risk


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Only just got my electricity back after an almost 3 day outage. As all my water [pumps] work based on electricity, that has meant no water.

Anyway lesson learned. Apparently Tesla batteries are available and can be fitted to charge from normal electric supplies and will take over in the even of a power cut. I’m going to buy at least 1, more if I can afford them and avoid this risk into the future.

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Compare the banking landscape today with that of 10 years ago, and it’s hard to miss the changes. Lenders typically hold more capital and are much less reliant on unstable wholesale funding than they were before the crisis. Regulators are generally warier of mounting risks in the financial system — whether these come from consumer debt or derivatives exchanged over the counter.

Yet the right question to ask is not what’s changed, but if these regulatory transformations have been sufficient. In at least three areas — the right level of bank capital, the use of risk weights, and structural reforms — many economists fear the financial system remains exceedingly vulnerable to shocks.

The largest gap between academics and practitioners is probably on the level of capital. As Sir John Vickers, a professor of economics at Oxford who presided over Britain’s Independent Banking Commission (IBC), noted in a recent speech, regulators are now accepting a level of leverage which is still around 25 or 30 times a bank’s core capital. Many outside economists believe a bank should only hold assets worth six to 10 times their key funds, if not less. “So one group or the other, if not both, would appear to be wrong by a large margin, on a policy question of deep importance,” Sir John noted.

Of course, most advocates of higher capital requirements don’t believe banks should get to the new ratio overnight. Lenders would do so by shedding assets rather than raising new capital, with dramatic impacts on the economy. But the question matters since regulators appear increasingly comfortable with the ambition of the existing rules and don’t want to go further. Vickers refers to the case of the Bank of England, which last year decided not to ask Britain’s largest lenders to raise significantly more capital over time, as it felt satisfied, among other things, with their plans for orderly resolution. It is hard to escape the feeling that some central bankers have become complacent over the level of risk they are willing to tolerate.

A related matter is our assessment of bank risk. At a conference held last week by the Centre for Economic Policy Research (CEPR), Tamim Bayoumi of the International Monetary Fund showed how the Basel Committee’s decision in 1996 to allow banks to use internal models for their valuation of risks radically changed the behavior of lenders, especially in Europe. Before 1996, banks with a higher ratio of risk-weighted assets to capital were also those with a higher leverage ratio. After that year, the relation between the two broke. Most likely, banks learned to game the system and pile up on assets simply by tweaking their risk models.

There is no guarantee that simpler leverage ratios can avoid a new financial crisis. After all, banks may simply hold on to their riskier assets and shed less remunerative but safer loans. The Basel Committee is introducing a simple leverage ratio as a backstop measure to internal risk models. Yet that doesn’t justify the prominence regulators continue to give to an approach which has shown manifest weaknesses during the crisis. Risk-weights, as the financial crisis made plain, can be pro-cyclical; risk falls when the level of an asset goes up and vice versa. The result is that not only are we asking banks to hold too little capital, but we are also underestimating how problematic their exposures really are.

The fear, therefore, is that sooner rather than later, governments may again be called upon to rescue a troubled bank. And here lies the third dangerous similarity with the pre-crisis world: Many Western countries have proven unable to separate investment banking from commercial lending. As a result, even if governments only wanted to keep the latter going, in many cases they will be forced to rescue everything, as it is impossible to split the two.

The worst offender is undoubtedly the European Union. In 2012, Erkki Liikanen, the Governor of the Bank of Finland, produced a reportrecommending, among other things, the separation of trading activity within universal banks. Five years on, the EU has failed to follow up on his suggestion in any meaningful manner, leaving mega-banks such as BNP Paribas and Deutsche Bank unchecked.

