Black Swans, Real Estate and Financial Stocks
-
Font Size:
The decline in the real estate and mortgage markets in 2007 has much to teach investors. Real estate has taken a bit hit after rallying for years. Banks that are active in the mortgage markets have also suffered substantial declines in their stock prices due to what is now perceived as indiscriminate lending practices. Major REIT indices are down 20% for the year and a range of bank stocks have double-digit declines. Many investors have lost a lot of money (at least on paper) from their holdings in even blue-chip banks like Citigroup (C) and Bank of America (BAC). The question that most investors really need to understand is whether this mortgage and real estate meltdown was unpredictable (a lightning bolt from the blue) or whether there were ways to anticipate and manage the probability of such an event.
The Economist has suggested that the scale of declines in the real estate and mortgage markets in 2007 was essentially a ‘black swan event’:
Although many anticipated a fall in American house prices for 2007, for example, few expected the scale of the ramifications for financial markets, as a whole system of structured finance appeared to unravel and the banking system was plunged into crisis.
In one of the defining phrases of 2007, the author and investor Nassim Taleb has called these occurrences “black swans”—unexpected events that have enormous consequences. These are, by definition, very hard to forecast.
Nassim Taleb defines ‘black swan events’ in the following way:
A black swan is an outlier, an event that lies beyond the realm of normal expectations. Most people expect all swans to be white because that's what their experience tells them; a black swan is by definition a surprise.
If the sub-prime / real estate meltdown was truly a ‘black swan’ event, the probability of it occurring should be estimated to be essentially zero or (at least) vanishingly small by portfolio management tools. There is a very important question here. Did good statistical models assign a non-trivial probability to the scale of losses that we have seen in financial stocks and real estate prior to the event? If the answer is no, then perhaps this ‘event’ in 2007 is an example of one of Mr. Taleb’s unpredictable surprises. On the other hand, if good statistical models assigned significant odds to such a decline, there is an entirely different lesson to be drawn. If this scale of decline was predicted at some reasonable probability, who knew and how did they know?
One of the core related themes of Mr. Taleb’s work is that the standards of mathematical finance are too simplistic to capture the risks of extreme events. In fact, he is highly dismissive of quantitative finance as a whole:
Quantitative economics, particularly finance, has not been a particularly introspective or empirical science….Financial economists built "portfolio theory" that is based on our ability to measure the financial risks. They used the Bell-Shaped (and similar) distribution which proliferated in academia and yielded a handful of Nobel medals…Everything reposes on probabilities being stationary, i.e. not changing after your observe them, assuming what you observed was true. They were all convinced of measuring risks as someone would measure the temperature. It led to series of fiascos, including the blowup of a fund called Long-term Capital Management, co-founded by two Nobel economists. Yet it has not been discredited — they still say "we have nothing better" and teach it in Business Schools. This is what I call the problem of gambling with the wrong dice. Here you have someone who is extremely sophisticated at computing the probabilities on the dice, but guess what? They have no clue what dice they are using and no mental courage to say "I don't know".
Was the sub-prime meltdown a ‘black swan’ event that the quantitative models suggested was impossible? Was everyone playing with ‘the wrong dice’ as Mr. Taleb suggests? I think not—at least based on the evidence before us today. The portfolio theory that Mr. Taleb is so dismissive of is widely in use in banks and on trading floors across the globe. I have worked on these types of models for a decade or so and I think that Mr. Taleb is throwing the baby out with the bath water in his sweeping critique (as above). This is not to deny that the probabilities of some extreme events are essentially totally unknown, but rather that there is a lot of middle ground in which portfolio theory can yield very useful insight into how to invest and manage risk.
Let us begin with a brief discussion of how portfolio theory can be used to estimate the probabilities of events when it is used properly.
The core precept in portfolio theory is that risk and return are coupled. Over long periods of time, asset classes which are more volatile should generate higher average return---and they do. At any given time in a widely traded (i.e. liquid) market for a security, there is a price at which you can buy or sell that security. The price of a security reflects the markets’ consensus opinion of the value of the discounted future earnings generated by the company or asset represented by the security. Of course, nobody knows the future earnings from a company or the future value of an asset with a high degree of certainty, so the price moves around as new information becomes available. There are speculative runs (up and down) in which investors become too enthusiastic or pessimistic about a given asset class, etc. Larger swings in price over time suggest that there is more uncertainty as to the future earnings (and vice versa). While investors converge to a price that represents the expected future earnings in the market for a stock, investors also converge to estimates of future volatility in the options markets. Good portfolio models combine market information on risk and return with long-term capital markets data. For the vast majority of investors, it is useful to be able to understand the broad features of risk in a portfolio. Good portfolio models cannot properly estimate the 1-in-10,000 events, but they can estimate risk reasonably well—enough to demonstrate to an investor or portfolio manager whether his or her portfolio is too risky or too conservative.
