Investing Expertise

What should investors expect to see in a typical investment advisor’s model portfolio? In their July 2019 paper entitled “Factors and Advisors Portfolios”, Brian Lawler, Andrew Ang, Brett Mossman and Patrick Nolan examine patterns and factor exposures in detailed holdings for a large number of model portfolios from many types of investment advisors. When holdings are funds, they examine contents of the funds. They assess exposures to economic growth, real interest rates and inflation. Within equity holdings, they assess exposures to size, value, momentum, quality and volatility factors. Using holdings of 9,940 model portfolios provided by investment advisors during October 2017 through September 2018, they find that: Keep Reading

Would retail investors improve portfolio performance by using robo-advisors to manage holdings they have selected? In their July 2019 paper entitled “Artificial Intelligence Alter Egos:Who benefits from Robo-investing?”, Catherine D’Hondt, Rudy De Winne, Eric Ghysels and Steve Raymond compare performances of portfolios held by each of a large sample of actual individual investors to that of a robo-investor constrained to the stocks and exchange-traded funds (ETF) held by that investor over a rolling 2-year historical window. They consider three robo-investor strategies:

1. Mean-variance optimization with guiding average and variance estimates based straightforwardly on 2-year rolling historical windows and parameters set to maximize Sharpe ratio.
2. Mean-variance optimization guided by machine learning algorithms and sophisticated covariance estimators, with two variations in variance estimation.
3. Equal weight.

Robo-investors may hold cash, but they may not sell short, with focus on quarterly portfolio rebalancing. They measure portfolio performance monthly and exclude trading frictions. Using common stock/exchange-traded fund (ETF) trading records for 20,622 individual Belgian brokerage accounts during January 2003 through March 2012, they find that:

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Do short-term trade ideas of professional stock analysts have merit? In their July 2019 paper entitled “Are Analyst Trade Ideas Valuable?”, Justin Birru, Sinan Gokkaya, Xi Liu and René Stulz examine the price impact of analyst trade ideas, which differ from stock ratings in that trade ideas:

1. Have horizons of only one week to three months.
2. Reflect expected short-term price changes in response to upcoming news (firm catalysts) or short-term overreaction/underreaction to recent news (temporary mispricing).
3. May be opposite in direction from the analyst’s rating on a stock.
4. Are typically issued on days with no firm news and no other analyst reports.

They estimate abnormal returns of trade ideas based on an equally weighted portfolios of stocks with similar size, book-to-market ratio and momentum characteristics. For trade idea buy and sell portfolios, they add a new stock at the close on the trading day after idea announcement and rebalance the portfolio on any day a stock is added or removed. Using a manually constructed sample of 4,167 buy ideas and 367 sell ideas from 688 analysts at 77 brokers involving 1,619 unique stocks during 2000 through 2015, they find that: Keep Reading

Did Bill Gross, the Bond King, generate significantly positive alpha during his May 1987 through September 2014 tenure as manager of PIMCO Total Return Fund (Fund)? In their March 2019 paper entitled “Bill Gross’ Alpha: The King Versus the Oracle”, Richard Dewey and Aaron Brown investigate whether Bill Gross generates excess average return after adjusting for market exposures over this tenure. They further compare evaluation of bond market alpha for Bill Gross to evaluation of equity market alpha for Warren Buffett. Following the explanation given by Bill Gross for his outperformance, their factor model of Fund returns includes three long-only market factors: interest rate (Merrill Lynch 10-year Treasury Index), credit (Barclays U.S. Credit Index) and mortgage (Barclays U.S. MBS Index). It also includes a fourth factor that is long U.S. Treasury 5-year notes and short U.S. Treasury 30-year bonds, with weights set to eliminate coupon and roll-down effects of their different durations. Using monthly returns for the Fund and the four model factors, and monthly 1-month U.S. Treasury bill yield as the risk-free rate during June 1987 (first full month of the Fund) through September 2014 (when Gross left the Fund), they find that: Keep Reading

Given the uniqueness of fine art objects and uncertainties in demand (at auctions), can investors in paintings get accurate estimates of market values of holdings and potential acquisitions? In their March 2019 paper entitled “Machines and Masterpieces: Predicting Prices in the Art Auction Market”, Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers compares accuracies of value estimates for paintings based on: (1) a linear hedonic regression (factor model), (2) neural network software and (3) auction houses. For the first two, they employ 985,188 auctions of paintings during 2008–2014 for in-sample training and 104,404 auctions of paintings during the first half of 2015 for out-of-sample testing. Neural network software inputs include information about artists and paintings (year of creation, materials, size, title and markings), and images of the paintings. Using information about artists/paintings and images and auction house estimates and sales prices for the specified 1,089,592 paintings by about 125,000 artists offered through 372 auction houses during January 2008 through June 2015, they find that:

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What are common cautions regarding exploitation of academic and practitioner papers on financial markets? To investigate, we collect, collate and summarize our cautions on findings from papers reviewed over the past year. These papers are survivors of screening for relevance to investors of a much larger number of papers, mostly from the Financial Economics Network (FEN) Subject Matter eJournals and Journal of Economic Literature (JEL) Code G1 sections of the Social Sciences Research Network (SSRN). Based on review of cautions in 109 summaries of papers relevant to investors posted during mid-March 2018 through mid-March 2019, we conclude that: Keep Reading

