Fallible and expensive: the primary reasons for dropping a human trader in favor of a computer. While pitting man versus computer feels like the start of a Ray Bradbury novel, it has become a very real thing, especially in the trading of a highly derivative product. Credit default swaps are a complicated product, and trading indices of CDSs add yet another layer of complication. Eventually tracing the line of credit back to the source of value is far too long and twisted. Details get lost, and, as we’ve often seen, trades go wrong, valuations prove incorrect, markets tumble.
Under the current model, trading divisions devise algorithms that process any number of details compared to the firm’s ability to take on risk. The answers these models provide are translated into a yes/no, buy/sell by human traders.
But, not being computers, traders often act beyond the scope of the algorithm. This has lead to the rise of “behavioral finance,” a study of the ways in which humans fail to make the best decision. We tend to make systematic mistakes because we have limited investor attention, we’re subject to overconfidence and excessive optimism, we fall prey to mimicry and herding, we are too inhibited by loss aversion. On the other end of the spectrum, we allow confidence in our own ability and brilliance to outweigh calculated risk potential; we believe in “winning streaks.”
Enter the computers. Their ability to store and sort data far outpaces ours. They can calculate risk more quickly and accurately. And, most importantly, they will always stick to the algorithm. Which means that, more often than not, they will make better decisions than human traders would using the same model. Not to mention computers will never ask for raises, won’t drive up the cost of the company’s insurance plan and don’t require a corner office.
The impact and implications of this new — and growing — practice go far beyond the firms that chose to use this form of non-employment. I’m not here to talk about what this means for job creation or to have a science fiction breakdown of what a world run by machines would be like. I want to take this space to think about the ways in which this is not just an attempt to circumvent the human failures that are abundant in risk calculation. I think in many ways this is just as much of an attempt to prevent abuse of power.
Scandals abound within the government, police forces and news networks, but these entities can do little to control that occurrence. Hope for more pure-hearted employees, write stricter contracts, up the consequences for immoral acts, even require ethics seminars, but little can change the human component of these jobs. When it comes down to it, these jobs will always be fighting the fact that “absolute power corrupts absolutely.”
The world of finance is no stranger to scandal, and I’m sure immoral acts of all sorts take place, but the most recent concerns over business ethics all center around acts that benefit the most powerful members of the firm at the expense of the stockholders, general public or even lower-level employees. There is clearly as much reason for concern when it comes to finance as any other of today’s most powerful careers. Early this year the chairman of Switzerland’s central bank was accused of manipulating the Swiss franc for the financial benefit of his and his wife’s investments. The dispute made quite a mess, and it’s interesting to me that in the year following two Swiss banks — UBS and Credit Suisse — have received attention for their use of computer-led trading.
Not only, then, is a computer less expensive in terms of salary and benefits, or less fallible in terms of calculations and applications of risk algorithms — computers are also far less of a scandal liability. A computer can’t leak insider tips to its girlfriend. A computer has no personal investments on the line, no national loyalties, no political ambitions. In a world where we fear we can’t train our business elite to uphold higher ethical standards, maybe deferring to computers offers far more than cost-saving benefits.
Lily Alberts is an economics major from Nashville, Tenn. She can be reached at lalberts@princeton.edu.
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