Distortions In Deriving Preferences

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Distortions In Deriving Preferences

Changes matter more than states. People’s preferences are especially sensitive to changes. Suppose you are asked two questions:

A: Imagine then you are richer by Euro 20,000 than you are today. Would you prefer an additional gain of 5,000 for sure or a 50–50 chance for a gain of 10,000 or nothing?

B: Imagine then you are richer by Euro 30,000 than you are today. Would you prefer an additional loss of 5,000 for sure or a 50–50 chance for a loss of 10,000 or nothing? Saturday 22 April 2023 08:38 AM

Make one choice.

Make one choice. Lecture 7

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Distortions In Deriving Preferences

Although the final outcomes in the two problems are exactly the same (€25,000 or 50/50 €20,000 or €30,000), most people choose the gamble in Question A and the sure loss in Question B. Apparently, they tend to favour the narrow framing based on gains and losses rather than the broader (and more relevant) framing based on the final wealth.

For a purely mathematical view see; can a gamble ever be right or wrong? Is there an objective way to say whether a particular bet was good value or not?

http://www.statslife.org.uk/social-sciences/1862-can-a-gamble-ever-be-right-or-wrong

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Distortions In Deriving Preferences - Loss

Aversion People’s sensitivity to losses is higher than their sensitivity to gains. Suppose you are asked the question:

Consider a bet on the toss of the coin. If heads, you lose Euro 100. What is the minimum gain, if tails, that would make you accept the gamble?

Most answers typically fall in the range from 200 to 250, which reflects a sharp asymmetry between the values that people attach to gains and loses.

Make a choice.

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Aversion One ubiquitous pattern stands out: Losses resonate more than gains.

In a wide variety of domains, people are more averse to losses than they are attracted to same-sized gains.

One of the main realms where loss aversion plays out is in preferences over wealth levels.

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Aversion What distinguishes loss aversion from conventional risk aversion is that people are significantly “risk averse” for even small amounts of money.

People dislike losing $10 more than they like gaining $11, and hence prefer their status quo to a 50/50 bet of losing $10 or gaining $11.

Tversky and Kahneman (1991) suggest that in most domains where sizes of losses and gains can be measured, people value moderate losses roughly twice as much as equal-sized gains.

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Aversion Finally, in prospect theory (which is a key concept in behavioural economics), the pain associated with a possible loss is much greater than the pleasure associated with a gain of the same magnitude. In the strip, Dilbert's garbage man clearly understands this concept much better than Dilbert. Cartoon (Kramer 2014)

Other models expressing loss aversion with a linear utility above the target and a specific concave utility below the target have been suggested (for a short and useful review see Jarrow and Zhao, 2006).

http://dilbert.com/strips/comic/2000-02-03/

http://www.psychologytoday.com/blog/markets-in-mind/201401/dilbert-does-behavioral-economics

http://dx.doi.org/10.1287/mnsc.1050.0486

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Aversion People fear losing more than they desire to win. This phenomenon, first demonstrated by Tversky and Kahneman (1991), is known as loss aversion, and it shows up everywhere.

Kahneman (2011) describes how economists Pope and Schweitzer (2011) reasoned that golf provides a perfect example of a reference point: par.

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Aversion Every hole on the golf course has a number of strokes associated with it; the par number provides the baseline for good — but not outstanding — performance. For a professional golfer, a birdie (one stroke under par) is a gain, and a bogey (one stroke over par) is a loss. The economists compared two situations a player might face when near the hole:

putt to avoid a bogeyputt to achieve a birdie

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Aversion Every stroke counts in golf, and in professional golf every stroke counts a lot. According to prospect theory, however, some strokes count more than others. Failing to make par is a loss, but missing a birdie putt is a foregone gain, not a loss. Pope and Schweitzer reasoned from loss aversion that players would try a little harder when putting for par (to avoid a bogey) than when putting for a birdie. They analysed more than 2.5 million putts in exquisite detail to test that prediction.

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Aversion They were right. Whether the putt was easy or hard, at every distance from the hole, the players were more successful when putting for par than for a birdie. The difference in their rate of success when going for par (to avoid a bogey) or for a birdie was 3.6%. This difference is not trivial. Tiger Woods was one of the “participants” in their study. If, in his best years, Tiger Woods had managed to putt as well for birdies as he did for par, his average tournament score would have improved by one stroke and his earnings by almost $1 million per season.

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Aversion These fierce competitors certainly do not make a conscious decision to slack off on birdie putts, but their intense aversion to a bogey apparently contributes to extra concentration on the task at hand.

The study of putts illustrates the power of a theoretical concept as an aid to thinking. Who would have thought it worthwhile to spend months analysing putts for par and birdie? The idea of loss aversion, which surprises no one except perhaps some economists, generated a precise and non-intuitive hypothesis and led researchers to a finding that surprised everyone — including professional golfers.

“Golf is a good walk spoiled” Mark Twain (30 November 1835 Florida - Missouri)

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Loss Aversion - Barings Bank How destructive is loss aversion? Perhaps the most

familiar case of loss aversion or “get-evenitis,” occurred in 1995, when 28-year-old Nicholas Leeson caused the collapse of his famous employer, the 233-year-old Barings Bank. At the end of 1992, Leeson had lost about £2 million, which he hid in a secret account. By the end of 1993, his losses were about £23 million, and they mushroomed to £208 million at the end of 1994 (at the time, this was $512 million). Instead of admitting to these losses, Leeson gambled more of the bank’s money in an attempt to “double-up and catch-up.”

Rogue Trader, Pub. Pathé, 1999 here also Rogue Trader: How I Brought Down Barings Bank and Shook the Financial World, Nick Leeson, Pub. Little, Brown & Company, 1996 here

https://en.wikipedia.org/wiki/Nick_Leeson

https://en.wikipedia.org/wiki/Barings_Bank

http://www.amazon.co.uk/Rogue-Trader-DVD-Ewan-McGregor/dp/B00004TIYT/ref=sr_1_2?ie=UTF8&qid=1413183680&sr=8-2&keywords=rogue+trader

http://www.amazon.co.uk/Rogue-Trader-Nick-Leeson/dp/0751517089/ref=sr_1_1?ie=UTF8&qid=1413183680&sr=8-1&keywords=rogue+trader

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Loss Aversion - Barings Bank On February 23, 1995, Leeson’s losses were about

£827 million ($1.3 billion) and his trading irregularities were uncovered. Although he attempted to flee from prosecution, he was caught, arrested, tried, convicted, and imprisoned. Also, his wife divorced him (Jordan et al. 2012).

For a review that focuses on banks rather than borrowers and associated risks see Beatty and Liao (2014). There is a very large literature on bank debt contracting, the literature is surveyed by Armstrong et al. (2010).

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Distortions In Deriving Preferences - Risk

Aversion Women are found to be more risk averse in making financial decisions than men (Donkers and Van Soest, 1999; Powell and Ansic, 1997; Weber et al., 2002).

Women also tend to own less risky assets than single men or married couples and reduce their risky assets when the number of children increases, in contrast to single men and married couples (Jianakoplos and Bernasek, 1998).

Furthermore, older people tend to take less financial risks than younger people (Jianakoplos and Bernasek, 2006).

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Aversion On the other hand, it has been shown that it was possible to teach effective risk diversification (Hedesström et al., 2006). “In 4 experiments, undergraduates made hypothetical investment choices. … In order to counteract naïve diversification, novice investors need to be better informed about the rationale underlying recommendations to diversify.”