A good portfolio model is tested to see whether its forecasts of expected return and volatility are borne out in the market. There are a range of standards for stress testing these models. It is certainly true that portfolio models cannot estimate the probability of massive dislocations like 9/11 or the collapse of Enron. On this Mr. Taleb and I agree. On the other hand, there is quite a spectrum of extreme events that good portfolio models can capture---and the current realty/sub-prime meltdown falls into this category, based on available evidence to date. I performed an analysis in which I used our portfolio management software, Quantext Portfolio Planner [QPP], to project the future returns for two financial stocks, Citigroup and Bank of America, and two REIT ETFs (iShares Cohen & Steers Realty (ICF) and SPDR DJ Wilshire REIT (RWR)). While every portfolio model is somewhat different, QPP’s results are broadly consistent with those used in the industry.
I used three years of trailing data through the end of 2006 to drive the model (my standard) and all default settings in QPP. The projections show what a user would have seen—and a number did—at the end of 2006.
The table below shows the percentiles for projected annual return for Citigroup (C) at the end of 2006. At the very bottom of this table, you can see what has actually occurred for 2007 (note this is through Dec 28). Citigroup has dropped 45% in 2007. The projected returns for C suggest that this loss was on the order of 2.5% probability. Things in this probability range (i.e. 1-in-40) are certainly not implausible. One-in-forty events (2.5%) are not like shark bites or getting hit by lightning.
Projected percentiles for Citigroup (C) at the end of 2006 and actual 2007 return
Without trying to get down to some estimate of whether the losses at Citi are a 1-in-50 even or a 1-in-30 event, I think that it is clear that this scale of loss is certainly not a ‘black swan’ kind of event. The whole idea of the ‘black swan’ is an event that is considered impossible or for which the probability is so small as to be ignorable. If 1-in-40 swans at your local park are black, you would not consider them to be incredibly rare. The 45% drop in Citi over 2007 may have been enormously surprising to many investors, but this does not mean that such an event was beyond a good portfolio model. There is a deep idea here: the market’s risk-return data for C carried with it the knowledge that such a drop was possible at a small but meaningful level. This is not to say that pure historical data provided this estimate, but rather than historical data combined with robust portfolio simulation tools showed that this level of drop was possible at a not-trivial level.
Bank of America (BAC) showed a considerably smaller loss of -19% for 2007, and QPP’s projections using data from the end of 2006 suggested that this level of loss would occur at the 10th percentile (a 10% chance of at least this level of loss)---see table below.
Projected percentiles for Bank of America (BAC) and actual 2007 return
The REIT market has also had a rough year in 2007, with 20% declines for the broad indices. The two tables below show the same QPP projections for ICF and RWR, two major REIT ETF’s, using data available through the end of 2006:
Projected percentiles for RWR and actual 2007 return
Projected percentiles for ICF and actual 2007 return
Both ICF and RWR suffered about 20% declines in 2007, and QPP projected that these losses would occur at between the 5th and 10th percentiles. Things that occur at between 1-in-10 (10th percentile) and 1-in-20 (5th percentile) are far from ‘black swan’ events, if we use the definition that a black swan event is beyond the realm of normal expectations.
Now, let’s pull things together. It is true that there are events that are simply beyond our capacity to calculate the odds---9/11 being an example. There are plenty of them in history. One response to such outliers in the investing world is to simply discount portfolio analysis---and this is what Mr. Taleb would suggest. If he is correct that portfolio theory is based on completely ill-founded models, then it is irrational for investors to try to compute risk or to try to discriminate between risky investments. If our capital markets are predicated on investors using their knowledge to establish a balance between risk and return, but our estimates of risk are without value, then the entire edifice is a pure random process. That is an extreme supposition, indeed.
