the fact that there are a lot of people who raised issues about problems emerging. But there are always a lot of people raising issues, and half the time they are wrong. And the question is, what do you do?

Indeed, there are always people predicting imminent financial disaster and they are usually wrong (see Ravi Batra).   That is, from a Bayesian perspective, given a small probability of say 2% of X, and many (but proportionately few) forecasting X, when X happens, should their opinions be upgraded to a probability of 4%?  If 4%, is that sufficiently large that we should make massive adjustments to incorporate their logic? What if this just reminds us they are outliers, emphasizing 2% events?  What if X happened, but it was because of Y, and not Z as they argued ex ante?

With a big issue like the economy there are always many Cassandras who will have called the crash, but then, the question is whether their theory seems helpful.  After all, many critics of capitalism celebrated the Great Depression as vindication of Marx, yet in retrospect this seems to have been a problem independent of either Marx’s conception of the laws of production, or of the relative attractiveness of communism. 

No financial expert focused on credit risk of mortgages as a serious threat to our financial system prior to 2006–though some, like Shiller, noted in 2005 that ‘significant further rises in these markets could lead, eventually, to even more significant declines’, which is typical of the hedged way this warning was framed.  Most of the risk in Fannie and Freddie was focused on too much interest rate risk from the notoriously difficult problem of estimating prepayment risk (Taleb see here, Mankiw here).  Others, like James Grant, Stiglitz or Nouriel Rabini, saw risks from disparate arenas like oil, globalization, or  secular increases in leverage.  Charles Morris’s "The Trillion Dollar Meltdown," David Smick’s "The World is Curved,"  George Soros’s "The New Paradigm for Financial Markets," Kevin Phillips’s "Bad Money," and Peter Schiff’s "Crash Proof," basically cover any possible cause (though I’m waiting for someone to blame it on Global Warming).  Putting a belt and suspenders on all these risks would be a major impediment to future productivity. 

The problem is that when a small probability event happens to a complex system it is not so simple as reverse engineering a bridge collapse or a space shuttle disaster.  The I-35 bridge collapsed in Minneapolis last summer, and it appears the main culprit was a simple gusset plate had insufficient thickness: one-half inch instead of the more appropriate one inch.  Doh!   But in a complex system like an economy there were multiple failures, from regulators and legislators encouraging subprime lending, to the investors taking on too much debt, to the rating agencies saying the more lenient lending standards were immaterial, to investors not appreciating that housing price increases masked underlying credit problem, to no one seeing the cascading implications of an increase in default risk within the non-transparent balance sheets of global financial institutions.  All were necessary, none sufficient, and each of the intellectual errors they made were pretty independent, with very different objectives.  Like the Great Depression, there was not one single bad act in our current problem.

Those most vociferously denouncing the stupidity underlying this mess have a theory that focuses on one cause–though sometimes incredibly vague, like ‘overconfidence’ or ‘greed’–because experts usually have very specific theories (eg, ‘peak oil’), but this neglects the other causes that were also necessary, and so are incomplete.  A robust risk system is a usually based on a theory, but is also quite klugey: it has several, if not 30, ad hoc rules too.  In reverse-engineering a complex system disaster, it is important to realize that high-profile experts with their pet theories tend to overemphasize a small part of the problem.

Disasters that happen one a generation are usually benchmarks for future stress testing.  They highlight flawed assumptions.  But using them to annoint new theories, especially those proposed by ‘correct’ forecasters, is usually not a good idea.  I’m afraid that as the securitization market has eliminated  subprime and Alt-A lending back in 2007 when this problem became apparent, the main remedy to a repeat of this crisis has already occured.  Now the question is, how many additional cures will be heaped on, loaded with pork, special interest rights, subsidies and protections, all hiding behind some permabear theory that  was incorrect for the 25 years prior to 2007.

