Chief Economists are for PR

Ezra Klein has a post promoting Blinder and Zandi's model that shows massive good effects from more government deficit spending. As the model is a 1970's vintage approach, an approach that attracted the nations best minds for decades, and was abandoned because they don't work better than rather simple alternatives (eg, a vector autoregression of GDP, Fed Funds, and the Baa-Aaa spread).

I found this amusing because it highlights that journalists grab whatever science supports their ends. The details are not important, you have a professor with lots of publications, he has a complicated scientific argument, it makes you an objective, rational journalist. He even quotes Narayana Kocherlakota saying macro models work, not realizing the Kocherlakota was actually talking about a very different class of models than the one Blinder and Zandi use, and forgetting that of course a macroeconomist would say macro theory works.

At one point, Klein reaches for this argument for believing in their results:

It's also worth noting that the private sector relies extensively on these models, and it would be odd for them to give Moody's all that money if they thought there was no predictive value.

Presumably, he infers that as Zandi works for Moody's, his results are somehow used by Moody's. They are, but not in the way he thinks. I used to work at Moody's. Moody's does not make money off their macro economic opinions, they make money issuing ratings on debt, something they are paid well for. The macro view is alluded to in any analyst opinion, but even within Moody's it's not like the analysts think their economist knows better than others. CNBC and the outlets need someone to comment on macroeconomic topics, so having a full time economist discuss these things makes sense. Yet, remember, economists can't predict business cycles, or explain why Mexico is poor, while the US is rich. Sure, people have theories, but there's no consensus, highlighting that macroeconomists don't understand the big issues on their plate.

I worked directly for Chief Economists at two major banks, First Interstate in the late 1980's, and KeyCorp in the 1990's. While it would be nice to know when the next recession or interest rate move will happen, no one thought the economist knew better than other random members of the executive committee. Economists are good at presenting the information that seems useful, but as per tying it together, they can't and most people making important decisions know that. This is why economists are always on TV and not in boardrooms. It is also why economics departments at banks have gone from large staffs in the 1970s (at the height of the Keynesian modeling boom), to basically one guy, because it was discovered his or her only value is getting the company name on TV. If someone presents himself as especially credible because he was a chief economist, I know he's a fool.

I spent 3 years of my life working directly for private sector macroeconomists, and the main thing I learned is they don't know anything useful. It's like studying the labor theory of value: if you really understand and have tested it empirically, you use such knowledge on the subject only in arguing with naive people who think the theory can buttress their arguments. I try to rationalize my waste of time on this subject by saying 'well, I now know really well what we don't know', but as the list of irrelevant theories is infinite, if I could redo my career I would have just ignored it all from the outset.

An Economic Slimming Algorithm

I think the key principle that underlies economics is that "individual incentives matter". Add some self interest (envy, greed), and look at the implications. I wouldn't think this way if I didn't think it worked.

I just lost 18 pounds via Stickk.com, a website that encourages weight loss and really any goal. It was founded by an economist based on the idea that incentives matter. I have wanted to lose some extra middle aged blubber for a while, but at the margin I had no real motivation (putting socks in the hamper would help my sex life more than buff abs). I knew I needed some outside force, so I created a contract at Stickk such that I had to lose 2lbs every week for 9 weeks or I would pay $50 for each violation to a charity that supported things I did not like (the anti-charity option).

The goal was cumulative, so basically my target weight goals were 231 lbs, 229, 227 etc. I gave them my credit card, and I had to report my weekly weight, and get a referee of my choosing to validate my progress. I chose a colleague at work, who would look at me on a scale I brought in. One could cheat the system, but when you involve another person, it's less likely. As an economist, I liked the idea: just give the incentive, make it strong, and let me figure how to get there. The algorithm is thus greatly simplified compared to Weight Watcher plans that suggest counting calories.

I only wish the website didn't try to do so much, classic feature creep. It seems to want to be a Facebook, allowing me to write commitment contracts on anything all while keeping up to date on my progress with pictures, etc.

A Batesian Mimicry Explanation of Business Cycles

I went to graduate school to become a macroeconomist and understand business cycles, but became convinced pretty quickly that this problem was not going to be solved anytime soon. The current theories today are pretty much the same is when I was in school 25 years ago. There are theories that recessions are caused by monetary shocks, interest rates being too low, insufficient consumption demand, sticky prices, technology shocks, changes in leisure preferences, cyclical investor optimism. None of these explanations are very convincing, which is why economists still have not coalesced around any one explanation. The debates of 50 years ago are pretty much the same as today, in substance if not in form.

Arnold Kling makes a good point when he states that macroeconomic activity consists of sustainable patterns of specialization and trade, and if a recalculation occurs such that the current pattern is recognized as unsustainable, the system re-allocates by exiting those unsustainable businesses. But then, whence the massive recalculation? Do we always have something like Felix Salmon's copulas, unique math errors that pop up every 5 to 10 years?

For theories that rely on mercurial investment or animal spirits, the cognitive errors that underlie business booms and busts have yet to be identified. Aggregates like investment spending, consumer durables, aggregate average liabilities/assets, do not show that increases into some zone over an expansion are correlated with future declines; the aggregate data show only a contemporaneous pattern with GDP. Understanding why people always become too optimistic in expansions in modern 'representative agent' models is like trying to understand why a drunk drinks too much every day. You can't model such investment rationally, and if you model it irrationally you imply a simple market timing model for the stock market should generate abnormal profits, and no such rule exists.


My argument is that business cycles are best understood through the framework of Batesian mimicry, an endogenous mechanism for booms and busts thru a misallocation in the horizontal structure of production. In ecosystems, Batesian mimicry is typified by a situation where a harmless species (the mimic) evolves to imitate the warning signals of a harmful species (the model) directed at a common predator (the dupe). For example, venomous coral snakes have red, yellow, and black bands, while the non-venomous scarlet king snake has the same colors in a different order. Animals afraid of venomous snakes would do well to avoid 4 foot long snakes with red, yellow and black stripes, in the process avoiding the scarlet king snake (alternatively, one could remember the rule "Red on yellow, kill a fellow; red on black, friend of Jack").

The process has been observed in insects, reptiles, mammals, and plants, and sometimes occurs between species. By parasitizing the true warning signal of the protected species, the Batesian mimic gains the same advantage without having to go to the biological expense of maintaining a poison. The species being mimicked, on the other hand, is disadvantaged, along with the dupe who misses out on tasty mimic meals. If imposters appear in high numbers, positive experiences by the predator with the mimic may result in the model species losing the benefits of signaling its poison.

Atsushi Yamauchi has shown that when there are density effects on the model species, there is no stable equilibrium. Nonlinear dynamics make the system’s aggregate features unpredictable in specifics, but most importantly, it is not a stable equilibrium to have no mimics over long periods of time: the gains are large to the mimic because predators obey the model’s high-quality signal.

While it’s conceivable one could generate a formal economic model with these qualitative results, note that the ecological literature mainly looks at comparative statics for one species, noting what assumptions generate stable equilibria, and which do not. There is no attempt to generate a dynamic model of the mimic or model's success over time, presumably because the highly nonlinear, recursive system is so sensitive to initial conditions results would merely be qualitative, like the comparative statics.

