CHAPTER 10
Systemic Threat

“I was looking for an opportunity to use my skills and knowledge. This is an interesting firm.”

—Former Chairman of the Federal Reserve Ben Bernanke accepts a position as senior adviser to the $25 billion high-frequency trading firm Citadel in 2015.

“Sunlight is said to be the best of disinfectants; electric light the most efficient policeman.”

—Louis Brandeis, Other People's Money and How the Bankers Use It (1914)

Because of the bankers' insistence on treating complex finance as a university end-of-term exam in probability theory, many of the risks in the system are hidden. And when risks are hidden, one is lured into a false sense of security. More risk is taken so that when the inevitable happens, it is worse than it could have been. Eventually the probabilities break down, disastrous events become correlated, the cascade of dominoes is triggered, and we have systemic risk. A risk to the whole financial system. None of this would matter if the numbers were small relative to world economic output, but the numbers are huge. The infiltration of derivatives into society is like an inoperable metastasized cancer. Underneath many of the most innocent of human financial arrangements there's likely to be a complex structured financial product, with some banker taking his cut. And ultimately it's your money he's taking his cut from. And when a bank goes bust, the stock market collapses, and house prices tumble, it's your bank account, your shares, and your house equity that suffer. This chapter shines some light on the murky depths of finance and politics, and asks if we've moved on since John Law first dazzled the French monarchy with his system.

In the summer of 2010, Paul was contacted by Her Majesty's Treasury to ask if he would like to join a project related to high-frequency algorithmic trading. The email referenced the volume of trading in equities generated by automated computer algorithms, and the May 6, 2010 Flash Crash that wiped a trillion dollars off the value of US stocks, fortunately only for a few crazy minutes. The UK government was worried about high-frequency trading having a similar impact on the UK financial markets, and was setting up the project to examine this possibility.

The UK government has organized many of these “Foresight” reports on a variety of important topics. With input from experts from industry, academia, etc., they have in the past looked at topics as diverse as “Exploiting the electromagnetic spectrum,” “Infectious diseases: preparing for the future,” “Reducing risk of future disasters: priorities for decision makers,” and naturally quite a few on climate change and sustainable stuff. The topic on which Paul was to be asked to comment turned out to be “Future of computer trading in financial markets: an international perspective.”

Paul was naturally delighted to be asked to advise Her Majesty's government. Finally, appreciation for his expertise and hard work. The knighthood was almost certain to follow. “Is it possible? Dare I even think it?” he thought, “A… peerage?!” Not to mention an opportunity to help prevent a crisis before it happened, rather than seeing it unfold from the outside.

And so began a series of meetings and exchanges of thoughts. Paul was shown a list of all the (other) eminent people who were being approached for their opinions, more anon.

Paul explained his worries about feedback effects, and bandwagons, and how computer trading broke the connection between share price and company value (the whole purpose of markets). Having been in on various discussions on this topic before he knew that the commonest defense of such trading was the provision of liquidity, Paul gave his reasons for why “playing the liquidity card” was totally fallacious, and only fooled people who didn't think very deeply.

Paul was asked to put together a list of “drivers,” important points for discussion.1 We've already seen most of these points in this book already.

  1. Incentives, moral hazard, and feedback. Does computerized trading facilitate coordination among traders whether deliberate (moral hazard) or via an evolutionary process? The current system of incentives for bankers encourages the taking of risk. Does coordination or competition among traders (man or machine) lead to an increase or a decrease in market risk? (Is the feedback positive or negative?) Will there be an increase in, or a new kind of, systemic risk in the markets? What new form will market movements take, will there be an increase in volatility or an increase in jumps? Should incentives be changed?
  2. Innovation and regulators. Regulators always seem to be on the back foot with respect to monitoring or even understanding new products, strategies, and structures. They do not move as quickly as banks or funds. How can regulations be designed so as to remain future proof? Regulators are not paid as much as bankers, and this may result in a lower caliber of regulator. How can regulators be given more bite or made to genuinely worry banks (rather than just being an inconvenience). Should regulators be better educated, so as to know the difficult questions to ask of the banks?
  3. The structure and purpose of exchanges. What are the fundamental purposes of exchanges? And how should they be designed now that the connections between them (in terms of latency and speed) have become part of the computerized trading game? Should there be competing exchanges subject to market forces? If so, how will that competition interplay with the computerized trading, and will that add to or decrease old and new risk factors? Or should exchanges be a public service, perhaps not for profit? And if so, should this be at a national or international level?
  4. Taxes, minimum holding periods, etc. Should there be any disincentives to short-term trading? Possibilities are taxes per trade,minimum holding periods, and restrictions on stock lending (and therefore limits on short selling), among others. What are the pros and cons of such “frictions”?
  5. National vs. international. How important is international cooperation on computerized trading? Restrictions imposed in one country will probably encourage financial institutions to move to a more friendly nation. Is this true? And does this matter if, for example, it leads to a more stable economy? Is there a natural and healthy fraction of a country's economy that should come from finance, or is more simply better?
  6. Value vs. price. The purpose of the markets is to enable companies to raise money to grow and benefit society. This requires the prices of stocks in the market to relate to fundamental values, with some subjectivity to encourage trading, so that those trading can fairly estimate rewards and risks. If prices and values are too far out of line, then the market becomes a casino. Is there a natural holding period for stocks so as to keep value and price in some alignment? Does computerized trading cause any dislocation? How quickly does a company's “value” change?