The inequality in these fields, where incomes have a power law distribution, is not bug, it’s a feature.  The presence of superstars, whose income and status is so high, is the offsetting basis of the dreams for those in the industry, why they are willing to accept less ‘on average’.  In dreams without such opportunities, you need security, or some other offset to compensate. If this is true, it suggests there is a general hope premium in industries, and even assets.  Many financial assets that have the highest volatilities have below average returns, if not negative returns: out-of-the-money call options, Junk bonds, highly volatile stocks, extreme-odds at the racetrack.  We pay to dream, and it can be frivolous, as with the $1 I recently spent on a $206MM lotto ticket Saturday (odds: 1 in 130 million, I did not win), but it can also be a significant part of one’s investment portfolio (not wise, in my opinion, but real). 

People take risk based on hope, and hope is a function of one’s dreams.  In 1961 Walter Gutman wrote a book You only have to get rich once, and noted that "growth stocks might better be called dream stocks", and that ‘dreams are real–we have them every day.  It’s a big mistake to think dreams are unreal and what is called real life is real’.   This is a simple, profound, model of the value effect, where stodgy, low beta firms without much upside generate a higher return than stocks that can be classified by some as the next ‘Yahoo!’ Dreams, in moderation of course, are paradoxically as real as anything, and assets that embolden dreams have extra value for investors, and they are willing to accept a lower ‘average’ return because in taking risk, because they are already ignoring the skepticism of the consensus.

Jonathan Alter recently wrote that we should perhaps pay teachers more, and pay their superstars a lot.  I think the data suggest that if we start paying really large salaries for superstar teachers, we could pay a few of them more, but pay them in aggregate less.  That is, we would have just as much supply if we offered them the hope of become very wealthy, with a lower average pay, than our current system, which pays them a rather solid pay but with very limited upside.  No one with really large ambitions goes into education precisely because they top has a low ceiling.  Hope is worth something, in terms of a higher price, and thus lower  return.

One particular area where the cognitive bias of overconfidence can be helpful is in entrepreneurship or investing.  Keynes hypothesized in The General Theory that if "spontaneous optimism falters, leaving us to depend on nothing but a mathematical expectation, enterprise will fade and die". Indeed, if you look at the data on stock picking, or investing with professionals, it is a puzzle why people don’t merely all use passive indices because their average alpha is negative, and further, the average returns to highly undiversified C-corps or partnerships, seems woefully insufficient to justify their significant volatility   But over-confidence of entrepreneurs is helpful because it offsets the underestimation in other benefits of investing or entrepreneurship. Basically, the totally rational investor won’t count what he can’t quantify, but much of the value in investing, or entrepreneurship, comes from an option value that is impossible to quantify. I know someone who built a product based on an optimization routine for superior investment strategy—the flagship idea failed, it never had a chance, but his system is now a popular risk management tool. I know someone else who created a system to consolidate earnings expectations in order to beat the market—it too failed, but his database of earning expectations was put together thoughtfully, and it became successful platform for disseminating this data. Actions affect one’s knowledge, reputation, and contacts in ways extremely difficult to quantify, because you can’t really define the probability state space.  But the idea is simple, that people often get into some field to find gold, but then make their fortune selling shovels. 

Investing for most people is irrational, in that regular retail investors and professionals don’t outperform the indices, trade too much and are too undiversified (transaction costs merely lower one’s returns, on average).  In sum, their Sharpe ratios would be much higher if they bought index funds as John Bogle, Burton Malkiel, or Eugene Fama recommend, and presumably we all want higher Sharpe ratios.  But think of the payoff to investing not merely the return from the investment, but rather, conditional upon success, access to a career in finance.  One can (must?) parlay a successful call into a career as a broker, investment adviser, or some middle management position in finance (see this Bloomberg article touting the expertise of recent ‘winners’).  Some initial filter, however imperfect, is needed by those who use financial professionals.  An individual taking risk in this context suggests they had private cues indicating they were better than average, because risk-taking is costly, and though highly noisy, successful investments are better indicators than losers.  Further, the act of investing can illuminate some parochial services where one does have an edge, and our specialized economy is based on a myriad of activities that are generally unknown until you get your hands dirty as a practitioner.  Sharpe ratios ignore this bigger picture, and the fact that millions of people work in finance, and many are very well paid, suggest this option value is nontrivial.

