On automation and algorithms

Add CDS traders to the list of jobs being automated out of existence.

It’s bad enough finding out that you’ve been made redundant when your pass fails to let you in to the building. But finding out that you’ve been sacked and replaced by a computer (which has more or less made your skills redundant)? That’s even worse. So spare a thought for David Gallers, former head of CDS index trading at UBS, who was let go last week, to be replaced by snazzy new algo.

We probably don’t need to feel too sorry for David, as head of CDS trading he was probably on at least 6 figures plus bonus and I doubt he’ll have to be signing up for workfare any time soon. But the transition away from real human traders towards completely automated algorithms is worth exploring.

Computerised trading isn’t anything new, since Black and Scholes first published their model for option pricing there have been attempts to automate the calculation of asset prices, hedge risks and trade away arbitrage. The first program trading started in the 70s and today the vast majority of US equities are traded by algorithms rather than real people, and markets in many other assets are now rushing to catch up.

On one hand this simply reflects a change we’ve seen across the economy in the last 30 years (and indeed since the start of the industrial revolution), an increasing organic composition of capital, the replacement of variable with fixed capital, firing workers and putting machines in their stead. What is true for manufacturing is now becoming just as true for services, with automated online help desks, phone lines, translation, even simple news articles now being produced entirely by bots with no direct human input.

Derivatives traders like David are expensive, yet even the best barely statistically significantly better than the average; a few good calls can make you a lot of money, but that doesn’t mean your next one won’t lose just as much. And at a time when banks are faced with public opprobrium over high wages, constant scandals about “rogue traders” and unnatural economic conditions that make banking potentially no longer economically worthwhile at all, replacing traders with algos looks to make a lot of sense.

But algorithmic trading isn't necessarily so sublime. In fact despite its rapid growth it has a history of implication in major financial crashes. Black Monday, in 1987, when the Dow Jones dropped over 22% in one day, was widely attributed to program trading, while the so called “flash crash” of 2010, where the stock market lost 9% of it’s value in a few minutes, only to immediately rebound has also been linked to high frequency automated trading.

It is, of course, impossible to know for certain what caused these events, and other theories do exist. But what we can say for certain is that the stock market is a vast, complex system, which experiences sometimes substantial fluctuations for which no easy explanation is forthcoming. At one time trading was based entirely on “fundamentals”, on how a profitable a company was, on its expected future profitability. Now traders rely on mathematical models which ignore entirely these fundamentals and simply seek to reproduce the statistical properties of past data, relying on a few economic assumptions, that markets contain no arbitrage, that past historical data, on average, will look like the future, that markets are efficient - mathematically that they are Markovian.

That these assumptions are likely incorrect, is not even the most substantial flaw - models do exist that attempt to generalise around these assumptions, and many heterodox economists are attempting to formulate an alternative to neoclassical economics which obviates some of these problems. Even if these models were right, they were created to simulate the economy as it existed when mainly traded by real humans, who based their individual decisions on a variety of factors, not all rational an mathematical. Determining a number of heuristic rules to describe that world and allowing them to then run and interact absolutely with themselves is a recipe for instability and chaos. We have no idea how high frequency automated trading even works, the trading is too quick for a human observer to decipher and so fast that even the time it takes light to cross the country can makes the difference between profit and loss.

Whilst the economic crises of the past few years undeniably have underlying real causes, the frameworks within which finance now operates can exacerbate and amplify these problems.

It is therefore, somewhat ironic that legislation intended to contain and regulate the banking system so as to increase its stability seems likely to lead to a vast increase in algorithmic trading.