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Aversion People’s financial history has a strong impact on their taste for risk. Malmendier and Nagel (2011) investigated whether individual experiences of macroeconomic shocks affect financial risk taking. As has often been suggested for the generation that experienced the Great Depression (1930s). Using data from the Survey of Consumer Finances from 1960 to 2007. They found that individuals who have experienced low stock market returns throughout their lives so far report lower willingness to take financial risk. They are less likely to participate in the stock market. Invest a lower fraction of their liquid assets in shares if they participate. They are more pessimistic about future share returns.

http://www.federalreserve.gov/econresdata/scf/scfindex.htm

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Aversion Exposure to economic turmoil appears to dampen people’s appetite for risk irrespective of their personal financial losses. That is the conclusion of Knüpfer et al. (2013) who suggest that labour market experiences are a natural candidate for explaining portfolio heterogeneity. In the early 1990s a severe recession caused Finland’s GDP to sink by 10% and unemployment to soar from 3% to 16%. Using detailed data on tax, unemployment and military conscription, the authors were able to analyse the investment choices of those affected by Finland’s “Great Depression”.

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Aversion The results suggest that workers who have experienced more adverse labour market conditions are significantly less likely to invest in risky assets. The decrease in risky investment is robust to controls for parental variables, family fixed effects, and cognitive ability. It is not fully attributable to the impact labour market shocks have on future income, unemployment risk, and wealth accumulation. They found that those hit harder by unemployment were less likely to own stocks a decade later. Individuals’ personal misfortunes, could explain at most half of the variation in stock ownership. They attribute the remainder to “changes in beliefs and preferences” that are not easily measured.

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Aversion Financial trauma appears to dampen people’s appetite for risk. Guiso et al. (2013) examined the investments of several hundred clients of a large Italian bank in 2007 and again in 2009 (i.e. before and after the plunge in global stock markets). The authors also asked the clients about their attitudes towards risk and got them to play a game modelled on a television show in which they could either pocket a small but guaranteed prize or gamble on winning a bigger one.

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Aversion Risk aversion, by these measures, rose sharply after the crash, even among investors who had suffered no losses in the stock market. They found that both a qualitative and a quantitative measure of risk aversion increases substantially after the crisis. After considering standard explanations, they investigated whether this increase might be an emotional response (fear) triggered by a scary experience. The reaction to the financial crisis, the authors conclude, looked less like a proportionate response to the losses suffered and “more like old-fashioned ‘panic’.” To show the plausibility of this conjecture, they conducted a laboratory experiment.

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Aversion The authors’ conclusions were reinforced by a separate test administered to a few hundred university students. About half were asked to watch a five-minute excerpt of a gruesome torture scene from a horror film. Then, the entire group answered the same questions about risk as the Italian bank’s clients. Watching the horror movie increased the students’ aversion to risk by roughly as much as the financial crisis had chastened the bank’s clients, although not among those who claimed to like horror movies. They found that subjects who watched a horror movie have a certainty equivalent that is 27% lower than the ones who did not, supporting the fear-based explanation.

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Risk Aversion – Seasonality Does the Caveman Within Tell You How to Invest? Changes in human biology acr

oss the seasons helps shape investment decisions - Psychology Today - 18 August 2014 - Lisa Kramer

Is there a rational theory of human behaviour that helps explain both the “Sell in May, then go away” effect observed in risky stock markets and an opposing seasonal cycle observed in safe Treasury bond markets. The paper by Kamstra et al. (2014 KKLW) is a bit technical, but it can be summed up easily with reference to our caveman ancestors. The study finds that the historical patterns of stock and bond returns through the seasons implies not only seasonally changing investor risk aversion, but also seasonal changes in the way investors decide between consuming now versus saving for the future.

http://www.psychologytoday.com/blog/markets-in-mind/201408/does-the-caveman-within-tell-you-how-invest



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Risk Aversion – Seasonality Many people experience severe seasonal depression

(seasonal affective disorder - SAD), during the dark seasons of autumn and winter. When people get depressed in the winter, they become more averse to financial risk. The implication is a much larger reward, on average, for investors who are willing to hold risky stock during the dark seasons. Once daylight returns, investors become much more tolerant of risk, and with droves of people heading back into stocks in the spring, the rate of return investors earn holding stocks reduces, on average, leading to lower rewards for holding risk in the spring and summer. Lo and behold, Wall Street has an adage for that: “Sell in May, and go away” (KKLW).

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Risk Aversion – Seasonality

How does an examination of our ancestors help shed light on all of this? The likelihood of our ancestors surviving another year was almost certainly enhanced by certain traits, such as willingness to save in the spring and summer so one could consume from stores of food in the autumn and winter. If our ancestors had acted like the proverbial grasshopper during the spring and summer — wildly consuming instead of judiciously saving — the odds of surviving through the next autumn and winter were certainly worsened. Thus, saving instead of consuming during the spring and summer would be the preferred behaviour for folks aiming to maximize the odds of survival. Additionally, by adopting cautious, “risk averse” habits through the autumn and winter, hunkering down until the daylight rebounded, they would increase their odds of surviving through to the spring (KKLW).

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Risk Aversion – Seasonality What are the key elements? Be aware of the way the

changing seasons can influence your mood and ultimately your behaviour. Be cautious about making important financial decisions during particularly emotional times. Recognise that what might look like a great investment idea in the summer might not feel so appropriate once the winter doldrums set in. Try to build a portfolio that can endure the seasons. Investors who “buy and hold” tend to do better on average than those who trade frequently in an attempt to outperform the market. Accordingly, consider holding investments you’ll be comfortable holding through thick and thin.Kamstra, M.J., Kramer, L.A., Levi, M.D. and Wang T. 2014 “Seasonally Varying Preferences: Theoretical Foundations for an Empirical Regularity” The Review of Asset Pricing Studies 4(1) 39-77.

http://raps.oxfordjournals.org/content/4/1/39.abstract

http://raps.oxfordjournals.org/content/4/1/39.abstract

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Risk Aversion – Seasonality Daylight-Saving Time Changes, Anxiety, & Investing. Disruptions i

n your sleep can have an impact on your portfolio - Psychology Today - 30 October 2013 - Lisa Kramer

During the first weekend of November, people will turn their clocks back an hour. The gain or loss of an hour on the clock typically translates immediately into the gain or loss of an hour of sleep. Psychologists refer to the fallout from such disruptions as “sleep desynchronosis”, with the symptoms of the changed sleep habits closely resembling jet lag. And the phenomenon can have significant consequences. For instance, psychologists have noticed that whenever we shift the clocks to or from daylight-saving time, car accident rates tend to rise, likely due to the cognitive changes that accompany disrupted sleep habits.

http://www.psychologytoday.com/blog/markets-in-mind/201310/daylight-saving-time-changes-anxiety-investing



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Risk Aversion – Seasonality Perhaps surprisingly, the phenomenon is observed

whether people are gaining or losing an hour of sleep. Several large-scale disasters have been associated with lack of sleep or disrupted sleep due to shift work, including the Exxon Valdez oil spill, the space shuttle Challenger explosion, the Three Mile Island near-meltdown, and the Chernobyl nuclear accident. Adverse effects of daylight-saving time changes can also spill over into financial markets, seemingly through increased anxiety that accompanies altered sleep patterns.

Daylight-Saving Time Changes, Anxiety, & Investing | Psychology Today

https://en.wikipedia.org/wiki/Exxon_Valdez

https://www.staff.ncl.ac.uk/mike.cox/PSY1011/ohp10.ppt#Slide%2020

https://en.wikipedia.org/wiki/Three_Mile_Island_accident

https://en.wikipedia.org/wiki/Chernobyl_disaster

https://www.psychologytoday.com/blog/markets-in-mind/201310/daylight-saving-time-changes-anxiety-investing

https://www.psychologytoday.com/blog/markets-in-mind/201310/daylight-saving-time-changes-anxiety-investing

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Risk Aversion – Seasonality In the language of financial economists, an increase

in anxiety translates into greater reluctance to bear financial risk (or greater risk aversion). If investors wake up on the Monday following the time change feeling more anxious (and more risk averse) than usual, they may be less willing to buy risky stock and could even consider selling the stocks they already own. With many investors simultaneously experiencing such a shift in their sentiment , the result is often a drop in stock markets on the Monday following the time change. Of course financial markets are impacted by many different factors (not least important of which is fundamental economic news), so naturally the stock market could go up or down following any given daylight-saving time change (Kamstra et al. 2000, Pinegar 2002 and Kamstra et al. 2002).

