Tim Palmer (Oxford): Status and Future of Climate Modeling — Manifold Podcast #16

 

Tim Palmer is Royal Society Research Professor in Climate Physics, and a Senior Fellow at the Oxford Martin Institute. He is interested in the predictability and dynamics of weather and climate, including extreme events. 

He was involved in the first five IPCC assessment reports and was co-chair of the international scientific steering group of the World Climate Research Programme project (CLIVAR) on climate variability and predictability. 

After completing his DPhil at Oxford in theoretical physics, Tim worked at the UK Meteorological Office and later the European Centre for Medium-Range Weather Forecasts. For a large part of his career, Tim has developed ensemble methods for predicting uncertainty in weather and climate forecasts. 

In 2020 Tim was elected to the US National Academy of Sciences. 

Steve, Corey Washington, and Tim first discuss his career path from physics to climate research and then explore the science of climate modeling and the main uncertainties in state-of-the-art models. 

In this episode, we discuss: 

00:00 Introduction 

1:48 Tim Palmer's background and transition from general relativity to climate modeling 

15:13 Climate modeling uncertainty 

46:41 Navier-Stokes equations in climate modeling 

53:37 Where climate change is an existential risk 

1:01:26 Investment in climate research 

Links: 

 

Tim Palmer (Oxford University) 

https://www.ox.ac.uk/news-and-events/find-an-expert/professor-tim-palmer 

The scientific challenge of understanding and estimating climate change (2019) https://www.pnas.org/doi/pdf/10.1073/pnas.1906691116 

ExtremeEarth 

https://extremeearth.eu/ 

Physicist Steve Koonin on climate change

https://infoproc.blogspot.com/2021/04/how-physicist-became-climate-truth.html

Note added: For some background on the importance of water vapor (cloud) distribution within the primitive cells used in these climate simulations, see:

https://infoproc.blogspot.com/2017/06/epistemic-caution-and-climate-change.html

Low clouds trap IR radiation near the Earth, while high clouds reflect solar energy back into space. The net effect on heating from the distribution of water vapor is crucial in these models. However, due to the complexity of the Navier-Stokes equations, current simulations cannot actually solve for this distribution from first principles. Rather, the modelers hand code assumptions about fine grained behavior within each cell. The resulting uncertainty in (e.g., long term) climate prediction from these approximations is unknown.

Embryo Screening and Risk Calculus

Over the weekend The Guardian and The Times (UK) both ran articles on embryo selection. 

Polygenic screening of embryos is here, but is it ethical? Philip Ball for The Guardian 

Genetics firms screen embryos to create designer babies Danny Fortson, The Sunday Times.

I recommend the first article. Philip Ball is an accomplished science writer and former scientist. He touches on many of the most important aspects of the topic, not easy given the length restriction he was working with. 

However I'd like to cover an aspect of embryo selection which is often missed, for example by the bioethicists quoted in Ball's article.

Several independent labs have published results on risk reduction from embryo selection, and all find that the technique is effective. But some people who are not following the field closely (or are not quantitative) still characterize the benefits -- incorrectly, in my view -- as modest. I honestly think they lack understanding of the actual numbers.

Some examples:

Carmi et al. find a ~50% risk reduction for schizophrenia from selecting the lowest risk embryo from a set of 5. For a selection among 2 embryos the risk reduction is ~30%. (We obtain a very similar result using empirical data: real adult siblings with known phenotype.) 

Visscher et al. find the following results, see Table 1 and Figure 2 in their paper. To their credit they compute results for a range of ancestries (European, E. Asian, African). We have performed similar calculations using siblings but have not yet published the results for all ancestries.  

Relative Risk Reduction (RRR): 

Hypertension: 9-18% (ranges depend on specific ancestry) 

Type 2 Diabetes: 7-16% 

Coronary Artery Disease: 8-17% 

Absolute Risk Reduction (ARR): 

Hypertension: 4-8.5% (ranges depend on specific ancestry) 

Type 2 Diabetes: 2.6-5.5% 

Coronary Artery Disease: 0.55-1.1%

I don't view these risk reductions as modest. Given that an IVF family is already going to make a selection they clearly benefit from the additional information that comes with genotyping each embryo. The cost is a small fraction of the overall cost of an IVF cycle.