The U.K. and the U.S. have undoubtedly moved further in this respect. Britain is pressing ahead with the recommendations issued in the IBC’s report, including building a ring-fence between investment and commercial banks when they are in the same institution. In the U.S., the Dodd-Frank act has led to the imposition of the so-called Volcker rule, prohibiting banks from engaging in proprietary trading under certain circumstances. Yet, at least in the U.S., the administration is now considering a dilution of these measures, which could turn the clock closer to the pre-crisis years.

When judging the shape of financial regulation after a crisis, we often hear the industry view that the new requirements have been overly burdensome. There is however another, equally plausible take: that we have not gone nearly far enough in shoring up the banking system. Trade-offs are always difficult to assess, but if this more conservative assessment is right, it is a terrifying prospect.

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In writing my latest Thoughts from the Frontline, I reached out to my contacts looking for an uber-bull—someone utterly convinced that the market is on solid ground, with good evidence for their view.

Fortunately, a good friend who must remain nameless shared with me an August 4 slide deck from Krishna Memani, Chief Investment Officer of Oppenheimer Funds.

The current bull market is the second longest and has the third-highest gain. It will be the longest stock bull market of the modern era if it can last another two years or so.

However, he thinks the present bull market will continue for another year.

Here’s Memani:

For some investors, the sheer age of this cycle is enough to cause consternation. Yet there is nothing magical about the passage of time. As we have said time and again, bull markets do not die of old age. Like people, bull markets ultimately die when the system can no longer fight off maladies. In order for the cycle to end there needs to be a catalyst—either a major policy mistake or a significant economic disruption in one of the world’s major economies. In our view, neither appears to be in the offing.

15 Events That Could Be a Catalyst for the Next Recession

He goes on to list 15 specific events he thinks would be necessary to make him abandon his bullish position. (Comments in parentheses and italics are mine.)

1. Global growth would have had to decelerate. It is not.

(European growth is actually picking up. Germany blinked on financing Italian bank debt, and the markets now have more confidence that Draghi can do whatever it takes.)

2. Wages and inflation would have had to rise. They are not.

3. The Fed would have planned to tighten monetary policy significantly. It is not.

(They should have been raising rates four years ago. It is too late in the cycle now. They may raise rates once more, but the paltry amount of “quantitative tightening” they are likely to do is not going to amount to much. In fact, if for some reason they decided to go further with rate hike and enter a tightening cycle, their monetary policy error would probably trigger a recession and a deep bear market. I think they realize that—or at least I hope they do.)

4. The ECB would have to tighten policy substantially. It will likely not.

(Draghi will go through the motions, though he knows he is limited in what he can actually do – unless for some unexpected reason Europe takes off to the upside. And while Eastern Europe is actually doing that, “Old Europe” is not.)

5. Credit growth would have had to be surging. It is not.

(Credit growth is generally picking up but not surging. And most of the credit growth is in government debt.)

6. Corporate animal spirits would have been taking off. They are not.

(That is basically true for most public corporations. There are a number of private companies and smaller businesses that are pretty optimistic.)

7. Equities would have had to be expensive relative to bonds. They are not.

8. FAANG stocks would have had to be at extreme valuations. They are not.

(I don’t think I buy this one.)

9. Investors would have had to be euphoric about equities. They are not.

10. The current cyclical rally within the secular bull would have had to be old and stretched. It is not.

(Not buying this one either.)

11. High-yield spreads would have to be widening. They are not.

(I pay attention to high-yield spreads, a classic warning sign of a turn in market behavior. Are they at dangerous levels? Damn, Skippy, I cannot believe some of the bonds that are being sold out in the marketplace. Not that I can’t believe the sellers are willing to take the money—you’d have to be an idiot not to take free money with no strings attached. I just don’t understand why major institutions are buying this nonsense.)

12. The classic signs of excess would have had to be evident. They are not.

(Kind of, sort of, but we are really beginning to stretch the point.)

13. China’s credit binge would have had to threaten the global financial system. It does not.

(Xi has somehow managed to push off the credit crisis, at least for the rest of this year, until after the five-year Congress. Rather amazing.)

