In our most recent blog, "What Drives Market Returns?" 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 investment.
This leads us to why we so strongly favor what is known as evidence-based investing. Grounding your strategy in a rational methodology helps you best determine your financial goals—and it helps you stay on course toward those goals even when your emotions threaten to take over.
The origins of evidence-based investing stretch back to at least the 1950s, when scholars began studying financial markets to answer key questions such as:
- What drives returns? Which return-yielding factors appear to persist over time, around the world and across a range of market conditions?
- How do the return drivers work? Once identified, can we explain why particular return-yielding factors exist, and exactly how they work?
Meanwhile, fund companies and other financial professionals seek to translate this academic inquiry into successful investments. Their job: to capture the theoretical return premium in the real world, and to preserve it even after implementation and trading costs are factored in.
In any discipline—from finance to medicine to quantum physics—it's academia's job to discover the possibilities; and it's the professionals' job to figure out what to do with the understanding. It's important to maintain the distinct roles of financial scholar and financial professional in order to ensure that each is doing what we they do best in their field.
In academia, rigorous research typically demands:
A Disinterested Outlook. Rather than beginning with a point to prove and then figuring out how to sell it, 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:
- 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.
- Apple-to-orange comparisons, such as using the wrong benchmark to evaluate a strategy's "success" or "failure."
- Insufficient use of advanced mathematics like multi-factor regression, which helps identify valuable factors within a 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 based on luck.
Peer Review. Last but not least, scholars must publish their detailed results and methodology, typically within an academic journal, so that their peers can review and their work. 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.
So, it's wise to step away from the popular financial media and take a look at academic research about the way investing really works. Wall Street's message tends to be "invest with us and we'll make you rich," in a
very short time horizon. Wall Street's profit motives also affect which research it shows clients, because the primary purpose of Wall Street research is to sell you products.
Our evidenced-based investment philosophy comes from the academic world and is based on more than 87 years of investment results, not on what happened last year or even over the past few investment cycles. Academic researchers are motivated by truth – not what sells product. Academics strive to win the Nobel Prize – not the "top producer of the year award".
In short, we firmly believe that an evidence-based approach to investing offers the best opportunity to advance and apply well-supported findings and strengthen your ability to build and preserve your wealth.