Foresight

At the meetings, various solutions were discussed. One that Paul remembers clearly was the suggestion that exchanges could implement circuit breakers in the event of a crisis. This is simply the idea that should markets fall by x% in y minutes, then the markets would be closed for z minutes. Paul laughed at this. He said that he thought hedge-fund managers would enjoy triggering such events. They'd put it into their computer algorithms and would inevitably find a way in which to benefit from them. Even if we couldn't think of how profit could be extracted from a market-cooling mechanism, that was just because we weren't as clever as the fund managers. Paul tried to explain that you had to understand the mentality of these people. You have to be more of a Miss Marple than a Sherlock Holmes. Paul said that he predicted hedge-fund managers would be in favor of circuit breakers.

And then nothing happened.

A year went by. Paul assumed that this was just how committees worked – inefficiently and slowly. Apart from the Oxford University Ballroom Dance Club in the 1980s, Paul had avoided committees like the plague. Anyway, Paul had plenty of other business matters to keep him off the streets. But then he got curious and sent a follow-up query.

Another meeting followed. It was explained to Paul that he was seen as being a little too academic. “The knighthood is on hold then,” thought Paul. But they were ever so polite. One thing Paul took away from these meetings was how incredibly charming civil servants are, even when telling you your services are no longer required.2 Later in 2012 the final report came out. You can find it easily online.

To put it briefly, the finding of the experts was that everything is fine. There is nothing to worry about. High-frequency and computer trading are nothing but good for everyone. Nine proposals had been made for pruning the impact of HFT, seven were deemed unnecessary or problematic.

Of the remaining two, one stood out. The experts were in agreement that circuit breakers were a good idea.

When he first read this Paul did that thing where you pretend to lick your finger and make a mark on an imaginary blackboard. “Yes,” he thought, “I may not be as clever as these people at moving the markets, but I can read their minds!” Miss Marple rules!

Paul then went into his investigative mode. He does this whenever he sees stupidity or smells something fishy. He looked at the list of people on the “High Level Stakeholders Group,” those whose advice was sought. (The group from which he had been dumped.) He noticed a pattern. He then rummaged through his old emails to find the original make-up of this panel.3 Now, Paul isn't going to reveal the names he was originally given. It's bad enough that this book and the blog he wrote at the time have effectively ruled out him ever getting that knighthood or peerage, but he doesn't want to fall foul of the Official Secrets Act.

At the start of the reporting process in 2010 the membership of the High Level Stakeholders Group was balanced between those within the finance industry and independents outside it. By the time this panel had been “reconfigured,” it was dominated by banking insiders. Over two-thirds were from the financial services and over half of the panel had links to high-frequency trading. The Bureau of Investigative Journalism reported “… a well-placed source close to the Foresight team said the High Level Panel ‘is dominated by providers, not by users’. The three- to four-hour meetings, the insider said, ‘tended to be dominated by industry… It is a concern that the group is like that.’”4

And you know what's most disturbing? This manipulation of the panel was not even subtle. Presumably the government got the answer it wanted, and the dominance of the City of London in the financial markets was assured.

