Preponderance of Evidence

There’s a reason we refer to our strategy for building durable, long-term wealth as evidence-based investing. There are a number of other terms we could use instead: Structured (getting warm), low-cost (definitely), passive (sometimes), smart beta (maybe), indexing (close, but) … the list goes on. But because the evidence is at the root of what we do, we believe it should also be at the root of what we call it. 

That said, there is a great deal of evidence to consider, and some purported findings seem to contradict others. How do we know which evidence to take seriously and which are false leads? 

Evidence-Based Investing: A Never-Ending Story

First, it’s worth noting that academic inquiry is never fully final, nor does it allow for absolutes in our application of it. As University of Chicago professor of finance and Nobel laureate Eugene F. Fama has said, “You should use market data to understand markets better, not to say this or that hypothesis is literally true or false. No model is ever strictly true. The real criterion should be: Do I know more about markets when I’m finished than I did when I started?” [Source]

With this caveat, there are still a number of important qualities to seek when assessing the validity of a body of academic evidence. 

A Disinterested Outlook – Rather than beginning with a point to prove, ideal academic inquiry is conducted with no agenda other than to explore intriguing phenomena and report the results. It is then up to us practitioners to apply the useful findings. 

Robust Data Analysis – The analysis should be free from weaknesses such as data that is too short-term or too small of a sampling; survivorship bias (wherein returns from funds that went under during the analysis period are disregarded); apples-to-oranges benchmark comparisons; or plain, old-fashioned faulty math. 

Repeatability and Reproducibility – Results should be repeatable in additional studies across multiple environments and timeframes. This helps demonstrate that the results weren’t just random luck or “data mining.” As AQR fund manager and founding principal Clifford Asness describes, “If a researcher discovered an empirical result only because she tortured the data until it confessed, one would not expect it to work outside the torture zone.” [Source]

Peer Review – To ensure that all of the above and more is taking place as required, scholars are expected to publish their detailed data sets, methodologies and findings in a credible academic journal or similar forum, so their credentialed peers can review their work and either agree that the results appear to be valid or refute them if they are not. 

The Alternative: Data Foolery

If our emphasis on deep and diligent peer review sounds like it doesn’t really apply to you and your tangible wealth, think again. Echoing the sentiment about lies, damned lies and statistics, when faulty conclusions are inappropriately applied, the results can send countless investors astray, with real dollars lost. 

Consider this fascinating exposé by journalist John Bohannon, “I Fooled Millions Into Thinking Chocolate Helps Weight Loss. Here’s How.” The evidence-based sting operation happened to take place within healthcare, but similar lessons apply to finance. 

Bohannon began by conducting a deliberately flawed “study” to serve as a glaring example of poorly done research. He intentionally used a paltry data set of 15 participants in a one-shot clinical trial, and then heavily tortured the resulting data to extract a technically accurate if essentially meaningless conclusion that chocolate consumption contributed to weight loss. 

Next, Bohannon mined his familiarity with the scientific publishing industry to submit his study to several journals that had questionable reputations with respect to their screening processes. Despite full disclosure of the study’s many weaknesses, Bohannon observed that his paper “was published less than 2 weeks after [our] credit card was charged.” He and his cohorts then launched an aggressive PR campaign to a global media who was apparently more interested in printing exciting sound bites than substantiating the validity of the sources involved. 

The title of his resulting exposé, tells us how the story ended. A blitz of television, Internet and print media coverage suggested to audiences around the globe that they might want to go on a chocolate diet to lose weight. Faulty evidence in, garbage conclusions out.  

The Preeminence of Peer Review 

The point is not to dismiss all evidence as bogus. The point is that substantive, meaningful peer review remains an essential component in separating real academic evidence from the wider glut of sloppy work that all too often occupies headline-grabbing news. Peer review also enables scholars to reference and build on their colleagues’ best work, which enables collective insights into important subjects to deepen and expand over time. 