14. Global trade would have had to be weakening. It is not.

15. The US dollar would have had to be strengthening. It is not.

That’s quite a list. Seeing it with the charts and Memani’s comments makes it even more compelling. To pick just one for closer scrutiny, let’s consider #7.

Are Equities Expensive Relative to Bonds?

That’s a good question because it really matters to big, long-term investors like pension funds.

Pension fund managers need to meet certain return targets, and they want to put the odds on their side. Treasury bonds offer certainty—presuming the US government doesn’t default. (Ask me about that again in October.)

Stocks may offer higher returns but more variation.

Memani explains this relationship by looking at earnings yield. That’s the inverse of the P/E ratio.

Essentially, it’s the percentage of each dollar invested in a stock that comes back as profits. Some gets distributed via dividends, buybacks, etc., and some is retained.

If you think there’s a stock mania today akin to the euphoria of the late 1990s, you’ll find no support in this ratio. Back then, bonds were dirt cheap compared to stock market earnings yield.

Now we have the reverse: stocks are cheap compared to bonds.

This is one of the most convincing bullish arguments I see now.

I remember the late ’90s very well. I called the top about three years early, never dreaming we could see a year like 1999. That will always be my mania benchmark—and today we are not even remotely near it. I don’t remember thinking much about bonds back then. No one else was, either.

But buying them would have turned out much better than buying stocks in 1997–99.

 

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BEIJING — What worries you about the coming world of artificial intelligence?

Too often the answer to this question resembles the plot of a sci-fi thriller. People worry that developments in A.I. will bring about the “singularity” — that point in history when A.I. surpasses human intelligence, leading to an unimaginable revolution in human affairs. Or they wonder whether instead of our controlling artificial intelligence, it will control us, turning us, in effect, into cyborgs.

These are interesting issues to contemplate, but they are not pressing. They concern situations that may not arise for hundreds of years, if ever. At the moment, there is no known path from our best A.I. tools (like the Google computer program that recently beat the world’s best player of the game of Go) to “general” A.I. — self-aware computer programs that can engage in common-sense reasoning, attain knowledge in multiple domains, feel, express and understand emotions and so on.

This doesn’t mean we have nothing to worry about. On the contrary, the A.I. products that now exist are improving faster than most people realize and promise to radically transform our world, not always for the better. They are only tools, not a competing form of intelligence. But they will reshape what work means and how wealth is created, leading to unprecedented economic inequalities and even altering the global balance of power.

It is imperative that we turn our attention to these imminent challenges.

What is artificial intelligence today? Roughly speaking, it’s technology that takes in huge amounts of information from a specific domain (say, loan repayment histories) and uses it to make a decision in a specific case (whether to give an individual a loan) in the service of a specified goal (maximizing profits for the lender). Think of a spreadsheet on steroids, trained on big data. These tools can outperform human beings at a given task.

This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-scale decimation of jobs — mostly lower-paying jobs, but some higher-paying ones, too.

This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)

We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?

Part of the answer will involve educating or retraining people in tasks A.I. tools aren’t good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and “cross-domain” thinking — for example, the work of a trial lawyer. But these skills are typically required by high-paying jobs that may be hard to retrain displaced workers to do. More promising are lower-paying jobs involving the “people skills” that A.I. lacks: social workers, bartenders, concierges — professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The solution to the problem of mass unemployment, I suspect, will involve “service jobs of love.” These are jobs that A.I. cannot do, that society needs and that give people a sense of purpose. Examples include accompanying an older person to visit a doctor, mentoring at an orphanage and serving as a sponsor at Alcoholics Anonymous — or, potentially soon, Virtual Reality Anonymous (for those addicted to their parallel lives in computer-generated simulations). The volunteer service jobs of today, in other words, may turn into the real jobs of the future.

Other volunteer jobs may be higher-paying and professional, such as compassionate medical service providers who serve as the “human interface” for A.I. programs that diagnose cancer. In all cases, people will be able to choose to work fewer hours than they do now.