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Risk Aversion – Seasonality The study (references follow) considers stock market

returns from four countries, some of which change the clocks on different dates. They found the market downturn associated with daylight-saving time changes amounted to a single-day loss of $31 billion in US markets, on average.

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Risk Aversion – Seasonality The finding does not imply ordinary investors should

do anything drastic in preparation for the time change. I am definitely not counselling anyone to time their purchase or sale of securities in accordance with the time change. In fact, experience tells us the best way to navigate through our emotions in the context of investing is to avoid making important decisions whenever feelings come in to play. Making investing decisions during emotional times often results in suffering dramatic financial consequences. Think of all the people who panicked during the recent financial crisis, selling just when their fears – and markets – were at their worst. Many of them ended up liquidating their assets at steeply discounted prices. Had they just sat tight and stuck to a buy and hold approach, they would eventually have noticed their stocks resuming their previous values.

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Risk Aversion – Seasonality At the best of times, people can be forgiven for

feeling a sense of anxiety when it comes to investing. Emotions can ride even higher than usual under some conditions, including times of sleep disruption such as those associated with daylight-saving time changes. By remaining calm and avoiding impulsive, emotion-driven investment decisions, there is no need to lose sleep over the market. Kamstra, M.J., Kramer, L.A. and Levi, M.D. 2000 “Losing sleep at the market: The daylight saving anomaly” American Economic Review 90(4) 1005-1011.Pinegar, J.M. 2002 “Losing sleep at the market: Comment” American Economic Review 92(4) 1251-1256.Kamstra, M.J., Kramer, L.A. and Levi, M.D. 2002 “Losing sleep at the market: The daylight saving anomaly: Reply” American Economic Review 92(4) 1257-1263.

http://www.jstor.org/stable/117321



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Or Is It A Gut Reaction?In new research (Thaiss et al. 2014), the microbes in the faeces of humans and mice were analysed, and it was discovered that gut microbes follow a rhythmic pattern throughout the day. The cycle depends on eating habits and the circadian cycle of the human or mouse.

The microbes were disrupted when the mice were exposed to an abnormal eating schedule and changes in their exposure to light and dark, the study found. In two people who suffered from jet lag, certain types of bacteria became more common. The germs are linked to obesity and problems in the body's metabolic system, according to the researchers.

Jet lag can cause obesity by disrupting the daily rhythms of gut microbes - ScienceDaily - 16/Oct/2014

Jet lag doesn’t just leave travellers feeling rotten – it is also being blamed for making them fatter - Telegraph - 16/Oct/2014

http://www.sciencedaily.com/releases/2014/10/141016123522.htm

http://www.sciencedaily.com/releases/2014/10/141016123522.htm

http://www.telegraph.co.uk/travel/11168554/Jet-lag-doesnt-just-leave-travellers-feeling-rotten-it-is-also-being-blamed-for-making-them-fatter.html

http://www.telegraph.co.uk/travel/11168554/Jet-lag-doesnt-just-leave-travellers-feeling-rotten-it-is-also-being-blamed-for-making-them-fatter.html

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Or Is It A Gut Reaction?

Thaiss et al. “Transkingdom Control of Microbiota Diurnal Oscillations Promotes Metabolic Homeostasis”, Cell, 2014.

http://www.sciencedirect.com/science/article/pii/S0092867414012367

http://www.sciencedirect.com/science/article/pii/S0092867414012367

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Risk Aversion – A Counter

Previous studies of loss aversion in decisions under risk have led to mixed results. Losses appear to loom larger than gains in some settings, but not in others. A paper by Ert and Erev (2013) highlighted six experimental manipulations that tend to increase the likelihood of the behaviour predicted by loss aversion.

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These manipulations include:

1 framing of the safe alternative as the status quo; 2 ensuring that the choice pattern predicted by loss aversion maximizes the probability of positive (rather than zero or negative) outcomes; 3 the use of high nominal (numerical) payoffs; 4 the use of high stakes; 5 the inclusion of highly attractive risky prospects

that creates a contrast effect; 6 the use of long experiments in which no feedback is provided and in which the computation of the expected values is difficult.

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Their results suggest the possibility of learning in the absence of feedback: The tendency to select simple strategies, like “maximize the worst outcome” which implies “loss aversion”, increases when this behaviour is not costly. Theoretical and practical implications are discussed (Ert and Erev, 2013).

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Distortions In Deriving Preferences - Endowment

EffectLoss aversion is related to the striking endowment effect identified by Thaler (1980, 1985).

Once a person comes to possess a good, they immediately value it more than before they possessed it.

How would you feel if you lost everything you owned, even if you were financially compensated? Like part of you had died? Or liberated? Examine the psychology of our lifelong relationship with objects (Jarrett, 2013).

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EffectKahneman et al. (1990) tested the endowment effect in a series of experiments, conducted in a classroom setting.

In one of these experiments a decorated mug (retail value of about $5) was placed in front of one third of the seats after students had chosen their places.

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EffectAll participants received a questionnaire.

The form given to the recipients of a mug (the “sellers”) indicated “You now own the object in your possession. You have the option of selling it if a price, which will be determined later, is acceptable to you. For each of the possible prices below indicate whether you wish to (x) Sell your object and receive this price; (y) Keep your object and take it home with you.”

The subjects indicated their decision for prices ranging from $0.50 to $9.50 in steps of 50 cents.

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EffectSome of the students who had not received a mug (the “choosers”) were given a similar questionnaire, informing them that they would have the option of receiving either a mug or a sum of money to be determined later.

They indicated their preference between a mug and sums of money ranging from $0.50 to $9.50.

The choosers and the sellers face precisely the same decision problem, but their reference states differ.

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EffectThe choosers face a positive choice between two options that dominate (their reference state).

The sellers must choose between retaining the status quo (the mug) or giving up the mug in exchange for money.

Thus, the mug is evaluated as a gain by the choosers, and as a loss by the sellers.

Loss aversion entails that the rate of exchange of the mug against money will be different in the two cases.

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EffectIndeed, the median value of the mug was $7.12 for the sellers and $3.12 for the choosers in one experiment, $7.00 and $3.50 in another. The difference between these values reflects an endowment effect, which is produced, apparently instantaneously, by giving an individual property rights over consumption goods.

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EffectThe behaviour described here is usefully conceptualised as a case of loss aversion comparable to that identified in choice among lotteries.

Individuals who are randomly given mugs treat the mug as part of their reference levels or endowments, and consider not having a mug to be a loss, whereas individuals without mugs consider not having a mug as remaining at their reference point.

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Distortions In Deriving Preferences - Status Quo

BiasAs established by Knetsch and Sinden (1984), Samuelson and Zeckhauser (1988), and Knetsch (1989), a comparable phenomenon - the status quo bias - holds in goods choice problems.

Here, loss aversion implies that an individual's willingness to trade one object for another depends on which object they begin with: Individuals tend to prefer the status quo to changes that involve losses in some dimensions, even when these losses are coupled with gains in other dimensions.

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BiasKnetsch and Sinden (1984) and Knetsch (1989), for instance, demonstrated the status quo bias by randomly giving one set of students candy bars, and the remaining students decorated mugs.

