A lack of portfolio diversification can severely impact the financial goals and
long term plans for individual retirement accounts, University Endowment funds,
and Municipal Pension funds alike. And as financial market tumult surrounding CoVid-19 has
revealed, many investors are often not as diversified as they initially believe.
While advisors are adept at packaging various financial products into
"diversified portfolios", relationships among various asset classes can change
suddenly during times of market crisis, upending the very diversification
an otherwise meticulous crafted portfolio was built to provide. Why does
diversification fail for so many precisely when it's most needed and what can be done?
In this project, you shall explore and implement several potential solutions for
enhancing portfolio diversification with R, as discussed in one of the best
investment references of the recent decade,
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 PerformanceAnalytics and PortfolioAnalytics,
but you will also need to write functions of your own to streamline workflows
and implement solutions.
Mentors will guide your understanding of the topic, support your use of best
practices in software development for quantitative finance using R, and
provide current professional grade market data for validating these approaches
in the midst of the current crisis.
Ultimately, your 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 deep understanding of:
i) Data Science applications in finance
ii) Quantitative Analysis of active portfolio management
We'll focus on three broad sections with specific subsection to explore
-
Approaches to Dynamic asset weighting
- Value-oriented equity selection
- Currency Carry
- Commodity Momentum and Trend following
-
Return Factors and their risk premia
- Inflation factor and inflation premium
- Liquidity factor and illiquidity premium
- Tail risks (volatility, correlation, skewness)
-
Time-Varying Expected Returns
- Endogenous return and risk: Feedback effects on expected returns
- Tactical return forecasting models
- Cyclical variation in asset returns
Most underlying data series are extracted from Bloomberg, including MSCI Barra’s equity indices, Barclays Capital and other banks’ bond indices, and S&P GSCI commodity futures indices. Other key sources include Kenneth French’s, Robert Shiller’s, and AQR's websites, as well as Arnott–Bernstein (2002), Dimson–Marsh–Staunton (2002, 2010), and Ibbotson Associates (now Morningstar) yearbooks.
To create long data histories for major asset classes, the author concatenated best quality recent data and best available older data series. Most exhibits display total returns denominated in U.S. dollars; but some exhibits show real (inflation-adjusted) returns or excess returns over cash or over maturity/duration-matched Treasuries.
Value-oriented equity selection, chapter 12.
On sector neutrality, style timing, and other refinements, see Asness et al. (2000), Barberis–Shleifer (2003), Cohen–Polk–Vuolteenraho (2003), Qian–Hua–Sorensen (2007), Lancetti–Nordquist (2008), Phalippou (2008), and Mezrich (2010).
On the link to distress and credit risk, see Campbell–Hilscher–Szilagyi (2008) and Avramov et al. (2010).
On value strategy’s success outside equity selection, see Arnott–Hsu–West (2008), Asness–Moskowitz–Pedersen (2009), and Blitz–van Vliet (2009).
Currency carry strategies, Chapter 13.
The academic literature on currency carry had already begun by around 1980 but the renaissance occurred in recent years with notable papers by Lustig–Verdelhan (2007), Brunnermeier–Nagel–Pedersen (2008), Farhi et al. (2009), Jurek (2009), and Burnside et al. (2010a).
Among practitioner work, see Bilson (1993) and Nordvig (2007).
On other predictors than carry, see Ilmanen–Sayood (2002), Yilmaz (2009), and Ang–Chen (2010).
On carry strategies in various asset classes, see Cochrane (1999), Asness–Moskowitz–Pedersen (2009), and Bacchetta–Mertens–van Wincoop (2009).
Commodity Momentum and trend following, Chapter 14.
Many authors have in recent years analyzed commodity momentum strategies: Erb–Harvey (2006), Ribeiro–Normand–Loeys (2006), Gorton–Hayashi–Rouwenhorst (2007), Miffre–Rallis (2007), Fuertes–Miffre–Rallis (2010), and Shen–Szakmary–Sharma (2010).
For a broad survey, see Schneeweis–Kazemi–Spurgin (2008).
On momentum or trend-following strategies in broader contexts, see Asness–Liew–Stevens (1997), Griffin–Ji–Martin (2005), Bhojraj–Swaminathan (2006), Ribeiro–Loeys (2006), Blitz–van Vliet (2008), Pukthuanthong–Levich–Thomas (2007), Asness–Moskowitz–Pedersen (2009), and Moskowitz–Ooi–Pedersen (2010). The last two studies also review explanations of why these strategies work so well.
On lead–lag relations across economically related firms or countries, see Cohen–Frazzini (2008) and Rizova (2010).
Inflation factor and inflation premium, Chapter 17
For a historical perspective on the inflation factor, see Ferguson (2007), Greenspan (2007), and Reinhart–Rogoff (2008, 2009).
On the economic impact of inflation, see Barro (1995).
