Thursday, November 19, 2009

My mother always told me if I concentrated, it would pay off

Upon reading a couple of articles lately regarding the pros and cons of holding concentrated portfolios (i.e., portfolios that hold relatively few securities), I decided to look at the impact of concentration and see if my mother’s advice would have paid off in terms of risk and risk-adjusted performance over the recent past.

Specifically I wanted to determine if investors were rewarded or penalized for investing in more concentrated strategies relative to more diversified portfolios. To investigate, I used the Morningstar U.S. Large Cap Equity Universe as my sample and quintiled the funds in that universe based on their number of holdings. I then used FactSet to calculate various measures of performance and risk for the first (most diversified portfolios) and last (most concentrated portfolios) quintiles. To allow for easy comparison I calculated averages for the quintiles at each point time for statistical measures I had chosen. I chose to focus on the period of January 2008 to October 2009 (the most recent 22 months). Since these are all supposed to be large cap portfolios, I used the S&P 500 as my benchmark for any benchmark relative calculations.

Let’s take a look at the results. First things first: how did the two groups compare in terms of performance? As you can see from the below chart, neither the Diversified (Q1) nor the Concentrated (Q5) groups outperformed the S&P 500 for first 13 months, though during this time the average of Quintile 1 portfolios clearly outperformed the average of Quintile 5 portfolios. What we can also see is a significant and consistent change of fortunes from February 2009 onwards, where Q5 seemed to benefit significantly from the market upswing.

The main thing I would like to understand is whether the improved performance we see above was achieved as a result of taking on substantially more risk. Also, on a risk-adjusted basis, which group of portfolios outperformed the other? Let’s take a look at a few different measures to see if we can uncover any trends.
Above we see the average volatility of the two groups of portfolios over time. The chart clearly shows that in terms of absolute volatility there is actually little difference between the two groups of portfolios over the last 22 months.
Now when we look at a relative measure of risk like the above Tracking Errors relative to the S&P 500 we do see that, as expected, the more concentrated portfolios (Q5) appear more risky relative to the broad market index. To answer our questions, we need to determine whether or not the managers of concentrated portfolios were able to more efficiently manage their portfolios by adding appropriate levels of performance for their increased relative risk.

To wrap things up, I examined the average annual Information Ratios of Quintile 1 vs Quintile 5, and it does appear that more concentrated portfolios (Q5) on average do a better job of managing the risk-return tradeoff as evidenced by the higher IRs. Obviously this is far from conclusive, but my quick analysis of the situation indicates that during the period of high volatility we have seen over the last 12 months or so, the more highly concentrated portfolios have done a better job than their highly diversified peers.

So maybe my mother was right; if you concentrate you will do better. I would like to know what the general consensus is amongst our readers – during volatile times would you rather hold a highly diversified portfolio or a very concentrated one, and why?

To receive future posts by e-mail, subscribe to this blog.

Monday, November 16, 2009

Register for our "Alpha vs. Risk: Where Should I Spend My Time" live webcast

FactSet's risk management webcast series, focusing on helping you produce alpha and create performance-enhanced portfolios, wraps up this week with Alpha vs. Risk: Where Should I Spend My Time? The live event will be Wednesday, November 18 at 2:00 p.m. EST/11:00 a.m. PST.

Register for this session now! Space is limited and available on a first-come, first-served basis.

The presentation is hosted by Steve Greiner, Ph.D., former Head Quantitative Strategist and Portfolio Manager for National City Bank.

Steve will discuss the practical and theoretical issues regarding which piece of the portfolio management process adds more value: searching for alpha or forecasting risk. First Steve will review tracking error and asset allocation, and how their misunderstanding and misapplication contributed to the 2008 meltdown. Second, he'll discuss risk vs. alpha and consider each of them separately, then show why you need to consider them simultaneously, concluding with examples.

