Felix Salmon

How does JP Morgan respond to a crisis?

Felix Salmon
Jan 16, 2013 15:17 UTC

If you have a bit of time today, the official JP Morgan post mortem on the London Whale affair is well worth reading. The whole thing is 132 pages long, although the executive summary — which is very clearly written — is only 17 pages.

One thing the report certainly does is reinforce my conviction that you can’t hedge tail risk. The losses all took place in something called the Synthetic Credit Portfolio, which was described as a “Tail Risk Book” — something designed to make money “when the market environment moves more than three standard deviations from the mean based on predictions from a normal distribution of historical prices”. In other words, JP Morgan is well aware that market moves are not normally distributed, and therefore it has a whole derivatives book in place to protect itself against inevitable unexpected events.

The whole point about tail risk, however (a/k/a “black swans”) is that you can’t anticipate exactly what it’s going to look like before you see it. In this case, the biggest tail event was the publication of stories, in the WSJ and Bloomberg, talking about JP Morgan’s positions. Those stories had a massive effect on the mark-to-market valuation of JP Morgan’s positions At the beginning of the first trading day after the stories appeared, it looked as though JP Morgan might be facing a one-day loss of $700 million; in the event, the final official number was $412 million. Ina Drew, the person in charge of the portfolio, sent an email to JP Morgan’s CEO and CFO, in which she observed that the move was “an eight-standard-deviation event”.

The report doesn’t say how many eight-sigma events the CIO has ever seen: my guess is that this is the only one. But here’s an idea of how crazy eight-sigma events are: under a normal distribution, they’re meant to happen with a probability of roughly one in 800 trillion. The universe, by contrast, is roughly 5 trillion days old: you could run the universe a hundred times, under a normal distribution, and still never see an eight-sigma event. If anything was a black swan, this was a black swan. And it didn’t help JP Morgan’s “tail risk book” one bit. Quite the opposite.

Another thing the report does is show just how difficult it is for any large organization to actually implement what managers want. At JP Morgan, for instance — where the CEO has an unusually large degree of power and knowledge of what is going on — the whole firm was meant to be reducing its “risk-weighted assets”, or RWA, since the higher a bank’s RWA, the more capital it needs under Basel III. And yet somehow, by the time this directive trickled down to the London Whale, it had been watered down and misinterpreted to the point at which the office’s RWA actually went up — substantially — rather than down.

What’s more, there’s a constant theme running through the report of managers being told what they want to hear, rather than the truth, especially with regard to substantial losses. When those appear, no one wants to tell Ina Drew about them; instead, the traders do everything they can to try to either fudge the numbers or attempt to trade their way out of the position.

Interestingly, one way that numbers were fudged was to use the favorite tool of quants around the world, the Monte Carlo analysis. After the Bloomberg and WSJ stories appeared, for instance, one trader drew up an analysis of just how bad the position could get. He modeled nine extreme event like a “bond market crash” or a “Middle East shock”, and found that in six of them, the portfolio lost money, with the losses ranging from $350 million to $750 million. This analysis did not go down well:

This trader sent his loss estimates to the other on April 7. According to the trader who prepared the loss estimates, the other trader responded that he had just had a discussion with Ms. Drew and another senior team member, and that he (the latter trader) wanted to see a different analysis. Specifically, he informed the trader who had generated the estimates that he had too many negative scenarios in his initial work, and that he was going to scare Ms. Drew if he said they could lose more than $200 or $300 million. He therefore directed that trader to run a so- called “Monte Carlo” simulation to determine the potential losses for the second quarter. A Monte Carlo simulation involves running a portfolio through a series of scenarios and averaging the results. The trader who had generated the estimates did not believe the Monte Carlo simulation was a meaningful stress analysis because it included some scenarios in which the Synthetic Credit Portfolio would make money which, when averaged together with the scenarios in which it lost money, would result in an estimate that was relatively close to zero. He performed the requested analysis, however, and sent the results to the other trader in a series of written presentations over the course of the weekend. This work was the basis for a second-quarter loss estimate of -$150 million to +$250 million provided to senior Firm management.

In the event, of course, the portfolio ended up losing not $150 million, not even $750 million, but more like $6 billion, with some $800 million of those losses taking place in the six trading days leading up to April 30, long before the decision was made to liquidate the position. Which just goes to show how useful stress tests are. (Remember, the initial worst-case estimates were put together after the WSJ and Bloomberg stories appeared, which means that JP Morgan was acutely aware, at this point, of the risk that the market would move against them just because their positions were public.)