But here is the important mathematical point which many people miss: We buy risk insurance even when the expected return is negative, in order to ameliorate the worst possible outcomes. 

Consider the example of home insurance. A typical family will spend tens of thousands of dollars over the years on home insurance, which protects against risks like fire or earthquake. However, very few homeowners (e.g., ~1 percent) ever suffer a really large loss! At the end of their lives, looking back, most families might conclude that the insurance was "a waste of money"!

So why buy the insurance? To avoid ruin in the event you are unlucky and your house does burn down. It is tail risk insurance.

Now consider an "unlucky" IVF family. At, say, the 1 percent level of "bad luck" they might have some embryos which are true outliers (e.g., at 10 times normal risk, which could mean over 50% absolute risk) for a serious condition like schizophrenia or breast cancer. This is especially likely if they have a family history. 

What is the benefit to this specific subgroup of families? It is enormous -- using the embryo risk score they can avoid having a child with very high likelihood of serious health condition. This benefit is many many times (> 100x!) larger than the cost of the genetic screening, and it is not characterized by the average risk reductions given above.

The situation is very similar to that of aneuploidy testing (screening against Down syndrome), which is widespread, not just in IVF. The prevalence of trisomy 21 (extra copy of chromosome 21) is only ~1 percent, so almost all families doing aneuploidy screening are "wasting their money" if one uses faulty logic! Nevertheless, the families in the affected category are typically very happy to have paid for the test, and even families with no trisomy warning understand that it was worthwhile.

The point is that no one knows ahead of time whether their house will burn down, or that one or more of their embryos has an important genetic risk. The calculus of average return is misleading -- i.e., it says that home insurance is a "rip off" when in fact it serves an important social purpose of pooling risk and helping the unfortunate. 

The same can be said for embryo screening in IVF -- one should focus on the benefit to "unlucky" families to determine the value. We can't identify the "unlucky" in advance, unless we do genetic screening!

Some useful references:

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations (Genes 2021 Special Issue) 

First Baby Born from a Polygenically Screened Embryo

Vineer Bhansali: Physics, Tail Risk Hedging, and 900% Coronavirus Returns - Manifold Episode #43

Steve and Corey talk with theoretical physicist turned hedge fund investor Vineer Bhansali. Bhansali describes his transition from physics to finance, his firm LongTail Alpha, and his recent outsize returns from the coronavirus financial crisis. Also discussed: derivatives pricing, random walks, helicopter money, and Modern Monetary Theory.

Transcript

LongTail Alpha

LongTail Alpha’s OneTail Hedgehog Fund II had 929% Return (Bloomberg)

A New Anomaly Matching Condition? (1992)
https://arxiv.org/abs/hep-ph/9211299

Added: Background on derivatives history here. AFAIK high energy physicist M.F.M. Osborne was the first to suggest the log-normal random walk model for securities prices, in the 1950s. Bachelier suggested an additive model which does not even make logical sense. See my articles in Physics World: 1 , 2


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Dangerous Knowledge and Existential Risk (Dominic Cummings)

Dominic Cummings begins a new series of blog posts. Highly recommended!

It's worth noting a few "factor of a million" advances that have happened recently, largely due to physical science, applied mathematics, and engineering:

1. Destructive power of an H-bomb is a million times greater than that of conventional explosives. This advance took ~20 years.

2. Computational power (Moore's Law) has advanced a million times over a roughly similar timescale.

3. Genome sequencing (and editing) capabilities have improved similarly, just in the 21st century.

How much have machine intelligence and AI progressed, say, in the last 20 years? If it isn't a factor of a million (whatever that means in this context), it soon will be ...

Dominic Cummings: ... The big big problem we face – the world is ‘undersized and underorganised’ because of a collision between four forces: 1) our technological civilisation is inherently fragile and vulnerable to shocks, 2) the knowledge it generates is inherently dangerous, 3) our evolved instincts predispose us to aggression and misunderstanding, and 4) there is a profound mismatch between the scale and speed of destruction our knowledge can cause and the quality of individual and institutional decision-making in ‘mission critical’ political institutions ...