Later, each student was offered the opportunity to exchange their gift for the other one - a mug for a candy bar or vice versa.

90% of both mug-owners and candy-owners chose not to trade.

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BiasBecause the goods were allocated randomly and transaction costs were minimal, the different behaviour for the two groups of subjects must have reflected preferences that were induced by the allocation.

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BiasOther experiments have shown that the more choices you are given, the more pull the status quo has. More people will, for instance, choose the status quo when there are two alternatives to it rather than one: For example A and B instead of just A.

Why? Choosing between A and B requires additional effort; selecting the status quo avoids that effort.

In business, where sins of commission (doing something) tend to be punished much more severely than sins of omission (doing nothing), the status quo holds a particularly strong attraction.

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Distortions In Deriving Preferences - Choice

Lipowski (1970) call this problem an approach-approach conflict: faced with enticing options, you find yourself unable to commit to any of them quickly. And even when you do choose, you remain anxious about the opportunities that you may have lost: maybe the “grass is greener”. He maintains that it is specifically the overabundance of attractive alternatives, aided and abetted by an affluent and increasingly complex society that leads to conflict, frustration, unrelieved appetitive tension, more approach tendencies and more conflict — a veritable vicious cycle. That cycle, in turn, likely has far-reaching and probably harmful effects on the mental and physical health of affected individuals.

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Distortions In Deriving Preferences - Choice

Iyengar and Lepper (2000) revived the idea of conflict created by an overabundance of choice — a concept called the paradox of choice (Schwartz 2004) and focusing on the concept of cognitive demands. When shoppers had to choose between jams or chocolates, it was found, they were more likely to make a selection when faced with six choices than when presented with twenty-four or thirty. They were also more satisfied with their ultimate selection. Too much choice would reduce motivation. That could be because an abundance of options may simultaneously attract and repel choice-maker, an emotion-based explanation. In a series of imaging studies Shenhav and Buckner (2014) observed students making various choices when inside an fMRI scanner. They found that given more “good” choices, makes you feel more anxious.

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Status Quo Bias - Personal C Current Bank Account A related study by the Office of Fair Trading (2008) examined the psychology of personal current bank account usage, with a focus upon account switching and bank charges.

Each year, only a small fraction of people switch current accounts and the overall switching rate is low. Each year, a large proportion of people incur bank charges.

Six psychological factors that may affect switching and charge-incurring behaviour were considered.

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Status Quo Bias - Personal C Current Bank Account

BiasKey findings include:

1. Interviewees believed that, when switching, payments may be missed and were averse to possible practical losses. The suggestion is that people are not concerned about general financial loss during switching, but more concerned about the inconvenience and hassle of losing features or having to correct missed payments.

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Status Quo Bias - Personal C Current Bank Account Key findings include:

2. However interviewees' perceptions of their control over switching were high, suggesting that, despite problems, they believed that they could switch as long as they had the necessary resources and ability to change and were not limited by outside forces.

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3. Interviewees showed a strong focus on the here-and-now at the expense of a future orientation, and may constantly defer taking action to switch. In order for a person to take action, the expected future gain received will need to be financially much larger than the future consequence.

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4. Interviewees were overconfident in their financial management, and underestimated the likelihood that they would become overdrawn and be charged. This overconfidence means that people probably underestimate the cost of banking and are more optimistic about the cost of banking. This optimism could result from psychological overconfidence in one's own abilities, but it may also result from a failure to correctly identify unpredictable outside consequences that may cause one to become overdrawn.

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5. Interviewees reported spending little time thinking about their finances.

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6. Perceptions of charges showed that, in principle, the existence of charges were not viewed as particularly unfair, with more favourable perceptions associated with increased awareness and advance warning of charges.

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The UK's 46 million current account holders will be able to switch banks in seven days from next month.

After two years of preparation, the Payments Council has confirmed a new switching service will start on 16 September 2013. (The Payments Council was an organisation of financial institutions in the United Kingdom, that set strategy for UK payment mechanisms from 2007 until 2015.)

Until now, transferring an account to a new provider has taken up to 30 days.

In anticipation of the new scheme, two banks are already offering customers an incentive of up to £125 to switch their current accounts to them.

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Watchdog tells banks to work harder for customers - FT - 22 Oct 2015

Banks in the UK will be forced to guide customers to rival current accounts in an attempt to stoke competition, but will not have to abandon free banking for people in credit or face being broken up.

The competition watchdog said on Thursday that the largest lenders — Lloyds Banking Group, Barclays, Royal Bank of Scotland and HSBC — must alert customers to switch to another current account provider in situations such as a branch closure or technology glitches.

http://www.ft.com/cms/s/0/9c09d722-782e-11e5-933d-efcdc3c11c89.html#axzz3pHGaRo2A

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/470032/Banking_summary_of_PFs.pdf

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Status Quo Bias - Personal C Current Bank Account The account switching service is being launched following a recommendation from the Independent Commission on Banking two years ago, which said that people only changed bank accounts once every 26 years on average.

BBC News - Bank account switching service to launch in September 16 August 2013

http://www.bbc.co.uk/news/business-23700012

http://www.bbc.co.uk/news/business-23700012

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Customers reluctant to ditch big four UK banks - FT - 3 Oct 2015

About 16 per cent would likely switch to a so-called challenger bank, such as Metro or TSB, within the next two years, up from 13 per cent last year, according to research by law firm Pinsent Masons and YouGov.

But three-quarters still expect to be banking with a traditional high street bank in two years’ time, undermining attempts to inject competition in the sector.

http://www.ft.com/cms/s/0/1076be76-813b-11e5-8095-ed1a37d1e096.html?ftcamp=crm/email/2015113/nbe/PersonalFinance/product#axzz3qPurY0hd

http://www.ft.com/cms/s/0/5c227696-73fb-11e5-bdb1-e6e4767162cc.html#axzz3qKFQsZ2i

http://www.pinsentmasons.com/en/media/press-releases/2014/banks-face-greatest-challenge-from-paypal-says-research/

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Status Quo Bias - AvoidanceHammond et al., 2006

1. Always remind yourself of your objectives and examine how they would be served by the status quo. You may find that elements of the current situation act as barriers to your goals.

2. Never think of the status quo as your only alternative. Identify other options and use them as counterbalances, carefully evaluating all the pluses and minuses.

3. Ask yourself whether you would choose the status-quo alternative if, in fact, it weren't the status quo.

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Status Quo Bias - Avoidance4. Avoid exaggerating the effort or cost involved

in switching from the status quo.

5. Remember that the desirability of the status quo will change over time. When comparing alternatives, always evaluate them in terms of the future as well as the present.

6. If you have several alternatives that are superior to the status quo, don't default to the status quo just because you're having a hard time picking the best alternative. Force yourself to choose.

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Status Quo Bias – Luke’s Axiom Of Choice

Luce’s choice axiom (Luce 1959 and 1977) is a theory of individual choice behaviour that has proven to be a powerful tool in the behavioural sciences for over 50 years. Luce’s choice axiom is grounded in two fundamental properties: choice is probabilistic and the probability of choosing an option from one set of alternatives is related to the probability of choosing the same option from a different set.

The probability of selecting one item over another from a pool of many items is not affected by the presence or absence of other items in the pool. Selection of this kind is said to have “independence from irrelevant alternatives”.

For instance do you usually choose the same meat (chicken chow mein or tandoori chicken) from the menu?

http://store.doverpublications.com/0486441369.html

http://dx.doi.org/10.1016/0022-2496(77)90032-3

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Status Quo Bias – In Marketing

Luce (1998) conducted a series of experiments which created purchasing decisions that pitted important values against each other. The more vivid she made these decisions for her subjects, the more intense the negative emotions that were aroused. Luce discovered that as these negative emotions became more intense, subjects' choice of the status quo alternative became more common.