In an expansion investors are constantly looking for better places to invest their capital, while entrepreneurs are always overconfident, hoping to get capital to fund their restless ambition. Sometimes, the investors (dupes) think a certain set of key characteristics are sufficient statistics of a quality investment because historically they were. Mimic entrepreneurs seize upon these key characteristics that will allow them to garner funds from the duped investors. The mimic entrepreneurs then have a classic option value, which however low in expected value to the investor, has a positive value to the entrepreneur. The mimicry itself may involve conscious fraud, or it may be more benign, such as naïve hope that they will learn what works once they get their funding, or sincere delusion that the characteristics are the essence of the seemingly promising activity. The mimicking entrepreneurs are a consequence of investing based on insufficient information that is thought sufficient, but they make things worse because they misallocate resources that eventually, painfully, must be reallocated.

Once the number of mimics is sufficiently high, their valueless enterprises become too conspicuous and they no longer pass off as legitimate investments. Failures caused by insufficient cash create a tipping point, notifying investors that some of their material assumptions were vastly incorrect. Areas that for decades were very productive, are found to contain exceptional levels of fraud, or operate with no conceivable expectation of a profit. Everyone outside the industry with excessive mimics marvels at how such people—investors, entrepreneurs, and their middlemen--could be so short-sighted, but the key is that the mimics and duped investors chose those business models that seemed most solid based on objective, identifiable characteristics that were, historically, correlated with success. An econometric analysis would have found these ventures a good bet, which is why investors did not thoroughly vet their business models. For example, banks stocks through 2007 were one of the best performing industries since industry data has been available in the US, and performed well in the 2001 recession. Another notable example: when I was head of economic risk capital allocations for KeyCorp in the 1990s, residential mortgages had the lowest risk allocation because of their historical minuscule loss rates; speaking with an economic risk capital allocator recently, they currently have the highest.


Historical Applications

In the 1990’s tech firms in general and internet firms in specific were doing very well. The internet bubble was filled with a naïve lack of skepticism that allowed otherwise absurd business ventures to get funding. Using hindsight there were so many businesses with doomed business models, you wondered how they could have been taken seriously, but investors were looking primarily at a few key criteria—net presence, branding—and these did work well for several years until the March 2000 crash, especially using the criteria of their stock price. Consider that Enron was able to engage in negative cash flow activities for at least 5 years while their stock price kept climbing, highlighting that if you hit the key signals investors are naively prioritizing, they can be fooled, just not forever.

AllAdvantage was a website that paid members to surf the Net. It paid to acquire these users, and supposedly leveraged its members’ eyeballs into advertizing dollars. At the initial fundraiser internet investment banking guru Frank Quattrone (who helped fund Cisco) and President Clinton both paid tribute to AllAdvantage. Yet even then, an investigation into AllAdvantage had determined that the clicks came mostly from bots that were explicitly gaming the system. As Buffet says: first the innovators, then the imitators, and finally the idiots.

Similarly, the housing bubble of 2008 was based on the idea that the borrower’s credit was irrelevant because the underlying collateral, nationwide, had never fallen significantly in nominal terms. This was undoubtedly true. The economics profession, based on what got published in top-tier journals, suggested that uneconomical racial discrimination in mortgage lending was rampant, lending criteria was excessively prudent (underwriting criteria explicitly do not note borrowers race, so presumably lenders were picking up correlated signals). Well-known economists Joe Stiglitz and Peter Orzag wrote a paper for Fannie Mae arguing the expected loss on its $2 trillion in mortgage guarantees of only $2 million dollars, 0.0001%. Moody’s did not consider it important to analyze the collateral within mortgage CDOs, as if the borrower or collateral characteristics were irrelevant. In short, lots of smart people thought housing was an area with extremely low risk.

Each major bust has its peculiar excesses centered on previously prudent and successful sectors. After the Panic of 1837, many American states defaulted quite to the surprise of European investors, who were mistakenly comforted by their strong performance in the Panic of 1819 (perhaps the first world-wide recession). The Panic of 1893 centered on railroads, which had for a half century experienced solid growth, and seemed tested by their performance in the short-lived Panic of 1873.

Note by focusing on what seems the essence of a good investment is basically looking over the past generation, which implies that the crux of the last crisis is probably less risky going forward. For example, subsequent to the 1990 Commercial Real Estate debacle, defaults in this asset class were well below-average for the subsequent 15 years. In the aftermath of that 1990crisis, newly issued Commercial Real Estate Asset Backed securities did well because everyone was especially cognizant of the risk factors involved. A similar thing happened in railroads after the Penn Central railroad defaulted in the early 1970s. Fooled once by a specific sector perversion investors are not fooled again, making the key risk characteristic of the latest recession interesting only in its meta sense, its higher-level commonality to the mixed bag of other recession essentials.

Inherently Unpredictable

In 1929, Irving fisher’s famously opined “Stock prices have reached what looks like a permanently high plateau." In 1993, Stanford macroeconomist Robert Hall said about the recession of 1990 that “established models are unhelpful in understanding this recession.” Econometricians James Stock and Mark Watson noted that the 1990 recession, which blindsided their new econometric forecasting model at that time, experienced a sharp fall in consumption, housing, and durable goods; whereas, in 2001, the updated model failed to note that the recession was centered on technology investment. Further, the signals change, as yield curves were not characteristically flat or inverted prior to the 1990 recession, and housing permits remained strong throughout the 2001 recession. Stock and Watson note “our conclusion—that every decline in economic activity declines in its own way—-is not new.” I think it's fair to say the latest recession has kept the string of unpredicted recessions perfect.

If mimicry is the essential driver of the misallocation of resources that inevitably must be corrected, it by definition occurs in places that do not have accurate quantitative signals; indeed, it preys upon areas where the essential data are beyond reproach (eg, mortgage underwriting did not matter to regulators, rating agencies, or investors prior to 2007). Safety creates risk in that eager overzealous entrepreneurs, once they figure out what sufficient statistics work on investors, quickly jump on these sectors with not merely excess capacity but business models that never stood a chance.

This model explains why business cycles are not forecastable, it’s inherent in the mimic's selection process. The recalculation Arnold Kling mentions relates to an investing error of a particular expansion, which is always unique. Mimicry explains why the biggest winners in a business cycle are also the biggest losers: their productivity was pervaded by fraudulent and incompetent mimics. It explains why the biggest losers of the prior business cycle often do relatively well in the next recession: investors are wary of mimics, so mimics only thrive where they are not expected. It explains why recessions are concentrated in certain sectors, and why these sectors are different each recession.

Efforts to prevent the next recession face a large difficulty, in that the impetus by necessity will be in the area that invites the least concern because that is where mimics fester. Any risk analysis that can identify risky ventures necessarily identifies safe ones, and when these safe investment characteristics become known to the mimics, they will be exploited. Top down risk management, the focus of so much policy talk in Basel, Washington, and wonky journals is futile, because risk grows dangerously only where one does not suspect it (G-7 sovereign debt, anyone?).