... Politics is profoundly nonlinear. (I have written a series of blogs about complexity and prediction HERE which are useful background for those interested.) Changing the course of European history via the referendum only involved about 10 crucial people controlling ~£10^7 while its effects over ten years could be on the scale of ~10^8 – 10^9 people and ~£10^12: like many episodes in history the resources put into it are extremely nonlinear in relation to the potential branching histories it creates. Errors dealing with Germany in 1914 and 1939 were costly on the scale of ~100,000,000 (10^8) lives. If we carry on with normal human history – that is, international relations defined as out-groups competing violently – and combine this with modern technology then it is extremely likely that we will have a disaster on the scale of billions (10^9) or even all humans (~10^10). The ultimate disaster would kill about 100 times more people than our failure with Germany. Our destructive power is already much more than 100 times greater than it was then.

Even if we dodge this particular bullet there are many others lurking. New genetic engineering techniques such as CRISPR allow radical possibilities for re-engineering organisms including humans in ways thought of as science fiction only a decade ago. We will soon be able to remake human nature itself. CRISPR-enabled ‘gene drives’ enable us to make changes to the germ-line of organisms permanent such that changes spread through the entire wild population, including making species extinct on demand. Unlike nuclear weapons such technologies are not complex, expensive, and able to be kept secret for a long time. The world’s leading experts predict that people will be making them cheaply at home soon – perhaps they already are.

It is already practically possible to deploy a cheap, autonomous, and anonymous drone with facial-recognition software and a one gram shaped-charge to identify a relevant face and blow it up. Military logic is driving autonomy. ...

Dangers have increased, but quality of decision making and institutions has not:

... The national institutions we have to deal with such crises are pretty similar to those that failed so spectacularly in summer 1914 yet they now face crises involving 10^2 – 10^3 times more physical destruction moving at least 10^3 times faster. The international institutions developed post-1945 (UN, EU etc) contribute little to solving the biggest problems and in many ways make them worse. These institutions fail constantly and do not – cannot – learn much.

If we keep having crises like we have experienced over the past century then this combination of problems pushes the probability of catastrophe towards ‘overwhelmingly likely’.

... Can a big jump in performance – ‘better and more powerful thinking programs for man and machine’ – somehow be systematised?

Feynman once gave a talk titled ‘There’s plenty of room at the bottom’ about the huge performance improvements possible if we could learn to do engineering at the atomic scale – what is now called nanotechnology. There is also ‘plenty of room at the top’ of political structures for huge improvements in performance. As I explained recently, the victory of the Leave campaign owed more to the fundamental dysfunction of the British Establishment than it did to any brilliance from Vote Leave. Despite having the support of practically every force with power and money in the world (including the main broadcasters) and controlling the timing and legal regulation of the referendum, they blew it. This was good if you support Leave but just how easily the whole system could be taken down should be frightening for everybody .

Creating high performance teams is obviously hard but in what ways is it really hard?

... The real obstacle is that although we can all learn and study HPTs it is extremely hard to put this learning to practical use and sustain it against all the forces of entropy that constantly operate to degrade high performance once the original people have gone. HPTs are episodic. They seem to come out of nowhere, shock people, then vanish with the rare individuals. People write about them and many talk about learning from them but in fact almost nobody ever learns from them – apart, perhaps, from those very rare people who did not need to learn – and nobody has found a method to embed this learning reliably and systematically in institutions that can maintain it. ...

Physics Envy

A reader referred me to this excellent paper by Andy Lo and Mark Mueller. Mark and I were both Harvard postdocs at the same time. I seem to remember long conversations about both physics and finance in the Dunster dining room :-)

Warning: Physics Envy May be Hazardous to Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, physics envy has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an uncertainty checklist with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.

While physics envy might be a problem for economists or theoretical biologists, making physics your career (as opposed to becoming a quant, as Mark did) is certainly hazardous to your net worth!