This is why we've opened this chapter on systemic risk with this story. Sure algorithmic trading is a systemic risk, but a larger one is toothless governments and regulators, or worse, governments and regulators whose interests are also aligned with those of the traders. Take our earlier examples from Chapter 9 out of the bank and onto the world stage. The amount that a hedge-fund manager can make in a couple of years is potentially enough on which to retire extremely comfortably. For that reason one can imagine that the business model is to get as much out, as quickly as possible, from any bandwagon, before its inevitable reversal. So if you are a hedge-fund manager and you happen to find yourself on any government advisory panel, just keep saying “There's nothing to see, move along now” for the short time that the bandwagon is rolling. You can be sure that regulators and governments will be trundling along behind your bandwagon, but at a snail's pace, giving you enough time to fill your boots.

Okay, having dealt with those in charge, let's look at the science and psychology of other aspects of systemic risk.

The MacGuffin

The next systemic risk after the cupidity and stupidity of governments is the role of bandwagons in high finance. Bandwagons beget bubbles, and bubbles beget crashes. Bubbles are not allowed according to efficient market theory, but speaking of bubbles, that one popped a while ago.5

Bubbles need something underlying on which to pin unrealistic prices. Alfred Hitchcock used the word MacGuffin for a device to keep a plot moving along, something that really did not matter in the final analysis: the [Spoiler Alert] stamps in his Charade, the briefcase in Tarantino's Pulp Fiction. And that's all that's needed to get a bandwagon going, and where there's a bandwagon, there's money. Some examples:

  • Tulips, for God's sake.
  • Louisiana gold (John Law's Mississippi Company).
  • The Internet! (It'll never catch on.)

On October 19, 1987, aka Black Monday, there was a MacGuffin as well – but this time the glowing object in the briefcase was a mathematical model. With their secret formula to eliminate portfolio risk, the firm LOR had found a way to make a bubble out of that same risk (see Box 10.1). And that's what burst in 1987. Twenty years later, the problem was the risk that was packaged up and hidden inside CDOs. Today, it is the risk that has been created by high-speed algorithms, all based on similar models, all racing to be the first to do the same thing.

Indeed, algorithmic trading may represent the ultimate MacGuffin – because even if someone opens the case, they still have no idea what's inside. The code could take the form of a genetic algorithm that has evolved to supposedly find the best method of forecasting, or a neural network so complex that even the creator doesn't know what's going on. As discussed in Chapter 8, this is a disadvantage if you want to understand or communicate the workings of the model. If you've got a black box making all the decisions based on some statistical analysis of share price moves over the last few seconds, and no one is allowed to look inside the box, and maybe even the manager doesn't know what the code is doing, then how much is there to say? “We've backtested it using… years' worth of data. It works. End of.”

But for the marketing pitch what counts isn't the technical explanation or the equations – it's the story and the promise that the box represents. Maybe the story is about macroeconomic conditions, or some inefficiency in the market, or faster execution of trades. It's got to be convincing and simple. And ideally true. And so it all boils down to your skills as a salesman. Market the heck out of it, raise funds, trade, and pray that it works. It if doesn't, then blame regime shifts.

The recent move to black box/algo trading means that there is less need than ever for any scientific basis for trading or modeling. Now finance has been distilled into the purest form of business, possibly zero-content, pure showmanship.

Don't get us wrong, we know that salesmen are the most important people on earth, without whom we'd still be living in caves. And since we've mentioned caves we ought to mention Gary Dahl, the inventor of the pet rock, talk about salesmanship! Black box trading is to the 2010s what pet rocks were to the 1970s. In either case, you're buying a story.

But High-Speed Trading Provides Liquidity!

The main defense of high-speed algorithmic trading is that it adds liquidity to the market. And that this is a good thing, and therefore such trading is also a good thing. This is a completely false argument. It fails in at least three ways, and it points toward yet another source of systemic risk, which goes to the heart of what markets are about. To get away with this feeble excuse it relies on people not thinking too deeply…

First, let's suppose that it is true: yes, there's greater liquidity and transaction costs are reduced by all that lovely liquidity. So you'll save a few cents here and there. But who cares about those few cents? Only others working in high finance care. The man in the street doesn't care. He buys or sells shares every few months, if that. He doesn't care two hoots about saving a few cents. He's either made or lost a much larger sum than that in that time. It's only the high-frequency boys themselves who care about the cents. But if, thanks to bandwagon/feedback effects, the market has crashed, or volatility has increased, then the man in the street could have lost a fortune. “I'm sorry you lost your life savings in a flash crash but, hey, you can console yourself with a nice latte, small mind you, thanks to lower transaction costs.” There's a saying for this, “Penny wise, pound foolish.”