This suggests focusing on robustness as opposed to prediction because the system works against rational expectations, especially those consensus ideas that come out of large bureaucracies. After all, what better sufficient statistic for a mimic to exploit than some well-known regulatory bullet point that supposedly ensures trivial risk? Recessions are not going away; they are endogenous because zero mimicry is not an equilibrium among insects, reptiles, or humans. Expect more unexpected recessions, just not real soon, and not in subprime housing.

The BP Oil Spill in Context

interesting note:

The Niger Delta, where the wealth underground is out of all proportion with the poverty on the surface, has endured the equivalent of the Exxon Valdez spill every year for 50 years by some estimates.

I'm not saying it's not bad, but it does highlight that to the extent the US stops drilling it happens elsewhere, probably with worse effects on Gaia.

Eddington's Experiment Was Bogus

Einstein stated that common sense was nothing but the collection of prejudices we acquire before the age 18. I think that's pretty correct. Most people don't change their minds on big issues during their working lives, though they may acknowledge certain tactics within their grand theory are not fruitful.

The Skeptics' Guide to the Universe has a podcast on the 90th anniversary of Sir Arthur Eddington's eclipse experiment that supposedly proved General Relativity was correct. While one discussant mentioned that the experiment had higher standard error than could have proved this, the next discussant said all measurement issues were addressed appropriately, and that claims Eddington engaged in fraud are pure myth. In the end, one discussant states, this is how real science is done: testing theories against reality.

I agree this is how real science is done, but contra the discussant, it was a bogus confirmation of Einstein's theory, tendentious cherry picking of what to leave in, what to leave out. The fact that the theory was correct does not change this, and it's very tempting to use hindsight to give the scientists involved the benefit of the doubt, especially when something is generally accepted. Deferral to conventional wisdom has some logic to it, but when you look at the past and note how many important beliefs were clearly untrue, there are probably many common scientific beliefs today that are untrue (unless somehow now we finally have everything right).

To recap the issue, in 1919, Einstein’s General Theory of Relativity was only a few years old, yet academics were eager to put the nightmare of World War I behind them and show the common bond of the old adversaries. Proving the German’s theory correct was greatly desired by scientists who rightly saw the conflict as simply madness. The null hypothesis, set up by standard Newtonian Physics, was that there should be a 0.85 arc-second deflection in light from stars behind the sun, while Einstein predicted a 1.7 arc-second deflection.

English physicist Arthur Eddington was a WorldWar I pacifist, and so had a predisposition to mend the rift between German and English academics. He made a trek to the island of Principe, off the coast of West Africa, one of the best locations for observing the eclipse. He used a series of complex calculations to extract the deflection estimate from the data and came up with an estimate of 1.6 arc-seconds. Data from two spots in Brazil from that same eclipse were 1.98 and 0.86, but Eddington threw out the 0.86 measurement.

In 10 further eclipses from 1922 to 1952 only one of these produced seemingly high-quality data, and in that case generated results much greater than Einstein predicted. In 1962, an English team tried to redo the experiment given a similar eclipse and methodology but newer equipment, and they found they could not. The tools and the event were simply too primitive to allow the kind of accuracy needed to prove general relativity via this experiment at that time. Eddington's dismissal of observations that would have lowered the key measurement's mean and increased its standard error used an objection that could just as easily been applied to the measurements he kept. In the late 1960s, using radio frequencies as opposed to pictures from an eclipse, Eddington’s results were confirmed, but that does not make that initial experiment good empirical research.

Eddington's method is held up as the epitome of science, and currently Wikipedia states "The myth that Eddington's results were fraudulent is a modern invention[citation needed]". Indeed.

Eddington died after a life of honors, his eclipse experiment his most conspicuous achievement, yet it was tendentious empirical work. Science is filled with fraud behind correct results: Mendel fudged his numbers, Pasteur was hardly fair to his critics. A great deal of science is motivated by confabulation, generating evidence for prejudices. In that sense scientists are little different than most professions, where one has ideals vs. reality. Lawyers aren't primarily interested in justice, teachers aren't primarily interested in children, even 'the best'. After all, unlike judges, most scientists have a rooting interest for whatever theory they are proposing, they want to be the founding father of some branch of knowledge. Those of us merely judging, meanwhile, are choosing between two choices, and as we don't have any skin in the game, we want the truth because that's what's helpful.

The Importance of Good Faith

I'm sure there's a need for prisoners to have banking services, but one would think security would be a high priority at such an institution. From the Wall Street Journal:

Dwelling House Savings & Loan Association, one of the country's few institutions to provide bank accounts to prisoners, failed last year after regulators warned the Pittsburgh S&L that its computer systems were vulnerable. Dwelling House was later hacked by cyber-thieves, who siphoned as much as $4 million out of its coffers.

Most criminals are truly stupid--unintelligent, uneducated--because criminality tends to not payoff in the long run, and stupid people overweight the short-run payoff. One might therefore conclude that computer theft would not be a concern, but this highlights that bad faith is very important because if there's a will, there's a way. Knowing your customer, or adversary, is very important because their end game often predicts tactical behavior.

Krugman's MindBlindness

Here's Krugman on Tyler Cowen:

If you believe stimulus is a bad idea, fine; but surely the least one could have expected is that opponents would listen, even a bit, to what proponents were saying.

So, people don't agree with Krugman on something, which means they can't be listening! If they listened, they would hear the truth, come around to his way of thinking, and the world would be as Keynes envisaged in The Economics Possibilities for our Grandchildren: the big problem would be how to spend leisure time.

What ill-tempered Krugman fails to appreciate, is that not many listened to Milton Friedman either, as his career ascended almost step-by-step with an increase in the size of government and the amount of regulation. I don't think many legislators do what Noam Chomsky wants either (anarcho-socialism).

Intellectuals should realize that they are influential only if they help rationalize a zeitgeist, as von Mises noted about Keynes:

His contribution consisted rather in providing an apparent justification for the policies which were popular with those in power…His achievement was a rationalization of the policies already practiced

Reading Marx, you become struck at how much of what he wrote was about just economics, what happens to wages, profits, production, with lots of little examples. No socialist mentions these arguments or examples anymore because they are so blatantly irrelevant, and rather talk endlessly about alienation, or vague notions like philosophical materialism. Marx was wrong whenever he was specific (eg, the falling rate of profits, business cycles becoming broader across industries, the state whithering aways under communism), but as Marx wrote such obscure long twaddle, despots of all types could simply point to Marx as proving their side was the vanguard of history, and few debaters could on the spot know how to respond to the specious argument taken from Marx's Das Kapital. Marx helped tyrants and naive revolutionaries rationalize what they wanted to do, which is why for many decades Marx was considered the greatest intellectual in history (after the fall of the USSR, this now seems absurd, but that's hindsight).

So, Paul, remember not to take offense, no intellectuals convince people to choose certain policies, rather, they help people rationalize their prejudices. Just break out of your intellectual echo chamber long enough to realize that emphatic reassertion is not argument, and if fiscal stimulus was the no-brainer you thought it was, it would have clearer empirical support.