That’s why the evidence that survives the gamut of academic peer review, and has withstood the test of time is the evidence that we are most interested in applying to a set of orderly (if never certain) principles to guide our practical, evidence-based investment strategies. 

The Adviser’s Essential Role: Separating Fact from Fiction

We are continuously scanning the work being presented to the public, filling in the due diligence that might have been missed, and translating the results with an eye toward helping investors appropriately view the big picture that emerges. 

Of all the ways we can go about investing, which ones are expected to best serve your personal interests and financial goals? Equally as important, which are more likely to distract or detract from our efforts? The evidence-based answers to these vital questions explain why we pay so much attention to qualities such as fund structure, cost management, patient trading, and global market exposure (beta) according to index-like asset classes. It’s also why we advise against trying to chase or flee current market trends or pick popular stocks, despite what seemingly erudite talking heads seem to forever be recommending. 

Which specific evidence has borne the most fruit over time? An exhaustive account of every meaningful contribution would be a lengthy list indeed, but it helps to be familiar with the most important insights that, in aggregate, offer investors a clearer pathway through the market’s daily twists and turns. 

For our purposes, most of the tenets underlying today’s evidence-based investment strategies originate in the 1950s with Modern Portfolio Theory, so we’ll begin there. 

Modern Portfolio Theory (MPT)

Harry Markowitz, “Portfolio Selection,” The Journal of Finance, 1952

Modern Portfolio Theory (MPT) represents one of the greatest equalizing breakthroughs in financial economics, paving the way for a radically different approach to investing. Prior to MPT, it was generally assumed that the best way to invest was to look at each security (or hire someone to do so), pick a few of the “best,” and hope you were right. For every winning trade, there had to be an equal and opposite losing one in the market’s transactional zero-sum game. This meant that individual success was a dicey proposition indeed, especially net of costs. 

MPT suggested that investors could abandon the cut-throat competition and play with rather than against the forces of the market by adopting a portfolio-wide approach to capturing returns. It introduced several, now widely accepted principles, including a strong relationship between market risk and expected returns, and the vital role that diversification plays in managing that risk. Most importantly, MPT described a way for any participant to earn market returns, simply by being patient and waiting for the market to do its thing. 

Separation Theorem 

James Tobin, “Liquidity Preference as Behavior Towards Risk,” The Review of Economic Studies, 1958

The Separation Theorem played an important role by proposing that investors could form portfolios of riskier stocks (equity), but temper that risk by offsetting it with an allocation to bonds (fixed income). Today, it’s widely assumed that a portfolio should consist of an appropriate mix of stocks and bonds reflecting the investor’s individual risk tolerances. In the 1950s, this notion was groundbreaking. 

Capital Asset Pricing Model (CAPM)

William F. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” The Journal of Finance, 1964 

How does the market set its prices? CAPM offered us some early, data-driven insights on this front. The Separation Theorem indicated that investors might be better off ignoring individual stock performance and focusing instead on their portfolio’s relative overall exposure to stocks versus bonds. This also was known as the single factor of general market risk. Sharpe expanded on the theme by analyzing newly available data on historic rates of returns. While CAPM left considerable room for additional inquiry, it established a stronger, data-driven platform from which to build. 

Efficient Market Hypothesis (EMH, or “Random Walk” Pricing)

Eugene F. Fama, “The Behavior of Stock-Market Prices,” The Journal of Business, 1965; and Paul Samuelson, “Proof That Properly Anticipated Prices Fluctuate Randomly,” Industrial Management Review, 1965 

In the mid-1960s, Samuelson, Fama and others contributed to what collectively became the Efficient Market Hypothesis. Complementing CAPM price-setting, EMH also crunched available data to determine that a security’s next price seemed effectively unpredictable – like trying to track a drunkard’s “random walk.” Instead, prices were generally established by the market’s collective wisdom in lieu of individual “smart” trades. The evidence continued to mount that investors seem better off consistently capturing wide swaths of the markets’ expected risks and rewards, instead of trying to chase individual stocks or particular market climates.