There is one big omission in the report — and that’s any discussion of how the ultimate losses in the portfolio grew to be so enormous. Where did the initial $2 billion estimate come from, and how did it grow to $6 billion by the time all was said and done? The report basically ends when the potential losses were made public, and doesn’t spend any time discussing how Jamie Dimon and his senior executives handled everything from there on in.

From the perspective of JP Morgan’s shareholders, there are two big things to worry about in this whole episode. The first is weaknesses in risk management, which the report goes into in great detail. The second is the way that senior management responds to a crisis, and whether it can do so while keeping its head and minimizing losses. On that front, the report is silent. Did a panicked reaction to the early losses result in a “dump everything immediately” response which ended up causing an extra $4 billion in damage? Who was responsible for those $4 billion in losses, and how avoidable were they? Those questions are never asked, let alone answered.

JP Morgan has looked in great detail at its crisis-prevention architecure: it’s time, now, too look at its crisis-response architecture, too. Because sometimes, it seems, the latter can cause more damage than the former.


Wow, wow, wow, wow…

Using a Monte Carlo simulation and averaging the outputs for a non-linear system is kind of missing the whole point of Monte Carlo simulations in non-linear systems.

Monte Carlo simulations are useful for, say, nuclear reactors. A nuclear reactor is a linear system from the POV of neutron transport. So Monte Carlo simulations can yield an accurate picture of the state of a running reactor as a superposition of many single particle simulations (google LANL MCNP).

In a non-linear system, Monte-Carlo simulations are useful to quickly explore the parameter space and sniff around for non-obvious, mmm, trouble, based on the assumption that the state space of most systems, even strongly non-linear systems, tends to be continuous. But you never, ever superpose states in a non-linear system. And a financial model is never linear (for they all have at least one very strong, very stateful non-linearity called ‘insolvency’).

And yes, the unfortunate conclusion is that financial models are more dangerous than nuclear reactors. Wall Street urgently needs a NRC of its own.

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Jamie Dimon’s failure

Felix Salmon
May 14, 2012 13:51 UTC

Ina Drew — the JP Morgan executive who famously “loves crises” — is out; it seems the buck for the $2 billion trading loss in her unit has stopped with her. And slowly, a few shapes are beginning to emerge from the fog of what exactly happened here.

For one thing, it’s becoming increasingly obvious that Drew got paid her eight-figure salary in return for being able to pull off a very neat trick: turning hedging operations into a profit center.

Drew’s Chief Investment Office quadrupled in size between 2006 and 2011, reaching $356 billion in total, and it’s easy to see how that happened. On the one hand, it was incredibly profitable, with the London team alone, which oversaw some $200 billion, making $5 billion of profit in 2010, more than 25% of JP Morgan’s net income for the year. At the same time JP Morgan accumulated enormous new deposits in the wake of the financial crisis, both by acquiring banks and by attracting big new clients wanting the safety of a too-big-to-fail bank. Historically, JP Morgan has served big corporations by lending them money, but nowadays, as the cash balances on corporate balance sheets get ever more enormous, the main thing these companies want from JP Morgan is a simple checking account — one where they can be sure that their money is safe.

With lots of deposits coming in, and little corporate demand for loans, it was easy for all that money to find its way to the Chief Investment Office, which could take any amount of liabilities (deposits are liabilities, for a bank) and turn them into assets generating billions of dollars in profits.

But the CIO does much more than just provide profits for JP Morgan. In contrast to the bank’s lending book, the CIO is nimble. Loans, as a rule, have to be held to maturity: that’s the essence of relationship banking. Investments, by contrast, can be sold at any time. Of course, an investment which can be sold at any time has another name: it’s a trade. Thus did the CIO become home to big traders, making huge bets and huge bonuses.

In the past couple of years, of course, that raised its own set of problems: how could this group of traders possibly be Volcker-compliant? The answer lay in Drew’s love of crises: her incredibly valuable ability to prevent losses and even make profits when the world is falling apart. In that sense, the CIO was one big hedge, and in a narrower sense the CIO was the go-to office whenever JP Morgan saw a risk which needed hedging.