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Everyone in sales and marketing is familiar with the status quo alternative. That's the choice customers make when they decide not to purchase anything at all and “make do” with what they have, e.g., get a few more miles out of the old Chevy or wear last year's bathing suit one more year. The attractiveness and popularity of the status quo alternative is a curse which bedevils salespeople, and we call it the status quo bias.

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Salespeople attribute many causes for the status quo bias, but Luce's experiments showed that people select the status quo primarily to relieve negative emotions - emotions aroused by competing values in the decision. These experiments also showed that negative emotions can be lessened if people are reminded that a status quo alternative is available.

Ordinarily, the only time salespeople call attention to the status quo alternative is to subtly disparage it, thereby hoping to discourage its selection, but Luce's experiments suggest this is a mistake.

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Luce points out that there are actually three status quo alternatives in most choice situations:

1) “make do” with what you have, 2) continue to search, and 3) select the dominant alternative.

The key here is in that third choice.

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Consider, for example, a car salesperson who recognises negative emotions building in a customer who is trying to resolve the safety versus economy conflict. This salesperson could call attention to the status quo alternative of the dominant choice by saying: “Most people resolve this problem by selecting model ABC. It offers the most popular balance of economy and safety.”

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The immediate effect of this tactic should be to reduce the negative emotions customers are experiencing, and Luce's research also reveals that it makes this choice more common.

Salespeople lose credibility when they use their knowledge of psychology to manipulate and exploit customers, but they gain credibility when they use this same knowledge to help customers make difficult decisions. Calling attention to all three status quo alternatives seems to fall into this latter category.

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Status Quo Bias – Utility Function

Knetsch (1989) experimentally demonstrates that such preferences can be usefully captured by utility functions defined over reference levels as well as consumption levels. The most prominent result is loss aversion - the observation that a loss is given greater value than a gain of an equal size - resulting in the S-shaped utility function (Dacey 2003), for reviews see Camerer (1995) and Rabin (1998).

In reality what is utility? If you have a bad back, would you bend down to pick up a penny? What about £50!

Consider the following rather stylised graph.

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The loci of points, IA and IB, represent indifference curves for bundles of Goods 1 and 2 (say apples and pears) for a consumer at, respectively, reference point A and reference point B.

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Such indifference curves capture the status quo bias, because they imply that the consumer strictly prefers A over B if they are at A.

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Such indifference curves capture the status quo bias, because they imply that the consumer strictly prefers B over A if they are at B.

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More generally, they will tend to prefer bundles that avoid losses of any goods; thus, they will prefer X over Y if they are at A.

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More generally, they will tend to prefer bundles that avoid losses of any goods; thus, they will prefer Y over X if they are at B.

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Distortions In Deriving Preferences - Probability

WeightingWith respect to the expected utility paradigm, people tend to weigh probabilities differently. Suppose you are asked:

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WeightingGiven a chance for a gain of Euro 20,000, would you pay more to raise the probability of a gain from 0% to 1%, from 41% to 42%, or from 99% to 100%?

While expected utility predicts that the answer should be the same, most people would pay significantly less for raising the probability to 42%.

In particular, low probabilities are over weighted: people tend to find a 1% chance of winning Euro 1,000 preferable to a sure Euro 10.

Your choice?

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WeightingEmpirical studies have shown that decision makers do not usually treat probabilities linearly. Instead, people tend to overweight small probabilities and underweight large probabilities. One way to model such distortions in decision making under risk is through a probability weighting function (Gonzalez and Wu 1999, Prelec 1998, Rachlin et al. 1991, Stewart et al. 2014).

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Distortions In Deriving Preferences - Diminishing

SensitivityIn addition to loss aversion, another important feature of how people assess departures from their reference levels is that they have diminishing sensitivity - the marginal change in perceived well being is greater for changes that are close to one’s reference level than for changes that are further away.

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SensitivityAs with loss aversion, Kahneman and Tversky (1979, p. 278) argue that diminishing sensitivity reflects a more fundamental feature of human cognition and motivation: Many sensory and perceptual dimensions share the property that the psychological response is a concave function of the magnitude of physical change (see definition and later slide) .

Definitions Slide

http://www.staff.ncl.ac.uk/mike.cox/PsyFin/definitions.pdf

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SensitivityFor example, it is easier to discriminate between a change of 3º and a change of 6º in room temperature, than it is to discriminate between a change of 13º and a change of 16º.

We propose that this principle applies in particular to the evaluation of monetary changes.

Thus, the difference between a gain of 100 and a gain of 200 appears to be greater than the difference between a gain of 1,100 and a gain of 1,200.

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SensitivitySimilarly, the difference between a loss of 100 and a loss of 200 appears greater than the difference between a loss of 1,100 and a loss of 1,200, unless the larger loss is intolerable.

Thus, we hypothesize that the value function for changes of wealth is normally concave above the reference point and often convex below it. That is, the marginal value of both gains and losses generally decreases with their magnitude.

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SensitivityThus, we hypothesize that the value function for changes of wealth is normally concave above the reference point and often convex below it. That is, the marginal value of both gains and losses generally decreases with their magnitude.

For those interested in evaluating this function for “real” individuals, refer to Swalm (1966) Booij and van de Kuilen (2009).

It is suggested that risk preference is related to skewness (the third standardised moment) and aversion to kurtosis (the fourth standardised moment) (Ebert, 2013).

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SensitivityIt is suggested that risk preference is related to skewness (the third standardised moment) and aversion to kurtosis (the fourth standardised moment) (Ebert, 2013). Hedge funds returns often exhibit high negative skewness and positive excess kurtosis (Zakamouline and Koekebakker, 2009 ).

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Sensitivity

Diminishing marginal utility

of wealth EU of the gamble < utility of a certain wealth Pref er certain income than uncertain (risky) income with the same expected

value

I ncreasing marginal utility

of wealth EU of the gamble > utility of a certain wealth Pref er uncertain income than certain income with the same expected value

Constant marginal utility of

wealth EU of the gamble = utility of a certain wealth I ndiff erence between certain income than uncertain income with the

same expected value

EU – expected utility

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SensitivityPeople behave as if the subjective value of an amount is determined, at least in part, by its rank position in the set of values currently in a person’s head.

So, for example, $10 has a higher subjective value in the set $2, $5, $8, and $15 because it ranks 2nd, but has a lower subjective value in the set $2, $15, $19, and $25 because it ranks 4th.

This suggestion — that subjective value is rank within a sample — is consistent with Parducci’s (1965, 1995) range-frequency model of magnitudes.

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SensitivityBooij and van de Kuilen (2009) present an experiment that completely measures the utility - and loss aversion component of risk attitudes, using a representative sample of N = 1935 respondents from the general public, in a parameter-free way. The study is valid under (cumulative) prospect theory, does not depend on prior assumptions about the underlying functional form of utility, is externally valid, and does not rule out heterogeneity of individual preferences. The results confirm the concave–convex pattern of utility as predicted by prospect theory, suggest that utility curvature is less pronounced than suggested by classical utility measurements, and show that women are significantly more loss averse than men.

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Sensitivity

Where low/high stimuli reflect the financial levels of the decision the participants had to make.

See the next slide for clearer plots

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What Might Utility Look Like?

The Rieskamp (2008) experiement, 180 trials on 30 individuals.

My interpretation

https://www.staff.ncl.ac.uk/mike.cox/PsyFin/case22.xls

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These are my numerical solutions to multiple choice experiments and may not always be a feasible solution (see case 22 on the module web site).