Harry Markowitz Inconsistent

Harry Markowitz is one of the patron saints of modern finance, who's contribution is, according to the Nobel committee, for having "constructed a micro theory of portfolio management for individual wealth holders." Mark Rubinstein spoke at one of the innumerable conferences honoring Markowitz, and stated that

Near the end of his reign in 14 AD, the Roman emperor Augustus could boast that he had found Rome a city of brick and left it a city of marble. Markowitz can boast that he found the field of finance awash in the imprecision of English and left it with the scientific precision and insight made possible only by mathematics

In a speech given in 2009 (and many other writings), Markowitz notes rather casually that many use 'tracking error' rather than portfolio variability as the risk to minimize. Yet this leads to totally different implications. As I have demonstrated (see blog post here or article here), this leads to no risk-expected return relation in equilibrium. There is a trivial 'efficient frontier', aping the benchmark--everything else is inefficient.

Without the concave efficient frontier, James Tobin's two-fund separation theorem does not work, and without the two-fund separation theorem, the Sharpe-Lintner-Mossin Capital Asset Pricing model does not work. Of course, in practice, we all knew that, given that the two-fund separation theorem implied there would be one mutual fund, and it would be a value-weighted index, whereas instead we have more funds than equities that go into them. That theory was as falsified as Tobin's 'transactions model of money demand'. The empirical failure of the CAPM, is also well known. These models all have a certain attractive elegance to them, but as they don't explain the real world after 50 years of looking for relevance, an idealized scientist would ignore them.

'Risk' prior to Markowitz was not well defined, and he showed that in the standard, new models of consumers (specific utility functions, like von Neumann-Morganstern Utility), the dispersion of wealth was what mattered. Other conceptions of risk were inconsistent, applying to assets or portfolios, depending. It seemed clear that applying rigor to preferences via utility functions, and applying statistics to portfolios via this new metric of risk, would lead to a philosopher's stone. Like so many things, it hasn't worked out as expected. In Markowitz's thesis, he remarked that "risk" and "variance of return" were interchangeable, and up to 1990 or so this idea was defensible, but we know know it's a dead end, that 'risk' is now a correlation to several unagreed upon factors such as FX rates, yield curve spreads, or micro-cap value stocks.

Markowitz's seminal work contains a lot of what can charitably be called quaint empirical analysis, and algorithmic tricks to do matrix math to get to this to irrelevant frontier. He also spent a lot of time on looking at refinements to his assumptions--fat tails, different utility functions, but these forays led to his conclusion that "mean-variance approximation is so good that there is virtually no room for improvement" via these extensions.

His big idea, that one should look at a portfolio to evaluate risk, not the individual asset, remains. I agree that's a good big idea. The particulars he notes around that are as irrelevant as Newton's writings on alchemy or Playboy's interviews. He does not seem to grasp that this is his bon idée, the essence of his contribution to science, his only lasting insight. It highlights that a good idea in economics is rarely inextricably linked to deep mathematics--in this case it is clearly not, demonstrable via noting the Law of Large Numbers (a sample means volatility diminishes as the sample grows), and noting that the saying 'don't put all your eggs in one basket' is a good idea (there are apt quotes from Shakespeare to Aristotle on this).

As mentioned, even Markowitz has given up the ghost, noting that minimizing benchmark risk is more prominent, as if this is totally consistent with his thinking. Yet in spite of this factual divergence between theory and practice, finance remains enamored by what Rubinstein calls the "the scientific precision and insight made possible only by mathematics." The essence of a Journal of Finance article is its rigor, defined as abstruse mathematics, similar too but slightly different than the genre it discusses. A trivial, even silly, idea, is considered publishable if within this paradigm because a nice property of such models is they can be tweaked by other mathematically inclined economists and the publication bubble festers.

The bottom line is these models are not useful in the real world, Markowitz's focus--as opposed to his big idea--has been a distraction, irrelevant. I've been to private wealth manager conferences, those people who daily deal with customers who have more than $5MM in wealth. They don't know much math and don't care too much to learn more, seeing little need for it. They do know a lot about taxes, the law, and communication skills. It simply hasn't been the case that investing is highly influenced by "scientific precision" of these financial founding fathers, because the main issues--what asset classes to invest in, what managers to choose within these classes--remains a very qualitative affair. Deviations from the consensus at any level usually involve a qualitative story. As they say, in theory, theory and practice are the same; in practice, they are not.

The Multiplier ... Yea, That's My Reason

Here in my state of Minnesota, we have a pretty large budget deficit, about 15% of spending, at $6.95 Billion for 2012. That's unsustainable long term. The Democratic candidate, Margaret Andersen Kelliher, is running on a program of increasing rspending by $2B to create jobs, but cutting $1B of 'fraud and waste. In other words, spending $2B more. The idea is that it 'would put 50,000 Minnesotans to work constructing roads, bridges and buildings'.

Given the logic of the multiplier, the government should always spend more. I think most people don't really have an opinion on the multiplier, rather, conservatives and liberals are more influenced by their appreciation or skepticism of government spending.

How to Think Like a Keynesian

Currently economists are debating whether the stimulus worked. The President's economists state that spending about $200B created about $400B in GDP net net. If the payoff is that high clearly we should spend more, way more. As Hayek said, ultimately, facts drive policy disagreements.

The empirical logic used, however, is often quite twisted. Here's Cato's Dan Mitchell with an analogy:

Next time I see my buddies, I’m going to claim that I enjoyed a week of debauchery with the Victoria’s Secret models. And if any of them are rude enough to point out that I’m lying, I’ll simply explain that I started with an assumption of spending -7 nights with the supermodels. And since I actually spent zero nights with them, that means a net of +7. Some of you may be wondering whether it makes sense to begin with an assumption of “-7 nights,” but I figure that’s okay since Keynesians begin with the assumption that you can increase your prosperity by transferring money from your left pocket to your right pocket.

ADCT Highlights Picking Stocks is Hard

ADCT is a telecom company headquartered within walking distance of where I live, and it was recently purchased by TYCO for $1.25B. I often jog by its sprawling 90 acre headquarters and long noticed that very few people actually worked there. I talked to people who did work there (eg, at my gym), and it always sounded like such people were looking to get out, or were being downsized. They trademark lots of minor innovations, like their OmniReach® FTTX Infrastructure Solutions, which to me highlights that their strategy was built on stupid marketing and legal basis, as opposed to merely providing high quality hardware efficiently.

In sum, they were a perfect short. They were caught up in the internet bubble and had the typical income pattern there, yet even post-2002 they cumulatively lost $315 million. Given the opportunity cost of capital this means they destroyed about half a billion since the post-bubble executive team got in there.

But, their stock price always remained pretty stable, around $10 to $20 (it was split adjusted a while back), recently taken out at $12.5. They were a classic short, in that their business model was pure gimmicks, you look in the company headquarters parking lot and saw no one, people who worked their were dispirited, and true to form, never made money. But, you also would have not made much money shorting them.

