ABCs of Behavioral Biases (A to F)
Four self-inflicted biases that knock a number of investors off-course are anchoring, blind spot, confirmation and familiarity bias.
In our last piece, “The Business of Investing,” we explored how markets deliver wealth to those who invest their financial capital in human enterprise. But, as with any risky venture, there are no guarantees. For any given investment, you might not earn the returns you are aiming for, or even recover your stake. This is one reason we strongly favor evidence-based investing.
It is easier to stick with your investment selections if you have used a rational methodology for choosing them.
This is especially important during volatile periods, when your hopes, fears and other emotional reactions threaten to steer you off-course. So, what does evidence-based investing entail?
Since at least the 1950s, a “Who’s Who” body of scholars has been studying portfolio management to answer key questions such as:
1. What drives market returns (factors)? Which factors (sources of return) appear to have persisted over time, around the world and through various market conditions? Once we have identified a potential factor, are there rational reasons for why it is likely to persist moving forward? The more robust a factor appears to be, the more confidently we can tilt a portfolio toward or away from its risks and expected returns, based on personal financial goals.
2. What drives portfolio performance (alpha vs. beta)? When comparing the performance of one diversified portfolio to the next, how much of the difference is explained by different exposures to these return factors, no matter which individual securities are involved? In financial parlance, that is a portfolio’s beta. Then, how much is explained by a portfolio manager’s stock-picking or market-timing skills? That is their value-added alpha.
In 1992, professors Eugene Fama and Kenneth French published their landmark paper, “The Cross-Section of Expected Stock Returns,” in The Journal of Finance. The paper gave birth to the Fama-French Three-Factor Model, which laid the groundwork for most factor-based inquiry that has continued ever since (and helped earn Fama a Nobel Prize in Economics in 2013).
Building on Harry Markowitz’s earlier Capital Asset Pricing Market (CAPM) model, the Three-Factor Model increased our ability to use beta to explain most of the difference between different portfolios’ returns. While CAPM found market beta alone could explain around 70% of the differences, the Three-Factor Model, with three sources of beta, offered over 90% explanatory power. (Source)
In 2015, Fama and French published “A five-factor asset pricing model” in the Journal of Financial Economics, which they also replicated in a 2017 paper looking at the same five factors in international markets.
Many other financial economists have added to the ongoing conversation about new and existing investment factors. How do they interact and contribute to a portfolio’s returns? Which combination represents a “perfect” portfolio? For a thorough review, consider reading “In Pursuit of the Perfect Portfolio,” by Andrew Lo and Stephen Foerster. The authors reach a telling conclusion (emphasis ours):
“We can see that the idea of diversification of a portfolio as a means to reduce risk is universally accepted, but this may be the only thing our experts fully agree on. Even an idea as fundamental as the market portfolio, a portfolio of all assets in the global market, is only viewed as a starting point for the Perfect Portfolio by most of our experts.”
Bottom line, the more we understand factor investing, the harder it has become to believe that the pursuit of extra, alpha-generated returns can add consistent value, especially after the costs involved and beyond what already is available through the beta returns found in a low-cost, well-structured, evidence-based portfolio.
Investors who focus on beta over alpha returns can then concentrate on which factors combine into their own “perfect” portfolio, based on their personal financial goals and risk tolerances.
As Fama has succinctly suggested:
“Pick your risk exposure, and then diversify the hell out of it.”
We will explore some of the key factors involved in our next installment of this series called “Factors That Figure in Your Evidence-Based Portfolio.” But first, let’s discuss the difference between the far less frequent academic milestones that contribute to factor investing, versus a barrage of industry analyses. While both can add value, they do so in different ways.
In academia, rigorous research calls for far more than a limited look at a few in-house spreadsheets. It typically demands:
A Disinterested Outlook: Rather than beginning with a conclusion and then figuring out how to prove it, purely academic inquiry is conducted with no agenda other than to explore intriguing phenomena and report the results of the exploration.
Robust Data Analysis: An academic analysis should be free from weaknesses such as:
Repeatability And Replicability: Academic research calls for results that can be repeated and replicated by the original authors and/or others across multiple environments. For example, do the outcomes only occur in the U.S., or are the same phenomena found elsewhere? This strengthens the reliability of the results and helps ensure they were not just random luck.
Peer Review: Scholars must publish their detailed results, data, and methodology, typically within an appropriate, peer-reviewed academic journal. Where transparency is tantamount, there should be no such thing as “proprietary information.” Others need to be able to scrutinize the work, and either agree that the results are sound or rebut them with counterpoints.
Building on this level of academic inquiry, fund companies and other financial professionals are tasked with an equally important charge:
Even if a relatively reliable return premium exists in theory, can we capture it in the real world, after the implementation and trading costs involved?
As in any discipline, it is academia’s interest to discover the possibilities. It is our interest to figure out what to do with the information. For example, as technology marches on, we have been able to add to real-life portfolios certain investment factors that were initially out of reach (when appropriate for individual circumstances). Emerging countries’ stocks and bonds is one example. So are some holdings beyond traditional stocks and bonds.
This is one reason it is important to maintain the roles of financial scholar and financial professional, to ensure each of us are doing what we can do best in our fields. It is also why studies, analyses, papers and other insights generated outside of academia typically require a higher level of scrutiny before accepting the results and incorporating them into our investment strategies.
As is the case in any healthy scholarly environment, those contributing to the lively inquiry about what drives market returns are rarely of one mind. Still, when backed by solid methodology and credible consensus, an evidence-based approach offers the best opportunity to:
Next in our series, we will continue to piece together our exploration of market factors and expected returns.
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This post was written and first distributed by Wendy J. Cook.
Four self-inflicted biases that knock a number of investors off-course are anchoring, blind spot, confirmation and familiarity bias.
Legendary economist Benjamin Graham once stated that ‘your own behavioral biases are often the greatest threat to your financial well-being.’
If you file tax returns separately AND are separated from your partner, colleges consider you a two-household family.
Take some time to map out how to pay for all four years before your student gets the bill for freshman year.