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Comment by bsdpython | Hacker News Reader

"The biggest gains came at funds that practiced a "trend following" strategy. Those managers—who use computer models to bet on price movements in either direction and often perform best when clear patterns emerge over several months"

This is headed for 2008 all over again because we've learned nothing. These algorithms rack up consistent wins for years on end, the markets rise and rise, everyone gets bonuses and all is great. Then the market turns on a dime, all the computer algorithms break at once because chaos theory takes over and your trend algorithms are useless. Liquidity disappears in an instant and the the federal reserve steps in and gives away free money to the banks until the banks become solvent again. This will continue until the parasite kills the host.

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Algos didn't cause the '08 crash. The '08 crash was caused by a lot of bad bets on the housing market. 100% of those bets were made by humans chatting on Bloomberg or over the telephone.

Edit: No, child posts, you cannot make the leap that trend following algos are the same thing as modeling MBS risk (which isn't an algo...).

Garbage in Garbage out. Algos directly caused the '08 crash (lot's of things where indirectly responsible though). Software Algorithms designed to model risks did not take into account a down economy which systemically undervalued risks leading to lots of over leveraged institutions to fail. Arguably, many of the people writing the software where instructed to 'tweak' their models to the point of uselessness.

One of the major failing of modern statistics instruction is 6+ sigma events don't look like 1-5 sigma events. Case in point global death totals per second probably fit a very well defined curve, but Hiroshima and Nagasaki where well outside that curve.

I believe, the underlying cause of '08 crash was short term profit mentality. Some large financial institutions knew that the financial instruments built on the top of sub-prime/very risky mortgages were bound to fail. So much, that some of them bet against the very instruments they sold off to investors. And there were other ones too, which predicted this successfully and made a lot of money.

This is a misattribution. It's like saying "The nails caused the building to collapse". The people using the tools are to blame, not the tools themselves. The ratings agencies gave incorrect ratings to mortgage backed securities, humans made assumptions based on greed and individual home buyers made poor purchasing decisions.

The flash crash was the result of bad algorithms, the tools acting on their own without significant human guidance or erroneous input. But that was a relatively minor event that temporarily affected professional market makers. Something like that could happen again, with a much larger impact. But the 2008 crash was not it, and wasn't related or even similar.

It is difficult to predict 6+ sigma events, but humans as a whole are no better at it than algorithms are. There were plenty of people who saw things that others didn't and made big short bets against the housing market. The problem there is that everyone else didn't listen to them. Eventually we can improve statistics to catch more outliers. Good luck improving human nature to get people to listen to predictions that go against the herd.

Full-on agreement with what you're saying here.

And if someone really wants to place blame, it's pretty tough to ignore the regulatory and interest rate changes that created the environment that allowed it all to happen. But again, like you said, good luck improving human nature.

Banks have shown themselves to be pretty irresponsible with (practically) free credit though. That's a mistake we shouldn't repeat again.

Edit: Direct Cause (businessdictionary.com) Alternative term for proximate cause. http://en.wikipedia.org/wiki/Proximate_and_ultimate_causatio... A proximate cause is an event which is closest to, or immediately responsible for causing, some observed result. This exists in contrast to a higher-level ultimate cause (or distal cause) which is usually thought of as the "real" reason something occurred.

Often bad nails are the direct cause of a collapse. As in someone used 3 inch nails when they should have used 6 inch nails, or they were not rust resistant etc.

In the end Root cause analysis is about looking past the direct cause to keep from repeating the same mistakes you need to look at both the direct case and dig further. In this case you look at algorithms misclassifying a lot of bad debt and then pealing back the layers. In the nails example it might be someone incompetent designed a foot bridge or someone tried to skim money by using 3 vs 6 inch nails or whatever.

In the bridge example replacing the crooked supplier is all well and good, but you also need to fix all the other bridges made with 3 inch nails.

PS: Where to place blame and the direct cause are far from the same thing.

Algos were stewards of the '08 crash, not the reason. It's dangerously naive to believe overexerted housing values and propped up fundamentals weren't to blame. Not to mention how institutional trust went out the window once it was discovered how poorly loans were written/packaged.

Why wouldn't trend following be the winning strategy when the market stayed on a steady climb? Anyone who hasn't continued with that strategy, attempting to call a top to this current market, has probably taken a beating many times over.

If anything, Algos helped to correct the market more quickly as those who chose poorly were washed away with greater speed.

See my other answer here but basically I see all of these things as the same: find a trend, abstract it with algorithms/risk models, exploit that trend and then walk away when the trend inevitably breaks.

Nice story, except the 2008 crisis happened in subprime mortgages backed securities, for which there were no such strategies. What you're describing sounds a bit more like the 1987 crash, which was linked to portfolio insurance strategies, which have far more insidious consequences than trend following (and have since then been deprecated).

