Posts Tagged ‘Quantitative investment’

Research Affiliates’ Jason Hsu has a new article Selling Hope (.pdf) in which he discusses the reason investors persist in the seemingly irrational behavior of paying high fees for active management despite the numerous studies that show most active managers fail to deliver “alpha” over time net of fees:

The empirical evidence that the average fund manager underperforms and the recent top-performing funds do not outperform subsequently are irrefutable. Why, then, do investors insist on paying for investment management expertise, which isn’t all that useful? Perhaps investors are not really that interested in holding their investment managers accountable for outperformance. The Economist’s Buttonwood column 5 argues that investors might only be interested in securing advice that confirms their own investment beliefs. The false sense of security that comes from hearing a “professional” concurring with one’s own opinions on unpredictable affairs makes the randomness that is inherent in investing almost tolerable. Clearly, not all aspects of investment management are related to generating outperformance; many managers and advisors are really in the business of preventing their clients from making bad financial decisions, such as overconcentrating the portfolio, trading excessively or making decisions under emotional distress. Barber and Odean, in their 2000 Journal of Finance paper, found that aggressive self-directed investors underperform the market by an average of 6.5% per annum.6 These investors simply own too few stocks and trade too much due to overconfidence in their own stock-picking and market-timing skills. Jason Zweig, in his 2002 investigative report, documented that retail mutual fund investors underperformed the average mutual fund by 4.7% per annum.7 Again, this poor result is driven by investors actively switching between funds and market-timing their investment contributions.

Read the article here (.pdf).

See an earlier post on fundamental indexation.

H/T Tom Brakke’s @researchpuzzler

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Greenbackd has been quiet over the last few days while I finished “Simple But Not Easy,” my latest white paper for Eyquem (embedded below). If you want to receive similar future missives, shoot me an email at greenbackd at gmail dot com. Thoughts, criticisms, and questions are all welcome too.

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James P. O’Shaughnessy’s What works on Wall Street is one of my favorite books on investing. The thing that I like most about the book is O’Shaughnessy use of data to slaughter several sacred value investing cows, one of which I mentioned yesterday (see The Small Cap Paradox: A problem with LSV’s Contrarian Investment, Extrapolation, and Risk in practice).

Another sacred cow put to the sword in the book is the use of five-year earnings-per-share growth to improve the returns from a price-to-earnings screen. O’Shaughnessy describes the issue in this way:

Some analysts believe that a one-year change in earnings is meaningless, and we would be better off focusing on five-year growth rates. This, they argue, is enough time to separate the one-trick pony from the true thoroughbred.

So what does the data say?

Unfortunately, five years of big earnings gains doesn’t help us pick thoroughbreds either. Starting on December 31, 1954 (we need five years of data to compute the compound five-year earnings growth rate), $10,000 invested in the 50 stocks from the All Stocks universe with the highest five-year compound earnings-per-share growth rates grew to $1,287,685 by the end of 2003, a compound return of 10.42 percent (Table 12-1). A $10,000 investment in the All Stocks universe on December 31, 1954 was worth $3,519,152 on December 31, 2003, a return of 12.71 percent a year.

O’Shaughnessy interprets the data thus:

Much like the 50 stocks with the highest one-year earnings gains, investors get dazzled by high five-year earnings growth rates and bid prices to unsustainable levels. When the future earnings are lower than expected, investors punish their former darlings and prices swoon.

The evidence shows that it is a mistake to get overly excited by big earnings gains.

Five-year growth rates are clearly mean reverting, and I love to see an intuitive strategy beaten by a little reversion to the mean.

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Yesterday’s post on LSV Asset Management’s performance reminded me of the practical difficulties of implementing many theoretically well-performed investment strategies. LSV Asset Management is an outgrowth of the research conducted by Josef Lakonishok, Andrei Shleifer, and Robert Vishny. They are perhaps best known for the Contrarian Investment, Extrapolation, and Risk paper, which, among other things, analyzed low price-to-book value stocks in deciles (an approach possibly suggested by Roger Ibbotson’s study Decile Portfolios of the New York Stock Exchange, 1967 – 1984). They found that low price-to-book value stocks out perform, and in rank order (the cheapest decile outperforms the next cheapest decile and so on). The problem with the approach is that the lowest price-to-book value deciles – that is, the cheapest and therefore best performed deciles – are uninvestable.

