#BehavioralFinance series: The Curious Case of Framing Bias
The framing effect is a phenomenon in which people react to a particular choice in various ways, depending on how the choice has been presented to them.
The framing effect is an example of something known as cognitive bias.
What bias, you ask.
I’m not biased, you assert.
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Truth is, biases, often subtle, often overt, often unintended and often deliberate, are an integral part of our world view. Even the most well-adjusted of us are prone to be biased about something, and this can affect the choices we make.
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Doesn’t necessarily make human beings unreasonable beings…but irrational, up to an extent.
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There are two popularly cited examples of framing bias- both lucid and illustrative:
Example 1: The Ground Beef experiment: Participants in an experiment were divided into two sets. One was given ground beef labeled 75% lean, and the other was given ground beef labeled 25% fat. They were asked to rate the products on a scale of 1–7. 75% lean got an average rating of 5.15, higher than 25% fat’s 2.83. Both labels essentially mean the same, but it’s the framing which made all the difference.
Example 2: This was devised by scientists Daniel Kahneman and Amos Tversky- the Asian disease framing experiment.
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An unusual Asian disease is purported to kill 600 people in the US. Two different programs have been planned to counter the disease.
Two alternative programs to combat the disease have been put forth.
Some experiment subjects received the proposal in a ‘survival’ format.
Program A will save 200 people.
With Program B, there’s a 1/3rd possibility that 600 people will be saved, and 2/3rd possibility that no one will be saved.
Another group of respondents received the proposal in a ‘mortality’ format.
If Program C is adopted, 400 people will die.
If Program D is adopted, there is a 1/3rd chance that nobody will die, and 2/3rd chance that everybody will die.
Needless to say, Program A and D were more popular- the ones most respondents went for. Even though B is the same as A and C is the same as D, the options were chosen because of the way they were framed.
This is illustrative of a general tendency in people- they tend to be risk-averse when exposed to a gains ( survival) format, and risk-seeking when exposed to a losses ( mortality) format.
The framing effect is a very important concept in behavioral economics and finance.
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But before we talk about that, let’s first try to understand the different kinds of framing bias.
The Different Kinds of Classifications for Framing Bias:
Levin, Schneider and Gaeth have classified framing bias into three kinds- risky choice, attribute and goal framing.
Hallanan classified framing into seven different categories, based on what is being framed: attributes, situations, choices, actions, issues, responsibility,and news.
Other types of framing biases discovered are number size framing, and framing risk in either or absolute terms.
Potential applications of framing bias in investment decision-making:
Attribute framing: A financial advisor may highlight the potentially large returns on an equity whose risk level is high, to make it attractive to certain potential investors. On the other hand, they may point to the huge risk of potential loss to be incurred on the same investment, to drive clients away from the investment.
Framing Risk in Absolute or Relative Terms: The risk of an investment might not seem that large in percentage terms, but could hit home when presented in absolute terms. For example, consider a person wanted to invest in a $4 call option on a $50 stock. An investment advisor can take two possible approaches here. A- make the risk of the option appear smaller than investing in the stock itself, by pointing out that while the investor loses only $4 on the call, they could lose more than that on the stock. B- the advisor points out that loss of $4 points out a 100% loss of investment, while investment in stock would be less likely to result in a 100% loss.
Number size framing: The theory here is that smaller numbers will appear more significant when compared with each other, than when larger numbers are compared the same way. A potential application this has in investment decision making is to see the way risk is expressed- using smaller numbers or larger numbers. For eg- if the chance of default risk of a bond increases from 5–6%, this can seem a much greater risk than when a decrease to 94% from 95% probability of obtaining promised yield from the bond. This maybe because an increase from 5 to 6% denotes a 20% yield in the chance of default, whereas the decrease from 95 to 94% denotes a 1.05% decrease in the chance of a full payout.
If used correctly, the framing bias can be an effective tool for finance companies to sell products to customers.