Thursday, April 23, 2015
Chitpuneet Mann, Mark Spitznagel, and Brandon Yarckin
The introduction of asymmetric beta to the CAPM framework can allow an investor to construct a portfolio with expectations well above the security market line. Incorporating asymmetric beta provides evidence of a mispricing in certain payoff profiles, namely tail hedged equities, that can be analyzed by using variants of the CAPM type of framework. CAPM based asset allocations are misspecified and ill-equipped to handle asymmetric returns.
The Capital Asset Pricing Model is a fundamental building block with which investors make allocation decisions over time. Investment decisions are made based on risk-return constructs, and in this framework, CAPM, for the most part, has stood the test of time. Due to its simplicity, it is widely used when an equity investor wants to roughly estimate the expected returns of one’s portfolio.
We want to appraise the value of tail hedging within a CAPM framework, and thereby show the efficacy of tail hedging and the misspecificity of the model itself.
By using Harry Markowitz’s efficient frontier, one can roughly compare different asset classes based on their consensus expected returns and observed risk (mostly computed using standard deviation of asset returns). However, this measure of risk is fairly naive since it has been well documented that most, if not all, asset classes have non-normal, fat-tailed and often asymmetric return distributions. Asymmetric properties are not well accounted for in a mean-variance framework as they underestimate tail risk in negatively-skewed portfolios. Stress tests should thus be used, as they are critical risk estimation tools that transparently demonstrate vulnerabilities to large deviations that can impact long-term expected returns. (We recognize successful empirical research stating that multifactor models can explain and predict investment returns, but they have similar limitations.)
Due to the principal-agent problem in the asset management industry, most money managers rationally have a propensity to use a negatively-skewed payoff distribution. This kind of behavior, in aggregate, is also evidenced in the historical data, which shows significant losses for professional investors during the largest market downturns. Most investors and asset allocators, in addition to these negatively-skewed positions, further view the returns of hedging strategies in a vacuum, rather than as a holistic part of their broader portfolio. Thus, they are likely to consider portfolio hedging programs to be a drag on their performance numbers and further undervalue them. We believe that these factors, among others, contribute to a market segmentation that creates an undervaluation in tail-risk hedges.
Assuming there are such opportunities in hedging tail risk, let’s evaluate how one can depict an asset class’s risk-return profile and see if using a fair proxy tail-risk hedging program could help investors better maneuver these not-so-uncommon market crashes. We use Markowitz’s efficient frontier type of framework to plot a ‘risk measure’ on the x-axis (which is the average semi-variance for three-year rolling monthly returns) and the corresponding asset’s annualized returns on the y-axis.
From Figure 1, we can safely ascertain that from a risk-reward standpoint, an investment in the S&P 500 Index plus short-term Treasuries could be considered a benchmark for validating a tail hedge argument. Thus, we choose a vanilla 60/40 portfolio — 60% invested in the S&P 500 and 40% in short-term Treasuries, rebalanced monthly. On the other hand, our tail-hedged portfolio consists of S&P 500 and out-of-the-money put options (specifically one delta which has a strike roughly 30-35% below spot) on the S&P 500. At the beginning of every calendar month, using actual option prices, the number of third-month options (with a maturity from 11 to 12 weeks, and also carrying over the payoff from unexpired options) is determined such that the tail-hedged portfolio breaks even for a down 20% move in the S&P 500 over a month. From practice, for scaling the payoff, we can safely assume that the S&P 500 options’ implied volatility, or IVol, surface would look similar to the one observed after the lows of the October 2002 crash (an observed in-sample data point for the backtest period).
Mark-to-market fluctuations in the options position of the tail-hedged portfolio (i.e. giving back small unrealized gains) can cause its risks to be overstated by semi-variance. We can, however, overcome this limitation by using model-free stress tests.
Posted by Bud Fox at 5:56 AM
Tuesday, April 14, 2015
Saturday, April 11, 2015
HOW INDEXATION KILLED GROWTH
By Carles Gave, Gavekal Dragonomics
Indexing, as I have written before, is a form of socialism, since capital is allocated not as it should be - according to its marginal return - but rather according to swings in the market capitalization of the underlying assets. It is hard to think of a more stupid way to allocate this scarce resource.
In this new world, the goal of every money manager is to achieve a performance as close as possible to the index against which he is benchmarked (see Indexation = Parasitism). As a consequence, the dispersion of results among money managers has become smaller and smaller over the years. Today you can even buy programs telling you how much IBM stock you should buy versus Johnson and Johnson in order to control your “tracking error”. As always in economics, there is what you see and what you don’t. What most people don’t see is how the spread of indexation has led to a collapse in the growth rate of the economy.
