Economists, closer to salt water, appear to be prone to thoughts of inefficiency and bubbles in the financial markets, something that can be cured by a single trip to the windy city. A recent study from Columbia University (1) asserts that they could find over 13,000 bubbles in the stock market between 2000 and 2013. Using supercomputers, no less, and "big data," they appear to have "conclusively shown" that stock prices take wild and persistent excursions from their "fair values." Unfortunately, these academics, who profess to be "data scientists," are yet to encounter the phenomenon of "random walk," further evidence that "data scientists" should stay away from financial markets. After all, the “physicists” who descended into Wall Street have had a checkered history of “abnormal returns” wrapped in consistent negative alpha.
The remark from a graduate student from Harvard - "I expected to see lots of bubbles in 2009, after the crash, but there were a lot before and a lot after," is symptomatic of the problem faced by “data scientists,” seeking problems to solve in super-domains they have no clue about, where participants, who determine outcomes are equipped with pattern finding technology. They may have better luck in real markets, for prices in financial markets are determined by a large number of participants, each with her own inefficient algorithms. The most troubling aspect of the study is that the authors of the study believe that “a bubble happens when the price of an asset, be it gold, housing or stocks, is more than what a rational person would be willing to pay based on its expected future cash flows.” In a world, immersed in intellectual property, where future cash flows cannot be forecasted precisely, the value of an asset cannot be determined by such simple constructs that have been rendered invalid for decades.
The lure of financial markets have been problematic for “data scientists” and “physicists.” However, a cure is readily available in academic literature emanating from the sixties.