The Moralistic Fallacy

The following quotes are considered patently idiotic by TalkingPointsMemo:

"I've literally had construction companies tell me, 'I can't get people to come back to work until...they say, I'll come back to work when unemployment runs out.'"
~Pennsylvania Attorney General Tom Corbett

"As bad as it sounds, ultimately we do have to sometimes accept a wage that's less than we had at our previous job in order to get back to work and allow the economy to get started again. Nobody likes that, but it may be one of the tough love things that has to happen."
~Rand Paul, the Republican nominee for senator of Kentucky

"continuing to pay people unemployment compensation is a disincentive for them to seek new work."
~Sen. Jon Kyl (R-AZ)

"facilitating the problem if you give an animal or a person ample food supply," he said. "They will reproduce, especially ones that don't think too much further than that. And so what you've got to do is you've got to curtail that type of behavior. They don't know any better."
~South Carolina Lt. Gov. Andre Bauer

I suppose they wish to presume that incentives don't matter and that no one should have to work for less than what their previous mortgage-banking job paid them, even if that job was, in retrospect, purely counterproductive. Why not also think that Madoff's investors should be able to peg their portfolio statements at their high water mark, and to suggest otherwise is mean-spirited lunacy?

Prior to this recession, it was generally acknowledged that increases in welfare increased unemployment spells. Now, saying such a thing is considered taboo, like saying that statistically, it's possible that women aren't as dominant at math at the highest levels (Larry Summers seems to have two opinions on all these subject)

Relative Status in Practice

Some may note that 'relative status' seems rather far-fetched to their daily lives, but it is equivalent with 'benchmarking', which is endemic in investing. If all the professionals are benchmarking, you get the same result.

for instance, take British Petroleum (BP). Their stock's implied volatility spiked up to 100%, well above its peers in the energy sector (around 25%). You might think, taking some of your exposure to BP would be prudent. Yet as BP represents $79B in market cap, if you are investing in the energy sector, having zero shares of BP generates the same benchmark risk and doubling down and having twice as much capital in BP as in the sector benchmark. Risk to BP is merely deviating from the consensus, which in this case is its proportional market cap as a percent of the energy sector.

And then there's the decision to allocate to stocks vs. bonds, small cap vs. large cap, having cash vs. paying down your mortgage. All these allocations are relative to a benchmark portfolio, which is generally the average of what everyone else does.

In Kenneth Fisher's book The Only Three Questions that Count, in the references under "Risk" he merely has 'see benchmarking'. If everyone benchmarks, risk is deviating from the consensus, and thus taking too little exposure to any popular investment is just as risky as taking on a lot of exposure. Cremers and Petajisto have a paper where the define portfolio manager risk this way. If risk is defined as a deviation from the benchmark, it becomes like idiosyncratic risk, unnecessary, so unpriced. Further, via arbitrage 'benchmark risk' can not be priced, because you can't get a risk premium from both having zero or twice the normal exposure to BP.

Status and Asset Pricing Thread Taking Baby Steps

A reader forwarded me a PhD student's job market paper on Asset Pricing with Status Risk. I guess there's a now an official thread on status and asset pricing. His paper, like the others he references in this thread, highlight what Richard Feynman famously noted in the Millikan's measurement of an electron's mass, where his seminal, original measurement was corrected subsequently, but always in baby steps.

The paper notes several papers that show that under certain circumstances, some investors can prefer particularrisky assets over-and-above that dictated by standard risk pricing models. He references Abel (1990), DeMarzo, Kaniel and Kremer (2004), and Roussanov (2009), and others. These papers generally focus on why people underdiversify, or hold too much of particular assets relative to standard theory. In a way, these are similar to papers that argue that asset skew (positive, negative, co-skewness--depending on author), could explain why some risky assets have lower returns than expected via the standard approach. Basically, add something really desirable as an extra to an asset (it increases your status), and in some situations its price is higher than otherwise.

Why I think these papers are inferior to my argument is there are too qualified when applied to the basic point: there are no risk measures that consistently, positively (let alone linearly) related to average returns--and average returns, in the data, is all we got. They attempt to explain a special case. Proposing a model that you can then selectively apply to assets and eras where the model appears invalid is lame given that the welter of data that does not even generate a positive sign for the risk-return is considered 'too short' to say anything (because expected returns are not actual returns). So, if the main theory is considered by many to be untestable because we haven't had enough time (like the Soviet Union's 70-years of dought), how about an ad hoc theory applicable towards some assets?

Here's the bottom line, which apparently is more difficult to understand than the theory, but ultimately much more convincing once you see it. The a negative risk-return relation holds for volatility (cross-sectionally and over time, total and idiosyncratic), beta, options, private investments, leverage, currencies, country returns,yield curves, financial distress, sportsbooks, lotteries, IPOs, junk bonds, and analyst uncertainty. It's pretty absent in country returns, commodities, movies, and private investments. It does work nicely in the the AAA-BBB spread or the short end of the yield curve. Now, if you were an alien, what's your generalization, that in general risk is related to return, not related, or the opposite? The Academy teaches that there's a linear relation, positively sloped!

I'm the only one who writes down several cases to show if people are status oriented, there is no risk-premium in general. End of story. There's no reason to subtilize this idea within a larger model so that it has only a selective implication. Embedding it within a larger model makes it easier for the academy to digest, in that like Millikan electron measurements, new is just slightly better than the old, but not so much as to say anyone in the past was 'wrong', we are just extending their approach. As in kids games, Everyone Wins! Yet, the result is a rather weak assertion, almost by definition non-falsifiable (some assets may show no risk premium--ORLY?), and does not advance the 'science' of finance because it allows professors to continue teaching the manifestly unhelpful risk-return story withing Modern Portfolio Theory (ie, that expected returns are increasing in risk, and only risk, properly defined).

Paul Erdös, the mathematician who collaborated with more people than anyone else on the planet, reckoned that up in heaven God had a book that contained all the best proofs. If Erdös was really impressed by a proof, he declared it to be "from The Book". G.H. Hardy explained such proofs are inevitable, succinct, and unexpected. Examples are Euclid's proof there are an infinite number of primes, or Cantor’s theorem of the non-enumerability of the continuum.

My proofs are in my SSRN paper Risk and Return in General: theory and evidence, but this simple example should suffice. In the table below, asset X is usually considered riskier, with a 30 point range in payoffs versus a 10 point range for Y. Yet on a relative basis, each asset generates identical risk. In State 2, X is a +5 out performer; in State 1, X is a -5 underperformer, and vice versa for asset Y. In relative return space, the higher absolute volatility asset is not riskier. The positions, from a relative standpoint, are mirror images. Buying the market, going with the consensus, generates zero risk.

Relative Payoffs Symmetric

	total return			relative return
	X	Y	avg	X	Y
State 1	0	10	5	-5	+5
State 2	30	20	25	+5	-5

Everything really flows from this simple insight. Implicitly the utility and arbitrage equilibria derive from the fact that when relative wealth is the objective, risk is symmetric, as the complement to any portfolio subset will necessarily have identical — though opposite signed — relative return. Thus arbitrage exits if there is a risk premium in a relative status world. That's it.

In contrast, job-applicant Krasny and the authors he references have incredibly hedged proofs that hold in certain cases to certain degrees (depending on the nature of the parameters), and follow from the third proposition of the fifth theorem. Gali's (1994) paper in this vien actually has a parameter that allows such increased risk to increase, or decrease, its expected return depending on whether the 'externality' from holding that asset is positive or negative. This is explains nothing and everything.

It reminds me of David Hakes's anecdote, that one of his papers was continually rejected, but then his coauthor suggested that the problem with the paper might be that we had made the argument too easy to follow, and thus referees and editors were not sufficiently impressed. He said that he could make the paper more impressive by

generalizing the model. While making the same point as the original paper, the new paper would be more mathematically elegant, and it would become absolutely impenetrable to most readers.