Replace "stock market" with "MBS/CDO type derivatives" and "computer trading algorithms" with "risk models" and you will see the similarities. I probably should have been more clear but the idea is that all of the financial markets work basically the same way: find a trend, abstract it with algorithms/risk models, exploit that trend and then walk away when the trend inevitably breaks. Every 10 years we keep having "10,000 year floods" according to the latest whiz-bang model/algorithm. It's all the same thing in a different variety.

There was no trend in MBS. What you had was

- a regulatory environment encouraging subprime loans

- high yielding securities which were rated as AAA (by a legal oligopoly of bond rating agencies) and thus could be used for bank reserves

- a short term interest rate that did not respond to market pressure (because determined by the fed) and thus created a gigantic carry trade

Mistakes in risk models always happen, and they can be caused by many things, including an over-reliance on mathematical model. However, for a real catastrophe, you need to remove all the feedback mechanisms such as

- a free market that would allow the short rate to rise with increased demand

- a free market that would allow bond rating agencies to compete

- a free market that would allow bank to compete for balance sheet quality rather than having a government insurance scheme creating a race to the bottom

How did the rating agencies come up with their AAA ratings on CDOs comprised of mostly subprime debt? They used historical models aka trends that basically assumed that housing prices never went down on a national basis. Throw a bunch of subprime MBS from different regions together into a CDO and bam our model spits out a AAA rating. It was based on a historical model aka trend. I don't dispute any of your other details.

Actually no, that assumption did not go in the pricing models. The key assumption that people got wrong, was the correlation between the risk of default of different borrowers. A single value was used for the middle of the distribution and for the tails, even though the correlation was actually much higher in the tails.

Vinod Khosla once said he was shocked at how good of a deal VC funding was for the entrepreneur. "Heads I win, tails you lose." It's the same thing for bankers, traders, and managers of 2/20 funds. If things collapse, they're already rich. Sure, it might be difficult to raise another fund, but if you've already stashed $10mm+, who cares? Gambling with other people's money, therein lies the incentive problem.

Agree. Everything gets amplified, whether that be to the upside or the downside. George Soros has a book about this [1].

Also, one big issue in 2008 is that no one thought so many different asset classes would be correlated together and so many were caught out when the market devolved into just 2 meta asset classes : "risk-on" and "risk-off". Can the algorithms account for this?

Can they also account for the fact that we have never seen such a persistently low interest rate environment in so many major economies at the same time? Or is that off-model? Looking at their human counterparts, a recent article speculated that it has been going on for so long, and there has been so much turnover at the financial institutions, that there may be many young traders who literally have no experience of what happens when interest rates are put to more normal levels.

Things like the major dislocation from the Swiss franc devaluation show the fallibility of trend following models that fail to capture the known-unknowns:

"When the “off-model” event was the breakdown of parts of the wholesale money market in 2007, their surprise was just about forgivable: in the case of the Swiss revaluation [which even Goldman Sachs called a '20-plus standard deviation' occurrence], to have failed to visualise the possibility is rank incompetence [2]."

-- [1] http://abcnews.go.com/Business/story?id=4842282&page=1&singl...

[2] http://www.ft.com/cms/s/0/5a06ef16-b5e4-11e4-a577-00144feab7...

> This is headed for 2008 ... and your trend algorithms are useless.

Wrong. Trend followers did extremely well in 2008-2010. As the article says it was the years 2011-2013 that they struggled with.

These things have kill switches built-in, don't they?

My question would be, what happens to the markets if there is a quick fall, the algorithms shut down, and all the HFT liquidity is removed?

> These things have kill switches built-in, don't they?

Not all of them have kill switches - Knight Trading had something with no kill switch that cost them $400M in a matter of hours. (It wasn't even a trading program - some kind of simulation system that was erroneously allowed on the real market).

You could say "it didn't need a kill switch, because it wasn't a trading algo!". But many trading systems are made of small parts that can break, and you only know your kill switch works after it saved you - otherwise, it's just an "untested feature before QA".

> My question would be, what happens to the markets if there is a quick fall, the algorithms shut down, and all the HFT liquidity is removed?

We've seen such mini events before. We see what's known as a "flash crash" (price goes down significantly within seconds, only to come back to more or less the same place), or alternately, a "flash smash" if it goes up (to come down later). This has been happening since 2007 at a frequency of about once every two months. Sometimes trades happening during the flash {sm,cr}ash get retroactively canceled, and sometimes they do not.

If it makes you feel better, my (quant and infrastructure) friends who work at algo firms that did phenomenally well this year all got bonuses way below what they were expecting.

People are pissed and jumping ship. There's way more people than there are positions available because the industry is contracting/consolidating.

Sure, the algos performed well this year, but these are the firms that survived. Lots of players got crushed playing the algo game and are no longer willing to play.

Are those who left staying in finance?

Yes and no.

Some are living off their savings for a little while and seeing where things go in the field before trying to return. A few have left to work at some startups. Most are planning on staying in finance though.

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