In an earlier post, Walking the talk: Applying back-tested investment strategies in practice, I noted that Aswath Damodaran, a Professor of Finance at the Stern School of Business, has a thesis that “transaction costs” – broadly defined to include brokerage commissions, spread and the “price impact” of trading – foil in the real world investment strategies that beat the market in back-tests. Damodaran made the point that even well-researched, back-tested, market-beating strategies underperform in practice:

Most of these beat-the-market approaches, and especially the well researched ones, are backed up by evidence from back testing, where the approach is tried on historical data and found to deliver “excess returns”. Ergo, a money making strategy is born.. books are written.. mutual funds are created.

The average active portfolio manager, who I assume is the primary user of these can’t-miss strategies does not beat the market and delivers about 1-1.5% less than the index. That number has remained surprisingly stable over the last four decades and has persisted through bull and bear markets. Worse, this under performance cannot be attributed to “bad” portfolio mangers who drag the average down, since there is very little consistency in performance. Winners this year are just as likely to be losers next year…

Damodaran’s solution for why some market-beating strategies that work on paper fail in the real world is transaction costs. But it’s not the only reason. Some strategies are simply impossible to implement, and LSV’s low decile price-to-book value strategy is one such strategy.

James P. O’Shaughnessy’s What works on Wall Street is one of my favorite books on investing. In the book, O’Shaughnessy suggests another problem with the real-world application of LSV’s decile approach:

Most academic studies of market capitalization sort stocks by deciles (10 percent) and review how an investment in each fares over time. The studies are nearly unanimous in their findings that small stocks (those in the lowest four deciles) do significantly better than large ones. We too have found tremendous returns from tiny stocks.

So far so good. So what’s the problem?

The glaring problem with this method, when used with the Compustat database, is that it’s virtually impossible to buy the stocks that account for the performance advantage of small capitalization strategies. Table 4-9 illustrates the problem. On December 31, 2003, approximately 8,178 stocks in the active Compustat database had both year-end prices and a number for common shares outstanding. If we sorted the database by decile, each decile would be made up of 818 stocks. As Table 4-9 shows, market capitalization doesn’t get past $150 million until you get to decile 6. The top market capitalization in the fourth decile is $61 million, a number far too small to allow widespread buying of those stocks.

A market capitalization of $2 million – the cheapest and best-performed decile – is uninvestable. This leads O’Shaughnessy to make the point that “micro-cap stock returns are an illusion”:

The only way to achieve these stellar returns is to invest only a few million dollars in over 2,000 stocks. Precious few investors can do that. The stocks are far too small for a mutual fund to buy and far too numerous for an individual to tackle. So there they sit, tantalizingly out of reach of nearly everyone. What’s more, even if you could spread $2,000,000 over 2,000 names, the bid–ask spread would eat you alive.

Even a small investor will struggle to buy enough stock in the 3rd or 4th deciles, which encompass stocks with market capitalizations below $26 million and $61 million respectively. These are not, therefore, institutional-grade strategies. Says O’Shaughnessy:

This presents an interesting paradox: Small-cap mutual funds justify their investments using academic research that shows small stocks outperforming large ones, yet the funds themselves cannot buy the stocks that provide the lion’s share of performance because of a lack of trading liquidity.

A review of the Morningstar Mutual Fund database proves this. On December 31, 2003, the median market capitalization of the 1,215 mutual funds in Morningstar’s all equity, small-cap category was $967 million. That’s right between decile 7 and 8 from the Compustat universe—hardly small.

The good news is, there are other strategies that do work.

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Insider Monkey has a great analysis of LSV Asset Management’s Value Equity Fund returns and alpha (LSVEX). LSV Asset Management is a quantitative value shop founded by Josef Lakonishok, Andrei Schleifer, and Robert Vishny, authors of the landmark 1994 Contrarian Investment, Extrapolation, and Risk paper, which is a favorite topic of mine (for more, see the archives on LSV and quantitative investment). Insider Monkey’s conclusions are not particularly positive for LSV:

LSV’s Value Equity Fund (LSVEX) uses quantitative methods to pick out-of-favor value stocks and does not employ any market timing strategies. LSV describes its investment process as follows:

A proprietary investment model is used to rank a universe of stocks based on a variety of factors we believe to be predictive of future stock returns. The process is continuously refined and enhanced by our investment team although the basic philosophy has never changed – a combination of value and momentum factors. We then overlay strict risk controls that limit the over- or under-exposure of the portfolio to industry and sector concentrations.We also limit exposures in individual securities to ensure the portfolios are broadly diversified, further controlling risk.

The competitive strength of this strategy is that it avoids introducing the process to any judgmental biases and behavioral weaknesses that often influence investment decisions.