Mark Dow has an intriguing thesis this morning:

We know that over the last 2-3 quarters of 2011 we were gripped by the fear of a European financial meltdown and a second recession in the US.

We know that that the Fed’s swap lines and the ECB’s LTRO reversed this market view and crushed credit spreads lower, hurting those who had been buying protection in the previous months.

Against this backdrop, it seems likely to me that the aggressive selling of protection we heard about in April 2012 was actually the unwinding of the hedge that had been accumulated in 2011 and was by then deeply underwater.

In other words, Jamie Dimon, like everybody else, was worried about a Europe-induced financial crisis at the end of 2011, and so he told Drew to put on positions which would protect against such a crisis. She did so — only this time around, the crisis never happened, and Drew’s positions had to be unwound.

That’s where things seem to have gone very, very wrong. Drew prided herself on turning every hedge into a profit center — having her cake and eating it too, basically. We’re deep in the realm of speculation, here, but it’s entirely possible that Drew positioned the CIO, at the end of 2011, to profit from a European meltdown. When that meltdown didn’t happen, simply selling those positions would involve realizing a substantial loss. And so rather than selling the positions, Drew decided to put on new, profitable positions which would offset the old hedge. Enter Bruno Iksil, the London Whale, and his enormous trade.

Iksil’s trade was fundamentally bullish, which would make sense for a trade designed to offset a fundamentally bearish hedge. Of course it wasn’t a perfect offset — there’s no such thing as a perfect hedge. Traders making multi-million-dollar bonuses don’t get paid to design perfect hedges, in any case. Iksil was being paid to put on a trade which would make money for the CIO, even as it was also hedging existing positions.

As with all imperfect hedges, however, especially when they’re big and public, the market can always move against you in exactly the way you don’t want. We don’t know the details of Iksil’s trade, but let’s say that the big underlying position was a bearish position in cash bonds, while Iksil’s trade involved a bullish position in the CDS market. In April, the cash and CDS markets stopped mirroring each other, and started behaving very oddly — you’d see bullish moves in cash bonds, combined with bearish moves in the CDS market. That combination, it seems, turned out to be the one thing that JP Morgan wasn’t hedged against, and the losses in the CIO started mounting rapidly.

How did Iksil’s trade go so horribly, massively, wrong? Partly it’s because his position was so big and so public. When hedge funds worked out what he was doing, they managed to get the word out, using stories in Bloomberg and the WSJ. And then it was just a matter of watching the market do what it always does, when it smells blood: I’m told that Boaz Weinstein’s Saba, for one, made a lot of money taking the other side of Iksil’s trade.

Taking a much bigger-picture view, however, what was really going on here was that JP Morgan had hundreds of billions of dollars in excess deposits, thanks to its too-big-to-fail status. And rather than lending out that money and boosting the economy, Jamie Dimon decided to simply play with it in financial markets, just as a hedge fund would. Here’s Bloomberg:

Dimon pushed Drew’s unit, which invests deposits the bank hasn’t loaned, to seek profit by speculating on higher-yielding assets such as credit derivatives, according to five former executives. The CEO suggested positions, a current executive said.

It’s always dangerous when a CEO suggests positions for an internal hedge fund to take, because the CEO by definition has no risk manager with enough authority to effectively constrain him. Dimon is powerful and secure enough that he’s not going to lose his job over this. But he probably should. Partly because the bank’s risk-management procedures were so weak that a $2 billion loss could suddenly appear out of nowhere. Partly because Dimon became too cocky, and started thinking that his job was to trade the bank’s billions for profit. But mainly because he’s lost sight of what JP Morgan has to be, in a post-crisis world.

Those excess deposits weren’t gifted to Dimon on a plate so that he could gamble them on the CDX NA IG 9. Rather, Dimon’s job is to take those deposits and lend them productively into the real economy. Every extra dollar in the CIO is a sign of his failure to do that. And the $2 billion loss is really just a symptom of what happens when banks get too much money, and don’t really know what to do with it all.


What I want to know is who was on the other side of the $2billion in trades. Every article, including this one, focuses on the $2billion as a trading loss, but their is a flip side of that coin. Someone or some company is $2billion richer and who are they? The money just didn’t disappear. So how about some reporting about that? Who made out like a bandit?!

