Thomas Maloney - Learning to Die

Of course he did know that Natalie had a meaningful boyfriend before he met her: they’d travelled a lot together, but the guy was at some other university and the long distance relationship hadn’t worked out. That was about all he knew. He asked her about it a few times in the early days and she was dismissive, so he let it drop. He doesn’t even know a name. He’ll call him Chris.

And all this time she’s kept Chris’ letters. Why shouldn’t she? Eighteen to twenty is an important chapter of any life. There was nothing in their marriage vows about erasing their browsing history, so to speak. Maybe, after all, she just wanted to remember what a loser the guy was.

The privacy of letters is, Dan felt instinctively, sacrosanct. Had it been a photo album, he could have looked. He’d have seen a teenage Natalie, with a bleached streak in her red hair, perhaps, and her man — Dan imagines him tall, dark and scruffy — happy and in love. There would have been lots of double selfies — a hit and miss composition in those days — their two faces pressed together (in one photo, kissing) with a wild landscape or ancient building in the background. There would probably have been photos of Chris looking relaxed and charismatic in exotic places: combat trousers, mirror shades, well-travelled rucksack. Perched daringly atop a rocky pinnacle; strumming a guitar on a beach; tearing up a blurry dance floor with unknown friends. And there would have been photos of the Nat he never knew: standing beside a waterfall, laughing, wearing not much and drenched by the spray; draped on the bonnet of a roofless car in the desert, relaxed in a short dress (she rarely wears dresses now, even on holiday). Carefree, fulfilled. So Dan imagines. As it happens, it wasn’t a photo album — just some letters that he didn’t read — but the effect is the same.

Now, with Dan, Nat has a mortgage, a Tesco Clubcard and office politics. However the relationship with Chris was broken off, are these letters likely to be anything other than a memento of profoundly happy times? How much of a problem is that?

‘I think we’d all agree the markets aren’t a hundred per cent rational,’ Mike declares to the row of intent but sceptical faces. One wise old owl — white hair with that lurid yellow tinge, like tarnished silver, the CIO — three golfing family chaps in their forties, and a young woman power-dressed in vivid green. Mike has been carefully distributing his eye contact, giving Jade only her fair share even though a delicious little spark flies off her at each glance.

‘They do reflect the fundamentals — growth, inflation, geopolitics, monetary policy—’ he lingers wistfully over those ponderous syllables ‘—and it’s hard enough forecasting those. But the markets mix in a lot of crazy shit.’ Americans love hearing that in his plummy accent. There are nods and chuckles: he has them so far. ‘Is that crazy shit easy to predict? No. But is it random? Absolutely not. It’s the combined effect of a dozen behavioural biases clouding the judgement, tilting the decisions of thousands of market participants. Sure, each single investor might trade for any number of reasons, but those investor-specific motives average to zero. The biases, on the other hand, infect us all. So they’re in the driving seat.’

No one argues. Mike flips open his laptop and two graphs appear on the giant screen behind him, one above the other. Like jagged rollercoasters, blue and red. The graphics are clean and spare: these graphs are more beautiful than any of the art on the walls.

‘Here are two anonymous financial assets,’ says Mike. He hits a key and the graphs change. ‘Here are two more.’ He starts tapping the key and the graphs flex and bulge into a parade of different shapes. ‘Here’s a whole zoo of them. Now, just to prove I’m not cherry-picking to tell a good story, you get to tell me when to stop.’ Tap, tap, tap, tap, tap —

‘Stop.’ It’s the CIO.

‘Done. Okay. Here are our two anonymous guinea pigs. Anyone like to guess what they are?’

No takers. Mike punches out a few more letters and hits enter. Five fainter lines, each in a different colour, slide onto both of the graphs from the left side of the screen. ‘Here are your macroeconomic variables — the real, stick-your-fingers-in-it stuff.’ He hits another key and the new lines slide off to the right. He pauses for effect.

‘We don’t care about those.’

The CIO raises an eyebrow.‘Economic forecasting isn’t our game,’ continues Mike. ‘You hire other managers to do that. We only care about the price. Decades of research — by psychologists, not economists — has identified the key behavioural traits that drive prices. Our challenge is to model those traits, and then — this is a technical term — to scrunch the bejesus out of them until the story of our chosen investment starts to emerge. Let’s see what happens.’

He hits enter, and a progress bar appears over the blue graph and begins smoothly to grow. Notifications appear beside it: ‘ Apply underreaction-to-news bias. Apply herding bias. Apply lottery bias. Apply disposition effect. Apply exuberance. Apply greed. Apply panic. ’ With each new message, the graph flickers but doesn’t change. After ten seconds, the progress bar completes and is replaced by message in bold text: ‘ MRI analysis effectuated. Model accounts for 0.0% of price variation. Signal: undefined. ’

‘And we’re done. Hold on — that doesn’t look right. Signal undefined?’

Mike stares at the screen with a comical frown, and can sense that glances are being exchanged behind him. He catches the eye of his sales rep, an amiable Texan who only joined six months ago and who now smiles nervously.

‘Gotcha,’ declares Mike, turning to his audience with a grin. ‘The blue line wasn’t a financial asset — it was randomly generated. There’s no signal, because there’s no story.’ Frowns all round. A chuckle. They’re thinking: Is this guy wasting our time? Mike proceeds calmly. ‘Let’s try the red one.’

Again the progress bar, but progress is slower this time, and with each new message the line on the graph shifts — it’s being dismantled, slowly collapsing, flattening onto the axis. After about thirty mesmerising seconds the bar completes. ‘ MRI analysis effectuated. Model accounts for 94.7% of price variation. Signal: long 56%. ’

‘And there we have it. Long fifty-six per cent. This asset is, judging by the price profile, the Italian stock index.’ His laptop tells him that discreetly.

‘Your system wants to go long Italian stocks, as of today?’ asks the CIO. He has an Italian name, Mike recalls. A lucky strike.

‘That’s correct. As of ten twenty-six this morning. Not full-throttle long, but decently long.’

‘I’d better call my broker,’ jokes the CIO, grinning.

Dan Mock loves cornering on his way to work, but his place of work itself, being circular, has no obvious corners. No, he’s not a cricketer, a gladiator, an actor or a clown: he’s a particle physicist. The synchrotron, a giant gleaming doughnut nestled in the downs, is all curves from the outside — but, as it happens, corners are what make it tick.

This accelerator isn’t used for incomprehensible (to most) particle physics research like its more famous big sister at CERN: here, the particle physics is just a means to more practical ends. ‘I make electrons dizzy,’ is how Dan describes it to the laity. He and his colleagues have to keep their little flock together and chivvy them up to about 99.9999% of the speed of light. At this rate they do half a million laps of the stadium-sized doughnut every second, which understandably makes them queasy. At each breakneck corner (the track is not actually a circle but a forty-eight sided polygon), the queasy electrons, which don’t like cornering as much as Dan does, spew out high-energy photons — these are what the synchrotron’s customers channel into their experiments. Dan’s official job is just to keep that dazzling photon-spew coming, in many shades and flavours, but he usually finds an excuse to poke around in the experiments too.