Successful strategies are based a kluge of assumptions, and the key is you are evaluating an assumption not in its effect on one act, but rather as part of a meta-strategy, as a general disposition that affects many acts, and every act has ripple effects on one’s life. Instead of evaluating assumption X on Y by looking at X and Y, you instead look at the effect of holding belief X on the person believing X. I suspect every novice investor is somewhat overconfident, and too many people  invest recklessly (‘gamblers’, Buffet derisively calls them). But there is a method to the madness, because the illogic of overconfident investing offsets the general underappreciation of the option-value of investing on one’s career, where you can provide a variety of auxiliary services in finance, and risk-taking is part of the application process.

As they say, Hofstadter’s Law is funny because it rings true to many programmers, who often work on complex projects that take years to complete.  Clearly an alternative to the Law of Iterated expectations.  Why might people involved in sufficiently complicated tasks–writing a paper, a book, building a deck–generally underestimate their length?  I think the main reason is that goals become self-fulfilling, so any lengthening of a goal time would add to the total time  the way bureaucracies spend the limit of their budget whatever it is.  Just like a group of people, people themselves have multiple goals; to watch tv, to get a project done, to be a better golfer.  A successful goal needs a  bias to compete with your other goals, who probably also have biased homunculus advocating for them in your mind. 

On one level an unbiased expectation is optimal because it allows us to allocate our resources more efficiently.  But there are many cases where this is not true, where a little too much hope and faith actually makes you a more successful person, and more fun to be around.  Just think about how annoying ‘brutally frank’ people are–they are jerks.  Think about the guy who thinks he is a better dancer than he really his confidence actually makes him a better dancer, because part of good dancing is not being self-conscious.  Robert Trivers has pointed out that self-deception is, in moderation, an evolutionary advantage, in that a liar who believes his own lies is a more effective persuader than a lie who knows he is lying, and fundamentally we are social animals trying to convince others to do this or think that.

Risk that can be diversified away does not generate returns in theory (see the CAPM ) or practice (see Ang, et al ).  That is, taking more of this risk does not imply a greater average return.  Given the general massive lack of diversification, the only conclusion is that most people think they can pick stocks or funds that will rise more than average. They must be wrong (on average investors are average), but they are the vast majority of investors.  Thus the traditional theory of mean-variance optimization is more prescriptive than descriptive, in that most people are not rational about their forecasts, they are biased upwards in an area of proficiency that seems to be more costly than trivial overestimation of one’s driving ability.  How can this massive delusion be an equilibrium? 

First, the evidence of suboptimal diversification.  From a mean-variance perspective, an optimal portfolio is well diversified, with literally hundreds of stocks underlying a portfolio, most cost efficiently implemented via an ETF or mutual fund.  One may add that diversification into real estate and other countries is optimal, but we can assume that frictions or ‘bounded rationality’ prevent that, and just look at the optimality of a long-only equity portfolio.  As opposed to owning hundreds of stocks, Goetzmann and Kumar (2001) and Polkovnichenko (2004) found the median number about three, not much different than what the Fed reported from a 1967 survey.  So compare an average S&P500 annualized volatility of 15%, with a portfolio of three stocks has an annualized volatility of 22% (assuming an average annualized vol of 34%, and an average correlation of 0.12).  If one is maximizing a Sharpe Ratio, and assuming an equity premium of 6%, an investor maximizing his return over the risk-free rate divided by the standard deviation (the Sharpe Ratio) would need an extra 2.8% return on his picks for this to be rational, so the average investor thinks his stocks return an average of 3% more than the average stock (of course, this is made more complicated by differing transaction costs, the marginal nature of a stock portfolio to one’s wealth, but these other effects are offsetting and the basic logic applies without loss of generality).  Goetzmann and Peles (1997) find that individuals overestimate their own investment performance by an average of 3.4%, so they have biased corroboration of their ability to pick stocks. 

It has been long documented that mutual fund do not outperform passive indices, yet mutual funds that clearly offer less diversification and higher expenses than alternatives like passive S&P500 funds are still a large investment class.  People–retail and professional–clearly think they have some kind of edge, or alpha. 