Who will pay for these jobs? Here is where the enormous wealth concentrated in relatively few hands comes in. It strikes me as unavoidable that large chunks of the money created by A.I. will have to be transferred to those whose jobs have been displaced. This seems feasible only through Keynesian policies of increased government spending, presumably raised through taxation on wealthy companies.

As for what form that social welfare would take, I would argue for a conditional universal basic income: welfare offered to those who have a financial need, on the condition they either show an effort to receive training that would make them employable or commit to a certain number of hours of “service of love” voluntarism.

To fund this, tax rates will have to be high. The government will not only have to subsidize most people’s lives and work; it will also have to compensate for the loss of individual tax revenue previously collected from employed individuals.

This leads to the final and perhaps most consequential challenge of A.I. The Keynesian approach I have sketched out may be feasible in the United States and China, which will have enough successful A.I. businesses to fund welfare initiatives via taxes. But what about other countries?

They face two insurmountable problems. First, most of the money being made from artificial intelligence will go to the United States and China. A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect; the more data you can collect, the more talent you can attract; the more talent you can attract, the better your product. It’s a virtuous circle, and the United States and China have already amassed the talent, market share and data to set it in motion.

For example, the Chinese speech-recognition company iFlytek and several Chinese face-recognition companies such as Megvii and SenseTime have become industry leaders, as measured by market capitalization. The United States is spearheading the development of autonomous vehicles, led by companies like Google, Tesla and Uber. As for the consumer internet market, seven American or Chinese companies — Google, Facebook, Microsoft, Amazon, Baidu, Alibaba and Tencent — are making extensive use of A.I. and expanding operations to other countries, essentially owning those A.I. markets. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.

The other challenge for many countries that are not China or the United States is that their populations are increasing, especially in the developing world. While a large, growing population can be an economic asset (as in China and India in recent decades), in the age of A.I. it will be an economic liability because it will comprise mostly displaced workers, not productive ones.

So if most countries will not be able to tax ultra-profitable A.I. companies to subsidize their workers, what options will they have? I foresee only one: Unless they wish to plunge their people into poverty, they will be forced to negotiate with whichever country supplies most of their A.I. software — China or the United States — to essentially become that country’s economic dependent, taking in welfare subsidies in exchange for letting the “parent” nation’s A.I. companies continue to profit from the dependent country’s users. Such economic arrangements would reshape today’s geopolitical alliances.

One way or another, we are going to have to start thinking about how to minimize the looming A.I.-fueled gap between the haves and the have-nots, both within and between nations. Or to put the matter more optimistically: A.I. is presenting us with an opportunity to rethink economic inequality on a global scale. These challenges are too far-ranging in their effects for any nation to isolate itself from the rest of the world.

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Portfolio management involves much more than just an investment idea. For sophisticated investors, it is also about diversification, volatility management, and exposure mitigation.

For a Bridgewater client, the investment process isn’t as much about the hedge fund as it is about the client’s risk management needs. After explaining how the fundamental investment machine works – which operates like a systematic, logic driven machine – Prince explained a portfolio customization method typically reserved for the most sophisticated of algorithmic investors.

While the core investment analysis and “truths” upon which investments are based does not change from investor to investor, the level of volatility and beta benchmark exposure can be adjusted like a dial on an oven. If an investor determines they want 7% volatility, for instance, Bridgewater customizes their investment based on this benchmark by keeping the mix of alphas the same, but adjusting their size. All other performance factors – absolute returns, drawdown, risk exposure – are driven by this volatility dial to various degrees.

The beta benchmark offers investors a method to calibrate the effectiveness of their investment to any one of 22 different benchmarks.

Bridgewater’s strategies are fundamental in nature but driven from a systematic standpoint. In fact, Ray Dalio, the fund’s founder, is credited with being among the early hedge fund leaders to embrace systematic, logic-based algorithms.