Second, what counts isn't the spread you see between buy and sell prices, it's the actual price of the transaction. So if the price is being moved around, for example by algos jumping in and out of the queue, then you might end up paying more even though the quoted spread seems small.

And finally, even if liquidity improves on average, that doesn't mean it will be there when you need it. The moment that the algos start seeing something out of the ordinary that they can't profit from, then the plug gets pulled. Precisely the moment you want to get out of the market, there's no market to get out of.

The purpose of a formal market is less about instant liquidity than reliable price discovery. With a work of art, you don't really know what it's worth until you sell it. A large house is similar. With a mid-terrace house among lots of similar houses then you might have an idea, if a similar house has sold recently. But with shares, and currencies, and many commodities, you have a much better idea of how much something is worth thanks to exchanges. And that share price in the market ought to bear some relation to some concept of the true value of the underlying company. Of course, company valuation is a tricky business. But if the share price and a plausible company valuation are too far out of line, then things can get strange.

For a brief period during the 2010 Flash Crash, Accenture shares traded for a few cents, down from around $40. As mentioned in Chapter 6, the crash lasted only a few minutes before the market returned to normal levels. Not everything fell. Some shares, including Apple, rose to six-figure amounts, before falling back. Fortunately the Flash Crash was short lived, and also so extreme as to be obviously an anomaly, but smaller versions of such events have become a recurrent phenomenon. In 2014, according to the US Commodity Futures Trading Commission, there were about 35 “flash” events involving the West Texas Intermediate crude oil contract alone.8 Instead of aiding price discovery, HFT is doing the opposite. Call it a new kind of pricing risk, on which more below. Those in favor are the exchanges, which benefit from the volume, and the algo firms themselves. And of course the regulators and politicians under their sway (Box 10.2).

A Million Billion Dollars

HFT is an example of systemic risk that arises directly from the models used by quants. In addition, there is a more general form of risk which has to do with the size and structure of the financial system. We've mentioned a few times that the notional value of all financial derivatives is over a quadrillion dollars, which everyone can agree is a large number. Now, notional value isn't the same as the amount at risk, because it represents the value of the underlying asset. For example, consider an interest-rate swap between two people, one earning interest at a fixed rate of 5% on a million dollars and the other earning interest at a variable rate, currently 4%. If they arrange to swap those income streams, then the notional value of the swap is a million dollars, but the actual value of the swap itself will be much less – around 1% of that (it corresponds to the difference in the interest rates, which will change with time). So, describing the swap as a million-dollar derivative seems to exaggerate its size.

But now consider a CDS, which (as discussed in Chapter 5) is used as a form of insurance against a firm going bust. Here the notional value is the amount insured. So if the firm does default, then the notional value becomes very real – as companies such as AIG discovered during the crisis. Or imagine that, in the interest swap example, one of the parties goes broke and the other discovers that they are legally responsible to replace that income stream. Again, this amounts to ponying up the equivalent notional value.

Credit events, as they are known, are part of the risk of doing business, and instruments such as CDSs are supposed to insure against them. But unlike proper insurance policies, whose writers are highly regulated and for obvious reasons need to maintain adequate reserves, financial derivatives are subject to none of the same regulatory scrutiny. Furthermore, because derivatives are often traded over the counter, rather than through a central exchange, it is impossible to see the net exposure throughout the economy to events such as defaults, or to know how many firms or individuals will be affected. We therefore have a situation where risk can be assessed at the individual level, for particular instruments or institutions, but not at the global level. Since the crisis, there has been some attempt to move derivatives trading to central clearing houses where exposure can be better monitored (e.g., the Dodd–Frank Act in the USA), but much of the risk still remains in the shadows.

Also, while derivatives can be used to insure individual parties against risk, what they are really doing at a system level is transmitting risk. Indeed, a main reason for their popularity is that by appearing to offload risk, they allow the purchaser to take on even more risk – and potential profit – somewhere else. But when a problem such as a bank failure occurs, the high degree of connectivity in the economy means that its effects rapidly propagate through the rest of the system, as loans are called in and risk tolerance deteriorates. Just as the high volume of international traffic is the best friend of viral pandemics, so our globalized financial system has prepared the perfect ground for financial contagion. The network is highly connected, but it is impossible to see where the connections are, or to turn them off in a crisis.

