The Fear Index (12 page)

For the first time Hoffmann risked a brief glance around the table. Quarry’s audience was listening intently. (‘The two most interesting things in the world,’ Quarry once remarked: ‘other people’s sex lives and your own money.’) Even Ezra Klein, rocking back and forth like a student in a madrasa, was temporarily still, while Mieczyslaw Łukasiński simply could not keep the grin off his plump peasant face.

Quarry’s right hand continued to rest on Hoffmann’s shoulder; his left was thrust casually in his pocket. ‘In our business we call the gap between market performance and fund performance “alpha”. Over the past three years, Hoffmann has generated alpha of one hundred and twelve per cent. That’s why we’ve twice been voted Algorithmic Hedge Fund of the Year by the financial trade press.

‘Now,’ he went on, ‘this consistency of performance is not, I can assure you, a matter of luck. Hoffmann spends thirty-two million dollars a year on research. We employ sixty of the most brilliant scientific minds in the world – at least I’m told they’re brilliant: I can’t understand a word they’re on about.’

He acknowledged the rueful laughter. Hoffmann saw that the British banker, Iain Mould, was chuckling particularly hard, and he knew at once that he was a fool. Quarry withdrew his hands from Hoffmann’s shoulder and from his own pocket and placed them on the table. He leaned forward, suddenly serious and urgent.

‘About eighteen months ago, Alex and his team achieved a significant technological breakthrough. As a result we had to take the very difficult decision to hard-close the fund.’ Hard-close meant turning away additional investment even from existing clients. ‘And I know that every single one of you in this room – because that is why we’ve invited you here – was disappointed by that decision, and also bewildered, and that some of you were actually pretty angry about it.’

He glanced at Elmira Gulzhan listening at the opposite end of the table. She had screamed at Quarry down the phone, Hoffmann knew, and had even threatened to withdraw the family’s money from the fund or worse (‘You hard-close the Gulzhans – the Gulzhans hard-close you …).

‘Well,’ continued Quarry, with the merest hint of a kiss blown in Elmira’s direction, ‘we apologise for that. But we took the view that we had to concentrate on implementing this new investment strategy based on our existing asset size. There’s always a risk with any kind of fund, as I’m sure you’re aware, that increasing size translates into decreasing performance. We wanted to be as confident as we could be that that wouldn’t happen.

‘It is now our opinion that this new system, which we call VIXAL-4, is robust enough to cope with portfolio expansion. Indeed, the alpha generated over the last six months has been significantly greater than it was when we were relying on our original algorithms. Therefore, as of today, I can announce that Hoffmann is moving from a hard-closed to a soft-closed position, and is willing to accept additional investment from existing clients only.’

He stopped and took a sip of water to allow the impact of his words to sink in. There was complete silence in the room.

‘Cheer up, everyone,’ he said brightly, ‘this is supposed to be good news.’

The tension was released by laughter and for the first time since Hoffmann entered the room the clients looked openly at one another. They had become a private club, he realised: a freemasonry bound together by a shared secret knowledge. Complicit smiles spread around the table. They were on the inside track.

‘At which point,’ said Quarry, looking on contentedly, ‘I think the best thing I can do is hand you over to Alex here, who can fill you in a bit more on the technical side.’ He half-sat down then stood again. ‘With a bit of luck I may even be able to understand it myself.’

More laughter, and then the floor was Hoffmann’s.

He was not a man to whom speaking in public came naturally. The few classes he had taught at Princeton before leaving the United States had been torture for lecturer and students alike. But now he felt himself filled with a strange energy and clarity. He touched his fingers lightly to his sewn-up wound, took a couple of deep breaths, then rose to his feet.

