Beyond Behavioral Biases

It's so easy to reduce Behavioral Finance to a bottomless list of biases.  As with many aspects of the financial industry, we get way too focused on labeling things instead of understanding them.

A recent article from Behavioral Scientist pointed out the limitations of defining Behavioral Economics as a series of fallacies.  (Note: I tend to use interchangeably with "Behavioral Finance" because as far as I'm concerned they're the same thing.)

I have weathered a great many presentations on behavioral economics.  I often leave with a laundry list of behavioral biases and a notable lack of actionable recommendations for what do about them.  

Here are three issues I see with reducing behavioral finance to a bunch of biases.

1) It's too simplistic.

I'm often entertained when investors make a comment like, "Interest rates are doing X, which means this bank stock should do Y."  The financial markets (much like human beings) are a complex system with many variables and unpredictable outcomes.  

Drawing a simplistic causal relationship ignores the fact that markets don't operate in a vacuum.  There are so many things (including just normal supply and demand) that can cause a stock's price to rise or fall on any given day.

Similarly, humans don't make decisions in a vacuum.  Your emotional state, your life experiences, your socioeconomic status, even what you ate for lunch can all affect your decisions.

As author Koan Smets explains:
"This focus on biases is unhelpful in several ways.  It fails to acknowledge that biases are broad tendencies, rather than fixed traits, and it oversimplifies the complexity of human behavior into an incoherent list of flaws. This leads to misguided applications of behavioral science that have little or no effect, or which can backfire spectacularly."

2) It's too descriptive.

When we focus too much on defining and categorizing biases, we leave little time to discuss what to actually do about them.  Any discipline has a theoretical side (what is it?) and a practical side (what do we do about it?), but behavioral finance has thus far remained fairly theoretical.

I've found technical analysis (a.k.a. momentum, trend-following, etc etc etc) to be an ideal toolkit with which to translate behavioral analysis into a practical set of guidelines and action steps.  This is based on the assumption that price is the best reflection of investor psychology, which is a basic premise of technical analysis.

Smets also made a great point on the issue of causality:
"A widespread misconception is that biases explain or even produce behavior. They don’t—they describe behavior. The endowment effect does not cause people to demand more for a mug they received than a mug-less counterpart is prepared to pay for one. It is not because of the sunk cost fallacy that we hang on to a course of action we’ve invested a lot in already. Biases, fallacies, and so on are no more than labels for a particular type of observed behavior, often in a peculiar context, that contradicts traditional economics’ simplified view of behavior."

I've seen a similar issue with quantitative factors and causality.  Some people think, "Stock XYZ is doing well because of the momentum factor," when in fact the factor does not cause anything!  Any factor like momentum, value, or volatility describes what has happened with stocks.  It does not cause any change in the stocks themselves.

By focusing too much on definitions, we forget to focus on what I think should be the whole point of behavioral economics: to move past the observations and think about how individuals can become better investors and better decision makers.

3) It's too negative.

As Smets wrote, "The conversation around biases is almost uniformly negative: they screw up our decision making, or undermine our health, wealth, and happiness."

Sit through any presentation on behavioral biases and it's hard to avoid becoming a bit disillusioned on how poorly humans are designed for making decisions.  It seems like any actual good decision you make involves overcoming millennia of evolution trying to prevent you from doing so.

My friend Steven Goldstein has done great work on the inherent negativity of behavioral biases, focusing instead on "behaviours and habits of highly effective and exceptional individuals who defy the odds to consistently outperform in Financial Markets."

Instead of beating people down with their bad decisions, he chooses to focus on what humans actually do well.  For example, he deals with actionable goals for performance improvement or understanding different individuals' risk appetite based on their personality types.

The Compliment Sandwich

When coaching people on communication, I tell them to use a "compliment sandwich" when delivering a tough message.

Start with a positive:
"Hey, you did a lot of things really well this year."

Then deliver the constructive feedback:
"Here are a couple things that you really need to work on."

And wrap it up with a positive:
"I'm confident you can get these things done!"

Basically you have delivered negative feedback in a way that you can feel good about and that the recipient will be motivated to address.

I humbly suggest using a compliment sa ndwich for a more positive tilt to discussions of behavioral economics...

Start with a positive:
"Humans are amazing and we do a lot of things really well."

Then deliver the bad news:
"Unfortunately there are plenty of hurdles to making good decisions."

Wrap it up nicely:
"The good news is that humans can overcome these challenges to be top performers."

As with many aspects of the financial industry, we get way too focused on labeling things instead of understanding them.

Having studied psychology as an undergrad, I've enjoyed seeing more and more people applying the concepts of behavioral psychology to the financial markets. 

I'm confident that by embracing the complexity of the human experience, focusing on practical approaches, and celebrating the positives, we can elevate behavioral finance to a discipline designed to empower people to make better decisions.