Portfolio turnover is approximately 30% for each strategy.


Insider Monkey downloaded LSV Value Equity Fund’s returns from Yahoo to calculate their alpha by using Carhart’s four factor model:

The LSV Value Equity Fund has $1.7 Billion under management, but the strategy is actually used to manage $22.2 billion in assets in various LSV funds. The fund’s objective is to achieve 200 basis points in excess returns before expenses. Considering that the LSV Value Equity Fund has an expense ratio of 0.65%, its alpha should not be less than 1.35%. The minimum investment is set at $100,000, so this fund is really not for small investors.

We calculated LSVEX’s alpha for the Oct 1999-Jun 2010 period. Though they call themselves a value fund, the LSV Value Equity Fund isn’t one. It had a slight value tilt in the first five years of the fund, but now it has a growth tilt. Neither of these are statistically significant though. Also during the first five years, the fund was investing in smaller companies. LSVEX had a monthly alpha of 31 basis points after expenses. This is exceptional for a mutual fund; usually mutual funds don’t have any alpha after expenses. But as assets grew, LSVEX was naturally tilted towards the large cap space. It doesn’t follow a momentum strategy. Unfortunately, the LSV Value Equity Fund’s alpha dropped to 10 basis points as assets grew, between 2005 and Jun, 2010. This level of alpha is actually 15 basis points below their goal of 1.35% annual alpha.

The top holdings of LSV funds are Chevron (CVX), Pfizer (PFE), AT&T (T), Conoco Philips (COP), Bank of America (BAC), and JP Morgan (JPM). Notice a theme here? This is the fundamental problem with talented mutual fund managers- they siphon most of their alpha into their pockets by inflating assets under management, because it pays to have large AUM but it doesn’t pay to have a large alpha. LSV could opt to manage a $3 Billion hedge fund and maintain a respectable 10% alpha. Instead of doing this, LSV added another $20 Billion of assets that follow the index funds and got a 1% alpha. Then they charged a 0.65% management for managing a $20 Billion index fund. That 0.65% fee from that $20 Billion is not much different from what could be collected from managing a hedge fund. LSV’s alpha is around $300-350 Million per year. They take $100-$150 Million from that in performance fees and leave the rest for their mutual fund investors.

See the article from Insider Monkey.

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I burned some digital ink on these pages discussing the utility of quantitative investment processes over more qualitative approaches. The thesis was, in essence, as follows:

  1. Simple statistical models outperform the judgements of the best experts
  2. Simple statistical models outperform the judgements of the best experts, even when those experts are given access to the simple statistical model.

The reason? Humans are fallible, emotional and subject to all sorts of biases. They perform better when they are locked into some process (see here, herehere and here for the wordier versions).

I also examined some research on the performance of quantitative funds and their more qualitative brethren. The findings were as one might expect given the foregoing:

[Ludwig] Chincarini [the author] finds that “both quantitative and qualitative hedge funds have positive risk-adjusted returns,” but, ”overall, quantitative hedge funds as a group have higher [alpha] than qualitative hedge funds.”

All well and good. And then Morningstar spoils the party with their take on the matter:

The ups and downs of stocks since the credit crisis began roiling the equity markets in 2007 haven’t been kind to most stock-fund managers. But those who use quantitative stock-picking models have had an especially difficult time.

What went wrong?

Many quant funds rely primarily on models that pick stocks based on value, momentum, and quality factors. Those that do have been hit by a double whammy lately. Value models let quants down first. Stocks that looked attractive to value models just kept getting cheaper in the depths of the October 2007-March 2009 bear market. “All kinds of value signals let you down, and they’re a key part of many quant models,” said Sandip Bhagat, Vanguard’s head of equities and a longtime quant investor.

Morningstar quotes Robert Jones of GSAM, who argues that “quant managers need more secondary factors”:

Robert Jones, former longtime head of Goldman Sachs Asset Management’s large quant team and now a senior advisor for the team, recently asserted in the Journal of Portfolio Management that both value and momentum signals have been losing their effectiveness as more quant investors managing more assets have entered the fray. Instead, he calls for quant managers to search for more-sophisticated and proprietary measures to add value by looking at less-widely available nonelectronic data, or data from related companies such as suppliers and customers. Other quants have their doubts about the feasibility of such developments. Vanguard’s Bhagat, for example, thinks quant managers need more secondary factors to give them the upper hand, but he also wonders how many new factors exist. “There are so many smart people sorting through the same data,” he said. Ted Aronson of quant firm Aronson+Johnson+Ortiz is more blunt: “We’re not all going to go out and stumble on some new source of alpha.”

