‘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 hypo
thesis – 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.’
Elmira Gulzhan said, ‘That is al-Qaeda.’
‘Partly. But why should al-Qaeda arouse more fear than the threat of mutually assured destruction did during the Cold War in the fifties and sixties – which, incidentally, were times of great market growth and stability? Our conclusion is that digitalisation itself is creating an epidemic of fear, and that Epictetus had it right: we live in a world not of real things but of opinion and fantasy. The rise in market volatility, in our opinion, is a function of digitalisation, which is exaggerating human mood swings by the unprecedented dissemination of information via the internet.’
‘And we’ve found a way to make money out of it,’ said Quarry happily. He nodded at Hoffmann to continue.
‘As most of you will be aware, the Chicago Board of Exchange operates what is known as the S and P 500 Volatility Index, or VIX. This has been running, in one form or another, for seventeen years. It’s a ticker, for want of a better word, tracking the price of options – calls and puts – on stocks traded in the S and P 500. If you want the math, it’s calculated as the square root of the par variance swap rate for a thirty-day term, quoted as an annualised variance. If you don’t want the math, let’s just say that what it does is show the implied volatility of the market for the coming month. It goes up and down minute by minute. The higher the index, the greater the uncertainty in the market, so traders call it “the fear index”. And it’s liquid itself, of course – there are VIX options and futures available to trade, and we trade them.
‘So the VIX was our starting point. It’s given us a whole bunch of useful data going back to 1993, which we can pair with the new behavioural indices we’ve compiled, as well as bringing in our existing methodology. In the early days it also gave us the name for our prototype algorithm, VIXAL-1, which has stuck all the way through, even though we’ve moved way beyond the VIX itself. We’re now on to the fourth iteration, which with notable lack of imagination we call VIXAL-4.’
Klein jumped in again. ‘The volatility implied by the VIX can be to the up side as well as the down side.’
‘We take account of that,’ said Hoffmann. ‘In our metrics, optimism can be measured as anything from an absence of fear to a reaction against fear. Bear in mind that fear doesn’t just mean a broad market panic and a flight to safety. There is also what we call a “clinging” effect, when a stock is held in defiance of reason, and an “adrenalin” effect, when a stock rises strongly in value. We’re still researching all these various categories to determine market impact and refine our model.’ Easterbrook raised his hand. ‘Yes, Bill?’
‘Is this algorithm already operational?’
‘Why don’t I let Hugo answer that, as it’s practical rather than theoretical?’
Quarry said, ‘Incubation started back-testing VIXAL-1 almost two years ago, although naturally that was just a simulation, without any actual exposure to the market. We went live with VIXAL-2 in May 2009, with play money of one hundred million dollars. When we overcame the early teething problems we moved on to VIXAL-3 in November and gave it access to one billion. That was so successful we decided to allow VIXAL-4 to take control of the entire fund one week ago.’
‘With what results?’
‘We’ll show you all the detailed figures at the end. Off the top of my head, VIXAL-2 made twelve million dollars in its six-month trading period. VIXAL-3 made one hundred and eighteen million. As of last night, VIXAL-4 was up about seventy-nine-point-seven million.’
Easterbrook frowned. ‘I thought you said it had only been running a week?’
‘I did.’
‘But that means …’
‘That means,’ said Ezra Klein, doing the calculation in his head and almost jumping out of his chair, ‘that on a ten-billion-dollar fund, you’re looking at making a profit of four-point-one-four billion a year.’
‘And VIXAL-4 is an autonomous machine-learning algorithm,’ said Hoffmann. ‘As it collects and analyses more data, it’s only likely to become more effective.’
Whistles and murmurs ran around the table. The two Chinese started whispering to one another.
‘You can see why we’ve decided we want to bring in more investment,’ said Quarry with a smirk. ‘We need to exploit the hell out of this thing before anyone develops a clone strategy. And now, ladies and gentlemen, it seems to me that this might be a suitable moment to offer you a glimpse of VIXAL in operation.’