The fundamental investment process starts by developing a timeless and universal investment thesis based on “how the world works.” Each performance driver and the logic behind the investment is made entirely transparent to the investment analysis team. Engaging in “radical transparency,” a strong critique and even attack of the investment thesis – modeling through positive and negative market environments – is encouraged. It is not an environment for those who take offense at their ideas or principles being challenged can typically handle. In this respect, it is a survival of the fittest environment to various degrees – and only the strongest uncorrelated ideas rise to the top.

After the research idea makes it through a strong due diligence process, strategies are assessed for goodness and correlation based on a series of qualitative and quantitative metrics. These fundamentally based systems measure the pressure on each market, with pressure dials that scale from fully bullish to fully bearish with smooth gradations in between.

This research, for which Ray Dalio, Bob Prince and Greg Jensen are intimately familiar, is then handed off to the asset management team where portfolio exposure impact is assessed. At each stage of the portfolio management process there are idiosyncratic methods that Bridgewater utilizes to deliver uncorrelated performance improvement.

After asset management, the third and final step of the process is trade execution. Bridgewater doesn’t invest in individual name stocks to the extent of most hedge funds but rather engages in significant derivatives exposure. In large part this is done for the sake of exposure efficiency but also allows the degree of volatility and risk management customization, much of which is done through various leverage adjustments.

Bridgewater is a machine, but a machine based on a systematic logic that is firmly run by humans. The output has been some of the most uncorrelated performance in the history of major hedge funds.

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Volatility tends to drop when market risk is building up and leverage is rising, luring investors into complacency. Indeed, the lower volatility justifies investors taking on more leverage; if volatility has dropped by a third, why not take one and a half times the leverage? This pro-cyclical dynamic arising from lower volatility in times of increasing risk-taking is the volatility paradox. The main take-away from the volatility paradox is that we shouldn’t use shorter-term, contemporary risk measures when they are very low.
But there isn’t really a paradox, and we shouldn’t ignore the low volatility. Unusually low volatility has value, it is just that if it is being viewed as a typical volatility measure it is being looked at in the wrong way. We can rely on short term volatility as a risk indicator, not as an exogenous measure of risk, but rather as endogenous manifestation of the dynamics of the market because low volatility may be telling you that everyone is levered to the hilt and is willing to snap up any asset that moves, that everyone is casting aside negative information with hardly a second thought.
When viewed as endogenously determined by the behavior of the market, the relationship between risk of crisis and unusually low levels of volatility is simple: If people are levered and are at the ready to snap up positions, if they are ready to arbitrage out price differences and make markets oblivious to risk at razor thin margins, then it won’t take much of a price move to find the other side of a trade. If people don’t care about negative information, then the information flows will hardly move prices. The result is low volatility, and this in turn leads to more leverage and then another round of the dynamics that feed the low volatility. The result will be a descending level of volatility that is telling you that the market had been lulled into complacency, or worse, is in full-speed-ahead risk taking fervor, and hence is vulnerable.
Of course even if it is more the latter, it still will be the case that a low volatility derived from recent history will likely reflect low volatility in the near future, because if people are levered and ready to buy anything, if they are at a level of exuberance that leads them to discount anything negative in the market, the odds are high that that the same behavior will persist for the next while. But then suddenly it won’t. There is the chance that the floor will fall out and a crisis will be unleashed, and more than anything else, that is what we need to know for risk management.
We can see this when we think look at things from the other direction: what happens to volatility when the crisis finally hits. At that point no one wants to take on any risk, delevering has led to a reduction in liquidity, and so prices have to move a lot to entice buyers. The market is skittish, and so any news or rumors find everyone scurrying for cover. So for both liquidity and information reasons, prices move a lot more and thus volatility rises to the point that it is again not a useful measure for risk, but for the opposite reasons..
The diversification paradox
Related to the volatility paradox is what we can term the diversification paradox, which I discussed in a post some time back. As with volatility, correlations are low pre-crisis. So as is the case with low volatility, the low correlation and resulting apparent potential for diversification will lull investors into taking more risk. And because of the dynamics that create the low correlation, this in turn will feed into further reductions in correlation, thus adding to pro-cyclicality.
At least this is what will happen if we take the correlations as exogenous – that is if we say “they correlations are what they are, so let’s throw them into our variance-covariance matrix and then let the optimizer rip”. But as with volatility, if we look at the correlations as being endogenous to the dynamics of the market, they give us warning signs. Low correlation tells us that everyone is evaluating the most subtle differences between assets – for example, are the transportation costs for the Ford’s supply chain dropping relative to those of GM’s – and is also searching out opportunities in hinterland, esoteric markets. One asset is being finely differentiated from the other, correlations are therefore low, and investors take more leverage and more exposure because of the apparent potential for risk reduction through diversification.
Of course we all know that when the crisis hits the correlations suddenly rise and the benefits of diversification go out the window. Thus, as I wrote in my earlier post, diversification works all the time, except when it really matters.
When the crisis finally hits, correlations shoot up from the same endogenous dynamics. Suddenly, the only thing that matters is risk, not the subtleties of earnings and the opportunities in Malaysian onyx mines. It is like high energy physics, where matter become an undifferentiated white-hot plasma; assets that are risky are all viewed the same way, all of the risky assets meld together. So correlations rise.
The Paradoxes and Risk Management
There are two points from this discussion of the volatility paradox and the related diversification paradox.
The first and well-known point is that if investors take these measures as exogenous – that is, if the data are treated as a given in the computation of the statistics and the statistics are then applied based on their statistical interpretation – then they will lead to pro-cyclical behavior. Higher leverage and risk taking in general will be apparently justified by the lower volatility of the market and by the greater ability to diversify as indicated by the lower correlations.
The second is that just because the volatility is not a good indicator of the risks lurking in the market doesn’t mean it is not useful. If we recognize that volatility and correlation are endogenous measure that are a manifestation of market dynamics rather than exogenous statistics of market risk to be thrown into our risk management engines, if we dig deeper into the dynamics that are generating them as endogenous parts of the market dynamic, we will find that they actually are telling us far more about the markets.