Should investors trust academic equity factor research? In their February 2019 paper entitled “A Census of the Factor Zoo”, Campbell Harvey and Yan Liu announce a comprehensive database of hundreds of equity factors from top academic journals and working papers through January 2019, including a link to citation and download information. They distinguish among six types of common factors and five types of firm characteristic-based factors. They also explore incentives for factor discovery and reasons why many factors are lucky findings that exaggerate expectations and disappoint in live trading. Finally, they announce a project that allows researchers to add published and working papers to the database. Based on their census of published factors and analysis of implications, they conclude that: Keep Reading

Do retail investors rationally account for risks as modeled in academic research when choosing actively managed equity mutual funds? In their March 2019 paper entitled “What Do Mutual Fund Investors Really Care About?”, Itzhak Ben-David, Jiacui Li, Andrea Rossi and Yang Song investigate whether simple, well-known signals explain active mutual fund investor behavior better than academic asset pricing models. Specifically, they compare abilities of Morningstar’s star ratings and recent returns versus formal pricing models to predict net fund flows. They consider the Capital Asset Pricing Model (CAPM) and alphas calculated with 1-factor (or market-adjusted), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) models of stock returns. They consider degree of agreement between signals for a fund (such as number of Morningstar stars and sign of a factor model alpha) and the sign of net capital flow for that fund. They also analyze spreads between net flows to top and bottom funds ranked according to Morningstar stars and fund alphas, taking the number of 5-star and 1-star funds to determine the number of top-ranked and bottom-ranked funds, respectively. Using monthly returns and Morningstar ratings for 3,432 actively managed U.S. equity mutual funds and contemporaneous market, size, book-to-market and momentum factor returns during January 1991 through December 2011 (to match prior research), they find that:

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Have long-short alternative beta (style premium) strategies worked well in practice? In their February 2019 paper entitled “A Decade of Alternative Beta”, Antti Suhonen and Matthias Lennkh use actual performance data to assess alternative beta strategies across asset classes from the end of 2007 through the end of 2017, including quantification of fees and potential survivorship bias in public data. Specifically, they form three equal volatility weighted (risk parity) composite portfolios of strategies at the ends of each year during 2007-2016, 2007-2011 and 2012-2016. Each portfolio includes all the strategies launched during the first year and then adds strategies launched each following year at the end of that year. When a strategy dies (is discontinued by the offeror), they reallocate its weight to surviving strategies within the portfolio. They also create two additional portfolios for each period/subperiod that segregate equities and non-equities. They further evaluate alternative beta strategy diversification benefits by comparing them to conventional asset class portfolios. Using weekly post-launch excess returns in U.S. dollars for 349 reasonably unique live and dead alternative beta strategies offered by 17 global investment banks, spanning 14 styles and having at least one year of history during 2008 through 2017, they find that:

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What are potential monthly returns and alphas from applying machine learning to pick stocks? In their February 2019 paper entitled “Machine Learning for Stock Selection”, Keywan Rasekhschaffe and Robert Jones summarize basic concepts of machine leaning and apply them to select stocks from U.S. and non-U.S. samples, focusing on the cross-section of returns (as in equity factor studies). To alleviate overfitting in an environment with low signal-to-noise ratios, they highlight use of: (1) data feature engineering, and (2) combining outputs from different machine learning algorithms and training sets. Feature engineering applies market/machine learning knowledge to select the forecast variable, algorithms likely to be effective, training sets likely to be informative, factors likely to be informative and factor standardization approach. Their example employs an initial 10-year training period and then walks forecasts forward monthly (as in most equity factor research) for each stock, as follows:

• Employ 194 firm/stock input variables.
• Use three rolling training sets (last 12 months, same calendar month last 10 years and bottom half of performance last 10 years), separately for U.S. and non-U.S. samples.
• Apply four machine learning algorithms, generating 12 signals (three training sets times four algorithms) for each stock each month, plus a composite signal based on percentile rankings of the 12 signals.
• Rank stocks into tenths (deciles) based on each signal, which forecasts probability of next-month outperformance/underperformance.
• Form two hedge portfolios that are long the decile of stocks with the highest expected performance and short the decile with the lowest, one equal-weighted and one risk-weighted (inverse volatility over the past 100 trading days), with a 2-day lag between forecast and portfolio reformation to accommodate execution.
• Calculate gross and net average excess (relative to U.S. Treasury bill yield) returns and 4-factor (market, size, book-to-market, momentum) alphas for the portfolios. To estimate net performance, they assume 0.3% round trip trading frictions. 

They consider two benchmark portfolios that pick long and short side using non-machine learning methods. Using a broad sample of small, medium and large stocks (average 5,907 per month) spanning 22 developed markets, and contemporaneous values for the 194 input variables, during January 1994 through December 2016, they find that: Keep Reading