Advisers vs. Machines

Professional and individual investors have long had a hard time beating the broader market. And, says Mark Hulbert, the rise of computer trading programs may be making it harder than ever.

Hulbert writes in The Wall Street Journal that it’s been “nearly impossible lately” for investors to consistently beat index funds, and just as difficult to predict which managers will be able to do so. “Consider the 51 advisers out of more than 200 on the Hulbert Financial Digest’s list who beat the market in the decade-long period that ended April 30, 2012, as measured by the Wilshire 5000 Total Market index, including reinvested dividends,” he says. “Of that group, just 11 — or 22% — have outperformed the overall market since then.”

Hulbert says that computerized trading has been winning out over traditional advisers in large part because computers can process vast amounts of financial data very quickly, which most people cannot do. He also says that investors “unwittingly let their emotions dominate their intellect”, something that is not a problem for computers. He references the work of behavioral finance pioneer Daniel Kahneman, who, in his 2011 book “Thinking, Fast and Slow,” reviewed more than 200 academic studies that analyzed competitions between human beings and mechanical algorithms. Whether the subject was medicine, economics, business, psychology, sports predictions, or the quality of Bordeaux wine, “the accuracy of experts was matched or exceeded by a simple algorithm,” Kahneman said.

Hulbert says all this means that investors should trade as infrequently as possible. Computer trading now dominates Wall Street so much that even professional managers “will lose out to them over time”. He recommends low-cost buy-and-hold index funds.

Hulbert does say that there may well be a place for human investors amid an increasingly computerized Wall Street. Brad Barber, a finance professor from the University of California, told him that computers cannot do some things, like determining whether a pattern makes sense. “If you don’t understand the reason for a pattern, you’re vulnerable to following a mindless algorithm that is quite likely to perform poorly,” Barber said.