After which, it was immediately accepted for publication.

I guess I'm rather impolitic on this issue, and so my straightforward approach not only prevents me from getting published, but also getting referenced. I don't find any gain from playing this game, which I find disingenuous, pretentious, and wasteful. Further, there is no value to the old paradigm, it doesn't help fresh-faced MBAs become better at ascertaining true value, merely better at becoming eloquent confabulators to whatever result you arrived at through totally orthogonal means. These piecemeal status approaches bury the lede within a welter of potential implications and parameter values. Any model with more parameters or a more complex functional form can explain more--including having some higher 'risk' assets with lower expected returns--I'm arguing change the argument in the utility function (relative vs. absolute wealth) and you get a novel result: no risk premium in general. My above table lays out the status-risk-return nexus without hiding it behind specious generality. If it's in Erdös' Book, it's my above 'proof' and not any of these qualified papers that don't even directly state this explains most things, not some things, in asset pricing. Take the jump guys! You have nothing to lose but your irrelevance.

I Live in Eden

Eden Prairie, Minnesota, that is. And it's the best place to live in the entire United States according to Money Magazine. Of course, there are nicer neighborhoods in my area, but one of the criteria is that it can't be so expensive your average Money Magazine reader could never live there ('overly expensive' housing they call it).

It's a fine suburb and has a lot to offer, but I've lived in other suburbs (Westfield New Jersey, Solon Ohio), that were as nice.

Unintentional Slam

Tyler notes Federal Reserve PhD elitist Kartik Athreya's research on bankruptcy law (he's the guy who writes non-PhDs make econ debates less fruitful). Then, as the 'other side', suggests blogger and non-PhD Megan McArdle. Equivalence implied.

What makes this all so harsh is I'm sure it was unintended. Kind a like when a boss says of a valued employee on her retirement, that she was the 'best secretary he's ever had', when she thought she was an analyst.

Mismeasuring GDP

Joseph Stiglitz and Amartya Sen, along with Jean-Paul Fitoussi recently released Mismeasuring Our Lives: Why GDP Doesn't Add Up, noting the fact that it ignores leisure, depreciation, externalities, inequality, and a host of other issues. The book was instigated by French President Nicolas Sarkozy with great fanfare.

National income accounting coincided with the development of 'macroeconomics' as a separate field of study in the 1930's. Simon Kuznets and Richard Stone won Nobel prizes for developing these measures, and Keynes was so excited by their potential that he gushed 'the era of joy through statistics' is starting, as if merely measuring GDP would show us how to manage it. Usually, we expect important data to follow a pattern, especially one as intuitively cyclical as the aggregated economy.

Later, Lawrence Klein and Wassily Leontief were given Nobel prizes for their models that presumably explained the laws of motion for macro variables. No one uses these models any more. They were very clever and rigorous and may have applications elsewhere, but they are totally irrelevant to understanding the aggregate economy. Like so many social science theories they were a considered foundational at one time, but then the new generation with no intellectual stake in the models examined them and found them useless. Yet another example of fads in science, and the current macro paradigm is probably no less ephemeral.

I too think GDP is flawed, in that investment spending by firms is treated the same as government spending, price adjustments contain subjective hedonic adjustments, but without an alternative, I'll leave it at that. These criticisms have been around for decades, and are prominent in every Introductory texbook that goes over GDP. Nonetheless, GDP still dominates macroeconomics. Clearly GDP is correlated with 'true' national income, and has the benefit of being fairly unambiguous.

It is a sign of wisdom that if someone really important asks you for the answer to a very important problem, and you have nothing new, true and important to say, say nothing. Stiglitz and Sen's cluelessness here is not an anomaly.

Schiff on Epistemological Errors

Kling notes the epistemological errors in the prior bubble. In Peter Schiff's prescient Crash Proof written in 2006, he notes the following story from the New York Times:

The July 31, 2006, New York Times had an article accompanied by a picture that might have been captioned "The Life of Riley."

It showed a smiling, well-coiffed, 53-year-old former steel-worker and sometime math teacher, relaxing in his jeans on a chaise lounge. The article title was “Men Not Working, and Not Wanting Just Any Job.”

The article explained that the man's life of leisure was being financed by home equity extractions. But that was not the articles's angle. That part seemed to be okay with the Gray Lady. The point was that our fried could afford to be idle and planned to stay that way until something befitting his dignity came his way.

To me, it was a telling example of how the idea that home equity is a modern form of wealth is routinely accepted.

The cognitive failure that with hindsight seems absurd, was real. See his 2006 Mortgage Bankers Speech, proof positive he is the most prominent, and most specific, big name who called the crash. Highlighting he wasn't using hindsight, he erred in expecting the dollar to fall, and that the US would do relatively worse than other economies, one problem with forecasting complex systems like economies: you can be right on some big assumptions but still screw up the implications.

Federalist Society Journal on the 2008 Crisis

The Spring Issue of the Harvard Journal of Law and Public Policy has many good articles on the economic crisis of 2008. It's pretty good. I really liked Arnold Kling's article, which argues the crisis was the result of a cognitive error--that home mortgages weren't risky irrespective of obligor risk--due to a confluence of factors: complexity, good intentions, regulations, simple carelessness.

Yet, he states this "may be our first epistemologically‐driven depression [ie, they didn't understand what they were doing]". I'm not so sure. The internet bubble of the 1990's also contained a lot of pervasive misconceptions that with hindsight are obvious.

Krugman sees Spending as Spending

Paul Krugman sees hypocrisy when fiscal conservatives bemoan the lack of investment spending:

There’s now a lot of talk about the fact that U.S. corporations are sitting on a lot of cash, but not spending it. I don’t find that particularly puzzling: with huge excess capacity, why invest in building even more capacity. But almost everyone seems to agree that if we could somehow get businesses to spend some of that cash, it would create jobs.

Which then raises the question: how can you believe that, and not also believe that if the U.S. government were to borrow some of the cash corporations aren’t spending, and spend it on, say, public works, this would also create jobs?....

I have never seen a coherent objection to this line of argument.

Here's the difference. If a company spends money investing, it expects to create more value than it spends. That is, if the NPV of spending is greater than 0, meaning not merely via some multiplier, but the spender himself expects to garner more money than he spends, otherwise the NPV is less than zero. Businesses may make mistakes, but surely this is their expectation, and generally they are correct, as profits, on average are positive (in spite of Marx's prediction in Book 3 of Das Kapital).

So, having GOOG spend $1B creates more than $1B in wealth via a virtuous circle of self-interested exchange. In contrast, if the government spends $1B on a light-rail environmental impact study and Global Warming research, the payback is much less than the $1B. You can say such spending creates $1B+ in wealth through the multiplier, but many of us fiscal conservatives are skeptical of such multipliers, especially given the political context in which such spending decisions are made.

When businesses invest it is not merely that they are writing checks that makes it productive, it is that they are spending money where they by definition expect to create value. Decentralizing investment decisions, relying on individuals to create wealth, is the Invisible Hand. When government spends, this is hardly ever the case. It's a distinction with a difference, one that a true Keynesian simply can't understand.