Steve was the Head Quantitative Strategist and a Portfolio Manager for the institutional asset manager arm of National City Bank (pre-merger with PNC). He was a key member of the Allegiant Structured Equity team, sitting on the Investment Committee and leading several strategies and being an integral contributor to other investment teams. In addition, Steven leverages his expertise to test quantitative processess employed by Allegiant's other investment teams and has firm-wide risk management responsibilities. Joining Allegiant Asset Mgmt in 2005, he previously served as the Large Cap Quantitative Head and Research Director for Harris Investment Managment and has 21 years of quantitative and modeling experience. Steven received his B.S. in Mathematics and Theoretical Chemistry from the University of Buffalo and his M.S. and Ph.D. in Chemical Physics from the University of Rochester, along with Post-Doctoral experience from the Fachberiech Physik from the Free University of Berlin.

Send your questions for Steve via Twitter @FactSet to be answered during the webcast.

Tuesday, November 10, 2009

Bets are looking Beta by nature

As a FactSet blogger focused on the nature of the risk being seen in the market, it has been interesting to watch how the rally that started back in February has turned from a "dead cat bounce" into the "start of a new bull" run via a "reaction to an overcorrection" as time has given commentators that little extra piece of information on which to base their comments. Indeed, in the history of this blog, I myself have tried myself to base a forecast of future events on what was available at that time only to see the market deliver a different result and subsequently looked for further explanation.

I thought that this week, therefore, I would merely commentate on what can be seen and let you the reader agree or disagree and then set your own expectations.

I wrote a month ago that there seemed to be a lack of conviction in the positive run on the market through observation of the amount of cash that active managers were still keeping in terms of asset allocation, and while the continuing rally has seen that allocation reduce, it is the nature of this reallocation that I want to examine.

I therefore present the below chart of percentage contribution of factor risk when comparing the active index Lipper Large Cap Core against the S&P 500. (I have generated the data using the APT United States factor risk model as by its nature it separates systematic risk from unsystematic without any pre-specification of factors and, therefore, is free from any potential bias that another construct might be accused of introducing.)

The chart shows how the nature of the risk over the last couple of years has varied in terms of the systematic contribution.

June 2006 showed the active risk taken on average to be split approximately 50/50 between systematic and stock specific, rising in late 2007 to around a 65/35 split. This timing coincides with the general realisation that a huge number of quantitatively driven processes were actually very parallel in nature, underlined through the liquidity squeeze and bringing about a rethink in these methods.

Late 2008 shows another spike where the general run on the markets (including the fall of Lehman Brothers) can be observed through an increase in factor correlations as again a large number of investors moved together. The fall back from that high is more a reflection of the rise in stock specific risk (e.g., holding RBS vs HSBC, Ford vs Chrysler) rather than a more uniform move towards stock selection.

Now consider the most recent observations of factor risk accounting for over 80% of total active risk. There has been a general rise in tracking error of the index without a parallel rise in general variance, implying that managers are taking on more risk. The reasons for this are perhaps an increasing confidence or alternatively through facing rising opportunity costs of not being in the market. What this chart does show is that the risk being taken on is extremely systematic in nature, asset allocation is the major factor in active management rather than stock picking.

I suggest several reasons for this:

  1. There is still a lack of confidence in the equity markets and therefore the systematic nature is reflecting a straight in/out decision.
  2. Managers are taking a short-term view and therefore getting exposure through liquid instruments such as ETFs which would bias the risk profile in this way.
  3. There is an acceptance that chasing alpha is much harder than used to be believed and that a more beta-biased methodology is more consistent.

Readers, I'd like to hear your thoughts. Do you agree or disagree with what I've written over this past year? What are your expectations as 2009 comes to a close?

To receive future posts by e-mail, subscribe to this blog.

Monday, November 2, 2009

Register now for our live "Finding Alpha" webcast

Join FactSet for a series of insightful risk management webcasts, focusing on helping you produce alpha and create performance-enhanced portfolios.

FactSet's Wednesday webcast series starts November 4 at 2 p.m. EST/11 a.m. PST with Finding Alpha.

Register for this session now! Space is limited and available on a first-come, first-served basis.

Led by Dorie Kim, FactSet Quantitative Analytics Specialist, the session will focus on finding an alpha factor that fits your portfolio management process. Finding this factor involves understanding the returns, correlation and predictive power of factors through time, across different subgroups of securities. Learn more about the workflow of generating a stock scoring model that will be used in backtesting and production environments. Most importantly, find out how to produce alpha and create performance-enhanced portfolios with stop loss and lock gain rules.