Building a portfolio is a very complex exercise. In a perfect world, one would start with the “expected” marginal increase in the return on invested capital of different investments. Once satisfied with a given position, one should try to ensure that the increase in the marginal return is not too correlated with other positions in the portfolio. The name of the game is to find assets with the same ROIC over the long run, but a negative correlation over the shorter term (for example, US shares versus. US government bonds over the last 20 years).
This aims at the Holy Grail of money management, which is to achieve a decent long term return, together with a low volatility of that return. As one can see, this involves a massively complex price discovery exercise, starting with an examination of the marginal variations of ROIC, followed by consideration of the prices at which one can buy the available assets, and finally ending with portfolio construction.
In such a world, one would expect the distribution of performances to be very wide. Indeed, a large dispersion of performances should reassure us that capital has been properly allocated. After all, not everybody can win the jackpot.
Alas, today’s world is not perfect, and this is not how capital is allocated. Instead capital is allocated according to the market capitalization of the assets under consideration. So nowadays, capital is directed to an investment if it outperforms. In simple terms, this means that capital is channeled to companies enjoying an increase, not in their ROIC, but in their share prices. In a world in which investments are made according to the marginal ROIC (i.e. the past), these two tended to overlap. As a result, indexation worked, but only as long as no more than about 5% of assets were managed by “free-riding” indexers.
Not in today’s world. Today indexing has become the dominant asset management style, and investments are dictated by market cap and changes in market cap; which is simply another way of saying that capital is now deployed according to momentum-based rules. This was very visible in 1999-2000, and is almost as visible today.
Intellectually, the old method of investment was based on a “return to the mean” approach. When the price movements of an asset became excessive compared to its expected ROIC, then one bought - or sold - the asset. Today, capital is allocated only according to marginal variations in the price of the asset. The more it goes up, the more money managers invest in it. The more it goes down, the less managers own.
A return to the mean methodology leads naturally to a stable, but moving, equilibrium. Momentum-based investing inevitably creates an explosive-implosive system, which swings wildly from booms to busts and back again. And if monetary policy is as silly as it has been since 2002, these swings will be even more pronounced.
The closer we get to a bust, the tighter the performance dispersion among money managers, as the poor fellows trying to manage efficiently and professionally lose their clients to benchmark optimization algorithms. I don’t have the necessary data, so cannot prove it, but I would not be surprised if a sharp fall in the dispersion of money managers’ results is a reliable warning that a bust is approaching.
The goal of every socialist experiment is for everybody to earn the same salary. In the world of money management, we seem to have achieved this remarkable ambition. Hurrah!
Of course, if everybody gets the same results, then no one is going to get fired for underperforming, which is great news for the people administering the capital (I hesitate to call them managers). But—and here is what we do not see—our capital is being massively misallocated, all the time.
People ask me why we have no economic growth. Why on earth do they expect economic growth in a socialist system?
Posted by Bud Fox at 5:27 AM
Friday, April 10, 2015
Thursday, April 09, 2015
The proportion of active US equity managers that underperformed the S&P 500 index in 2014 was “extraordinarily high”, according to S&P Dow.
“In a low-dispersion environment, the value of skill goes down.” —Chris Bennett & Craig Lazzara, S&P Dow JonesResearch by the index provider reported that record-low measurements of stock dispersion in the benchmark had wiped out many opportunities for stockpickers to outperform.
“This is not primarily a reflection of manager skill,” wrote Senior Index Analyst Chris Bennett and Managing Director Craig Lazzara. “The problem is that in a low-dispersion environment, the value of skill goes down.”
Of a sample of 362 US equity funds, just 37 outperformed the S&P 500’s 20.76% return in 2014, a significantly lower proportion (10%) than in the previous two years, according to data from FE Analytics collated by CIO.
“For low-dispersion, high correlation sectors, the most important decision is the sector call, rather than individual stock recommendations.”Bennett and Lazzara reviewed historical dispersions within S&P 500 industry sectors and between sectors, as well as comparing the index to mid-cap and small-cap benchmarks. Small caps offered the highest dispersion and volatility measures, the pair found, meaning better stock-picking opportunities were likely.
The pair also argued that managers should focus on sector calls rather than always trying to pick the best individual stocks—at least when it came to the S&P 500.
“For low-dispersion, high correlation sectors, the most important decision is the sector call, rather than individual stock recommendations,” Bennett and Lazzara wrote. “A correct sector call will be reflected relatively consistently across all stocks in the sector.”
Analysts covering utilities or energy companies “would be well advised to spend most time and effort deciding whether to be in or out of the sector”, the report concluded, citing the low levels of dispersion between such stocks. In contrast, analysts working on technology or healthcare “may be better off trying to separate the sectoral wheat from the sectoral chaff.”
Bennett and Lazzara’s paper, “Some Implications of Sector Dispersion”, can be downloaded from the S&P Dow Jones website.
Posted by Bud Fox at 7:49 AM