Aswath Damodaran on Applying the Equity Risk Premium

Aswath Damodaran has a set of lectures from his class at NYU available online. He's an expert on 'valuation', which is kind of like being an expert on 'efficiency'. Obviously, if you could make something specific more efficient, or know the true value of a certain stock, you can make a lot of money, so it's understandable he glosses over some things and instead just gives the kiddies a framework to become successful green-eyeshade equity analysts.

He's a very clear lecturer, not getting bogged down on second order adjustments that are technically correct but practically irrelevant. He's very unpretentious, which is refreshing. Alas, he seems oblivious to the fact that his method has zero empirical support, and not for lack of trying.

In contrast to those deriving risk premiums, he's all application, and he clearly assumes that a risk premium exists and must be accounted for. His number for the US, is currently around 4.5% (he has a website with this info here). While he notes that historically, the return on equities above the risk-free rate is pretty volatile, and that the geometric average is a couple points below the arithmetic average, he does not make a big deal about it, as if 2% doesn't matter much, and given the ultimate set of ad hoc decisions, this gives a flavor of where he's going. He mentions the survivorship bias of the US as well. Taxes, adverse market timing, and transaction costs, are presumably irrelevant, though estimates for each of these is on the same order of magnitude as the geometric vs. arithmetic difference, or the survivorship bias (see here). All those 2% adjustments add up.

He favors adjusting for risk even if no betas are used, however one likes to dice up risk, so that higher risk firms have higher expected returns (and thus, higher discount rates or costs of capital). But his preferred approach is bottom up betas and they involve many different assumptions. He outlines several methods, including one that takes the percent of revenues from various countries and sectors (consulting vs. software), and applies separate risk premiums based on these exposures. You can map these exposures into regions, which can have their risk premium derived from Moody's rating for government debt, and their implied currency-specific risk free rates. You can also use the relative aggregate volatility in that country and the equity premium implied by the currency's libor premium to the dollar.

He mentions risk adjustments to individual companies so that less diversified companies have a higher risk than diversified companies, manufacturing companies higher risk than service companies, companies that use futures to hedge their risk have a lower risk than companies that do not, or growth companies having higher risk than value companies. Of course, higher risk implies higher expected returns, presumably.

These all seem reasonable, but there's no empirical evidence that such distinctions show up in actual returns, in fact, most of his examples go the other way. Sure they all seem plausible, indeed, given an economist’s assumptions on the utility function—that utility is increasing in wealth at a decreasing rate—this is both a necessary and sufficient reason for there to be a risk premium, so presumably just construct an intuitive risk metric and expect higher returns. Alas, the risk premium does not appear. It’s simply not true that growth outperforms value, or certain stock sectors have higher returns, even countries do not have obviously higher returns, or that more volatile stocks have higher returns than low volatility stocks. A beautiful theory killed by data, it happens all the time.

I explain this as due to utility being primarily envious, about status as opposed to wealth. While correlated ideas, one produces risk premiums, the other not. If you want to get rich, find your niche, don't expect to make money merely by tolerating great pain (ever notice those 'dirty jobs' don't actually generate a premium--there's a surplus of masochists out there). The returns in equities is like returns derivatives, the risk free rate. There are exceptions, but as a general rule this is better than assuming that risk begets return. The benefits of this approach is not merely a more accurate though less ambitious risk premium, but the effort saved can then be applied to something more fruitful, such as actually looking at the company's business model and forecasting revenues.

At one point, Damodaran notes Warren Buffett's use of simple Treasuries for discount rates, seemingly making his bottom-up betas a huge waste of time, but you had to figure he's got an answer for that. He says that when the Oracle of Omaha projects cash flows, he calculates 'certainty equivalent' cash flows. Now, I know Buffett tries to be conservative, and has a 'margin of safety' in his hurdle rates, but I really doubt he generates 'certainty equivalent cashflows' in the academic sense. Such cash flows are not adjusted for imprecision, or volatility, but rather correlation with something like the S&P500 or GDP. Then, the adjustment is a linear function of this covariance. To say that's what Buffett is doing by making conservative cash flow assumptions, implicitly, is one of those assertions that seems plausible, but under scrutiny absurd.

Damodaran talks about applying this to specific companies overseas, where he's apparently an international expert, flying all over the globe. This highlights that credentialed experts are very useful when dealing with imprecise assumptions, just tell him the answer you need and you can get the objective datum you want.
With his bottom-up approach betas can be very complicated, yet never so much that a noob can’t understand each little step in his algorithm. I love the idea of taking a company like SAP, with years of history, and instead of using the obviously irrelevant top-down beta, use a less irrelevant-seeming 'bottom up' beta by separating the revenues by continent and then into consulting and software. As a consultant whose main input is the discount rate, if he merely applied the beta taken from regressions of the stock price it wouldn't seem worth $50k. Going over all the bottom up logic requires lots of face time with senior execs, something both consultants and insecure executives love.

Yet ultimately there's absolutely no evidence this approach produces either more accurate forward covariances with the aggregate market or better predicts expected returns than simply using a number like Libor (the Buffett approach). It's typical consulting, in that the process allows the consultant to give the answer a particular firm-insider want, all with the pretense of rigor and objective analysis. It's hardly more scientific than astrology in its actual implementation, even though its filled with many reasonable assumptions and interpolations. I can see how someone unaware of the failure of the CAPM, and any risk proxy, could find him totally convincing and convenient.

How can so many smart people be fooled like this? Well, like many popular erroneous beliefs, it is not obviously wrong. The false syllogism "you must take risk to get large returns implies risk begets return" seems true. It's popular among scientists, and their consensus has a lot of weight. It's consistent with other economic assumptions that have been around for a while. It creates a pedagogical structure that is perfect for teaching, as there are fundamental assumptions, mathematical implications, and empirical applications.

It's just wrong, a waste of time, like calculating different expected returns on options.

Beware the Technocrats

Brad DeLong over on Blogggingheads.tv frets:

...It makes me feel very sad for my Republican technocratic opponents

Anyone who thinks "technocrat" implies a scientific, rational, top-down way to manage an economy--as I'm sure DeLong does--is a Keynesian, and probably only a Republican for the social agenda. They exist, but they aren't the kind of Republicans I like.

Reagan, Coolidge, these are my kind of Presidents, and neither were considered very enlightened, because they prioritized doing less at the federal level. Unfortunately, most really smart, educated, people fall prey to such scientism as the assertion that there exists a simple positive correlation between total employment and the size of the aggregate demand for goods and services; it leads to the belief that we can permanently assure full employment by maintaining total money expenditure--from any source--at an appropriate level. Thus to them it's obvious that that top-down management of a large complex process is better than letting it alone. As Fredrich Hayek put it in his Nobel Prize speech:

While in the physical sciences it is generally assumed, probably with good reason, that any important factor which determines the observed events will itself be directly observable and measurable, in the study of such complex phenomena as the market, which depend on the actions of many individuals, all the circumstances which will determine the outcome of a process, for reasons which I shall explain later, will hardly ever be fully known or measurable. And while in the physical sciences the investigator will be able to measure what, on the basis of a prima facie theory, he thinks important, in the social sciences often that is treated as important which happens to be accessible to measurement. This is sometimes carried to the point where it is demanded that our theories must be formulated in such terms that they refer only to measurable magnitudes.