Risk and the spurious air of technicality

The following appeared in a letter to the editor in response to the Times article Risk Mismanagement by John Nocera.

I'll never forget a referee report I once saw which referred to the equations and graphs in a paper as "lending a spurious air of technicality" to otherwise pure speculation. The same comment might be applied to whole fields of research :-)

Charles Mackay’s 19th-century book “Memoirs of Extraordinary Popular Delusions and the Madness of Crowds,” recounting John Law’s Mississippi Scheme, the South Sea Bubble, Tulipomania and others, was reissued in 1932 (for obvious reasons). In a foreword, Bernard Baruch wrote: “All economic movements, by their very nature, are motivated by crowd psychology. . . . I never see a brilliant economic thesis expounding, as though they were geometrical theorems, the mathematics of price movements, that I do not recall Schiller’s dictum: ‘Anyone taken as an individual, is tolerably sensible and reasonable — as a member of a crowd he at once becomes a blockhead.’ ” Baruch speaks of “crowd madness” and says that “these are phenomena of mass action under impulsions and controls which no science has explored.” Sir Isaac Newton, who lost a life’s savings in one of these bubbles, wrote, “I can calculate the movements of the heavenly bodies, but I cannot calculate the madness of men.” Einstein said Newton was the greatest scientific mind who ever lived, so if he couldn’t do it, who can?

Catastrophe bonds and the investor's choice problem

Consider the following proposition. You put up an amount of capital X for one year. There is a small probability p (e.g., p = .01) that you will lose the entire amount. With probability (1-p) you get the entire amount back. What interest rate (fee) should you charge to participate?

What I've just described is a catastrophe bond. A catastrophe bond allows an insurer to transfer the tail risk from a natural disaster (hurricane, earthquake, fire, etc.) to an investor who is paid appropriately. How can we decide the appropriate fee for taking on this risk? It's an example of the fundamental investor's choice problem. That is, what is the value of a gamble specified by a given probability distribution over a set of payoffs? (Which of two distributions do you prefer?) One would think that the answer depends on individual risk preferences or utility functions.

Our colloquium speaker last week was John Seo of Fermat Capital, a hedge fund that trades catastrophe bonds. Actually, John pioneered the business at Lehman Brothers before starting Fermat. He's yet another deep thinking physicist who ended up in finance. Indeed, he claims to have made some fundamental progress on the investor's choice problem. His approach involves a kind of discounting in probability space, as opposed to the now familiar discounting of cash flows in time. I won't discuss the details further, since they are slightly proprietary.

I can discuss aspects of the cat bond market. Apparently the global insurance industry cannot self-insure against 1 in 100 year risks. That is, disasters which have occurred historically with that frequency are capable of taking down the whole industry (e.g., huge earthquakes in Japan or California). Therefore, it is sensible for insurers to sell some of that risk. Who wants to buy a cat bond? Well, pension funds, which manage the largest pools of capital on the planet, are always on the lookout for sources of return whose risks are uncorrelated with those of stocks, bonds and other existing financial instruments. Portfolio theory suggests that a pension fund should put a few percent of its capital into cat bonds, and that's how John has raised the $2 billion he currently has under management. The market answer to the question I posed in the first paragraph is roughly LIBOR plus (4-6) times the expected loss. For a once in a century disaster, this return is LIBOR plus (4-6) percent or so. Sounds like a good trade for the pension fund as long as the event risk is realistically evaluated.

Note there is no leverage or counterparty risk in these transactions. An independent vehicle is created which holds the capital X, invested in AAA securities (no CDOs, please :-). If the conditions of the contract are triggered, this entity turns the capital over to the insurance company. Otherwise, the assets are returned at the end of the term.

In the colloquium, John reviewed the origins of present value analysis, going back to Fibonacci, Fermat and Pascal. See Mark Thoma, who also attended, for more discussion.

Taleb or not Taleb?