On the other hand, there is an enormous amount of evidence that investors are consistently more highly rewarded for taking on riskier assets over less risky assets (in a statistical sense). While we cannot quantitatively estimate the odds of a 9/11-like event, portfolio theory actually provides an enormous amount of useful insight when used properly. Using data available at the end of 2006, QPP projected outcomes for financial stocks (like C and BAC) and REIT indices (like ICF and RWR) in which the events that occurred in 2007 were estimated to occur with non-vanishing probability. If I am looking at a stock with a 1-in-20 chances of losing 36% in the next year (as C was projected to do), I would be very careful about how I would include it in my portfolio. I would combine C with other assets that are not highly correlated to it, in order to manage the risks of a major decline as we have experienced this year. I own BAC—and its decline has not caused me any lost sleep. I have other things in my portfolio that have done disproportionately well even as BAC has done very poorly---and this was by design.
Now, I assume that Mr. Taleb would say that the 45% decline in C or the 20% declines in ICF and RWR are not ‘black swan’ events, precisely because a standard portfolio model like QPP calculated that they could occur at non-vanishing probability. The ability to estimate the relative risk in investments is very useful to investors, but Mr. Taleb completely dismisses portfolio theory as worthless and its practitioners as intellectually lazy. This argument is ultimately a philosophical debate rather than a practical one. Mr. Taleb is saying, in effect, that he would rather have no model at all rather than attempt to use an imperfect model. At its core, this is a circular argument. If a portfolio model estimates that something is possible, the event is (by Mr. Taleb’s definition) not a ‘black swan.’ If portfolio theory estimates that the odds of something happening are vanishingly small, and it occurs, it is (by definition) a ‘black swan’ and represents a failure of portfolio theory. How can portfolio theory ever be validated under this sort of scoring?
While the models are far from perfect, and our ability to compute the odds of extremely improbable events is almost nil, the use of portfolio analysis to capture the broad features of the risk in a portfolio is extremely valuable. While The Economist article cited earlier suggests that the scale of the sub-prime mortgage meltdown in 2007 was something of a ‘black swan,’ our results show that an investor or portfolio manager with a good portfolio tool could see that declines that we have experienced this year in real estate and financial stocks were well within the range of the possible.
Get Seeking Alpha Free Stock Alerts by Email!
Get Free Stock Alerts by Email!
ETFs In Focus
-
Editor's Picks
-
Most Popular
- Has Jim Cramer Crossed the Line with Sirius XM?
- Why Core Inflation?
- Apple's China Debacle: The Corporation as an Agent of Social Change
- High Prices Cut Demand for Metals
- On Recent Financial Stories
- Yes, Virginia, There are High Dividend ETNs
- Full list of Editor's Picks »
- Wall Street Breakfast: Must-Know News »
- Apple's Problems - Bad to the Core? »
- Looming Financial Catastrophe: A Real Inconvenient Truth »
- Apple's Biggest Rumor: iPod or Jobs? »
- Solarfun's Huge Run: Time To Lock in Solar Profits »
- Sirius XM Cramer Wars Part II »
- What Did Buffett Buy: American Express or Wells Fargo? »
- Grab Your Shorts, the Tide Has Turned »
- Wall Street Breakfast: Must-Know News »
- Wall Street Breakfast: Must-Know News »
- Sirius XM Belt Tightening Begins »
-
Long Ideas
-
Short Ideas
-
Cramer's Picks
- Office Depot vs. Staples: Discounted Book vs. Superior ROE
- Top 5 Stock Picks for September
- Obama Plays - Fast Money Recap (8/27/08)
- Diversified Portfolios - Cramer's Mad Money (8/27/08)
- Gustav Moves Overdone - Cramer's Stop Trading! (8/27/08)
- GrafTech is Too Cheap - Cramer's Stop Trading
- Borders: Earning Call Notables
- Mexico’s Guillermo Ortiz: The Anti-Greenspan
- Silvercorp: Canadian Mining Profits in China
- Amylin, Lilly: Another Case of a Panic Driven Sell-Off
- Full list of Long Ideas »
- Short Thesis Still Intact at FirstFed
- Short Story: Lehman
- 'Buy, But Sell' - What Are Analysts Thinking?
- Nordson's Rally Is Over, For Now - Barron's
- What's So Special About RadioShack? - Barron's
- Salesforce.com: It's All About the Guidance
- Three Casino Stocks Rolling Over
- New Web Site For Short Sellers: You Gotta Love Capitalism
- Commodity Carnage: Where to Turn Next?