You can measure total employment, which includes the military, people doing census surveys, diversity outreach PowerPoints, environmental impact studies. Having people in jobs that are the best fit for them, in the context of what others really appreciate and what they enjoy doing, isn't in any aggregate data.

Volatility and Returns in the Long Run

A recent paper looks at volatility in German stocks in the long run. Stefan Koch documents that high 'idiosyncratic' volatility firms have lower returns than high idiosyncratic firms. Now, these are actual returns, not expected returns, so, they aren't proof of anything according to some. But theoretically, expected returns (population means) converge to actual returns (sample means) in the long run. I suppose like the Soviet Union and it's 70 years of bad weather, the globe had an anomalous century in every developed country. Looking at data from 1974 through 2006, the result is the following chart.


This confirms the results in Ang, Hodrick, et al (2009), but that had data only going back to 1980 in Deutschland, and as a German, Stefan was probably more careful about this analysis (it was one country among many for Ang et al). I suppose every country will find this same pattern upon close examination, as opposed to some freak finding by some careless researcher.

I suppose also the academic conventional wisdom is that volatility is inversely correlated with risk via a correlation with higher moments or information dissemination. When the experts start this kind of crazy-talk--that total derivatives have opposite sign of partial derivatives because of some feedback loop--you know they are in an intellectual dead-end, oblivious to the absurdity of their statements. Let me qualify: I agree that many things, like some charities, often have perverse consequences; but then I would never also assert that such charities are beneficent. If risk is reduced by systematic and idiosyncratic volatility via these effects, then 'risk' means something quite different than what is implied by Markowitz or his progeny (eg, the Stochastic Discount Factor). It may work in holding off the inevitable until they retire, but in their dotage they'll be like Marxist profs seeing the collapse of the Soviet Union, and while they may have ruled the roost in their day their work will be irrelevant.

My dissertation in 1994 was on idiosyncratic variance and total return. I found, and continue to find, that idiosyncratic variance, total variance, even beta, were/are all inversely related to returns. It does not matter which metric of volatility you use, you get the same pattern.
The main idea at that time, was that beta was mismeasured (it was time varying, the true index was proxied imperfectly by the S&P500), leading to weak evidence of the beta-return nexus. Yet as mismeasured betas should show up in the residual, or idiosyncratic variance, it was a puzzle that this residual was actually inversely related to returns. I found great skepticism if not simple disbelief in my results, as they thought it was some sort of measurement error, or some omitted variable that made actual returns inversely correlated with expected returns. I tried to motivate my finding by noting that mutual funds disproportionately invested in the more volatile stocks, which made sense given the convex relation between fund inflows and mutual fund returns, but this was before Freakonomics or popularity of investor biases, so partial equilibrium stories were not considered worthy as an explanation back then. More evidence, if needed, that what counts as kosher for a science is not some logical necessity, but rather what's popular amongst practitioners, the current zeitgeist's ephemeral definition of 'rigorous science'.

That I was correct on something new and important highlights, it's not a good thing to be too ahead of one's time. I got no fly-outs for an academic job, so my professional life went a different way, and as I'm pretty blessed now I wouldn't want it any different. But it's interesting that true facts, when you find them, have a tendency of showing up independently again and again.

Lesnar Defeats Carwin

Another excellent Mixed Martial Arts event, with Brock Lesnar defeating Shane Carwin in an epic battle of very large and dangerous men. Carwin was the better striker, and deftly landed an upercut that sent Lesnar to the ground (for some reason, applying g-forces directly to the jaw causes one's brain to momentarily reset). Then, Carwin reigned down shots, but couldn't get a clean enough blow to finish him. In the second period, Carwin again landed some good shots, but then Lesnar took Carwin down with a double leg, and went to work. He went for an arm triangle choke, which is a great move for a wrestler because it plays into their strength in knowing how to torque the neck when going for a pin (note that 3 time NCAA champ Jake Rosholt applied the same move to put Chris Leben to sleep last year).

I know many consider MMA to be rather base because it seems so savage, but its strategy space is large, necessitating skills in boxing, wrestling, and jiu-jitsu. This makes it fun to watch. Several champions have undergraduate degrees in real subjects (Shane Carwin, Rich Franklin, Chuck Liddell), unlike boxing. Recently, the seemingly invincible Fedor Emelianenko was submitted by a jiu-jitsu specialist, highlighting that in a sport with such range, everyone can get beat because no one is best at all three (wrestling, striking, jiu-jitsu), and any one of these skills can be decisive if you don't defend it intelligently. It's a relatively cognitively demanding sport.

Someday the mainstream media will catch on. Sports Illustrated and the New York Times still give it pretty short shrift in their coverage. ESPN's coverage is growing, but they still hold on to boxing which is relatively boring (and, any MMA fighter would dispatch quickly by simply taking him off his feet).

I like amateur wrestling, and many college wrestlers now go directly into MMA rather than train for the Olympics (eg, Johnny Hendricks, Ben Askren, Muhammad Lawal, Cole Conrad). Its very popular among the US military, and Russian ex-President Vladmir Putin likes to show up for Fedor's matches. It sure beats a 1-0 game of soccer.

In Defense of Elitism

I understand economist Kartik Athreya's frustration, in that it's hard to have a rational discussion when so many voices are profoundly stupid. I remember once teaching a class on International Finance (grad school students could be 'instructors' in their 4th plus year). I tried, a couple times, taking a subject and letting the class discussion grow endogenously: what do you think of the currency crisis (this being 1992), or the Fed? It didn't take long for the most voluble speakers to monopolize the debate, and unfortunately they were wrong on so many dimensions it always became a pointless discussion within a few minutes. Confused thinking leads nowhere in particular and can be indulged indefinitely without progress.

I'm an unabashed elitist. I chose my wife because I think she's better than other women, evaluate wines, books, investments, new hires, all on an ordinal scale from bad to great. This is often confused with racism or other repugnant thoughts because such discrimination tends to disproportionately affect socially disadvantaged groups, but that's incidental. Economists are generally very Liberal, but they also obsess over how smart various economists are, closet IQ idolaters. The key is moderation in all things. Most signals of quality are not linear, so you have to not focus too much on one dimension of someone or something.

The problem is, where to draw the line on who's qualified to voice an opinion. Now, the line must be related to some obective credential. Unfortunately, having a degree in economics is not very impressive, because so much economics is irrelevant or wrong (based on bad assumptions--the logic from assumption to conclusion is usually right). That is, say you really understand Debreu's Theory of Value, or Stokey and Lucas's Recursive Methods in Economics. That may get you tenure, but those books are only related to economics in theory, not practice. Further, as a PhD, you can't draw the line right beneath your credential, but rather have to choose something like an undergraduate degree--otherwise it seems too self serving.

It would be helpful to have some metric of analytical competence to keep the discussion, at the least, logical, but it's hard to think of one that would not exclude too many thoughtful, energetic people. Any exclusive club tends to corrupt itself, and develop insular, self-serving logic.

So, I sympathize with his point--there's too much crappy economics out there--but unfortunately, a PhD is too noisy a signal. I don't have a solution.