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AQR's Momentum Funds (A) 9-211-025 R E V : M A R C H 2 9 , 2 0 1 2 ________________________________________________________________________________________________________________ Professors Daniel Bergstresser (HBS), Lauren Cohen (HBS), Randolph Cohen (MIT Sloan School of Management) and Christopher Malloy (HBS) prepared this case. HBS cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. Copyright © 2010, 2012 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545- 7685, write Harvard Business School Publishing, Boston, MA 02163, or go to www.hbsp.harvard.edu/educators. This publication may not be digitized, photocopied, or otherwise reproduced, posted, or transmitted, without the permission of Harvard Business School. D A N I E L B E R G S T R E S S E R L A U R E N C O H E N R A N D O L P H C O H E N C H R I S T O P H E R M A L L O Y AQR's Momentum Funds (A) In early 2009, after significant research and reflection, Cliff Asness, founder and principal at AQR, was considering the launch of three new retail mutual funds that would offer investors exposure to ‘Momentum,’ a new investment style. While momentum strategies were commonplace among hedge funds, the new AQR funds would become the first retail funds to focus on this strategy. The Momentum Strategy AQR defined stock momentum as ‘the phenomenon that stocks which have performed well in the past relative to other stocks (winners) continued to perform well in the future, and stocks that have performed relatively poorly (losers) continue to perform poorly.’ This relative performance was a key component of momentum, as it implied existence and ability to implement the strategy irrespective of up or down markets. The first academic paper demonstrating the high returns associated with the momentum strategy was a 1993 publication by Narasimhan Jegadeesh and Sheridan Titman. Among the most important extensions of this work were Clifford Asness’s 1994 paper showing the robust profitability of momentum investing strategies, and a paper by Mark Grinblatt and Tobias Moskowitz in 1999 demonstrating the role that industry affiliation played in momentum’s performance.1 Asness was a founding partner at AQR, and Moskowitz, a professor at the University of Chicago, served as a consultant at AQR. Since the original academic work, hundreds of papers had been published on momentum. While explanations for the phenomenon differed, there was widespread agreement about its existence and pervasiveness. In particular, the momentum phenomenon had also been found to exist in international equity markets and across various asset classes. 1 See Jegadeesh, Narasimhan, and Sheridan Titman, “Returns to buying winners and selling losers: implications for stock market efficiency,” Journal of Finance, 48, 1993; Asness, Clifford S., "The power of past stock returns to explain future stock returns," Goldman Sachs Asset Management, 1994; Grinblatt, Mark, and Tobias Moskowitz, "Do industries explain momentum?" Journal of Finance, 54, 1999. For the exclusive use of D. Ghosh, 2020. This document is authorized for use only by Devleena Ghosh in Behavioral Corporate Finance (Summer 2020) taught by DUCCIO MARTELLI, Universit?? degli Studi di Perugia from Jun 2020 to Aug 2020. 211-025 AQR's Momentum Funds (A) 2 Some argued that momentum investors experienced outsized average returns as compensation for bearing undiversifiable risk. Evidence in support of this explanation was the fact that high- momentum stocks had a strong tendency to move together, making it difficult to obtain momentum profits without exposing oneself to the possibility of substantial loss. Others argued that momentum profits were caused by cognitive errors and irrationalities in the market. One set of theories saw momentum as a result of overreaction to news – e.g., a company made a positive announcement, leading to a price increase, which then caused additional buying in the form of an irrational bandwagon effect. This effect was driven by investors who mistakenly expected that good news should be followed by more good news. This follow-on buying would create a momentum effect. A second set of behavioral theories took the opposite approach, explaining momentum as a consequence of underreaction, or slow reaction to news. For example, consider an announcement of news that, in a rational world, would merit a 10% increase in the stock price. Holders of the underreaction view would argue that it is common for the stock to increase, say, 6% immediately, and then have the remaining 4% price growth over the subsequent year. The momentum effect would be driven by this “drift” resulting from the information slowly leaking into prices. While the debate raged on in the halls of academia as to momentum’s cause, investment vehicles were designed by a variety of firms in an attempt to profit from the