Now the answer, actually, two answers.  The first is that it is not an equilibrium, as suggested by the continued growth of passive indices as an investment class.  People have been moving away from active management as information about passive indices has grown with experience, and perhaps this growth will only end when informed agents generating above-market returns are the only active traders in the market.  The second answer, the proof I can’t fit in the margins of this blog post (heh), is that risk taking as a general strategy does generate above average returns, but only in domains that are not zero sum.  For example, my investment into a sole-proprietorship is not necessarily zero-sum, I can create value. My investment into a firm via a private-equity placement is not zero-sum because I often receive warrants, options, or rights that are basically an exchange for special financing at the expense of existing shareholders: my investment adds value to the asset, not merely capital, and I capture that value via off-balance sheet contracts. 

When I think of the wealthy people I know, most have built their wealth this way, investing in parochial situations where they captured value via non-straightforward means.  It doesn’t show up in the data–either aggregate stock picking prowess, or the returns to entrepreneurial investment–because there aren’t any good databases with these types of contracts, which are not standardized, and for public equities often not fully documented in the 10-ks and 10-Qs.  Further, people have an incentive to under-report these assets because 1) they would be taxed 2) dissemination would incite the other claim holders who are expropriated and 3) people like to think they generate higher returns through proficient selection, and not from mere better negotiating. The dataset of this type of information does not exist, and it is quite understandable why.  The best empirical support I can give comes from the work of Susan Chaplinsky at Dartmouth, who finds PIPE (Private Investment in Public Equity) investors outperform existing investors by a substantial margin, generating large returns even though the average stock underperforms subsequent to a PIPE offering.  Where are they getting this excess return?  Primarily warrants and rights, because without these PIPE investors would be losing money year after year, while PIPE investors within hedge funds in aggregate make decent returns (ie, you have to add the warrants and rights to have this make sense).  People merely mistakenly apply the risk-reward notion to zero sum arenas, so they mistakenly apply this to areas where it is mathematically impossible, such as me merely buying your existing IBM shares. 

Thus, I posit the theory that risk is compensated by return, but only in areas that are non-zero sum.  When investors merely buy existing claims from each other, they are engaged in overconfidence (see Milgrom-Stokey’s No Trade Theorem ).  People self-select into non-zero sum situations based on their informational advantages and above-average ability, and on average prosper accordingly.  Thus you need not only a high tolerance for risk, but moxie, because it takes energy and negotiating prowess to create and capture these non-standard options from one’s investments.  They are active investments even if the activity is merely in the negotiation of rights. 

While rich people are usually somewhat fortunate, there is an element of skill or ability that suggests it is more than mere tolerance to volatility that is driving their wealth growth rate.  That is, while the CAPM would suggest that the only difference between a large group of investors to outperforms the average is their luck and risk tolerance, I think it is reasonable to think this is not the whole story.  I can’t prove it, but the alternative, that risk is everywhere uncorrelated with risk, is simply too irrational to bear (an assertion in Keynes’ General Theory).   That mere idiosyncratic risk is clearly not compensated is in stark contrast to the behavior of most investors who are massively undiversified, and  even systematic risk appears non-compensated (see Why Risk is Not Related to Returns by yours truly).

In contrast, market disagreements are a refreshingly empirical and rational alternative to those with different prior beliefs about politics. Portfolio managers consistently ‘agree to disagree’ and no portfolio managers use the words dogmatic or biased. Instead they use the terms fool, idiot, and moron. These are less morally charged words, aimed at errors in thinking, as opposed to malice. One mutual fund manager may be long Google, the other may ignore it. In the hedge fund world, things can be crazier because investors aren’t constrained to be long: one investor can be short something another investor is long. In markets these disagreements are common, and investors consider their own beliefs ‘wiser’ than the others. Market participants realize, however, that this is largely an empirical matter, and further, that small sample sizes relevant to investors are tentative (eg, the market can be irrational longer than you can be liquid).  Further, they worry much more about their beliefs, as to whether they are correct or wrong, much more so than a typical pro/anti-abortion zealot. No one talks about a ‘dogmatic’ investor, because if he invests based on dogma, he will suffer—he’s merely a fool, full of hubris, thus posterity will punish him so you don’t have to. There is no ill-will towards those who disagree, only envy when you are wrong, and (patronizing) pity when they are wrong.