‘Ladies and gentlemen, we have to be secretive about the detail of what we do in this company, to avoid having our ideas stolen by our competitors, but the general principle is no great mystery, as you well know. We take a couple of hundred different securities and we trade them over a twenty-four-hour cycle. The algorithms we have programmed into our computers pick the positions we hold based on a detailed analysis of previous trends, mostly liquid futures – the Dow, say, or the S and P 500 – and the familiar commodities: Brent crude, natural gas, gold, silver, copper, wheat, whatever. We also do some high-frequency trading, where we may hold positions for only a few milliseconds. It’s really not that complicated. Even the S and P two-hundred-day moving average can be a pretty reliable predictor of the market: if the current index is higher than the preceding average, the market is likely to be bullish; if lower, bearish. Or we can make a prediction, based on twenty years of data, that if tin is at this price and the yen at that, then it is more likely than not that the DAX will be here. Obviously we have vastly more pairs of averages than that to work with – several millions of them – but the principle can be simply stated: the most reliable guide to the future is the past. And we only have to be right about the markets fifty-five per cent of the time to make a profit.

‘When we started out, not many people could have guessed how important algorithmic trading would turn out to be. The pioneers in this business were frequently dismissed as quants, or geeks, or nerds – we were the guys who none of the girls would dance with at parties—’

‘That’s still true,’ interjected Quarry.

Hoffmann waved aside the interruption. ‘Maybe it is, but the successes we have achieved at this firm speak for themselves. Hugo pointed out that in a period when the Dow has declined by nearly twenty-five per cent, we’ve grown in value by eighty-three per cent. How has this happened? It’s very simple. There have been two years of panic in the markets, and our algorithms thrive on panic, because human beings always behave in such predictable ways when they’re frightened.’

He raised his hands. ‘“The space of heaven is filled with naked beings rushing through the air. Men, naked men, naked women who rush through the air and rouse gale and snowstorm. Do you hear it roaring? Roaring like the wing-beat of great birds high in the air? That is the fear of naked men. That is the flight of naked men.”’

He stopped. He looked around at the upturned faces of his clients. Several had their mouths open, like baby birds hoping for food. His own mouth felt dry. ‘Those are not my words. They’re the words of an Inuit holy man, quoted by Elias Canetti in
Crowds and Power
: when I was designing VIXAL-4 I used them as a screensaver. Can I have some water, Hugo?’ Quarry leaned over and passed him a bottle of Evian and a glass. Hoffmann ignored the glass, unscrewed the plastic cap and drank straight from the bottle. He didn’t know what effect he was having on his audience. He didn’t much care. He wiped his mouth on the back of his hand.

‘Around 350 BC, Aristotle defined human beings as “
zoon logon echon
” – “the rational animal” or, more accurately, “the animal that has language”. Language, above all, is what distinguishes us from the other creatures on the planet. The development of language freed us from a world of physical objects and substituted a universe of symbols. The lower animals may also communicate with one another in a primitive way, and may even be taught the meaning of a few of our human symbols – a dog can learn to understand “sit” or “come”, for example. But for perhaps forty thousand years only humans were
zoon logon echon
: the animal with language. Now, for the first time, that is no longer true. We share our world with computers.

‘Computers …’ Hoffmann gestured towards the trading floor with his bottle, slopping water across the table. ‘It used to be the case that we imagined that computers – robots – would take over the menial work in our lives, that they would put on aprons and run around and be our robot maids, doing the housework or whatever, leaving us free to enjoy our leisure. In fact, the reverse is happening. We have plenty of spare, unintelligent human capacity to do those simple, menial jobs, often for very long hours and poor pay. Instead, the humans that computers are replacing are members of the educated classes: translators, medical technicians, legal clerks, accountants, financial traders.

‘Computers are increasingly reliable translators in the sectors of commerce and technology. In medicine they can listen to a patient’s symptoms and are diagnosing illnesses and even prescribing treatment. In the law they search and evaluate vast amounts of complex documents at a fraction of the cost of legal analysts. Speech recognition enables algorithms to extract the meaning from the spoken as well as the written word. News bulletins can be analysed in real time.

‘When Hugo and I started this fund, the data we used was entirely digitalised financial statistics: there was almost nothing else. But over the past couple of years a whole new galaxy of information has come within our reach. Pretty soon all the information in the world – every tiny scrap of knowledge that humans possess, every little thought we’ve ever had that’s been considered worth preserving over thousands of years – all of it will be available digitally. Every road on earth has been mapped. Every building photographed. Everywhere we humans go, whatever we buy, whatever websites we look at, we leave a digital trail as clear as slug-slime. And this data can be read, searched and analysed by computers and value extracted from it in ways we cannot even begin to conceive.

