Thorpe Abbotts Capital

DEEPER DIVE

Understanding the role behavioral bias plays in security mispricing lies at the heart of our investment strategy.  If we define the intrinsic value of a security as the value of its future cash flows discounted for both the time value of money and risk, then to understand how bias affects security prices we must understand how investors form beliefs about future cash flow generation, as well as their preferences regarding risk. 

The Mispricing Opportunity

Behavioral economics has shown that humans are prone to make mistakes in forming beliefs about the probabilities of future events. We also have inconsistent risk preferences. Analyzing the underlying nature of these mistakes illuminates situations where a biased forecast of the future is being discounted into security prices. Thorpe Abbotts seeks to profit from these situations.

  • Behavioral Finance
  • Wisdom Of The Crowd
  • System 1 & System 2
  • Prospect Theory
Behavioral finance lies at the intersection of psychology and finance.  It’s raison d’etre is to study and resolve inconsistencies between traditional financial theory and observed deviations from this theory in the real world. Behavioral finance recognizes that human psychology often conflicts with the rational agent model employed by financial theorists.  It recognizes that humans are not always the perfectly rational, optimizing machines required by efficient market theory and the law of one price.  In this sense it tries to explain how financial markets actually work, not how they should work. Behavioral finance is the lifeblood at Thorpe Abbotts Capital.  At a fundamental level it drives everything we do—how we search for ideas, how we research and evaluate these ideas, and most importantly, how we select ideas and manage our portfolio.   At our core we are contrarians, generating alpha by exploiting the mistakes of other investors.  Behavioral finance is our roadmap.
Ask a large group of people to independently guess the weight of a steer, the number of pennies in a fifty-gallon drum, or even the probabilities surrounding some random outcome, and you might be surprised by the accuracy of the crowd response.  If you take the average of the individual guesses, you’ll often find the result is extremely close to the actual answer.  The reason is simple: Everyone’s guess is the right answer plus an error term.  If we assume that the errors are not systematically correlated, and that they crowd is sufficiently diverse in their views, then the errors should in theory cancel out, given a large enough crowd. Problems begin to emerge, however, when the individual error terms become correlated and a diversity of opinions is lost.  When crowds no longer aggregate independent judgements—when individual beliefs verge towards one viewpoint—the wisdom of the crowd can quickly turn into madness.  Thorpe Abbotts actively searches for situations where systematic bias has taken ahold of the market’s judgement.  This is the start of our search process.  Our strategy is simple: Look for mispriced opportunities by searching for situations where the crowd’s judgement is skewed by correlated errors.
When it comes to decision making there is the easy way and there is the hard way.  System one is the easy way.  It is the quick, intuitive decision maker inside us that has been honed to survive in the wild.  While it serves us well in many instances, system 1 makes terrible investment decisions.  It is easily fooled into suboptimal behavior through a panoply of behavioral biases.  The hard way, system two, is the logical part of our decision process that a rational economic agent should use to price securities correctly.  For better or worse it is not used as often as it should be. Understanding how these systems are reflected in the market’s pricing of securities lies at the heart of the Thorpe Abbott’s investment process.  We actively seek out situations where the market’s beliefs regarding the fundamental worth of a security are being disproportionately influenced by system 1 thinking at the systematic level.
Prospect theory was first developed by the behavioral psychologists Daniel Kahneman and Amos Tversky as an alternative to traditional expected utility theory. The dominant theory in decision science, expected utility theory postulates that humans make decisions under uncertainty based on the expected utility they will derive from the alternative choices they face.  Key to the theory is the idea that utility is linked to the given levels of wealth the decision maker will experience with the alternative outcomes associated with these choices. Kahneman and Tversky argued that outcomes and utility should not be linked to absolute levels of wealth.  Rather, according to prospect theory, utility is a function of changes in wealth relative to a given reference point.  This was the key insight that drove the development of prospect theory. The logic is explained in a simple example:  If two people are both worth $10 million, expected utility theory would postulate that the psychological value of that level of wealth is the same for both, regardless of the path each took to get to $10 million in wealth.  If one person had just turned $1 million into $10 million, and the other had just turned $100 million into $10 million, utility theory stipulates that they will be equally content. Prospect theory assumes that the utility of wealth is path dependent.  Said another way, the path someone takes to arrive at a level of wealth, relative to their starting point, affects their perception of utility.  This has huge implications for asset pricing and investor behavior, given that the static utility function used in most asset pricing models fails to explain risk preferences accurately. 

HOW RELIABLE ARE OUR THINKING PROCESSES?

think-03

Simple question: A baseball and a baseball bat cost a total of $1.10. The bat costs $1 more than the ball. How much does the ball cost?

If you said $1 and $.10, unfortunately you’re wrong, as there’s only $0.90 difference, but you’re certainly not alone! The correct answer is $1.05 and $0.05 but even Yale students get this wrong.

This was System 1, as author of the seminal book ‘Thinking Fast and Slow’ Daniel Kahneman describes it, your ‘fast’ brain giving you the answer without engaging System 2 which would have done a more considered ‘fact check’ and realized the ‘gut’ response was incorrect!

This mistake is common in investing.  At Thorpe Abbotts we are big believers in the use of checklists to prevent investment decisions from falling into the same traps.

think-01

‘Thinking Fast and Slow’ author Daniel Kahneman describes how people were stopped on a London street and asked to estimate the cost of a bottle of champagne.

Prior to doing this, they had to choose a single ball from a bag of numbered balls. Unbeknownst to the participants, one bag only contained balls with the number 10 and the other with the number 65.

The people who picked out the number 10 estimated the champagne price to be around £17. The people who picked out 65 guessed at £57 for the exact same bottle! This phenomenon is known as ‘anchoring’ and is widely used by expert negotiators.

A common way this affects how investors misprice assets is in their over-reliance and anchoring on a company’s past growth rate when they forecast future cash flows. Instead of heeding the mean-reverting nature of abnormal growth rates (both high and  low), investors simply extrapolate the recent past into the future.

quiz-image

If you got the answer Zuze because your brain recognized the pattern a, e, i and o in the names 1-4 that’s System 1 in action! 

System 2 would tell you that the answer is ‘Johnny’ as the opening piece of information you’ve been given is that ‘Johnny’s father has five sons’ and then you’ve been given the other four names.

If you thought the answer was ‘Zuze’ you’ve experienced System 1 doing what it does, looking for shortcuts and recognizable patterns to help you quickly navigate your world. As you can see, it is not always correct! This puzzle appears on LinkedIn from time to time and the comments clearly demonstrate how powerful System 1 can be. 

Recognizing patterns where there are none is a key contributor to asset mispricing.  Knowing how to look for these types of situations–and exploit them–can be a powerful source of alpha generation.

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