Behavioral Finance

Amos Tversky and Daniel Kahneman, Judgment under Uncertainty: Heuristics and Biases,” Science, 1974

Parallel to financial economics, behavioral finance empirically analyzes the “human factor” in factor-based investing. Tversky and Kahneman published one of the earliest inquiries in this ongoing field, describing a number of behavioral biases to which investors seem predisposed. Particularly as improved brain-imaging techniques have advanced, so too has our understanding of what is going on in the deepest recesses of our brains that may be causing us to make seemingly irrational investment decisions, to the detriment of our end returns.

The Role of Bonds/Fixed Income

Eugene F. Fama, “Term premiums and default premiums in money markets,” Journal of Financial Economics, 1986 

Turning to the bond side of the bond/stock mix, Fama and others have shed additional light on why it is usually a good idea to diversify one’s portfolio into a measure of stocks and bonds. With bonds, the primary risks – and thus sources for expected returns – include a bond’s term (its maturity date) and credit rating (the likelihood it might default on its obligations). These and other differences contribute to a relatively low correlation between stock and bond markets’ differing risks, expected returns and price movements. This in turn helps us understand why bonds are better suited to serve as a stabilizing rather than a returns-generating force within an investor’s total portfolio, offsetting stocks’ more volatile mood swings.

The Three-Factor Model

Eugene F. Fama and Kenneth R. French, “Common risk factors in the returns on stocks and bonds,” Journal of Financial Economics, 1993

In one of the most important advances beyond the single-factor model for stock pricing, Fama and French provided key, data-driven evidence indicating that three distinct market factors went further than a single factor in explaining why one stock portfolio would be expected to perform better or worse than another over time. This became known as the Three-Factor Model. The three factors include: the market factor (stocks vs. bonds), the size factor (small-company vs. large-company stocks) and the stock-price factor (value vs. growth companies, typically as measured by price-to-earning ratios). 

The Three-Factor Model has been monumental in enabling fund companies to create practical, low-cost solutions for building diversified portfolios in which holdings can be tilted toward or away from each of these factors, so investors can efficiently tailor their expected levels of risk and return. 

International Diversification

Steven L. Heston, K. Geert Rouwenhorst, and Roberto E. Wessels, “The structure of international stock returns and the integration of capital markets,” Journal of Empirical Finance, 1995

In our related paper, “Evidence on the Evidence,” we explained that one way to differentiate persistent results from fleeting patterns is whether the results are repeatable in other samples. In financial economics, this often means determining whether a factor shows up in multiple markets around the globe. This landmark study (among others) shored up existing evidence by testing a number of markets across a dozen European countries and the U.S., and finding that they shared multiple risk factors in common. The study also initiated exploration into potential benefits of diversifying not only across risk factors, but also across various global markets. 

What Does the Future Hold? 

In the latest capital market research, scholars cited here as well as the next generation of their peers have been exploring whether the Three-Factor Model should evolve into a model incorporating additional factors – such as stock-price momentum, company profitability and company reinvestment costs. Do these factors represent additional, distinct sources of expected return? If so, can we expect them to remain persistent over time? Can they be practically implemented after the costs involved? 

These are the questions unfolding even as we publish this piece. They get to the heart of why we feel an evidence-based investment approach is crucial to our own and our clients’ well-being. We begin by harnessing the most robust evidence available to us today, using the practical solutions that have been built from that evidence to help our clients invest confidently toward their long-term goals. Keeping a watchful eye on additional evidence as it emerges, we also remain vigilant to new possibilities, applying the same criteria we described in “Evidence on the Evidence” to assess their credibility. 

In this context, we believe the best way to participate in ever-volatile markets was, is and will remain those strategies and solutions that are grounded in the most durable academic evidence. While we can never promise certain success, evidence-based investing gives investors their best shot at likely success. That’s one factor we don’t see changing over time.