On the relation between inflation and equity returns, see Fama–Schwert (1979), Modigliani–Cohn (1979), Boudoukh–Richardson (1993), Campbell–Vuolteenaho (2004b), Piazzesi–Schneider (2007), Bekaert–Engstrom (2010), and Lee (2009).
On different assets’ inflation-hedging abilities at various horizons, see Normand (2006), Amenc–Martellini–Ziemann (2009), Briere–Signori (2009), Bekaert–Wang (2010), and Martin (2010).
Liquidity factor and illiquidity premium, Chatper 18.
Given the growing interest on liquidity, an excellent survey by Amihud–Mendelson–Pedersen (2005) is gradually becoming outdated.
On the relation of liquidity premia with business and monetary cycles, see Jensen–Moorman (2010) and Naes–Skjeltorp–Odegaard (2011).
On time-varying liquidity premia, see Watanabe–Watanabe (2008).
On market timing using liquidity indicators, see Guo et al. (2010).
On the profits of liquidity provision strategies, see Nagel (2009) and Rinne–Suominen (2010).
On liquidity droughts, see Brunnermeier (2009), Brunnermeier–Pedersen (2009), Garleanu–Pedersen (2009a), Nagel (2009), Pedersen (2009), and Duffie (2010).
Tail risks (volatility, correlation, skewness), Chapter 19
On the time series relation between volatility and future market returns, see French–Schwert–Stambaugh (1987), Glosten–Jagannathan–Runkle (1993), Whitelaw (1994), Ghysels–Santa-Clara–Valkanov(2005), and Bollerslev–Zhou (2006).
On leverage constraints and the relative performance of low-beta and high-beta assets, see Black (1972), Baker–Bradley–Wurgler (2010), and Frazzini–Pedersen (2010).
On correlation-related patterns, see Deng (2007), Driessen–Maenhout–Vilkov (2009), Krishnan–Petkova–Ritchken (2008), Pollet–Wilson (2008) for equity markets and Bhansali–Gingrich–Longstaff (2008), Longstaff–Rajan (2008), Coval–Jurek–Stafford (2009) for debt markets.
On hedge fund sensitivities to higher moments, see Bondarenko (2004), Agarwal–Bakshi–Huij (2007), Lo (2008), and Buraschi–Kosowski–Trojani (2010).
Endogenous return and risk: Feedback effects on expected returns, Chapter 20
On feedback effects that create endogenous return and risk, see Shiller (2000), Lo (2004), Soros (2008), Brunnermeier–Nagel–Pedersen (2008), Brunnermeier–Pedersen (2009), Danielsson–Shin–Zigrand (2009), Geanakoplos (2009), Summers (2009), and Shin (2010).
On crowded trades, see Perold–Sharpe (1988), Khandani–Lo (2007), Pedersen (2009), and Stein (2009).
On short-term momentum and long-term reversals, see Cutler–Poterba–Summers (1991) and Ghayur et al. (2010).
Tactical return forecasting models, Chapter 24:
For examples of simple forecasting models in the bond market context, see Ilmanen (1997), Ilmanen–Sayood (2002), and Naik–Balakrishnan–Devarajan (2009).
For an example of fair value models, see Panigirtzoglou–Loeys (2005).
For books on quantitative forecasting models and trading approaches, all with equity orientation, see Grinold–Kahn (1999), Qian–Hua–Sorensen (2007), and Fabozzi–Focardi–Kolm (2010).
On factor-mimicking portfolios, see Melas–Suryanarayanan–Cavaglia (2010).
On econometric issues see Campbell–Lo–McKinlay (1996) and Cochrane (2005a).
Cyclical variation in asset returns, Chatper 26.
For examples of similar business cycle analysis, see Naik–Devarajan (2009) and Lustig–Verdelhan (2010). See Kaya–Lee–Pornrojnangkool (2010) and Ang–Bekaert (2002) for applications of regime-switching models.
- Get familiar with sections of the text above
- Work with the GSoC mentor(s) to lay out the script for each section
- Gather data related to the project, which your GSoC mentors can access.
- Write
Rscripts to gather, format, and save public data used in the text from Kenneth French’s, Robert Shiller’s, and AQR's websites - Reproduce key sections using existing
Rpackages & write custom functions where needed - Organize your work as an
Rpackage, in collaboration with Mentors - Complete minimal function and data documentation using roxygen2
- Create short vignettes using
Rmarkdown to share your code and findings.
- EVALUATING MENTOR Prof. Justin M. Shea
- Prof. Brian Peterson
- Erol Biceroglu, Senior Investment Policy Analyst
- Peter Carl, Portfolio Manager
- Soumya Kalra, Senior Quantitative Risk Specialist
Vito Lestingi, MSc Finance and Insurance - Quantitative Finance track, Sapienza University of Rome.
Ilmanen, Anti. 2011. “Expected Returns.” John Wiley & Sons Ltd. ISBN: 978-1-119-99072-7