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When investment banks hire risk-takers

Felix Salmon
Sep 15, 2011 21:42 UTC

Matt Taibbi is quite right about the $2 billion of rogue-trading losses at UBS. Basically, investment banks hire for risk-takers; they shouldn’t be surprised when this kind of thing happens.

The brains of investment bankers by nature are not wired for “client-based” thinking. This is the reason why the Glass-Steagall Act, which kept investment banks and commercial banks separate, was originally passed back in 1933: it just defies common sense to have professional gamblers in charge of stewarding commercial bank accounts.

Investment bankers do not see it as their jobs to tend to the dreary business of making sure Ma and Pa Main Street get their $8.03 in savings account interest every month. Nothing about traditional commercial banking – historically, the dullest of businesses, taking customer deposits and making conservative investments with them in search of a percentage point of profit here and there – turns them on.

In fact, investment bankers by nature have huge appetites for risk, and most of them take pride in being able to sleep at night even when their bets are going the wrong way.

Taibbi is receiving some blogospheric pushback, because the term “investment banker” means two very different things depending on the context. On the one hand, there’s investment banking as in M&A advice and old-fashioned merchant banking. A typical sentence would be “traders have replaced bankers in the executive suite at Goldman Sachs”. And then there’s Taibbi’s meaning: investment bankers as opposed to commercial bankers, or people who work at investment banks rather than at commercial banks. These are the people that the Vickers report is scared of.

The fact is that old-fashioned advisory bankers are pretty irrelevant here: the big money in finance has always been where the balance sheet is. And balance sheet is used on the trading floor and in commercial banking. So let’s put the fee-based bankers to one side: it’s absolutely true that investment bankers tend to love risk, even as commercial bankers have historically shunned it.

I’m reading The Devil’s Derivatives right now, Nick Dunbar’s fantastic book about credit derivatives traders. (I’ll have much more on the book when I’m done with it.) In the introduction, he makes this distinction really well, introducing the hotshot traders he dubs “the men who love to win”:

This rare, often admirable, but ultimately dangerous breed of financier isn’t wired like the rest of us. Normal people are constitutionally, genetically, down-to-their-bones risk averse: they hate to lose money. The pain of dropping $10 at the casino craps table far outweighs the pleasure of winning $10 on a throw of the dice. Give these people responsibility for decisions at small banks or insurance companies, and their risk-averse nature carries over quite naturally to their professional judgment. For most of its history, our financial system was built on the stolid, cautious decisions of bankers, the men who hate to lose. This cautious investment mind-set drove the creation of socially useful financial institutions over the last few hundred years. The anger of losing dominated their thinking. Such people are attached to the idea of certainty and stability. It took some convincing to persuade them to give that up in favor of an uncertain bet. People like that did not drive the kind of astronomical growth seen in the last two decades.

Now imagine somebody who, when confronted with uncertainty, sees not danger but opportunity. This sort of person cannot be chained to predictable, safe outcomes. This sort of person cannot be a traditional banker. For them, any uncertain bet is a chance to become unbelievably happy, and the misery of losing barely merits a moment’s consid- eration. Such people have a very high tolerance for risk. To be more precise, they crave it. Most of us accept that risk-seeking people have an economic role to play. We need entrepreneurs and inventors. But what we don’t need is for that mentality to infect the once boring and cautious job of lending and investing money.

When you’re hiring people for the UBS trading floor, you’re hiring men who love to win, congenital risk-takers. And then you surround them with risk-management protocols designed to keep them under some semblance of control. There’s a natural tension there. And if you take the hundreds of thousands of risk-takers working on trading floors in London and Hong Kong and New York and Paris, it’s a statistical inevitability that one or two of them will go rogue every year or so.

Risk-managment protocols are important, but they can never be foolproof, because they’re run by humans. So we really shouldn’t let investment bankers — by which I mean risk-hungry traders with access to billions of dollars of balance sheet — anywhere near the systemically-important balance sheets of our largest commercial banks. Losses like the $2 billion at UBS are manageable. But they’re small beer compared to the entirely legitimate losses made by the likes of Morgan Stanley’s Howie Hubler during the financial crisis. He managed to lose $9 billion, and get paid millions for doing so.

