Trusting a Rules Based Strategy Isn’t Easy

by | Sep 2, 2015 | Investor Behavior, Podcasts

The NFL season is about to get underway. I know I’m anxiously anticipating next Thursday’s showdown between the Patriots and Steelers. Can you imagine either of these teams not planning for this matchup? We all know Bill Belichick will have something up his sleeve, and Mike Tomlin isn’t one to be caught flat-footed. Like football, investing also requires a disciplined game plan.

Even more integral than having a defined investment strategy is actually sticking with it. There are plenty of investment strategies that work over time, but the results aren’t pretty when they’re prematurely abandoned. If you can’t follow your investment rules, results are going to disappoint. This is especially true when the market, or a particular strategy, experiences inevitable periods of volatility or underperformance. Volatile time periods are challenging because they’re often when you’ll want to abandon an investment strategy the most. However, they also tend to be the worst possible time you could bail.

The 2015 DALBAR Quantitative Analysis of Investor Behavior shares some interesting data about the average mutual fund investor. Over the last 20 years, the average equity fund investor earned annualized returns of 5.19%. Over that same time period, the S&P 500 earned annualized returns of 9.85%. Why the gap of 4.66% between investors and the index? They don’t call it the behavior gap for nothing! The main reason the S&P 500 outperforms most mutual fund investors is because it cannot deviate from its strategy. The S&P 500 will own the 500 largest US stocks weighted by their market capitalization regardless of market conditions. These are its rules and it must abide them. We should all take a note out of its book and become that loyal to our own strategies.

We’ve been told time and time again that rules-based, statistically driven models outperform humans, but when underperformance or market volatility rears its ugly head, our brains continue to tell us that “we know better than the stupid broken model”.

Our preference for human intuition over rules-based models has been dubbed “algorithm aversion”. Hat tip to the guys of Alpha Architect for bringing an excellent paper on the subject to my attention. The paper states that despite proven research showing the superiority of evidence-based algorithms to human forecasters, people continue to show more willingness to trust an emotional human-being over a set of emotionless rules. Here’s an excellent example of this behavior from the paper:

“Imagine that you are driving to work via your normal route. You run into traffic and you predict that a different route will be faster. You get to work 20 minutes later than usual, and you learn from a coworker that your decision to abandon your route was costly; the traffic was not as bad as it seemed. Many of us have made mistakes like this one, and most would shrug it off. Very few people would decide to never again trust their own judgment in such situations. Now imagine the same scenario, but instead of you having wrongly decided to abandon your route, your traffic-sensitive GPS made the error. Upon learning that the GPS made a mistake, many of us would lose confidence in the machine, becoming reluctant to use it again in a similar situation. It seems that the errors that we tolerate in humans become less tolerable when machines make them.”

Simply put, we’re less apt to trust a system of rules than our own instincts or those of another human. This is problematic because emotionless, rules-based investment strategies have been proven to outperform most human investors. Even the S&P 500’s simplistic strategy of owning large cap US stocks outperforms, mostly because it just plays by the rules.

Trusting a rules-based strategy isn’t always going to be easy, but in the long run you’ll likely be happy you stuck with it.


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