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For more details see Rieskamp, J. 2008 “The probabilistic nature of preferential choice”, Journal Of Experimental Psychology. Learning, Memory, And Cognition, 34, 1446-65 also refer to Abdellaoui, M. 2000 “Parameter-free elicitation of utility and probability weighting functions” Management Science 46(11) 1497-1512 and later works by this author.

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What Might Utility Look Like?However, beware “Thus, the data imply that despite their historical importance and incorporation in many psychological and economic decision theories, the most widely assumed models of utility are incorrect” (Kirby 2011). For a nice, relatively simple, mathematical introduction see Špirková(2013), who aims to calibrate utility using a short questionnaire. For a theoretical account of the origin of the shapes of utility functions, see Stewart et al. (2014).

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What Might Utility Look Like?Malul et al. (2013) describe three different experiments that explore participants’ risk attitude. When they analysed the average results, they found that participants behave as the S-shape value function predicts. However, breaking the data down on the individual level reveals that the S-shape is valid just for about one-third of the cases.

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What Might Utility Look Like?In the first experiment, they used lotteries with different stakes and found that in the high stake only 31% of the participants behave as the S-shape value function predicts. The percentage decreases to 16% when the stakes were lowered. In the second experiment, they used a prepayment mechanism to create a more realistic experimental environment. In this case, 37% of the participants behaved consistently with the S-shape value function. In the third experiment, they used allocation tasks. The results revealed that most subjects could not be classified into one of the classical risk attitude groups. More than one value function is needed to characterize individuals’ attitudes toward risk.

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What Might Utility Look Like?There might also be wider applications, Hsu and Vlaev (2014) measured utility curves for the hypothetical monetary costs as a function of time engaged in three everyday physical activities: walking, standing, and sitting. They found that activities requiring more physical exertion resulted in steeper discount curves, i.e., perceived cost as a function of time. They also examined the effects of gain versus loss framing (whether the activity brought additional rewards or prevented losses) as well as the effects of the individual factors of gender, income, and BMI. The results also demonstrate a general method for examining the costs of effort associated with everyday activities.

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What Might Utility Look Like?It has been said “Essentially, all models are wrong, but some are useful” G.E.P. Box (Box, G.E.P., and Draper, N.R., 1987 page 424).

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Cumulative Prospect Theory

For many years this has been a useful descriptive model. In recent years a more complex alternative has been introduced (Tversky and Kahneman, 1992) now called the Cumulative Prospect Theory. Much recent work has investigated this problem; in particular obtaining fits to experimental data.

See case 22 on the module web site.

https://www.staff.ncl.ac.uk/mike.cox/PsyFin/case22.xls

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Distortions In Deriving Preferences -

AltruismSimple altruism does parsimoniously capture important phenomena, and is psychologically valid in many contexts.

But there is a mass of psychological evidence - and, more recently, experimental economic evidence – that indicates it is often an importantly wrong model of social preferences.

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AltruismTo get a sense for how social preferences differ from simple altruism, I turn to a (far from complete) account of what can be called “behavioural distributive justice”: How do individuals choose to divide resources among themselves and others?

There are two aspects to this question: First, what do people, when disinterested, feel are proper rules for allocation? Second, to what degree do people sacrifice self-interest for the sake of these principles?

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AltruismTo address the question of disinterested assessments, suppose two people together find $10 on the ground.

How would the average person, in their role as a third party, decide to split the money between the two?

One answer, following from the simple-altruism perspective, is that the person who is poorer, or who can otherwise benefit most from the money, should get it.

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AltruismThere is no doubt that people often consider comparative needs in allocation decisions.

There are similar norms about how to allocate goods whose usefulness is different for the two parties: We often find it appealing to allocate goods to maximize the benefit of each good.

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Distortions In Deriving Preferences - Reciprocity and

AttributionThe previous subsection (see extend example 3) considered evidence about social preferences defined over the allocations of goods.

Psychological evidence indicates, however, that social preferences are not merely a function of consumption levels, or even changes in consumption levels: People do not seek uniformly to help other people, nor do they seek uniformly to share equitably.

https://www.staff.ncl.ac.uk/mike.cox/PsyFin/ohp3.pdf

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AttributionRather, people’s concerns regarding the experiences of other people depend on the behaviour, motivations, and intentions of these other people.

The same people who are altruistic towards deserving people are often indifferent to the plight of undeserving people, and often motivated to hurt those whom they believe to have behaved egregiously.

If somebody is being nice to you or others, you are inclined to be nice to him. If somebody is being mean to you or others, you are inclined to be mean to them.

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AttributionThis “reciprocal” nature of preferences manifests itself in the distinction between simple altruism, as outlined earlier, and reciprocal altruism.

Consider the question of why people conserve water during a drought. Clearly they perceive that conservation contributes to the general good, which at a small cost is something they eagerly do. How might we model such preferences?

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AttributionFirst note that probably there are diminishing social benefits of conservation because the marginal social value of water is greater the less water there is.

If other people conserve, it is less urgent for you to do so; if other people don’t conserve, it is more urgent for you to do so.

If you were a simple altruist, therefore, learning that others were not conserving should cause you to intensify your conservation efforts — if nobody else conserves, it becomes all the more urgent that you do.

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AttributionThis prediction does not accord with actual sentiments toward cooperation. People are more inclined to conserve energy or water if they think other people are doing their share, but not if they think that others are not doing their part. People reciprocate public spiritedness in others rather than counteract it.

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More On Altruism

Almost 10 million NHS admissions in England related to alcohol last year - Mirror - 15 Oct 2014

Heavy boozers are putting the NHS under “intolerable strain” and risk sparking a health crisis which will cost the country billions.

Alcohol Concern said 9.9million NHS admissions in England – including hospital patients and clinic and A&E visits – were related to alcohol last year.

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More On Altruism

Some 9.6 million people are now drinking in excess of Government guidelines - including 2.4 million who are classed as “high risk”, according to the charity.

High risk drinking is defined as people who drink more than six to eight units of alcohol a day, with one unit equating to less than a small glass of wine or a half pint of beer.

The charity’s chief executive, Jackie Ballard, said: “The NHS is now facing an intolerable strain from alcohol-related illnesses."

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Distortions In Deriving Preferences – Prisoner’s

DilemmaA scenario where cooperation and trust wins and blind pursuit of self-interest loses, is illustrated by the problem faced by two accomplices locked in separate cells. Each is offered three choices by the police:

(1) if both confess to the charges, both will be jailed for five years

(2) if only one confesses, he will be freed but the non- confessor will be jailed for twenty years

(3) if neither confesses, both will be tried for a minor offence and will be jailed for one year

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Dilemma

vvvvvvvvvvvvvvvv

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DilemmaIf both know that the other will not be selfish and will take the collective interest into consideration, neither will confess and serve one year in jail.

Otherwise, where one cannot depend on the other, both have no choice but to confess and serve five years. It is an example of non-zero sum game.

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AttributionThe results of a competition to devise a computer programme capable of optimally solving the prisoner’s dilemma problem are reported (Axelrod and Hamilton 1981, Axelrod 1984). The winning programme always initially uses a co-operative strategy until the opposing player defects and then retaliates until the opponent co-operates again. Their “model is based on the more realistic assumption that the number of interactions is not fixed in advance. Instead, there is some probability, that after the current interaction the same two individuals will meet again. Factors that affect the magnitude of this probability of meeting again include the average lifespan, relative mobility, and health of the individuals.”

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AttributionThis ‘tit-for-tat’ strategy turns out to be optimal almost whatever the opponent does (Hertwig and Todd, Gigerenzer 2007 and Gigerenzer 2008). If my opponent is continually uncooperative he gets one period of grace before my retaliation kicks in. If my opponent always co-operates so will I. Interestingly the strategy that performed worst is the most seemingly ‘‘sophisticated’’ invoking learning and probability distributions to constantly update behaviour.

