Computerized systematic funds, also known as “quants”, have grown in popularity since the 1990s and are now more common than the “human”, discretionary method of trading currencies. Of 43 currency funds tracked by Parker Global, 27 are driven by computer models. They returned an average 10.7% a year since the Stamford, Connecticut-based consulting firm started compiling the data in 1986, compared with 8.6% for discretionary funds, However, as of June this year, quants made 0.7%, whereas human made 2.3%, beating computer with the biggest margin since 2008.
Quants were “wrong-footed” in the second quarter by the Fed’s signals that it would start scaling back monetary easing, according to Caio Natividade, the head of foreign-exchange and commodity quantitative research at Deutsche Bank AG, the world’s biggest currency trader. Humans are proving more adept than computers in reacting to the Federal Reserve’s mixed messages on when policy makers will reduce their unprecedented stimulus.
Quant funds use computer models based on inputs such as price history and correlations, and typically execute buy or sell orders automatically at certain trigger points. Apparently, the breakdown of long-held asset correlations is proving problematic for many computer-based funds. For instance, the Mexican peso, which used to be a barometer of risk sentiment, is no longer moving in tandem with the Standard & Poor’s 500 Index of stocks. The 60-day correlation declined to 0.4, the lowest since 2009, according to data compiled by Bloomberg. A reading of 1 indicates the two move in lockstep.
Quant investing dates back to the 1960s, when Edward Thorp, a mathematics professor at the Massachusetts Institute of Technology, used probability theories to bet, first on blackjack and then on Wall Street. Banks and hedge funds such as London-based Man Group Plc and Winton Capital Management Ltd. popularized their use over the past two decades, expanding trading from stocks to bonds and currencies.
As for the human – discretionary fund managers may use technical analysis in their research, they commonly rely on economic indicators to dictate their trading strategies. The disadvantage of the human is its decision could handicapped by its emotion, i.e. a human being does not have an independent mind. For instance, a trader who sits on a profit evaluates the economic, political and market situations differently to a trader who sits on a loss.
Nonetheless, the fact that human had beaten quants has proved that human is more adaptive and responsive in a market whereby policy makers “change the rules all the time”, at least for now.
Bloomberg, Ye Xie & Liz Capo McCormick, 2013-07-25, “Humans Beating Robots Most Since ‘08 as Trends Shift: Currencies”