Next, you'll move into a discussion of market risk and creating your own custom risk models. FactSet merges our alpha factor with common market risk factors such as beta, size, valuation and sectors, to create a risk model. This risk model will measure how well our alpha factor has been working and determine if the portfolio has effectively incorporated the potential alpha.

Dorie J. Kim is a Quantitative Analytics Specialist at FactSet. She is responsible for providing factor modeling, portfolio backtesting, and optimization tools as well as risk management solutions to a diversified client base in the West Coast. Prior to joining the group in 2008, she worked as a FactSet consultant supporting more than 20 buy-side firms in the Bay Area and New Mexico. She holds a BS degree in electrical engineering from the University of California, San Diego.

See the full series at www.factset.com/huntforalpha.

Extrapolation revisited

In a previous post, we discussed the prospect of inflation and how one might create appropriate stress for such a scenario on FactSet. The specific example we discussed raises a more general question of the proper design of stress tests. Remember, the problem that we encountered was that most models did not have history that contained any significant rise in CPI. The highest CPI rise observed in the past 20 years was about 5%, while we wanted to see what a 10% rise will look like. The premise of our search was that 5% is fundamentally different from a 10% rise when we are talking about CPI, and thus it would be inappropriate to simply linearly extrapolate from 5% environment to the 10% environment (a scalar of 2).

What is the general idea at work here? Do we always have to observe in history some number of exactly the same impacts as we are trying to model, in order for the covariance structure to make sense? If not, what kind of extrapolation is appropriate and what kind is not? We will first provide some guidelines, and in the next post will dwell more on the reasoning and theoretic economic issues involved.

In general, the process of stress testing should force the practitioner to answer the following questions (we are talking only about factor stress tests, since historical stress testing design is quite obvious):

Question 1: What kind of impact are we trying to model?

Stress testing is not about predicting specific events like particular company’s default or a natural disaster. It is all about impacts. In order to properly design stress tests, you have to think about stress testing as a tool that allows you to examine systematic weakness in your portfolio. The best analogy for the process is car crash testing, where a designer cares not about what may cause a particular accident, but rather about a limited number of possible impacts. That is why we use the term Portfolio Crash Testing when referring to stress testing.

The impacts can come from a few categories:
  • Broad market impacts described by indices such as S&P 500

  • Sector impacts described by indices such as S&P Financials or S&P Technologies

  • Economic variables in a loose sense of this term (e.g. oil, gold, CPI, GDP…)
Sometimes it is useful to combine from the same or multiple categories. Our multiple factor stress testing functionality was designed specifically for that purpose.

Question 2: Now that we decided on the financial impacts we are modeling we have to ask; is the model in use well suited for modeling this impact?

The model is well suited for our task if there were similar impacts observed in its history. Similar does not mean exactly the same. A useful simplification for the purposes of stress testing is to think about each of the above mentioned factors as roughly two kinds of behavior. One kind could be characterized as more or less trading range; this environment as we will see in the second part of the post is what is usually described by the economic theory as an equilibrium or near equilibrium state. The relationships between assets (described in the case of most risk models as correlations) are mostly stable.

The second kind of an environment is one in which changes are sharp and the relationships can rapidly change (e.g., rise in correlations). This is the extreme environment in which market participants lose any sense of equilibrium, and supply and demand fluctuate sharply, possibly becoming strongly mismatched. If we have some observations of the extreme variety, it is fair to extrapolate from them, even if the magnitude of our stress testing shock is considerably larger. For example, if we saw a 30% decline in S&P 500, it is fair to extrapolate to 50% or even 60%, because the events differ in degree, but do not differ qualitatively in a major way. However, if nothing that we could call a major shock was observed in the sample to a given factor, linear extrapolation is likely to be hopelessly wrong. This goes for inflation. A 10% inflation is fundamentally different to the economy that 2 0r 3%, even 5%. That is why linear extrapolation from existing CPI data will not work.