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AttributionThe most direct evidence of reciprocal altruism in the prisoner’s dilemma with which I am familiar comes from Shafir and Tversky (1992).

When subjects were told that their anonymous partner in a Prisoners’ Dilemma had cooperated, 16% also cooperated; when subjects were told that their partner did not cooperate, only 3% cooperated.

This idea that people are motivated to retaliate when they feel they have been mistreated is a fairly obvious intuition, and well understood by psychologists.

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AttributionMore recently, it has been widely explored by experimentalists who have investigated many variants of the “ultimatum game” (Mousazadeha and Izadkhahb 2015).

The ultimatum game consists of two people splitting some fixed amount of money according to the following rules: A Proposer offers some division of (say) $10 to a Decider. If the Decider accepts, they split the money according to the proposal. If the Decider rejects, they both get nothing.

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AttributionThe result of rational self-interest is clear: Proposers will never offer more than a penny, and the Decider will accept any offer of at least a penny.

Experiments clearly refute such behaviour: Even in one-shot settings, Deciders are willing to punish unfair offers by rejecting them, and Proposers tend to make fair offers.

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AttributionThe decision by Proposers to make fair offers can come from at least two motivations:

Proposers themselves may have a preference for being fair,

or self-interested Proposers might correctly predict that Deciders will retaliate against unfair offers by rejecting them.

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AttributionCriminals show cooperation and prosocial behaviour in economic games. It's easy to demonise people who have broken the law. However, recent studies using economic games that test fairness and cooperation show that this is short-sighted. Researchers observed prisoners' performance on a famous game known as the "prisoner's dilemma" (Khadjavi and Lange 2013) - the convicted criminals actually displayed more cooperation during the game than undergraduate students. Similarly, another study (Birkeland et al. 2014) found that people with a criminal record displayed just as much "prosocial motivation" (i.e. they distributed money fairly) in the "dictator game" (see below) as those without such a record.

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AttributionFor those who wish to put the problem into practice the Betrayer's Banquet is an evening of theatrical dining crossed with the prisoner's dilemma. Mind you at around £100 a ticket it is not a cheap experience.

How the prisoner's dilemma changes diners' etiquette - physics-math - 26 September 2013 -

New Scientist

Betrayers' Banquet

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AttributionIn a similar vein consider the “dictator game” as reviewed by Krupka and Weber (2013) and the “ultimatum game” reviewed by Mousazadeha and Izadkhahb (2015).

For an easy reading introduction see Pauli (2009).

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Distortions In Deriving Preferences - Prospect

TheoryThe distortions in this section are often modelled using the prospect theory proposed in Kahneman and Tversky (1979), which suitably modifies the expected utility formulation.

The major modifications are three.

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TheoryFirst, the utility function is defined over changes in wealth rather than wealth levels.

Second, the slope of the utility function over changes in wealth is greater for losses than for gains.

Third, the agent generates decision weights wi from the probability distribution and maximizes Σwiu(xi).

Note that the decision weights are not necessarily interpretable as probability weights.

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TheoryThis basic model is often enriched by the assumption that the utility function is concave over gains and convex over losses: people are risk averse when dealing with gains and risk prone when dealing with loses.

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TheoryIn his review of 30 years of research in Prospect Theory, Barberis (2013) notes that support for Prospect Theory had come mainly from the laboratory. In a paper (Abdel-khalik 2014), writes about recurring phenomenon in real life that are consistent with Prospect Theory predictions in the decision-making loss domain. The 58 cases noted in his paper are associated with specific risk seekers that had cost more than $126 billion (an average of $2.3 billion per case). Synopses are presented for 14 cases. It is striking that these cases are costly, all participants are young men, and almost all had followed the gambler’s martingale strategy — i.e., double down. While these cases are informative about risk-seeking behaviour, they are not sufficiently systematic to be subjected to stylized archival research methods.

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Prospect Theory - Decision Weights

This discussion closely follows that of Kahneman (2011). Many years after they published prospect theory, Tversky and Kahneman (1992), carried out a study in which they measured the decision weights that explained people’s preferences for gambles with modest monetary stakes. The estimates for gains are shown in the table and graph. probability 0 1 2 5 10 20 50

weight 0 5.5 8.1 13.2 18.6 26.1 42.1 probability 80 90 95 98 99 100

weight 60.1 71.2 79.3 87.1 91.2 100

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The estimates for gains are shown in the previous table and graph below.

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Prospect Theory - Decision Weights The particular probability-weighting function originally proposed by Tversky and Kakneman (1992) is

where p is the cumulative probability of the distribution of gains and losses. In this case the value fitted to the experimental data is . The weighting function has an inverted S-shape and is sub-proportional. That is if for p > q and then

The property allows for the Allais paradox (lecture 7) and can explain the preference for lottery tickets (Ingersoll 2008).

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For a review of several different probability weighting functions, plus a cautionary discussion see Ingersoll (2008). Köbberling and Wakker (2003) apply the tradeoff technique to three popular theories of individual decision under uncertainty and risk, i.e., expected utility, Choquet expected utility (Etner et al. 2012), and prospect theory.

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You can see that the decision weights are identical to the corresponding probabilities at the extremes: both equal to 0 when the outcome is impossible, and both equal to 100 when the outcome is a sure thing. However, decision weights depart sharply from probabilities near these points. At the low end, we find the possibility effect: unlikely events are considerably over weighted.

probability 0 1 2 5 10 20 50 weight 0 5.5 8.1 13.2 18.6 26.1 42.1

probability 80 90 95 98 99 100 weight 60.1 71.2 79.3 87.1 91.2 100

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For example, the decision weight that corresponds to a 2% chance is 8.1. If people conformed to the axioms of rational choice, the decision weight would be 2 — so the rare event is over weighted by a factor of 4. The certainty effect at the other end of the probability scale is even more striking. A 2% risk of not winning the prize reduces the utility of the gamble by 13%, from 100 to 87.1.



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Prospect Theory - Decision Weights To appreciate the asymmetry between the possibility effect and the certainty effect, imagine first that you have a 1% chance to win $1 million. You will know the outcome tomorrow. Now, imagine that you are almost certain to win $1 million, but there is a 1% chance that you will not. Again, you will learn the outcome tomorrow. The anxiety of the second situation appears to be more salient than the hope in the first. The certainty effect is also more striking than the possibility effect if the outcome is a surgical disaster rather than a financial gain. Compare the intensity with which you focus on the faint sliver of hope in an operation that is almost certain to be fatal, compared to the fear of a 1% risk.

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The combination of the certainty effect and possibility effects at the two ends of the probability scale is inevitably accompanied by inadequate sensitivity to intermediate probabilities. You can see that the range of probabilities between 5% and 95% is associated with a much smaller range of decision weights (from 13.2 to 79.3), about two-thirds as much as rationally expected.



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Prospect Theory - Decision Weights Neuroscientists (Hsu et al. 2009) have confirmed these observations, finding regions of the brain that respond to changes in the probability of winning a prize. The brain’s response to variations of probabilities is strikingly similar to the decision weights estimated from choices.

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Prospect Theory - Decision Weights Probabilities that are extremely low or high (below 1% or above 99%) are a special case. It is difficult to assign a unique decision weight to very rare events, because they are sometimes ignored altogether, effectively assigned a decision weight of zero. On the other hand, when you do not ignore the very rare events, you will certainly overweight them. Most of us spend very little time worrying about nuclear meltdowns or fantasizing about large inheritances from unknown relatives.

