We certainly know that contrarian valuation-based factor timing tilts are low Sharpe ratio strategies (very low short-term, somewhat better medium- to long-term) that are rarely immediately rewarded (few have such timing luck). 2 2 Close Though, despite bitter experience, the day after you put on such a tilt part of you does come in to the office genuinely expecting it to start working ASAP. We are all that naïve… Thus, as you’re all likely well aware by now as we keep repeating ourselves, we recommend “sinning a little” and only doing so at extremes. We wrote about (and implemented in the appropriate places) such a timing sin late last year, moving to a small overweight of the value factor (remember – value is only a part of our process). We only sinned a little (thank God for small favors!). Again, while we know such tilts are rarely, if ever, instantly rewarded, it’s also rare for them to be instantly incredibly punished (simply because “incredibly punished” is, thankfully, a rare thing). Well, welcome to 2020.
To get a sense of the magnitude, let’s start with the most basic data. 3 3 Close To keep this simple, and because the message is the same, I only address the USA here. But the extreme value drubbing this year-to-date is a global phenomenon (Europe terrible, emerging markets terrible, Japan merely bad). I’m confident that were we to assess the rarity and extremeness of this year-to-date globally the results would be even more extreme than I document here. Also, the data here ends on Feb 13th. Updating it through today would be still worse. Take the Russell 1000 Growth and Value series starting in 1991. We consider this a pretty simplistic form of value investing, but it captures the core concept and is widely followed. From January 1st until February 13th of 2020 the cumulative daily return difference between the two is -6.4% (take a guess which one of the two was worse 4 4 Close It rhymes with “schmalue“. ). Comparing this period to all rolling periods of the same number of days going back to 1991, this difference falls below the 3rd percentile. Of course, that includes such famous events as the technology bubble of 1998-2000 and the GFC of 2008-2009. If you only look at 2010 to today, this is the zeroth percentile event. That is, in a decade quite bad for value investing, the start of 2020 is the absolute worst 6-week period. 5 5 Close It’s not precisely six weeks but I’m going to call it that as it’s very close and much simpler to write.
Doing the same comparison among small stocks (Russell 2000 value vs. growth) from 1993 onward yields nearly identical results. Now, if we use Fama and French’s HML (using AQR data as Ken doesn’t update fast enough for this!), it’s a bit more extreme. This 6-week period falls below the first percentile since 1963 and, of course, is the worst such period since the value drawdown began a decade ago. 6 6 Close It’s really “more extreme” only because we can go back to 1963 and thus the value destruction in the tech bubble and the GFC gets less weight. Using what we think is a more accurate method for measuring value, using more updated prices, which we call HML-Devil, it’s been much better at exactly 1st percentile bad since 1963. Take that, dissertation advisors! 7 7 Close Hopefully it’s clear that is self-deprecating sarcasm, not bragging. They are pretty much the same. Adjusting HML-Devil for industries, something we’ve advocated since 1994, creates an exceptionally mild improvement. This value strategy comes in at only the 1.4th percentile back to 1963 and is off the zeroth schnide at 0.3th percentile in this ten-year value drawdown (it’s only not the zeroth because of a worse rolling period a few days earlier!). 8 8 Close Adjusting for industries was actually helpful from 2010-2017, but not in the last two years (a minor part of why we prospered during value’s troubles from 2010-2017 but have suffered along with it afterwards).
The point is a simple one. Value has started 2020 with an extremely severe loss versus very long-term history, and, defined in a wide variety of ways, the worst loss yet (examining all of the same 6-week length periods) over the entire long 2010-2020 value drawdown.
So, what are we going to do? Well, when it comes to making big changes to the process, very little. It would be a fair critique to say that this piece is largely just “quantitative whining.” First and foremost we’re executing our preferred strategy of not making panicky changes to our process that would have (note the tense) alleviated recent pain. Nothing has changed save value has gotten cheaper this year. We will continue to watch the value spreads, and consider doing a bit more of a tilt if they ever, which we hope not to see but will persevere if necessary, get to tech bubble levels, or conversely if they remain high but are running into less of a negative trend headwind.
I have been a pooh-pooher (if that’s not a word, it should be) of some who compare this current value pain to the tech bubble. We have found value spreads are quite wide today, but not tech-bubble wide. 9 9 Close You will come across some research that shows value is even cheaper than the tech bubble. We’ve generally found these pieces to be using fairly cherry-picked value measures over strange universes and using odd portfolio construction. It’s quite enough for us to be much of the way there without going full tech bubble… Though I have to admit, while you don’t come to me for my feelings about markets (I make no claim to be better than anyone else in this regard – and I don’t think anyone is that great!), I will say comparisons to the tech bubble, in terms of seeing more radical events (no more slow steady losses for value, now it’s very quick big ones!), and the widespread embracing by many of all the reasons (which they usually have never mentioned before) as to why value is never going to work again (my colleagues have a paper on this I hope to blog about soon), are converting me. It’s getting very bubbly out there (number two here details how I try to use this word as little as possible – but more than I would’ve when emerging from the University of Chicago many, many years ago).