Nassim Taleb, love him or hate him, has appeared quite a few times on this blog. Personally I find him quite amusing. I like his irreverence for "expert" opinion (especially that of economists) and his skepticism toward finance theory (in particular, towards Black Scholes and assumptions about perfect hedging and normally distributed risks). His earlier book Fooled By Randomness is largely devoted to making the simple point (amazingly, not appreciated by many otherwise very smart people) that it is quite difficult to tell whether success is due to ability or plain luck. In Wall Street terms, when is there enough data to be confident about someone's alpha?

I recently found this interesting essay by Eric Falkenstein, which is quite critical of Taleb and his book The Black Swan. Many of Falkenstein's points are well taken, although one should evaluate his arguments carefully -- his background (worked on VAR, an economics PhD) might predispose him to dislike Taleb. He criticises Taleb's hero Mandelbrot and the use of fractal ideas in finance, but these criticisms point to the fact that the ideas do not lead to easily implementable models, not that they are wrong as a fundamental description of the underlying phenomena. See, e.g., here and here for more discussion. Eventually, he cuts to the chase and notes that Taleb's earlier hedge fund was probably a loser, and that if he had had any success as a trader he wouldn't be out there hawking books and giving lectures on the rubber chicken circuit :-)

Taleb's trading strategy, based on the idea that others in the market are insufficiently aware of fat tailed distributions, was to buy out of the money puts in hopes of a profiting from a catastrophe. Under this strategy his fund constantly lost small amounts of money in hopes of making a big killing. Sadly for Taleb, he never hit the jackpot, although (see the Fooled By Randomness comment above) that doesn't necessarily undermine the validity of the strategy. More damaging, however, is the fact that insurance companies are basically on the other side of Taleb's trade all the time, and they seem capable of generating steady profits for long periods of time. My guess is that any trader who sold a put to Taleb's fund would pad out the price so much that even if their probability distribution were off at the tails, they would still exact a premium over the real value of the option. The deeper out of the money you go, the more careful and suspicious your counterparty is likely to be.

Finally, here's a recent paper by Taleb which is harshly critical of Black-Scholes-Merton.

Falkenstein: ...Taleb argues that the unpredictability of important events implies we should basically forget about all that is predictable, because that’s not where the real money or importance is. So from a risk management perspective, we should ignore Value at Risk, which measures anticipated fluctuations. Further, we should ‘go long’ on these unanticipated events by engaging in quirky activities on the off-chance that we randomly find something, or someone, really valuable.

Success in markets, like life, is a combination of ability, effort, and chance. Much of intelligent thought is distinguishing between what is predictable v. what is unpredictable; it is to any organism's advantage to find out what we can figure out and change, and what is forever mysterious and unalterable (eg, the Serenity Prayer). The brain is constantly predicting the environment, trying to figure out cause and effect so it can better understand the world. Most of what humans process is predictable, but because we take predictable things for granted, they are uninteresting. We can't predict some things, but instead of resorting to nihilism, we merely buy insurance or manage our portfolios--in the broad sense of the term--to have an appropriate robustness. Discovering certain things are basically unpredictable does not diminish our constant focus on trying to predict more and more things. People will disagree on which risks at the margin are predictable, but that's to be expected, and we all hope to be making the right choices that optimize our serenity at the margin of our predictable prowess.

Of course, in the face of being totally wrong in his evaluation of the usefulness of VAR as a tool — it’s ubiquitous in practical management of diverse trading books — Taleb now says he merely warned against naive usage of VAR. However, it was only his absurdly strong statement that VAR was for charlatans that got him mentioned in the Derivatives Strategy article that propelled him into public discourse (conveniently removed from his website, but you can read it online here). Then, as now, he points to anecdotes of imperfection to "prove" his points.

From Taleb's Wikipedia entry circa July 2006, we see where Black Swan thinking goes when applied to an investment strategy:
When he was primarily a trader, he developed an investment method which sought to profit from unusual and unpredictable random events, which he called "black swans." His reasoning was that traders lose much more money from a market crash than they gain from even years of steady gains, and so he did not worry if his portfolio lost money steadily, as long as that portfolio positioned him to profit greatly from an extremely large deviation (either a crash or an unexpected jump upwards).

