How should we think about risk?
Anyone who has taken a finance course should come away with a basic knowledge of asset pricing models such as the CAPM and concepts such as Beta, which examines investment risk as a function of the standard deviation of historical returns.
Although many of the details of these pricing models are empirically very shaky, it's taken for granted by most people that it's very difficult to beat the market, and that most investors are better off investing in index funds.
One of the more interesting papers I've read recently that calls these topics into question is called Is Modern Portfolio Theory Harming Your Portfolio? by hedge fund manager Scott Vincent.
In it, the author claims that it's not as rare to find managers that beat the market as academic research would suggest, and that modern academia has defined risk in a way that loads the dice in favor of the academic standard of index fund investing.
First, Vincent observes that many supposedly "active" equity managers examined in the literature are not "active" at all. Rather, they are trying to meet or beat similar, standard benchmarks such as the S&P500 index. In this case, actively managed funds are destined to perform poorly against their index counterparts. But it's inaccurate to say these these benchmark tracking funds are really "active:"
Stripping away the influence of portfolio theory involves isolating and evaluating the relatively small group of equity managers who rely heavily on judgment to build concentrated equity portfolios. Empirical data from multiple studies show that these concentrated managers, in fact, persistently outperform indexes....There’s compelling evidence that the core theories behind the push to passive management do not work and they distort the facts around the passive versus active debate, giving passive management the false appearance of having an edge.
Next, Vincent argues that academic tend to define risk in a wrong-headed way. Not only are past historical returns not necessarily indicative of future returns...
Amazingly enough, there’s not much empirical “proof” as to why we should use variance as a measure of risk, yet it plays a critical role in almost all large financial transactions. It seems that academicians needed a way to quantify risk to fit mathematical models and they grabbed variance, not because it described risk very well, but because it was the best quantitative option available. But just because it is convenient, and it carries a certain intuitive appeal, doesn’t make it right.
...but this view also ignore the very subjective nature of what risk really is. If a stock has fallen by a large percentage, for example, does this represent great risk or a significant margin of safety?
Risk is often in the eye of the beholder. While “quants” (who rely heavily on MPT) might view a stock that has fallen in value by 50 percent over a short period of time as quite risky (i.e. it has a high beta), others might view the investment as extremely safe, offering an almost guaranteed return. Perhaps the stock trades well below the cash on its books and the company is likely to generate cash going forward.
When academic studies do not back up the prevailing thoughts on the nature of risk, other studies attempt to account for new variables to fit the results to the prevailing theory, which amounts to data mining:
Numerous empirical studies have shown that taking on more risk (as represented by volatility) doesn’t reliably deliver additional reward. So, the quantitative cooks continue to tinker with recipes to fit variables to an equation that can make sense of financial markets. New multi-variable regression models are introduced to describe alternative factors that influence returns most, but these efforts amount to data mining. Just because these new and improved formulas generate more respectable correlations doesn’t mean there’s a causal link between their variables and the returns they predict – as such, observed relationships can be fleeting. While the multi-variable models can solve some of the problems in certain instances, these reworked formulas still suffer from many obstacles to successful, practical implementation.
He then argues that risk thought be re-considered to be thought of in terms of a bottom-up view of the business itself rather than the volatility in its stock price:
Perhaps then the risk in a portfolio is better described by taking a bottoms-up view of the fundamentals of the businesses owned, and how those fundamentals manifest themselves in stock prices, rather than computing the portfolio’s historic variability with respect to the market?
Vincent ends by arguing that funds with a very high degree of active management show persistence in outperforming passively managed funds:
Multiple studies indicate that funds which are more actively managed, or more concentrated, outperform indexes and do so with persistence (Kacperczyk, Sialm and Zheng (2005), Cohen, Polk, Silli (2010), Bakks, Busse, and Greene (2006), Wermers (2003), and Brands, Brown, Gallagher (2003), Cremers and Petajisto (2007)). Funds with the highest Active Share [most active management] outperform their benchmarks both before and after expenses, while funds with the lowest Active Share underperform after expenses …. The best performers are concentrated stock pickers ….We also find strong evidence for performance persistence for the funds with the highest Active Share, even after controlling for momentum. From an investor’s point of view, funds with the highest Active Share, smallest assets, and best one-year performance seem very attractive, outperforming their benchmarks by 6.5% per year net of fees and expenses.
Most of the wealth in the world has resulted from individual entrepreneurs using their judgment to invest in opportunities (inefficiencies) in a highly concentrated, even exclusive, fashion. Think about that for a moment, because it’s a big statement. Sure, wealth has been lost using this formula, but the good has dramatically outweighed the bad. Although far from perfect, human judgment has advanced us a very long way. While public markets are much more efficient than the entrepreneurs’ private markets, they still contain inefficiencies. Accordingly, good judgment will reward investors over time. Demoting a time-tested, highly successful system that favors judgment in preference for one supported by an unsound infrastructure of quantitative theories and formulas, doesn’t make a lot of sense. Make this flaw your opportunity.
I found these arguments compelling. Going forward, I'm going to be much more likely to really research actively managed funds for my personal investing, after having drunk the efficient markets Kool-Aid for so long.
Monkeys, what do you think? Have any of you reviewed Vinson's sources? Does the academic approach the the definition of risk hold any weight? How might this impact your own investment decisions?
Comments
I've always been a supporter
I've always been a supporter of passively managed investments, so you can keep that in mind when evaluating my thoughts, but...
It seems to me like this research doesn't mention a simple idea - for every actively managed dollar beating the market, there's an actively managed dollar losing to the market (and that's before fees). I don't know how efficient the market is, and frankly it makes sense to me that academics would overestimate how efficient it is. But nonetheless, an inefficient market doesn't mean active management is better, it just means there are a lot of winners and losers in active management. When I'm investing my own money, I don't think I can tell the difference between the winners and the losers, and I'm not about to pay higher fees on the belief that I can pick the winners. So I see merits in passive management regardless of how efficient/inefficient the market is
There is already a book on you. That book is already being written. And if I talked to your friends, your teachers, your professionals, your family, I would know so much about you I wouldn't even have to meet you. You write the book the way you want to be
I have nothing to add, but in
I have nothing to add, but in response to the thread title this is pretty much a necessity:
http://cache.dealbreaker.com/uploads/2010/08/Pictu...
Edit: can we not embed images anymore?
I hate victims who respect their executioners
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Nice read OP -- SB for you.
Nice read OP -- SB for you.
For my aspiring Entrepreneurial Nomads, check out my blog.
Have only two points that
Have only two points that might help:
First Klarman says beta is inherently flawed (and I agree) because it makes no distinction between upward and downward movements. I believe the following illustration holds true, but quant geeks please butt in if this is wrong:
Stock A: when the market is down 10, stock A goes down 10, i.e. perfect 1:1 correlation on the downside, but when the market goes up 10, stock A only goes up 5. (limited upside)
Stock B: when the market is UP 10, stock B goes up 10, but when market goes down 10, stock B only goes down 5. (limited downside)
I believe these two securities would yield the same beta since an OLS regression of either stock to the market index would yield the same coefficient (that is, Beta in the Re = Rf + Beta * MRP). However, stock B is clearly the superior pick. Maybe my regression concepts are a little off but I think in any case the basic principles hold.
Second point, when funds report their performance, I don't think they say "and by the way our risk number was X" (at least mine didn't). They simply present their strategies, a list of securities they went for, and leave it to the investors to decide whether the risk/return blend was worth it.
Interesting post, curious to hear the crowd on this one.
if you like it then you shoulda put a banana on it