strategy. In general this opportunity was only available to deep-pocketed institutional investors. Hedge funds frequently included momentum among their strategies, and institutional equity funds often had a momentum tilt to their portfolios. AQR wondered whether momentum would also be an attractive strategy if offered to retail investors. Empirical evidence on momentum There was ample empirical evidence on price momentum in equities. The most common such strategies followed Jegadeesh and Titman in using a prior or “backtest” period to define good performing (winner) stocks and poorly performing (loser) stocks, and then formed a portfolio that bought winner stocks and sold loser stocks, and held this portfolio into the future. Although there were many variations of this strategy, including changing the past time window over which to define winners and losers, or altering the horizon of the holding period after portfolios were defined, a very common formulation was based on a pre-ranking period of roughly past year returns, and then a future holding period of 1 month. Specifically, at the beginning of each month all firms were assigned to one of ten portfolios ranked according to past year returns (skipping the prior month).2 Stocks in the lowest decile of past returns (decile 1) were assigned to the loser portfolio of stocks, while stocks in the highest decile of past returns (decile 10) were assigned to the winner portfolio. Momentum returns were then computed as the returns to the long-short portfolio that went long (decile 10) winner stocks and short (decile 1) loser stocks over the following month. At the end of every month, stocks were re-ranked, and a new portfolio of (winner stocks - loser stocks) was formed and held during the next month. 2 It was common to skip the prior month (t-1) when computing past year returns (i.e., to use t-12 to t-2 returns rather than t-12 to t-1 returns), because of evidence in Jegadeesh (1990) that stocks with high returns last month tend to reverse in the following month. See Jegadeesh, Narasimhan, “Evidence of predictable behavior in security markets," Journal of Finance, 45, 1990 and Asness, Clifford S., "The power of past stock returns to explain future stock returns," Goldman Sachs Asset Management, 1994. For the exclusive use of D. Ghosh, 2020. This document is authorized for use only by Devleena Ghosh in Behavioral Corporate Finance (Summer 2020) taught by DUCCIO MARTELLI, Universit?? degli Studi di Perugia from Jun 2020 to Aug 2020. AQR's Momentum Funds (A) 211-025 3 Jegadeesh and Titman (JT) published their findings in 1993. Many investors believed that an easily explained strategy like momentum would lose its efficacy once it became well known. Thus the performance of momentum both before and after the publication of the JT paper was particularly relevant. Kenneth French of Dartmouth’s Tuck School of Business maintained a data library online3 which showed the historical performance of many quantitative strategies. Included was data on a momentum-based factor that its creators, French and University of Chicago economist Eugene Fama, called UMD (for Up minus Down) or MOM; in this formulation, momentum was computed from the highest 30th percentile of past return stocks (UP) minus the lowest 30th percentile of past returns stocks (DOWN). According to the average returns from the annual UMD data on French’s site, UMD’s returns were actually slightly larger in the post-1992 period (1993-2008): Pre- Jegadeesh and Titman paper, 1927-1992: 10.79% Post- Jegadeesh and Titman paper, 1993-2008: 11.48% Full sample period, 1927-2008: 10.92% Exhibit 1 shows the annual returns to the Fama-French UMD momentum strategy over the entire 1927-2008 period, while Exhibit 2 shows the cumulative performance of a dollar invested in UMD in 1927 versus a dollar invested in the overall market (CRSP NYSE/Nasdaq value-weight portfolio) in 1927. Exhibit 3 provides summary statistics over the entire sample period for the momentum "10 minus 1" portfolio, as well as the individual long and short component portfolios (i.e., deciles 1 and 10). Exhibit 4 presents the average monthly returns from 1927 to 2008 for all ten portfolios formed on past returns, as well as the long-short "10 minus 1" momentum portfolio. AQR AQR was established in 1998 and headquartered in Greenwich, CT. The founding principals of the firm included Clifford Asness, David Kabiller, Robert Krail, and John Liew, who had all worked together at Goldman Sachs before leaving to start AQR. Asness, Krail, and Liew had all met in the Finance PhD program at the University of Chicago, where Asness’ dissertation had focused on momentum investing. AQR had grown substantially from its start in 1998, and by early 2009 AQR had over 200 employees and managed over $19 billion in assets. A large portion of these assets were invested in hedge fund strategies.