Behavioral Finance: The Three A's - Availability, Anchoring, and Adjustment

August 27, 2014

In our last paper, we explored biases inherent to heuristics. Now we explore availability, anchoring, and adjustment - shortcuts that are rooted in the inclination to project our current frame of reference to situations where it may not be applicable.

  • Many biases are rooted in the projection of our current frame of reference to other non-applicable situations.
  • Availability is the tendency to assess frequency and probability based on our recollection.
  • Anchoring occurs when we rely on estimates derived from an initial value and then fail to make accurate adjustments.

In Behavioral Finance: Rules of Thumb and Representativeness, we explored the biases inherent to heuristics, or rules-of-thumb. Among these mental shortcuts, we isolated representativeness as a common source of potential behavioral pitfalls. The tendency to group dissimilar objects together based on shared characteristics takes place without applied thought, and can shape our expectations based on these misperceptions. Representativeness is one of several biases rooted in the inclination to project our current frame of reference to situations where it may not be applicable. 

Availability

Pioneering behavioral finance researchers Kahneman and Tversky (K&T) describe availability as the impulse to "assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind." 

For example, which of the following causes more fatalities in the United States in a year?
a) shark attacks, or 
b) airplane parts falling from the sky
and 
c) Homicide and car accidents, or 
d) Diabetes and stomach cancer

If you guessed a and c sharks and homicide or car accidents, you are in the majority, but you are also wrong1. Airplane parts falling from the sky are thirty times more likely to cause death in the U.S. than shark attacks, and diabetes and stomach cancer are twice as likely as homicide and car accidents. It is the ease by which we recall shark attacks and homicides or car accidents from news reports or sightings in our neighborhood that causes us to think otherwise.

The following paragraph contains references to publically traded companies. We selected these companies to illustrate the behavioral finance characteristics discussed in this paper and because they’re familiar to many investors. These references are for ILLUSTRATIVE PURPOSES ONLY. The references are NOT RECOMMENDATIONS to transact in any securities issued by these companies.

As with representativeness, we must actively apply slow thinking processes and seek information that requires the employment of System 2, which allocates attention to higher-level mental activities such as complex computations, rather than System 1, which relies on rules-of-thumb or shortcuts.

For a hypothetical investing example of availability, consider this question: Most great advances in the commercialization of information technology have occurred in the last two decades. If we ignore dividends, which company’s stock provided the greatest price appreciation over the last twenty years?

a) Kansas City Southern, a railroad company or
b) Microsoft, which provides the operating system for countless personal computers and servers 

By ignoring dividends, we leveled the playing field for Microsoft because it only began paying dividends in 2009. Microsoft’s stock price appreciated an astounding 1,193% during the 20-year period from August 1, 1994 through July 31, 2014. Meanwhile, Kansas City Southern appreciated 13,775%; more than 11 times as much as Microsoft2.

Perhaps many guessed in favor of Microsoft because it is considered one of the greatest growth businesses of all time, or because founder Bill Gates is one of the world’s richest men. How many people can recall the last time they bought an item that had spent time on a Kansas City Southern railcar? The availability heuristic can cause us to miss great opportunities if we fail to uncover those that do not come to mind easily.

As with representativeness, we must actively apply slow thinking processes and seek information that requires the employment of System 2, which allocates attention to higher-level mental activities such as complex computations, rather than System 1, which relies on rules-of-thumb or shortcuts (see Behavioral Finance: Rules of Thumb and Representativeness for a more thorough explanation). Using information technology, we can scan entire data sets for evidence of opportunities with which we are not familiar. Often the best investment ideas are those that investors dislike or do not know exist.

Anchoring and Adjustment

"In many situations, people make estimates by starting from an initial value and adjusting to yield a final answer." K&T wrote that an issue arises because those adjustments "are typically insufficient. That is, different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring."

For example, the financial results of most companies rise and fall over time, affecting their business valuations. Yet, investors often reference a particular value estimate, even when new evidence suggests the original estimate is no longer accurate. week high prices. Investors often anchor on and sell near the high price without sufficiently adjusting for positive news, which allows momentum investors to achieve statistically significant outperformance asweek high3.

Fundamental investors value businesses using several methods, often with ratios that compare price to other objective fundamental measures for apples-to-apples comparisons between different companies. A focus on ratios rather than current prices helps investors adjust properly and minimizes the effects of this bias. The next paper in our series, Behavioral Finance: Loss and Regret Aversion, examines subsequent behavioral investment bias discoveries. 

1 Plous, Scott. (1993). The Psychology of Judgment and Decision Making
2 Bloomberg Terminal Data
3 The 52-Week High and Momentum Investing;" George, Thomas J. and Hwang, Chuan-Yang (October 2004). Journal of Finance, pp. 2145 – 2176  

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