Multiply that by an entire company, and you get Lehman Brothers, or Merrill Lynch. One of the great good fortunes of the financial crisis was that neither of them was attached to a commercial bank at the time; one of the great bad fortunes of the financial crisis is that the sins of Merrill Lynch weigh down BofA’s balance sheet to this day, and are in large part responsible for the fact that, still, no one really knows whether the bank is solvent or not.


FifthDecade, LloydsTSB didn’t have a US boss either in the sense of a company in the US or an american CEO or chairman.

NRK and HBOS and B&B got into trouble over vanilla commercial and retail banking loans that went bad. HBOS was a “victim” of a massive fraud – and i mean actual fraud, not a lazy rubber stamper not ticking all the boxes – that costs it billions of pounds in its SME loans operation. Absolutely sweet FA to do with investment banking.

Frannie were always what people seem to think IBs are, that is government back-stopped hedge funds where the profits went to the shareholders and management and the massive losses were socialised, yet weirdly they are the “victim”.

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How has VaR changed over time?

Felix Salmon
Aug 6, 2009 13:44 UTC

Whenever I write about banks’ rising Value-at-Risk, a bunch of commenters tells me that duh of course VaR is rising, because VaR is a function of volatility, and volatility has gone up. So here’s my question: can someone come up with a baseline VaR chart, for a hypothetical bank which had, say, a fixed $1 million investment in the S&P 500. What would its quarterly Value-at-Risk have looked like over the past couple of years?

Would the decrease in volatility this year have shown up as decreased VaR in say the second quarter? Or do the volatility calculations go back so far that only now are we losing the Great Moderation datapoints and using volatility numbers only from the era of increased volatility?

Armed with that kind of baseline chart, we’ll be able to tell much more easily, for any given bank, whether it’s actually increasing the size of its bets, or whether increased VaR is simply a statistical necessity given the recent history of volatility. Does such a chart exist?

Update: Phorgy comes through. All banks calculate VaR differently, but this is a really useful resource. Basically Var increased enormously in 2008, and after that slowed down sharply in 2009, possibly even dropping significantly. Which means, I think, that any significant increases in VaR in 2009 can’t be blamed on increased volatility.


In a similar vein, here’s something I wrote at the end of 2008 based on the DJIA over the last 100 or so years.

http://www.riskmetrics.com/publications/ research_monthly/20081100

McNamara and model risk

Felix Salmon
Jul 7, 2009 19:47 UTC

Philip Delves Broughton notes that Robert McNamara, one of Harvard Business School’s most notorious graduates, basically did in the field of war what Wall Street quants did in the field of finance:

The journalist David Halberstam wrote that McNamara mistrusted people who did not speak his language of statistics and hard data. If it ever came down to one person saying something “just didn’t feel right” or that it “smelled wrong”, he would always go with his facts over their feeling. Fatally, in the case of Vietnam, the data he received was not accurate.

When Wall Street quants fail to account for model risk, they can end up losing hundreds of billions of dollars. But that’s an improvement over what happened when McNamara failed to account for model risk: those losses were much worse.


Based on my reading of Halberstam’s account of the War,
McNamara had reservations about America’s progress in Vietnam operations even in the Kennedy years (when the war was still at an incipient stage).

His shortcoming was not a tendency to implicitly believe modeled forecasts. Rather, it was the desire to please the boss (Johnson) with pleasant tidings.

Modelling model risk

Felix Salmon
Jul 2, 2009 15:46 UTC

Paul Wilmott has words of wisdom for anybody in the financial-services industry who’s putting a model together:

At every stage of valuation and model development you must be asking questions about risk and robustness. It is dangerous to come up with some fancy model and only afterwards start asking questions about model error. Anyone who has ever calibrated a model knows that the methods used to mitigate model risk almost come as an afterthought, and are totally inconsistent with the original model. This need not be the case.

The problem is that developing a model is the sort of thing which (a) quants are trained to do, and (b) can, eventually, make money. While mitigating model risk is a very recondite field which very few people have any expertise with; what’s more, it doesn’t really make money in and of itself. Where will all the model-risk modellers come from?


I aint no hotshot at all, just debating ideas…which seems to be how these forums typically work. If I can bend my head around others ideas, perhaps there’s can bend around a few of mine as well.

If market policy and climate policy can intersect without egregious and/or superficial economic cost I’m on board. But as we’ve seen with ethanol mandates (Grow corn. grow corn now!) there is / can be decidedly after-effects which aren’t always fully considered on the interim prior to implementation.