Jones’s comments echo Robert Litterman’s refrain (also of GSAM) in Goldman Sachs says P/B dead-as-dead; Special sits and event-driven strategies the new black. Litterman argued that only special situations and event-driven strategies that focus on mergers or restructuring provide opportunities for profit:

What we’re going to have to do to be successful is to be more dynamic and more opportunistic and focus especially on more proprietary forecasting signals … and exploit shorter-term opportunistic and event-driven types of phenomenon.

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In A Crisis In Quant Confidence*, Abnormal Returns has a superb post on Scott Patterson’s recounting in his book The Quants of the reactions of several quantitative fund managers to the massive reversal in 2007:

In 2007 everything seemed to go wrong for these quants, who up until this point in time, had been coining profits.

This inevitably led to some introspection on the part of these investors as they saw their funds take massive performance hits.  Nearly all were forced to reduce their positions and risks in light of this massive drawdown.  In short, these investors were looking at their models seeing where they went wrong.  Patterson writes:

Throttled quants everywhere were suddenly engaged in a prolonged bout of soul-searching, questioning whether all their brilliant strategies were an illusion, pure luck that happened to work during a period of dramatic growth, economic prosperity, and excessive leverage that lifted everyone’s boat.

Here Patterson puts his finger on the question that vexes anyone who has ever invested, made money for a time and then given some back: Does my strategy actually work or have I been lucky? It’s what I like to call The Fear, and there’s really no simple salve for it.

The complicating factor in the application of any investing strategy, and the basis for The Fear, is that even exceptionally well-performed strategies will both underperform the market and have negative periods that can extend for three, five or, on rare occasions, more years. Take, for example, the following back-test of a simple value strategy over the period 2002 to the present. The portfolio consisted of thirty stocks drawn from the Russell 3000 rebalanced daily and allowing 0.5% for slippage:

(Click to enlarge)

The simple value strategy returns a comically huge 2,450% over the 8 1/4 years, leaving the Russell 3000 Index in its wake (the Russell 3000 is up 9% for the entire period). 2,450% over the 8 1/4 years is an average annual compound return of 47%. That annual compound return figure is, however, misleading. It’s not a smooth upward ride at a 47% rate from 100 to 2,550. There are periods of huge returns, and, as the next chart shows, periods of substantial losses:

(Click to enlarge)

From January 2007 to December 2008, the simple value strategy lost 20% of its value, and was down 40% at its nadir. Taken from 2006, the strategy is square. That’s three years with no returns to show for it. It’s hard to believe that the two charts show the same strategy. If your investment experience starts in a down period like this, I’d suggest that you’re unlikely to use that strategy ever again. If you’re a professional investor and your fund launches into one of these periods, you’re driving trucks. Conversely, if you started in 2002 or 2009, your returns were excellent, and you’re genius. Neither conclusion is a fair one.

Abnormal Returns says of the correct conclusion to draw from performance:

An unexpectedly large drawdown may mark the failure of the model or may simply be the result of bad luck. The fact is that the decision will only be validated in hindsight. In either case it represents a chink in the armor of the human-free investment process. Ultimately every portfolio is run by a (fallible) human, whether they choose to admit it or not.

In this respect quantitative investing is not unlike discretionary investing. At some point every investor will face the choice of continuing to use their method despite losses or choosing to modify or replace the current methodology. So while quantitative investing may automate much of the investment process it still requires human input. In the end every quant model has a human with their hand on the power plug ready to pull it if things go badly wrong.

At an abstract, intellectual level, an adherence to a philosophy like value – with its focus on logic, discipline and character – alleviates some of the pain. Value answers the first part of the question above, “Does my strategy actually work?” Yes, I believe value works. The various academic studies that I’m so fond of quoting (for example, Value vs Glamour: A Global Phenomenon and Contrarian Investment, Extrapolation and Risk) confirm for me that value is a real phenomenon. I acknowledge, however, that that view is grounded in faith. We can call it logic and back-test it to an atomic level over an eon, but, ultimately, we have to accept that we’re value investors for reasons peculiar to our personalities, and not because we’re men and women of reason and rationality. It’s some comfort to know that greater minds have used the philosophy and profited. In my experience, however, abstract intellectualism doesn’t keep The Fear at bay at 3.00am. Neither does it answer the second part of the question, “Am I a value investor, or have I just been lucky?”

