One of the most common arguments I hear against passive investing (which we can define as the use of a systematic approach to gain exposure to a factor or factors) goes like this: How can good management that is “thinking” not be superior to “nonthinking” management? I have found most investors harbor a strong opinion on this question.
Fortunately, we have evidence to help settle this matter. We’ll begin with a study by Lewis Goldberg, a psychology professor, who in 1968 analyzed the Minnesota Multiphasic Personality Inventory (MMPI) test responses of more than 1,000 patients and their final diagnoses as neurotic or psychotic.
As I discussed last week, Goldberg used this data to develop a simple model to predict the final diagnosis based on the MMPI test results. He found that, out-of-sample, his model had a 70% accuracy rate. He then gave the MMPI scores to experienced as well as inexperienced clinical psychologists and asked them to diagnose the patient. Goldberg found that his simple model outperformed even the most experienced psychologists.
Taking it one step further, Goldberg reran the test, this time providing the clinical psychologists with the model’s predictions. Goldberg was shocked that, while their performance did improve, they still underperformed the model, even armed with the benefit of its predictions.
The conclusion one might draw is that the results of quantitative models may be a performance ceiling from which humans are more likely to subtract (due to our behavioral biases, such as overconfidence) than exceed.
But, does the field of investing produce the same results in the contest of man versus machine?
Man Vs. Machine: Hedge Funds
Campbell Harvey, Sandy Rattray, Andrew Sinclair and Otto Van Hemert provide evidence on the subject with their December 2016 paper, “Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance.”
They analyzed and contrasted the performance of systematic hedge funds, which use rules‐based strategies involving little or no daily intervention by humans, with the performance of discretionary hedge funds, which rely on human skills to interpret new information and make the day‐to‐day investment decisions.
The study covered the period 1996 through 2014, and included data on more than 9,000 macro and equity hedge funds. To adjust returns for exposure to common factors, they used stock factors (beta, size, value and momentum) and bond factors (term and credit), as well as FX carry and volatility.