THREE KILOMETRES AWAY, in Cologny, forensics had completed their examination of the Hoffmanns’ house. The scene-of-crimes officers – a young man and woman, who might have been students or lovers – had packed up their equipment and left. A bored gendarme sat in his car on the drive.
Gabrielle was in her studio, dismantling the portrait of the foetus, lifting each sheet of glass out of its slot on the wooden base, wrapping it in tissue paper and then in bubble wrap, and laying it in a cardboard box. She found herself thinking how strange it was that so much creative energy should have flowed from the black hole of this tragedy. She had lost the baby two years ago, at five and a half months: not the first of her pregnancies that had ended in a miscarriage, but easily the longest and by far the most shattering. The hospital had given her an MRI scan when they began to get concerned, which was unusual. Afterwards, rather than stay on her own in Switzerland, she had gone with Alex on a business trip to Oxford. Wandering round a museum while he was interviewing PhDs in the Randolph Hotel, she had come across a 3D model of the structure of penicillin built up on sheets of Perspex in 1944 by Dorothy Hodgkin, the Nobel laureate for chemistry. An idea had stirred in her mind, and when she got home to Geneva, she had tried the same technique on the MRI scan of her womb, which was all she had left of the baby.
It had taken a week of trial and error to work out which of the two hundred cross-sectional images to print off, and how to trace them on to glass, what ink to use and how to stop it smearing. She had sliced her hands repeatedly on the sharp edges of the glass sheets. But the afternoon when she first lined them up and the outline had emerged – the clenched fingers, the curled toes – was a miracle she would never forget. Beyond the window of the apartment where they had lived in those days, the sky had turned black as she worked; brilliant yellow flashes of forked lightning had stabbed down over the mountains. She knew nobody would believe it if she told them. It was too theatrical. It had made her feel as if she were tapping into some elemental force: tampering with the dead. When Alex came home from work and saw the portrait, he had sat stunned for ten minutes.
After that she had become utterly absorbed by the possibilities of marrying science and art to produce images of living forms. Mostly she had acted as her own model, talking the radiographers at the hospital into scanning her from head to toe. The brain was the hardest part of the anatomy to get right. She had to learn which were the best lines to trace – the aqueduct of Sylvius, the cistern of the great cerebral vein, the tentorium cerebellum and the medulla. The simplicity of the form was what appealed to her most, and the paradoxes it carried – clarity and mystery, the impersonal and the intimate, the generic and yet the absolutely unique. Watching Alex going through the CAT scanner that morning had made her want to produce a po
rtrait of him. She wondered if the doctors would let her have his results, or if he would allow her to do it.
She wrapped up the last of the glass sheets tenderly, and then the base, and sealed the cardboard box with thick brown sticky tape. It had been a painful decision to offer this, of all her works, to the exhibition: if someone bought it, she knew she would probably never see it again. And yet it seemed to her an important thing to do: that this was the whole point of creating it in the first place – to give it a separate existence, to let it go out into the world.
She picked up the box and carried it out into the passageway as if it were an offering. On the handles of the doors leading off the corridor, and on the wooden panels, were traces of bluish-white powder where the surfaces had been dusted for fingerprints. In the hall, the blood had been cleaned off the floor. The surface was still damp, showing where Alex had been lying when she discovered him. She carefully skirted the spot. A noise came from inside the study and she felt her skin rise into gooseflesh just as a man’s heavy shape loomed in the doorway. She gave a cry of alarm and almost dropped the box.
She recognised him. It was the security expert, Genoud. He had shown her how to use the alarm system when they first moved in. Another man was with him – heavyset, like a wrestler.
‘Madame Hoffmann, forgive us if we startled you.’ Genoud had a grave professional manner. He introduced the other man. ‘Camille has been sent by your husband to look after you for the rest of the day.’
‘I don’t need looking after …’ began Gabrielle. But she was too shaken to put up much resistance, and found herself allowing the bodyguard to take the box from her hands and carry it out to the waiting Mercedes. She protested that at least she wanted to drive herself to the gallery in her own car. But Genoud was insistent that it was not safe – not until the man who had attacked her husband had been caught – and such was his blunt professional inflexibility that eventually she surrendered again and did as she was told.
The Fear Index Page 9