As an aside, whenever I see back-test results like the ones above (or like those in the Net current asset value and net net working capital back-test refined posts) I am reminded of Marcus Brutus’s oft-quoted line to Cassius in Shakespeare’s Julius Caesar:

There is a tide in the affairs of men,

Which, taken at the flood, leads on to fortune;

Omitted, all the voyage of their life

Is bound in shallows and in miseries.

As the first chart above shows, in 2002 or 2009, the simple value strategy was in flood, and lead on to fortune. Without those two periods, however, the strategy seems “bound in shallows and in miseries.” Brutus’s line seems apt, and it is, but not for the obvious reason. In the scene in Julius Caesar from which Brutus’s line is drawn, Brutus tries to persuade Cassius that they must act because the tide is at the flood (“On such a full sea are we now afloat; And we must take the current when it serves, Or lose our ventures.”). What goes unsaid, and what Brutus and Cassius discover soon enough, is that a sin of commission is deadlier than a sin of omission. The failure to take the tide at the flood leads to a life “bound in shallows and in miseries,” but taking the tide at the flood sometimes leads to death on a battlefield. It’s a stirring call to arms, and that’s why it’s quoted so often, but it’s worth remembering that Brutus and Cassius don’t see the play out.

* Yes, the link is to classic.abnormalreturns. I like my Abnormal Returns like I like my Coke.

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I’m a huge fan of James Montier’s work on the rationale for a quantitative investment strategy and global Graham net net investing. Miguel Barbosa of Simoleon Sense has a wonderful interview with Montier, covering his views on behavioral investing and value investment. Particularly interesting is Montier’s concept of “seductive details” and the implications for investors:

Miguel: Let’s talk about the concept of seductive details…can you give us an example of how investors are trapped by irrelevant information?

James Montier: The sheer amount of irrelevant information faced by investors is truly staggering. Today we find ourselves captives of the information age, anything you could possibly need to know seems to appear at the touch of keypad. However, rarely, if ever, do we stop and ask ourselves exactly what we need to know in order to make a good decision.

Seductive details are the kind of information that seems important, but really isn’t. Let me give you an example. Today investors are surrounded by analysts who are experts in their fields. I once worked with an IT analyst who could take a PC apart in front of you, and tell you what every little bit did, fascinating stuff to be sure, but did it help make better investment decisions, clearly not. Did the analyst know anything at all about valuing a company or a stock, I’m afraid not. Yet he was immensely popular because he provided seductive details.

Montier’s “seductive details” is reminiscent of the discussion in Nicholas Taleb’s Fooled by Randomness on the relationship between the amount of information available to experts, the accuracy of judgments they make based on this information, and the experts’ confidence in the accuracy of these judgements. Intuition suggests that having more information should increase the accuracy of predictions about uncertain outcomes. In reality, more information decreases the accuracy of predictions while simultaneously increasing the confidence that the prediction is correct. One such example is given in the paper The illusion of knowledge: When more information reduces accuracy and increases confidence (.pdf) by Crystal C. Hall, Lynn Ariss, and Alexander Todorov. In that study, participants were asked to predict basketball games sampled from a National Basketball Association season:

All participants were provided with statistics (win record, halftime score), while half were additionally given the team names. Knowledge of names increased the confidence of basketball fans consistent with their belief that this knowledge improved their predictions. Contrary to this belief, it decreased the participants’ accuracy by reducing their reliance on statistical cues. One of the factors contributing to this underweighting of statistical cues was a bias to bet on more familiar teams against the statistical odds. Finally, in a real betting experiment, fans earned less money if they knew the team names while persisting in their belief that this knowledge improved their predictions.

This is not an isolated example. In Effects of amount of information on judgment accuracy and confidence, by Claire I. Tsai, Joshua Klayman, and Reid Hastie, the authors examined two other studies that further that demonstrate when decision makers receive more information, their confidence increases more than their accuracy, producing “substantial confidence–accuracy discrepancies.” The CIA have also examined the phenomenon. In Chapter 5 of Psychology of Intelligence Analysis, Do you really need more information?, the author argues against “the often-implicit assumption that lack of information is the principal obstacle to accurate intelligence judgments:”

Once an experienced analyst has the minimum information necessary to make an informed judgment, obtaining additional information generally does not improve the accuracy of his or her estimates. Additional information does, however, lead the analyst to become more confident in the judgment, to the point of overconfidence.

Experienced analysts have an imperfect understanding of what information they actually use in making judgments. They are unaware of the extent to which their judgments are determined by a few dominant factors, rather than by the systematic integration of all available information. Analysts actually use much less of the available information than they think they do.

Click here to see the Simoleon Sense interview.

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