‘Most people are barely aware of what has happened. Why would they be? If you leave this building and go along the street, everything looks pretty much as it’s always looked. A guy from a hundred years ago could walk around this part of Geneva and still feel at home. But behind the physical facade – behind the stone and the brick and the glass – the world has distorted, buckled, shrunk, as if the planet has passed into another dimension. I’ll give you a tiny example. In 2007, the British government lost the records of twenty-five million people – their tax codes, their bank account details, their addresses, their dates of birth. But it wasn’t a couple of trucks they lost: it was just two CDs. And that’s nothing. Google will one day digitalise every book ever published. No need for a library any more. All you’ll need is a screen you can hold in your hand.

‘But here’s the thing. Human beings still read at the same speed as Aristotle did. The average American college student reads four hundred and fifty words per minute. The really clever ones can manage eight hundred. That’s about two pages a minute. But IBM just announced last year they’re building a new computer for the US government that can perform twenty thousand trillion calculations a second. There’s a physical limit to how much information we, as a species, can absorb. We’ve hit the buffers. But there’s no limit to how much a computer can absorb.

‘And language – the replacement of objects with symbols – has another big down side for us humans. The Greek philosopher Epictetus recognised this two thousand years ago when he wrote: “What disturbs and alarms man are not the things but his opinions and fancies about the things.” Language unleashed the power of the imagination, and with it came rumour, panic, fear. But algorithms don’t have an imagination. They don’t panic. And that’s why they’re so perfectly suited to trade on the financial markets.

‘What we have tried to do with our new generation of VIXAL algorithms is to isolate, measure, and factor into our market calculations the element of price that derives entirely from predictable patterns of human behaviour. Why, for example, does a stock price that rises on anticipation of positive results almost invariably fall below its previous price if those results turn out to be poorer than expected? Why do traders on some occasions stubbornly hold on to a particular stock even as it loses value and their losses mount, while on other occasions they sell a perfectly good stock they ought to keep, simply because the market in general is declining? The algorithm that can adjust its strategy in answer to these mysteries will have a huge competitive edge. We believe there is now sufficient data available for us to be able to begin anticipating these anomalies and profiting from them.’

Ezra Klein, who had been rocking back and forth with increasing frequency, could no longer contain himself. ‘But this is just
behavioural finance
!’ he blurted out. He made it sound like a heresy. ‘Okay, I agree, the EMH is bust, but how do you filter out the noise to make a tool from BF?’

‘When one subtracts out the valuation of a stock as it varies over time, what one is left with is the behavioural effect, if any.’

‘Yeah, but how do you figure out what caused the behavioural effect? That’s the history of the entire goddam universe, right there!’

‘Ezra, I agree with you,’ said Hoffmann calmly. ‘We can’t analyse every aspect of human behaviour in the markets and its likely trigger over the past twenty years, however much data is now digitally available, and however fast our hardware scans it. We realised from the start we would have to narrow the focus right down. The solution we came up with was to pick on one particular emotion for which we know we have substantive data.’

‘So which one have you picked?’

‘Fear.’

There was a stirring in the room. Although Hoffmann had tried to avoid jargon – how typical of Klein, he thought, to bring up EMH, the efficient market hypothesis – he had nevertheless sensed a growing bafflement among his audience. But now he had their attention, no question. He continued: ‘Fear is historically the strongest emotion in economics. Remember FDR in the Great Depression? It’s the most famous quote in financial history: “The only thing we have to fear is fear itself.” In fact fear is probably the strongest human emotion, period. Whoever woke at four in the morning because they were feeling happy? It’s so strong we’ve actually found it relatively easy to filter out the noise made by other emotional inputs and focus on this one signal. One thing we’ve been able to do, for instance, is correlate recent market fluctuations with the frequency rate of fear-related words in the media – terror, alarm, panic, horror, dismay, dread, scare, anthrax, nuclear. Our conclusion is that fear is driving the world as never before.’