We should be clear that for vast majority of the impact the risk model has some observations from the extreme sample; therefore it is quite fair to extrapolate as long as those extreme observations get enough weight in a calculation of the covariance matrix (see next question). In summary, we assert that market conditions when, for example, the S&P 500 went down significantly in 1998 are similar to those observed in the 2008 crash and will be similar should another major sell-off occur. There are many reasons for this and we will elaborate on some in the follow-up to this post.

Question 3: Should I use the Event Weighted or Time Weighted method for stress testing?

A detailed discussion along with empirical testing can be found in Tail Risk and VaR: Reconciling Theory with Reality in FactSet’s Portfolio Analysis. In short, Event Weighted is suited best for extreme impacts, because it overweighs the extreme observations in the calculation of the covariance matrix. Since stress testing is mostly concerned with major impacts, the Event Weighted method is preferred in majority of cases. The Time Weighted method should be used when we want to determine portfolio moves in the environment where the relationships will stay as they are now and were recently (i.e., no sharp disequilibrium occurs). It is important to note that in times of major market reversals Time Weighted and Event Weighted methods converge, because the Time Weighted method assigns higher weight to the recent observations which also happened to be extreme.

There is one more question remaining.

Question 4: What if we are trying to model something that has no precedent in the history of the model, e.g., a significant rise in CPI?

The way to approach this problem is to consider other impacts that may become highly correlated with the one we are trying to model if a major move in one of them occurs. For example, when we were designing our stress testing product in 2006-2007, one of the most interesting shocks that we wanted to test was a significant decline in housing prices. However, significant declines in housing had not yet occurred, and there were no broad home price declines in the history of the models. This led us to consider what else would happen if housing were to drop significantly. The first and obvious observation was that the financial sector was likely to suffer a great deal in a falling home price environment. Thus, we designed tests around major declines in S&P Financials and called them housing price stress tests. Subsequent events showed that our hypothesis was correct and portfolio reactions were reasonably accurately predicted. Another useful, if more complicated, example is inflation. In the previous post, we described such an inflation proxy test by simultaneously shocking gold up 40% and keeping real estate flat. We chose to stress gold up 40% and housing prices 0% (flat) for the following reasons. Gold up is a well known inflation hedge, because it really has no reason to move outside of inflation of the money supply. However, it was rising very significantly from 2001 to now, even though there was not a large consumer inflation for most of this period. But how is that possible?

Our hypothesis was that the inflation really was there all along, but it was channeled into assets like real estate and financial assets. It was kept out of the consumer products, because China was exporting deflation. The way that China did it was by keeping their currency artificially low vs. the U.S. Dollar. In other words, they forced their citizens to underconsume, since their currency was worth less than it otherwise would have been had the market forces been allowed to play out. This coupled with the fact that China is a key exporter of consumer products to the U.S. kept consumer prices artificially low. The significant inflation of money supply which was going on since at least 2002 was channeled into assets like real estate and financial assets, as we said above.

What did China get out of this symbiotic relationship? It got to build a huge production base to position itself as the economic powerhouse by using U.S. consumption at the expense of the underconsumption of its citizens as the engine of growth. What did the U.S. get out of it? The U.S. got the ability to lower the Fed Funds rate without paying the price of the consumer price inflation and the resulting instability.

All this long reasoning explains why creating a stress test with only gold prices up 40% was not enough to model this scenario. It would have simply given us the asset inflation scenario that we observed in 2002-2008. That is why we explicitly added the cap to the real estate return of 0%. The flat real estate return suggested that we want to see the impact of a 40% rise in gold prices without the asset inflation component (since real estate was the major beneficiary of asset inflation), that is we want to see a consumer inflation.

As you can see the process of creating proxy tests is quite elaborate and requires much more effort in the design stage. We believe that it is fairly infrequently that we have to resort to this approach, but when we do it can be of great value.

In my next post, we will trace the origins of the equilibrium thinking back through Harry Markowitz to the work of Leon Walras and will show why the Event Weighted method corrects for the problems inherent in assuming financial market runs as “a constant and known statistical process” ( as quoted in a Basel Committee on Banking Supervision report).

Make sure you see part two of this entry by subscribing to this blog.