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Hedge funds, you read here in June, are often riskier than they are made out to be. Putting your money into ‘a fund that blows up, closes down or disappears with all your money’, I suggested, is a real risk for the unwary investor. The danger, I could have written, is that you will find your money being looked after by Brian Hunter, a 32-year-old energy trader from Calgary who last month single-handedly accounted for the largest hedge-fund meltdown since records began.

In the space of two short weeks, Mr Hunter worked his way through some $6.5 billion when his complex strategy of forward bets on the price of natural gas went badly wrong, wiping out 70 per cent of the capital deployed by his hedge fund employers, Amaranth Advisors. In one day alone, Mr Hunter and his colleagues on the energy trading team lost $560 million as the price of natural gas futures plunged and they were unable to liquidate their positions fast enough to meet their margin calls and preserve their lines of credit.

The fund is now attempting to close down what remains of its operations in an orderly fashion. The energy trading positions have been sold to other market participants and what is left of its $9 billion of capital (not much) will be returned to investors. This being North America, a carrion-seeking flight of lawyers is hovering over the scene, looking to institute a legal action of some sort for the unhappy victims.

Aside from wealthy individuals, those feeling the heat from the meltdown include the San Diego county pension scheme, which has lost some $100 million, and two fund-of-hedge-funds run by Morgan Stanley and Goldman Sachs, whose allegedly sophisticated monitoring systems proved unable to spot the trouble ahead. Man Group, the quoted UK hedge fund group, also had a small exposure to Mr Hunter’s trading activities.