- Fannie and Freddie Shareholders Run for the Exit
- Full list of Short Ideas »
- Diversified Portfolios - Cramer's Mad Money (8/27/08)
- Gustav Moves Overdone - Cramer's Stop Trading! (8/27/08)
- GrafTech is Too Cheap - Cramer's Stop Trading
- The Rebound List - Cramer's Mad Money (8/26/08)
- The List - Cramer's Stop Trading! (8/26/08)
- Can't Turn My Back - Cramer's Lightning Round (8/26/08)
- The Pelosi Factor - Cramer's Mad Money (8/25/08)
- Buy Tech Weakness - Cramer's Lightning Round (8/25/08)
- Fannie & Freddie Too Difficult - Cramer's Stop Trading! (8/25/08)
- Attractive and Single - Cramer's Mad Money 8/22/08)
- Full list of Cramers Picks »
Trading Center
Hedge Fund Jobs
Job Seekers: Search jobs by category, get job alerts by email or live feed, apply online See full list of jobs »
Employers: See all recruitment options, get applications online or by email Post a job »
This article has 26 comments:
Teric
No. Having a model that gives the illusion of knowledge is worse than no model. Mr. Taleb points out that the effects of black swans are overwhelming.
Example:before/ after WW1, European monarchies gone, Russian revolution, England in rapid decline, Influenza epidemic, USA ascendant. As in the book, Goodbye to All That by Graves - the world changed.
Not an example: overeating by a factor of two on Thanksgivng. Does not effect your weight in the long run.
You missed the difference between Mediocristan and Extremistan which is central to the argument. Traditional modeling works in Mediocristan. Traditional modeling is dangerous in Extremistan.
Sometimes a culture suspends critical judgements out of wishful thinking and then cries "black swan" when the inevitable happens.
His main issue is lack of numbers in his arguments.
Considine
Now, I agree with Susan, too: Mr. Taleb's arguments are often worthy of thought and discussion. When he says, quite bluntly, that portfolio theory and quantitative methods as a whole are useless (as in the quote above) and even itellectually lazy, he is taking it too far.
I am a big fan of logic and philosophy, and Mr. Taleb often uses strategies that are ill-founded. By using LTCM as an example, he is saying "hey, look at this case where smart people put too much faith in quantitative finance" and extrapolating to saying that the entire edifice of portfolio theory is wrong. There is a big unsupported leap in logic here. The insurance and banking industries have used these types of models very successfully for quite some time--and will continue to.
Anyway, this is a useful topic and the 'black swan' metaphor motivates good discussions. Thanks.
I think the basic premise of Taleb's book is that these models helps us get into a false world were we think we know! and that's dangerous..
Considine
To FH: your comments suggest that you are not really familiar with modern financial modeling. Modern risk modeling is very useful in helping investors estimate the risk levels of their portfolios. These models are well-tested and benchmarked (the good ones at least). This does not mean that users should be naiive with regards to the limits of the models. They are far from perfect representations of reality--but they are still very useful.
Mr. Taleb is correct that we need to understand the limits of models. Professional energy traders rely on weather forecasts--knowing that these forecasts decrease in accuracy as you go out in time. If I know that there is a 5% chance of 100+ Deg high temperatures in Chicago on a given day, I would manage my positions accordingly. I would not simply ignore the forecast because I knew that the forecast model was imperfect.
Now, I do not hope to convince you or anyone else in such a short article or comments below. I do hope to stimulate interest in the solid use of portfolio management tools such as those that Mr. Taleb dismisses as useless. There is an enormous well-established discipline of quantitative portfolio management and I believe that the basic lack of understanding of this discipline is a major source of information asymmetry between investors. Companies like RiskMetrics, SunGard, and (on a much smaller scale) my own little firm provide well-tested tools. No reasonable practitioner would suggest that people have too much belief in such tools, but they are an important tool in one's arsenal.
When David Swensen of Yale says that he is targeting a specific average return on the Yale endowment with a specific target standard deviation in return, where do you think he gets these numbers?
If you want to get a sense of what it being done by practitioners, simply do a web search on "Basel II VaR."
Have a look at the CFA curriculum if you want more information on modern methods, too.
BUT the biggest nail in your silly probability coffin is that C had a >40% decline just 6 years ago in the August '01 to June '02 timeframe (less than a year; Black Swan=9/11). C had nearly a 50% decline from June -Sept '98 (Black Swan=Russia default). And another from June '90 to Oct. '90 (Black Swan=S&L?, Gulf War I?). And another from from Sept - Dec '87 (Black Swan=October '87 crash). And another from May '81 to March '82 (help me out on that one--interest rates?).
FIVE such declines in the past 25 years. This is not a 1:40 event. Period. That is what Taleb was hammering on. These probabilistic models always underestimate the "tails" because stock returns do not follow a standard bell curve.