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Are the new securitization regulations workable?

Felix Salmon
Jun 16, 2009 15:12 UTC

Binyamin Appelbaum has details of the new regulations surrounding securitization, and it all looks incredibly unworkable to me, especially the central plank:

Lenders would be required to retain at least 5 percent of the risk of losses on each package of loan pieces, known as an asset-backed security…

The plan also would prohibit firms from hedging that risk, meaning that they could not make an offsetting investment.

What on earth is a bank’s chief risk officer supposed to do with an edict that she cannot hedge a certain chunk of the bank’s balance sheet? I can see that she might not be able to explicitly create a synthetic CDO to bring that idiosyncratic risk down to zero. But if a bank has exposure from securitizing credit-card receivables, say, or commercial real estate leases, or student loans, or residential mortgages, then similar risk will reside elsewhere on the balance sheet, and the bank will — and should — just reduce the amount of that risk instead. It has the same result, from an all-over risk perspective, and the new regulation is rendered utterly toothless.

As for the idea that the ratings agencies should make it clear that ABS aren’t corporate bonds, well, as Agnes points out, that’s pretty silly: I think everybody knows that by now. The problem is that if there’s a separate second scale for ABS (and maybe even a third scale for munis), no one will have a clue what the new ratings are supposed to mean. It would be a bit like being given two apples, and told that one costs 15 foos while the other costs 17 bars.

I would rather encourage the ratings agencies to try and make credit ratings as laterally comparable as possible, and to try to set ratings so that you don’t have the enormous default-rate discrepancies that exist right now between different asset classes. Investors could then judge for themselves the degree to which the ratings agencies had succeeded.

My fear is that ratings agencies might start issuing separate credit ratings for munis, which will be considered much safer than the equivalent credit ratings on corporate bonds, just as a wave of muni defaults is about to hit. In general, the key here is to decrease the importance of the ratings agencies, rather than trying to regulate them on the grounds of how important they are.

But a couple of the proposals make sense: paying originators of securitized loans gradually, over time, for instance, rather than up-front when the loans are securitized; or standardizing contract language in the ABS market to make such securities easier to compare to each other. They just won’t make an enormous amount of difference.


There is another argument for using a separate rating scale for securitized products – which is to prevent them from passing through the “loophole” in investment guidelines for pension funds, insurance companies, etc., that allows them to hold anything with a certain rating. If the senior piece of a sub-prime RMBS is no longer “AAA” but is now “SP-1″ or whatever, then the investment guidelines of these institutional investors would have to be rewritten specifically to allow them to hold these assets, whereas before the issue never came up, even though they should have known that these were not the same as corporate AAAs.

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Taleb’s data dump

Felix Salmon
Apr 16, 2009 14:02 UTC

Back in March, Rick Bookstaber published a blog entry entitled “The Fat-Tailed Straw Man”, which seems to imply that Nassim Taleb attacks Wall Street practices on the grounds that its practitioners believe in normal distributions.

The result of that blog entry, and of a few other criticisms of Taleb in various places, is that Nassim has now published an extremely useful technical appendix to The Black Swan. It includes substantially all his relevant scientific and technical papers, and essentially comprises a gauntlet being thrown down to those who would criticize him. If you want to attack my ideas, he’s saying, that’s fine, but please first do me the favor of looking at where they’re laid out in detail, as opposed to where they’re laid out in newspaper quotes or in a literary book.

There is certainly a lot of confusion in the public mind when it comes to philosophical arguments about fat tails and risk management, and it’s easy for journalists to distill things into easy soundbites about normal distributions and the like. I, for one, would like at some point to write about Taleb’s important paper on Errors, Robustness, and the Fourth Quadrant, which looks at the history of thousands of data series going back decades — but I haven’t found a very clear way of trying to explain its details in English.

The Black Swan has proved to be a very popular book largely because it doesn’t even attempt to do that: instead it talks philosophically about the conclusions one draws after looking at the data. But if you want to attack Taleb for drawing the wrong conclusions, I think he’s right: you shouldn’t attack the book, but rather the empirical research which underlies it.


I would say the problem is that traders and risk managers focused far too much on complicated models and statistics. Those models will never be perfect and if they had spent more time thinking about the ways they could get in trouble instead of trying to improve the models it would have worked out a lot better.