The losses at Amaranth dwarf even those of Long Term Capital Management, the now infamous hedge fund that boasted two Nobel Prize-winning economists among its founders yet still went down in flames in 1998. It lost $4 billion in a few weeks when an even more complex series of bets on a range of financial derivative contracts proved to be less fireproof than its ultra-sophisticated risk modelling had suggested. LTCM was eventually bailed out by a consortium of leading Wall Street banks at the instigation of the Federal Reserve.

The failure of Amaranth has fortunately had few such repercussions. While there is no question that hedge funds are here to stay, the Amaranth case is an unwelcome setback for the industry at a time when its advocates are pitching hard to persuade pension funds that hedge funds are a valuable new investment class, and regulators that private investors should be allowed much broader access to these new and poorly understood investment vehicles.

Amaranth was not some fly-by-night bucket shop, but an A-list fund operating from Greenwich, Connecticut, the hedge fund capital of the world, a place cutely described at a recent industry dinner as ‘New York on steroids’. Morgan Stanley and Goldman Sachs were both happy to put clients’ money with Amaranth — and if they can’t spot a blow-up coming, we may well ask, what hope has anyone else?

All hedge funds trumpet the fact that they have sophisticated risk systems that allow them to pursue absolute returns — that is, make money in both up and down markets — in a controlled manner. The best, to be fair, do just that, but the lure of big bucks is now attracting a much more diverse crowd of wannabes, to the point where even veteran hedgies such as Steven Cohen, founder of SAC Capital Advisors, says that it is getting harder to make outsize returns. ‘We’re entering a new environment. The days of big returns are gone,’ he told the Wall Street Journal this summer.

At Amaranth Mr Hunter routinely held hundreds of positions in an array of derivative contracts linked to future prices in the natural gas market. These bets were ‘geared up’ by using borrowed money and margin calls to magnify the gains and losses. As so often happens, it appeared for a while that he had the Midas touch. In 2005 Amaranth made $1.3 billion from his trading activities, and $2 billion more in the first four months of this year.

But there were warning signs too. In May he lost nearly $1 billion. At his previous employers, after a similar period of success, he departed abruptly having lost two thirds of his gains in the last month of the year, something he apparently attributed to faults in the bank’s trading systems. Some seasoned hedge fund investors, it now appears, declined to invest in the Amaranth fund because of its flawed risk controls.

In September, triggered by a sharp fall in energy prices and a disappointing (for some) absence of hurricanes, the whole operation blew up in Mr Hunter’s face. According to his boss, the dramatic losses were the result of a highly improbable combination of events, a sharp decline in the future price of natural gas coupled with the rare inability of the fund to unwind its positions in the market. The reality, everyone else in the business suspects, is rather different.

Hubris and overconfidence surely played a part, as evidenced by the ever bigger bets that Mr Hunter appeared to be taking. Whatever risk systems the hedge fund had in place, losing two thirds of the firm’s capital in two weeks suggests a certain (shall we say) inadequacy on that score. Judging by the way that natural gas prices have bounced back up since the Amaranth meltdown, other traders were happy to put the squeeze on when it became clear that the firm’s trades were not working out. Mr Hunter’s fall from grace is further proof of the adage that in investment, as in the Battle of Britain, ‘there are old pilots and there are bold pilots, but there are no old, bold pilots’.

There is a more fundamental issue too, one that increasingly exercises regulators who worry about the damage that so much highly leveraged trading could do to the financial system the next time something goes wrong — as it surely will. Taking huge leveraged bets on such volatile phenomena as future natural gas prices, where the weather is a major factor, is in truth more akin to gambling than investing, as properly understood.

The lopsided reward structure of hedge funds — heads the fund manager wins, tails the investor loses — actively encourages aggressive managers to take big bets with other people’s money, a factor that industry apologists typically neglect to mention. There is nothing wrong with the hedge fund concept, nor with taking calculated risks, but investors who have the gambling instinct, as Keynes perceptively wrote some 70 years ago, ‘must pay to this propensity the appropriate toll’.

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