There are two big, ongoing debates relative to index (or, more broadly, passive) investing. The first is whether active or passive (which I define as neither market timing nor individual security selection) management is the winner’s game (the one most likely to allow you to achieve your goals). The overwhelming evidence, as presented in my book, “The Incredible Shrinking Alpha,” shows that passive investing is the prudent choice.
The other debate that rages on is if a total-market approach (with John Bogle often seen as the standard-bearer) is “best,” or whether “titling” a portfolio to well-documented factors is likely to produce higher returns, and perhaps higher risk-adjusted returns.
During discussion about this second issue, perhaps the most-asked question I hear goes something like this: We know the historical evidence shows premiums for these factors, but how can you be confident that factor premiums will persist after research about them is published and everyone knows about them? After all, we are all familiar with the phrase “past performance does not guarantee future results.” I thought it worth sharing my answer.
Argument For Persistence
The first thing I point out is that we live in a world of uncertainty. There is simply no way to know for certain whether a factor premium will persist in the future; that goes for all factors, including market beta.
A good example demonstrating this point is that, using data from Dimensional Fund Advisors, from 1969 through 2008, the Fama-French U.S. large-cap growth index ex-utilities returned 7.8% and underperformed long-term (20-year) Treasury bonds, which returned 9.0%. That’s a 40-year period in which investors took all the risks of stocks in this asset class (which today constitutes about 50% of the U.S. market’s total capitalization, down from about 70% at the end of 1999) but still underperformed long-term U.S. Treasuries.
Given that any strategy of investing in risky assets can fail to deliver a risk premium, no matter how long the horizon, we must make decisions in the face of uncertainty, recognizing that the best we can do is to put the odds in our favor. That raises another question: How can we best put the odds in our favor?
Potential Problems With Research Findings
A well-documented problem with factor-based investing is that smart people, with even-smarter computers, can find factors that have worked in the past but are not real—they are the product of randomness and selection bias, referred to as data snooping or data mining. I’m reminded of the saying, “If you torture the data long enough, it will eventually confess.”
The problem of data mining compounds when researchers snoop without first having a theory to explain the result they expect, or hope, to find. Without a logical explanation for an outcome, one should not have confidence in its predictive ability.
“P-hacking” refers to the practice of reanalyzing data in many different ways until you get a desired result. For most studies, statistical significance is defined as a “p-value” less than 0.05—the difference observed between two groups would not be seen even one in 20 times by chance. That may seem like a high hurdle to clear to prove that a difference is real. However, what if 20 comparisons are done and only the one that “looks” significant is presented?
The problem of data mining and p-hacking is so acute that economist John Cochrane famously said that financial academics and practitioners have created a “zoo of factors.”
For example, Campbell Harvey (past editor of The Journal of Finance), Yan Liu and Heqing Zhu, in their paper “…and the Cross-Section of Expected Returns,” which was published in the January 2016 issue of The Review of Financial Studies, reported that 59 new factors were discovered between 2010 and 2012 alone. Furthermore, as reported in a May 2017 Wall Street Journal article, “most of the supposed market anomalies academics have identified don’t exist, or are too small to matter.”
To address this problem, some financial economists have argued that the hurdle for statistical significance should be raised to a p-value of less than 0.01. But there is another way to solve the issue. To minimize the risk of p-hacking, and to give investors sufficient confidence in allocating a portion of their portfolios to a factor (or any alternative investment), my co-author Andrew Berkin and I established the following criteria in our book, “Your Complete Guide to Factor-Based Investing.”