In fact, Mandelbrot also argues for this strategy. Taleb co-authored a paper arguing that most people systematically underestimate volatility. Furthermore, he argues there exists not only a lack of appreciation of fat tails, but a preference for positive skew, in that people prefer assets that jump up, not down, which would imply the superiority of buying out-of-the-money puts as opposed to calls because those negative tails that increase the price of puts are unappreciated.

These assertions present some straightforward tests, which a Popperian like Taleb should embrace. Specifically, buying out-of-the-money options, especially puts (because of negative skew), should, on average, make money. But insurance companies, which basically are selling out-of-the-money options, tend to do as well as any industry (Warren Buffet has always favored insurance companies, especially re-insurers, as equity investments). Studies by Shumway and Coval (2001) and Bondarenko (2003) have documented that selling puts is where all the extranormal profit seems to be. Of all the option strategies, selling, not buying, out-of-the-money puts has been the best performer historically.

Famed New Yorker writer Malcom Gladwell in a 2002 New Yorker article contrasts the thoughtful, pensive Taleb versus the brash cowboy Victor Neiderhoffer: Taleb buys out-of-the-money puts, Neiderhoffer sells them. Taleb is betting on the big blow up, Niederhoffer on the idea that people overpay for insurance. Who was right? Well, Neiderhoffer still ran his flagship fund until September 2007 from a chalet-style mansion in Weston Connecticut . Taleb shut down his Empirica Kurtosis fund at the end of 2004, and the only public data on it suggest a rather anemic Sharpe ratio, below that of the S&P500 (60% in 2000, about zero for the next 4 years, see here), which is consistent with shutting it down, and trying to redescribe it as a hedge or laboratory, and then move into the more profitable business of teaching how to invest. While neither strategy was great, Niederhoffer's was better, if you just look at their lifetimes (management, in this case Taleb, always likes to say that people left positions of power due to desires to be with family or other opportunities, but the bottom line is, selling puts remained immune to family considerations longer than buying puts).

Taleb's big problem is that he misinterprets the mode-mean trade. A mode-mean trade is where a trader finds a strategy with a positive mode, but zero or negative mean. He then uses someone else’s capital to make money off several years of good returns, making good money for creating or managing the strategy, then, when the strategy gives it all back, the investor bears all the loss. That’s a bad strategy for the investor, and the trader who manages it is either naïve or duplicitous. That is, selling extreme options or writing insurance on extreme events at any prices generates a good mode return, but if it underestimates the probability or severity of the bad times, it may generate a zero or negative average return. Buying High Yield debt is a good example. However, just because selling puts is a bad strategy, it doesn't mean buying puts is a good strategy. A Sharpe of 0.2 is a bad long position, but a worse short (because a - 0.2 Sharpe is worse than a 0.2).

Fake alpha, tail risk and compensation in finance

I highly recommend this essay in the Financial Times. It notes that current banking and money management compensation schemes create incentives for taking on tail risk (which is really beta) and disguising it as alpha. The proposed solution: holdbacks or clawbacks of bonus money. This would probably be a big improvement over the status quo (although how long would one have to wait to be sure that risk was properly priced on a group of thirty year loans?). When will shareholders smarten up and enforce this kind of compensation scheme on management at public firms? Clawbacks already happen in VC when early success turns into losses for a fund.

A minor quibble with what is written about VCs: in many cases "activism" is too strong a characterization -- it is the inventor/entrepreneur who does all the work.

FT: Bankers’ pay is deeply flawed

By Raghuram Rajan

Published: January 8 2008 18:04 | Last updated: January 9 2008 16:21

Summary: Raghuram Rajan says bogus alpha is created by hiding long-tail risks, as with structured products linked to subprime mortgages. A solution would be to hold in escrow a big chunk of bonuses until the full risks play out, meaning only true alpha gets jumbo rewards and reducing the hidden risks in the financial system.


