Welcome to the next instalment in our series of evidence-based investment insights; The essence of evidence-based investing. To check out the rest of the series, click here.
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 that you’ll earn the returns you’re aiming for, or even recover your stake. This leads us to why we so strongly favour evidence-based investing. Grounding your strategy in rational methodology helps you best determine and stay on a course toward the financial goals you have in mind, especially when your emotional reactions threaten to take over the wheel.
So, what does evidence-based investing entail?
Alpha, beta, and sources of returns: The essence of evidence-based investing
Since at least the 1950s, a ‘Who’s Who’ body of scholars has been studying portfolio management to answer key questions such as this critical pair:
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’ve identified a potential factor, are there rational reasons for why it’s 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 can be explained by different exposures to these return factors – no matter which individual securities were involved? In financial parlance, that’s a portfolio’s beta. How much can be explained by the portfolio manager’s stock-picking or market-timing skills? That’s their value-added alpha.
The more we know…
In 1992, 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 earned Fama a Nobel Prize in Economics in 2013).
Building on an earlier, Capital Asset Pricing Market (CAPM) model, the Three-Factor Model increased our ability to use beta to explain the differences 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.
Fama, French, and many others have since expanded on this early work. In 2014, they published A five-factor asset pricing model in the Journal of Financial Economics, which they’ve also stress-tested in a 2015 working paper looking at the same five factors in international markets.
In short, over time, the academic community has continued to study new and existing factors, and how they appear to interact and contribute to beta-generating returns. The more we understand about factor investing, the harder it has become to believe that the pursuit of extra, alpha-generated returns can add consistent value – after the costs involved and beyond what already is available through a low-cost, well-structured, evidence-based portfolio.
Or, as Fama has explained more succinctly: “Pick your risk exposure, and then diversify the hell out of it.”
We’ll explore some of the factors involved in our next section, Factors That Figure in Your Evidence-Based Portfolio. Before we do, let’s take a moment to discuss the difference between the far less frequent academic milestones vs. what can often feel like a never-ending barrage of industry ‘studies’. While both can add value, they do so in different ways.
The rigors of academic inquiry
In academia, rigorous research calls for considerably more than an arbitrary sampling or a few in-house spreadsheets. It typically demands:
A disinterested outlook – Rather than beginning with a point to prove and then figuring out how to prove it, ideal academic inquiry is conducted with no agenda other than to explore intriguing phenomena and report the results of the exploration.
Robust data analysis – The analysis should be free from weaknesses such as:
- Suspect data that is too short term, too small of a sampling to be significant, or otherwise tainted
- ‘Survivorship bias’, in which the returns from funds that were closed during the study (usually because of poor performance) are omitted from the results
- Comparing apples to oranges, such as using the wrong benchmark against which to assess a fund’s or strategy’s ‘success’ or ‘failure’
- Insufficient use of advanced mathematics like multi-factor regression, which helps pinpoint the critical factors from among an otherwise confusing, noisy mix of possibilities
Repeatability and reproducibility – Academic research requires results to be repeatable and reproducible by the author and others, across multiple, comparable environments. This strengthens the reliability of the results and helps ensure they weren’t just random luck.
Peer review – Last, but hardly least, scholars must publish their detailed results and methodology, typically within an appropriate academic journal, so similarly credentialed peers can review their work and agree that the results are sound or rebut them with counterpoints.
Financial scholar vs. financial professional
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?
In short, as in any discipline, it’s academia’s interest to discover the possibilities; it’s our interest to figure out what to do with the understanding.
This is in part why it’s important to maintain the bifurcated roles of financial scholar and financial professional, to ensure each of us are doing what we can do best in our field. It’s also why ‘studies’, ‘analysis’, ‘papers’, and other insights generated outside of academia typically require a higher level of scrutiny before we even accept the results at face value, let alone incorporate them into our investment strategies.
Your take-home
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 to investing offers the best opportunity to advance and apply well-supported findings; eliminate weaker proposals; and, most of all, strengthen your ability to build and/or preserve long-term personal wealth according to your unique goals.
Next up, we’ll continue to piece together our exploration of market factors and expected returns.
Continue exploring the rest of the evidence-based investment insights here.