Again, our plan is to do very little. That doesn’t mean we don’t question everything constantly (“doing very little” does not apply to research into what’s going on or trying, as we always are, to improve strategies). But, if that questioning doesn’t result in damning evidence (again, the paper by my colleagues is forthcoming!), it means sticking with the process.
We’ve seen this movie before a few times and we know how, but definitely not when, it ends. We believe that sticking with the process is the only way to achieve the long-term gains we seek (and which won’t always be provided by a long-only market that continues to levitate). We also know that sticking with something that’s good through its occasional very bad times, and even acting as a contrarian when others are finding newly created (and creative) reasons to throw in the towel, is very difficult. 10 10 Close I often go further noting that changing your process, not because you’re following some disciplined pre-specified plan (e.g., using trends, which we in fact do a bit in our alpha oriented multi-factor processes), but because you’re in pain (of course, you never say you’re changing because of the pain, it’s always justified with some ex post research) is the only -5 Sharpe ratio strategy (gross, there are plenty of -5 net strategies! – and, of course, please don’t take the -5 too seriously!) I’ve ever encountered (yes, this is an anecdotal observation, not data). If I could bottle it and do the opposite I would (it’s hard to bottle the precisely calibrated point of human caving/rationalization!). But this very difficulty is a large part of why we believe it’s long-term rewarded, and much harder to arbitrage away than some seem to think. As they say, if something were easy, everyone would do it. 11 11 Close It’s ironic that one of the reasons some say value won’t work anymore is it’s too crowded and everyone knows about it. Uh, no. It’s out of favor, hated, and cheap, even if widely known. If Yogi Berra was a value critic he’d say “that strategy is so popular nobody does it anymore.”
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The information contained herein is only as current as of the date indicated, and may be superseded by subsequent market events or for other reasons. Charts and graphs provided herein are for illustrative purposes only. The information in this presentation has been developed internally and/or obtained from sources believed to be reliable; however, neither AQR nor the author guarantees the accuracy, adequacy or completeness of such information. Nothing contained herein constitutes investment, legal, tax or other advice nor is it to be relied on in making an investment or other decision. There can be no assurance that an investment strategy will be successful. Historic market trends are not reliable indicators of actual future market behavior or future performance of any particular investment which may differ materially, and should not be relied upon as such.
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Russell indices, source: Bloomberg
The Russell 1000 Growth Index is a composite derived from the Russell 1000 Index that includes large and mid-cap companies located in the United States that also exhibit the growth investment style.
The Russell 1000 Value Index is a composite derived from the Russell 1000 Index of large and mid-cap companies located in the United States that also exhibit the value investment style.
The Russell 2000 Value Index is a composite derived from the Russell 2000 Index of small cap companies located in the United States that also exhibit the value investment style.
The Russell 2000 Growth Index is a composite derived from the Russell 2000 Index of small cap companies located in the United States that also exhibit the growth investment style.
HML series, source: AQR, AQR Data Library:
Pricing and accounting data are from the union of the CRSP tape and the Compustat/XpressFeed Global database. The universe is all available common stocks in the merged CRSP/XpressFeed data.
HML: book equity (BE) divided by current total market value of equity (ME). To obtain shareholders’ equity we use Stockholders’ Equity (SEQ) but if not available, we use the sum of Common Equity (CEQ) and Preferred Stocks (PSTK). If both SEQ and CEQ are unavailable, we proxy shareholders’ equity by Total Assets (AT) minus the sum of Total Liability (LT) and Minority Interest (MIB). To obtain book equity (BE), we subtract from shareholders’ equity the preferred stock value (PSTKRV, PSTKL or PSTK depending on availability). We assume that accounting variables are known with a minimum 6-month gap and align book price of the firm at the end of the firm’s fiscal year ending anywhere in calendar year to June of calendar year
Fama French’s HML: to compute book to market ratios, we scale BE by the ME at fiscal year end following Fama and French (1992, 1993 and 1996).
HML Devil: to compute book to market ratios, we scale BE by the current ME at the end of each month following Asness and Frazzini (2013).
Industry neutral: The intra-industry version ranks stocks within industries only so as to take no industry bets. The industry classification is based on SIC (Standard Industrial Classification) codes before 1986 and MSCI GICS (Global Industry Classification Standard) codes after 1986. The long side of each portfolio includes the best (cheapest) 30%, while the short side includes the worst (richest) 30%. The long and short sides are then market-cap weighted.
Hypothetical AQR Long/Short Factor, source: AQR:
The AQR Long/Short Valuation factor is a U.S valuation theme backtest utilizing the full set of underlying factors that compose the Valuation theme within AQR’s Global Stock Selection strategy to evaluate stocks and create a long-short, market-neutral and industry-neutral equity portfolio based exclusively on these signals within the U.S. region. Within the composite of Value signals are: B/P, E/P, S/P, CF/P, and some other proprietary value measures. The Valuation Theme is designed to capture the tendency for relatively cheap assets to outperform relatively expensive ones. Backtest returns are gross of advisory fees and transaction costs from February 13, 2020 (when data is available). The backtest utilizes a monthly rebalancing schedule and target 7% annual volatility. The investment universe is U.S. large cap. The risk model used is the Barra U.S. Equity Risk Model (USE3L).
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