By not correctly estimating your downside risk, you greatly increase the odds you'll get blown up. Period. That's one of the big themes in Taleb's work.
Mr. Considine: I'm sorry if my knowledge of statistics is relatively shallow - I'm assuming some kind of Monte Carlo simulation is at the heart of these predictions. Wouldn't it be possible to give a probability curve on the accuracy of the predictor itself, thus at least giving an estimate of how wide the black swan event window is?
After reading your article i found the article suffers from
1. cognitive dissonance
2. Framework dependence
3. Heuristic-driven bias
4. Confirmation bias
5. Round trip fallacy
and the following things might also apply
Empty-suit problem
Future blindness
Locke’s madman
Reverse-engineering problem
Black Swan blindness
and
Finally we are all fooled by randomness
The way you look at the market you can be insane or sane.
Accept that one cant predict all the things and try to take advantage of the black swans.
For that you need lot of patience finally that makes the difference between bleed or blowing up
I don't think Considine ever implied he tried to "predict all the things". Black swans will make any investment method fail whether they are based on fundamentals, technicals, psychology or whatnot.
Yes, to one with a hammer everything looks like a nail, aka. the data fit problem.
The flipside is to one with no (or inadequate) knowledge everything looks like randomness....
Considine
I appreciate your enthusiasm, but you are not grasping the numbers. First of all, the probability of a cumulative 12-month decline is different than the various partial years, etc. Just to prove a point, I have gotten the 25-year history of C and analyzed the 2.5th percentile in returns for all 12-month periods. Guess what--its -36%! So, my analysis was predicting a substantially higher probability of the observed loss than history.
Considine
While testing models after the fact it is always a good idea to be careful. The model I used QPP was run with default inputs used in my articles for years. This is not a case of over-fitting. There is no reverse engineering issue here.
The bottom line here is that you and everyone else are free to disregard available models because you feel they have the potential for 'black swan blindness.' You may as well argue that weather forecasts are pointless--and you are free to do so. Just carry your umbrella and slicker every day.
Considine
dmnieren points out all the "crashes" endured by C over the years. However, over the long-run C has produced a tidy return for its investors. It's split-adjusted price in 1981 hovered just over $1. Today, the battered stock trades around $28. This does not have the look of a disastrous investment.
Considine
I cannot help but feel that some portfolio of the popularity of black swan thinking (as expressed here) has to do with a lack of understanding of statistics.
I've read Taleb's "Black Swan," and have profited from it, both personally and financially. Besides selling his Black Swan idea, Taleb advocates that we all swallow a healthy dose of what I think he calls "pragmatic skepticism/empiricism,... and revisit accepted theories--especially those in finance and economics, and see if they stand up to the no-nonsense viewpoint of the empirical skeptic. A viewpoint that appeals to me especially because of my background in physics.
So in this vein, I'll make a feeble attempt at picking apart your defense of portfolio theory, below :)
"The core precept in portfolio theory is that risk and return are coupled."
Yes, but a good number of successful value investors think this is nonsense. They buy, when in their judgment, the downside risk is minimal, and the upside, large. That is, they don't believe in this model; they believe in another model, and they have money to show for it.
"The price of a security reflects the markets’ consensus opinion of the value of the discounted future earnings generated by the company or asset represented by the security. "
Really? Sure, the estimated value of discounted future earning means something to a long term investor. But to say that whenever I buy or sell a stock I am voting on the value of its discounted future earnings, is plainly nonsense. My decision to buy or sell depends on other factors as well. These include recent volume and price action, market sentiment, etc. Most traders, and investors, in fact, consider second order effects, such as *expected* market sentiment, *expected* price, etc. That is, a kind of hysteresis seems to govern the price action: every trade affects the trades after it. To say, that every transaction constitutes some kind of vote on the future value of earnings, it would seem, is an academic exercise removed from reality.
All the same, thanks for your insightful article! I love to see this black swan idea being discussed!!
-Babak
P.S.
I have a more fundamental gripe with the tenet of risk/reward: it may be a kind of tautology. That is, I have a sneaky suspicion that such a relationship can be made to hold true for any model. That is, it may suffer from what Taleb calls the "confirmation bias." One interesting simulation experiment may be to see if one can devise an imaginary model in which risk/reward are *not* correlated, or perhaps even a model risk/reward are inversely related. Would such a thing be even possible?
Further commentary can be found at this tibet from October:
Quants meet Reality
hingefire.blogspot.com...