Banks have recently been acknowledging enormous losses, yet those losses are barely reflected in employee compensation. For example, Morgan Stanley announced a $9.4bn charge-off in the fourth quarter and at the same time increased its bonus pool by 18 per cent. The justification was that many employees had a banner year and their compensation should not be held hostage to mistakes that were made in the subprime market. The chief executive, John Mack, however, assumed some responsibility and agreed to take no bonus for 2007 – although he got a $40m payout for 2006.

Even so, most readers would suspect something is not right here. Indeed, compensation practices in the financial sector are deeply flawed and probably contributed to the ongoing crisis.

The typical manager of financial assets generates returns based on the systematic risk he takes – the so-called beta risk – and the value his abilities contribute to the investment process – his so-called alpha. Shareholders in asset management firms, such as commercial banks, investment banks and private equity or insurance companies are unlikely to pay the manager much for returns from beta risk. For example, if the shareholder wants exposure to large traded US stocks she can get the returns associated with that risk simply by investing in the Vanguard S&P 500 index fund, for which she pays a fraction of a per cent in fees. What the shareholder will really pay for is if the manager beats the S&P 500 index regularly, that is, generates excess returns while not taking more risks. Hence they will pay for alpha.

In reality, there are only a few sources of alpha for investment managers. One of them comes from having truly special abilities in identifying undervalued financial assets. Warren Buffett, the US billionaire investor, certainly has it, yet this special ability is, by definition, rare.

A second source of alpha is from what one might call activism. This means using financial resources to create, or obtain control over, real assets and to use that control to change the payout obtained on the financial investment. A venture capitalist who transforms an inventor, a garage and an idea into a fully fledged, profitable and professionally managed corporation creates alpha.

A third source of alpha is financial entrepreneurship or engineering – creating securities or cash flow streams that appeal to particular investors or tastes. As long as the investment manager does not create securities that exploit investor weaknesses or ignorance (and there is unfortunately too much of that), this sort of alpha is also beneficial, but it requires constant innovation.

Alpha is quite hard to generate since most ways of doing so depend on the investment manager possessing unique abilities – to pick stocks, identify weaknesses in management and remedy them, or undertake financial innovation. Such abilities are rare. How then can untalented investment managers justify their pay? Unfortunately, all too often it is by creating fake alpha – appearing to create excess returns but in fact taking on hidden tail risks, which produce a steady positive return most of the time as compensation for a rare, very negative, return.

For example, an investment manager who bought AAA-rated tranches of collateralised debt obligations (CDO) in the past generated a return of 50 to 60 basis points higher than a similar AAA-rated corporate bond. That “excess” return was in fact compens ation for the “tail” risk that the CDO would default, a risk that was no doubt perceived as small when the housing market was rollicking along, but which was not zero. If all the manager had disclosed was the high rating of his investment portfolio he would have looked like a genius, making money without additional risk, even more so if he multiplied his “excess” return by leverage. Similarly, the management of Northern Rock followed the old strategy of taking on tail risk, borrowing short and lending long and praying that the unlikely event of a liquidity shortage never materialised. All these strategies essentially earn the manager a premium in normal times for taking on beta risk that materialises only infrequently. These premiums are not alpha, since they are wiped out when the risk materialises.

True alpha can be measured only in the long run and with the benefit of hindsight – in the same way as the acumen of someone writing earthquake insurance can be measured only over a period long enough for earthquakes to have occurred. Compensation structures that reward managers annually for profits, but do not claw these rewards back when losses materialise, encourage the creation of fake alpha. Significant portions of compensation should be held in escrow to be paid only long after the activities that generated that compensation occur.

The managers who blew a big hole in Morgan Stanley’s balance sheet probably earned enormous bonuses in the past – Mr Mack certainly did. If Morgan Stanley managed its compensation correctly those bonuses should be clawed back and should be enough to pay those who did well this year without increasing the bonus pool. At the very least, shareholders deserve better explanations. More generally, unless we fix incentives in the financial system we will get more risk than we bargain for. Unless bankers offer these better explanations, their enormous pay, which has been thought of as just reward for performance, will deservedly come under scrutiny.

The writer is a professor of finance at the Graduate School of Business at the University of Chicago and former chief economist at the International Monetary Fund