettes to replace functions
from the plm
package with the fitFfm
or fitFfmDT
functions associated with
the FactorAnalytics
package. This may include debugging upstream issues with
merging data series, as well as reformatting data to match requirements of the
new function arguments.
Harder: Reflect on the steps above. How do you interpret the results of the new functions? 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. What data transformations or models might have benefited from writing unit tests? Please include examples for these as well.
Students, please post a link to your test results here.
Ilmanen, Anti. 2011. “Expected Returns.” John Wiley & Sons Ltd. ISBN: 978-1-119-99072-7
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