With complex systems, there is usually a trade-off between efficiency and robustness. Increasing bank reserves makes banks less profitable, but also more secure. Introducing friction into the system – for example by putting regulatory brakes on HFT – will slow the markets, but also make them more transparent and reliable. And imposing a degree of modularity on the financial system – say by restructuring large global banks into smaller, more local entities – would reduce efficiency but also the likelihood of contagion. As ecologists such as Robert May have pointed out, robust ecosystems such as food webs tend to be organized into separate, weakly connected subnetworks.10 However, the banking system has only become more concentrated since the crisis, as weaker banks were taken over by the survivors.

Perhaps the greatest structural risk to the financial system, though, is – the financial system. Or rather, money itself. As we've seen, money and debt don't play much of a role in mainstream economics. This is related to the fact that money has traditionally been treated as an inert medium of exchange, rather than something with powers of its own.11 The so-called dynamic stochastic general equilibrium models favored by macroeconomists, for example, model the entire global economy, except for the finance part. Models are a way of interpreting the world, and if something is not in the model then we tend not to see it. This blind spot toward money and debt helps explain why we have let debt become so large – rather like a newbie subprime housing customer, lured in by teaser rates, but on a more magnificent scale. According to a 2014 report from McKinsey, global debt has reached almost $200 trillion, up by 40% just in the seven years following the crisis.12 Debt is inherently destabilizing, for the simple reason that during an economic downturn, debts don't decline as well – they just grow mathematically over time. Leverage also amplifies the effect of price changes and feedback loops.

One solution would be to… actually, we don't have a solution (apart from the obvious ones, default or debt forgiveness). But a first step will be to rethink our models – not just the mathematical ones, but the entire way in which we see the economy.

The Bionic Hand

Discussions about regulating or fixing the system usually return to the old debate over free markets, and whether state interference helps or hinders progress. Opinions on this have long been shaped by the picture – our collective mental model – of the economy as a fundamentally stable and optimal system, controlled by the negative feedback of the invisible hand. For Adam Smith, this process not only led to market prices stabilizing at an optimal “center of repose,” but ensured “ease of body and peace of mind” for kings and beggars alike. Neoclassical economists fleshed out the story with mathematical equations, and tried to prove it by making what amounted to symmetry assumptions about things such as fairness and stability. Other theories – such as the efficient market hypothesis, with its rational, independent investors driving prices to equilibrium – are essentially updated elaborations of this theme. Quants put it to work by modeling asset prices as a probabilistic, mechanistic system, spreading and dispersing in time like plumes of smoke but without the annoying turbulence. Money throughout was mostly treated as just a metric, rather than something of importance in itself.

Today, Keynesian economists promote government attempts to stabilize markets, and central banks tinker with them at will. However, the touted ability of properly managed markets to drive prices to their “natural” level remains the lynchpin of mainstream economics, and much quant finance, and even markets themselves, because it means that those prices correspond to something solid and reliable, instead of just being a transient, emergent phenomenon of the world economy. Portfolio management assumes that prices encode a relationship between growth and risk. Financial derivatives are valued on the basis that markets set prices correctly even for things like volatility and correlation. Without such assumptions, the calculations just don't add up. To quote Blaise Pascal (on the perils of home renovation), “our whole foundation cracks and the earth opens into the depth of the abyss.”

Smith's idea that selfishness at the individual level can paradoxically lead to positive societal outcomes wasn't a new one, even in the 18th century – as Czech economist Tomáš Sedláček points out, it has been around in one form or another since antiquity – but he put the story into a form and language suitable for the industrial age.13 The world was experiencing an economic boom unlike anything ever experienced before – a truly singular event in human history – and Smith was its Poet Laureate. Even now, the invisible hand remains conflated with the ideas of economic efficiency and technological progress. On the one hand, it drives prices to an equilibrium level where no firm makes excessive profits; but on the other hand, it also drives innovation and evolution, by acting as a kind of Darwinian selection mechanism for the markets. High-frequency trading, in the words of one commentator, is a way of “giving Adam Smith's invisible hand a bionic upgrade by making it better, stronger and faster like Steve Austin in the Six Million Dollar Man.”14

But as we know, the story is more complex than the one portrayed in the standard model. Algorithms share similar models so are not independent; power and influence distort the playing field by allowing privileged access to market information; positive feedback loops accentuate sudden changes and make the system unstable; and having robots mindlessly compete does not automatically benefit society. There may be tiny savings in transaction costs, but as shown above these are little compensation for the true costs, in terms of both profits and financial stability. It is hard to believe in “no arbitrage” when a major portion of the economy depends on exactly that. The financial sector protects its turf through its influence in power centers such as Washington and London, and pushes for bailouts when it screws up. If this is the bionic invisible hand, then it has the economy by its throat – less Steve Austin than the Peter Sellers character Doctor Strangelove (we're sticking with old pop culture references).

If we see high-frequency trading not as the perfect realization of the invisible hand, but as an emergent property of the financial system, which itself has emerged from a nexus of cultural, political, legal, technological, and other factors, then things become more complicated and nuanced. Unrestricted competition in the system can be good when it is between relative equals, but bad when it leads to excessive concentration of power; cooperation can be good when it leads to the productive sharing of ideas and resources, but bad when it becomes a bandwagon. And just because something emerges – be it a trading strategy or a tumor – doesn't mean we want to keep it.

The System (John Law feat. Isaac Newton)

As we have seen in this book, high-frequency trading can be viewed as the latest step in the long evolution of quantitative finance. The principal characters in the story, in descending order of distance from the money, have been bankers, quants, economists, and scientists – with a fair degree of overlap and crossover between them. Some scientists become quants, some quants become bankers. Some bankers fund scientists, some economists are scientists. But all have important roles.

While Smith idealized Isaac Newton, he had no time for John Law, with his idea of “multiplying paper to almost any extent… the most extravagant project both of banking and stock-jobbing that, perhaps, the world ever saw.” But today our financial system owes less to the former than the latter. Instead of Newton's gold standard, with its clearly defined units of value weighed out in precious metal, we have Law's fiat currency, only on a much larger scale. And in place of Newtonian stability, the dominant theme seems to be chaos and uncertainty. However, our financial institutions still look and behave as if they were in the gold-standard era. Remarkably little has changed, on the surface, at the Bank of England or the Federal Reserve, or in university economics departments, since that time. The system is Law's, but Newton serves as the straight man, the public face of reason. The role of economists since Smith has been to channel Newton, and give the system the gold-standard stamp of certification.

And the role of quants? Everything was fine, back when they were just making calculations and valuing derivatives and doing a better job than their competitors of balancing growth and risk. But something changed – they became too big. Beginning in the 1980s, their models first influenced, then took over, the system, creating unanticipated feedback loops between the models and reality. In the early 2000s their CDOs and CDSs literally made money, Law style, by allowing banks to lend out more and more credit, which consisted of new money, and charging commission on the process. When their schemes – their quantitative seizing – cratered, the central banks stepped in to fill the hole with quantitative easing. This at least reinflated asset prices to roughly where they were, but global debt levels inflated too.

The crisis didn't slow quants down. Instead, they turned markets into a plaything for algorithms. No longer content to just analyze markets, quants – or at least their robot avatars – actually became the markets. But the danger is that their cover has been blown. The old story about markets being stable and self-correcting looks more uncertain with each disaster and every flash crash. The idea that we all behave like independent atoms no longer appeals in an age of social networks and complexity theory. Newton has left the building.

And here we have the biggest systemic risk of all. Money is a way to attach number to the concept of value, and markets are a way to make sure the numbers have meaning. Because number is involved, it is easy to get sucked into the idea that the economy is a physical system, governed by mechanical laws. But maintaining that fiction, that illusion of validity, requires an epic amount of denial – a recalibration of reality. Number is – literally – only half of it, only one side of the coin, because money and markets also depend on human factors such as trust and belief. So for the system to function, the story – the bond between number and value – has to hold. Break that, and we are back in John Law territory, where the miraculous “system” is revealed as a Wizard-of-Oz-like fraud.

While algorithms are good at many things, one skill which continues to elude them is the ability to understand stories – or make up new ones (which is why humans are still employed to write screenplays and books, and even trade stocks). And in a world where robotic, twitter-fed algorithms race to react to the decoded mutterings of equally robotic central banks, finance appears to have lost the plot.

So the question is, how does this end? Can quants turn it around, or will the straight man become the fall guy? After all, if most of the trading is being done by algos, how are the humans going to react the next time things really go wrong? In an unequal world, that's called political risk – and the outcome might be worse than Law's exile in Venice.

Answers to wilmott.com, please. We'll wrap this up with some thoughts on reforming the field we call quantitative finance – and ask if it needs to become more qualitative.

Notes

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