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Prospect Theory - Decision Weights However, when an unlikely event becomes the focus of attention, we will assign it much more weight than its probability deserves. Furthermore, people are almost completely insensitive to variations of risk among small probabilities. A cancer risk of 0.001% is not easily distinguished from a risk of 0.00001%, although the former would translate to 3,000 cancers for the population of the United States, and the latter to 30.

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Prospect Theory - Decision Weights When you pay attention to a threat, you worry — and the decision weights reflect how much you worry. Because of the possibility effect, the worry is not proportional to the probability of the threat. Reducing or mitigating the risk is not adequate; to eliminate the worry the probability must be brought down to zero.

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Prospect Theory - Decision Weights The question below is adapted from a study of the rationality of consumer valuations of health risks (Viscusi et al. 1987), which was published by a team of economists in the 1980s. The survey was addressed to parents of small children. Suppose that you currently use an insect spray that costs you $10 per bottle and it results in 15 inhalation poisonings and 15 child poisonings for every 10,000 bottles of insect spray that are used. You learn of a more expensive insecticide that reduces each of the risks to 5 for every 10,000 bottles. How much would you be willing to pay for it?

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Prospect Theory - Decision Weights Parents were willing to pay an additional $2.38, on average, to reduce the risks by two-thirds from 15 per 10,000 bottles to 5. They were willing to pay $8.09, more than three times as much, to eliminate it completely. Other questions showed that the parents treated the two risks (inhalation and child poisoning) as separate worries and were willing to pay a certainty premium for the complete elimination of either one. This premium is compatible with the psychology of worry but not with the rational model.

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The Fourfold Pattern This discussion closely follows that of Kahneman (2011). When Tversky and Kahneman (1992) began work on prospect theory, they quickly reached two conclusions: people attach values to gains and losses rather than to wealth, and the decision weights that they assign to outcomes are different from probabilities. Neither idea was completely new, but in combination they explained a distinctive pattern of preferences that we called the fourfold pattern. The name has stuck. The scenarios are illustrated below.

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The Fourfold Pattern

The top row in each cell shows an illustrative prospect.

GAINS LOSSES HIGH 95% chance to win $10,000 95% chance to lose $10,000

PROBABILITY Fear of disappointment Hope to avoid loss Certainty Effect RISK AVERSE RISK SEEKING

Accept unfavourable settlement Reject favourable settlement LOW 5% chance to win $10,000 5% chance to lose $10,000

PROBABILITY Hope of large gain Fear of large loss Possibility Effect RISK SEEKING RISK AVERSE

Reject favourable settlement Accept unfavourable settlement

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The second row characterizes the focal emotion that the prospect evokes.






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The third row indicates how most people behave when offered a choice between a gamble and a sure gain (or loss) that corresponds to its expected value (for example, between “95% chance to win $10,000” and “$9,500 with certainty”). Choices are said to be risk averse if the sure thing is preferred, risk seeking if the gamble is preferred.






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The fourth row describes the expected attitudes of a defendant and a plaintiff as they discuss a settlement of a civil suit.






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The Fourfold Pattern The fourfold pattern of preferences is considered one of the core achievements of prospect theory. Three of the four cells are familiar; the fourth (top right) was new and unexpected.

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The Fourfold Pattern The top left is the one that Bernoulli (1700–1782) discussed: people are averse to risk when they consider prospects with a substantial chance to achieve a large gain. They are willing to accept less than the expected value of a gamble to lock in a sure gain.

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The Fourfold Pattern The possibility effect in the bottom left cell explains why lotteries are popular. When the top prize is very large, ticket buyers appear indifferent to the fact that their chance of winning is minuscule. A lottery ticket is the ultimate example of the possibility effect. Without a ticket you cannot win, with a ticket you have a chance, and whether the chance is tiny or merely small matters little. Of course, what people acquire with a ticket is more than a chance to win; it is the right to dream pleasantly of winning.

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The Fourfold Pattern The bottom right cell is where insurance is bought. People are willing to pay much more for insurance than expected value — which is how insurance companies cover their costs and make their profits. Here again, people buy more than protection against an unlikely disaster; they eliminate a worry and purchase peace of mind.

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The Fourfold Pattern The results for the top right cell initially surprised Kahneman and Tversky.

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The Fourfold Pattern The results for the top right cell initially surprised Kahneman and Tversky. They were accustomed to think in terms of risk aversion except for the bottom left cell, where lotteries are preferred. When they looked at the choices for bad options, they quickly realized that individuals were just as risk seeking in the domain of losses, as they were risk averse in the domain of gains. They were not the first to observe risk seeking with negative prospects — at least two authors had reported that fact (Markowitz 1952 and Williams 1966 and further discussed in Kahneman and Tversky 1979), but they had not made much of it. However, Tversky and Kahneman (1992) were fortunate to have a framework that made the finding of risk seeking easy to interpret, and that was a milestone in their thinking. Indeed, they identified two reasons for this effect.

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The Fourfold Pattern First, there is diminishing sensitivity. The sure loss is very aversive because the reaction to a loss of $900 is more than 90% as intense as the reaction to a loss of $1,000.

The second factor may be even more powerful: the decision weight that corresponds to a probability of 90% is only about 71 (refer back to the table of weights), much lower than the probability. The result is that when you consider a choice between a sure loss and a gamble with a high probability of a larger loss, diminishing sensitivity makes the sure loss more aversive, and the certainty effect reduces the aversiveness of the gamble.

The same two factors enhance the attractiveness of the sure thing and reduce the attractiveness of the gamble when the outcomes are positive.

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The Fourfold Pattern The shape of the value function and the decision weights both contribute to the pattern observed in the top row of the table. In the bottom row, however, the two factors operate in opposite directions: diminishing sensitivity continues to favour risk aversion for gains and risk seeking for losses, but the over weighting of low probabilities overcomes this effect and produces the observed pattern of gambling for gains and caution for losses.

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The Fourfold Pattern Many unfortunate human situations unfold in the top right cell. This is where people who face very bad options take desperate gambles, accepting a high probability of making things worse in exchange for a small hope of avoiding a large loss. Risk taking of this kind often turns manageable failures into disasters. The thought of accepting the large sure loss is too painful, and the hope of complete relief too enticing, to make the sensible decision that it is time to cut one’s losses. This is where businesses that are losing ground to a superior technology waste their remaining assets in futile attempts to catch up. Because defeat is so difficult to accept, the losing side in wars often fights long past the point at which the victory of the other side is certain, and only a matter of time.

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Distortions In Deriving Preferences - Shape And

AttractivenessThe following table list eight gambles in Euros as ranked by financial analysts according to their attractiveness to investors. They have the same expected value and the same number of possible outcomes (Kahneman and Riepe 1998 ).

Which would you prefer?

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AttractivenessGamble Payoff 1 Probability (%) Payoff 2 Probability (%)

A 5,000 95 105,000 5

B 5,000 50 15,000 50

C 1,000 10 11,000 90

D 1,000 90 91,000 10

E 2,000 50 18,000 50

F 0 50 20,000 50

G -2,000 90 118,000 10

H -5,000 50 25,000 50

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A 5,000 95 105,000 5

B 5,000 50 15,000 50

C 1,000 10 11,000 90

D 1,000 90 91,000 10

E 2,000 50 18,000 50

F 0 50 20,000 50

G -2,000 90 118,000 10

H -5,000 50 25,000 50

The ideal gamble combines a high probability of a moderate gain and a small probability of a very large gain.

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A 5,000 95 105,000 5

B 5,000 50 15,000 50

C 1,000 10 11,000 90

D 1,000 90 91,000 10

E 2,000 50 18,000 50

F 0 50 20,000 50

G -2,000 90 118,000 10

H -5,000 50 25,000 50

Its prefferability may be explained by a combination of the over weighting of the small probability of a large gain and of the different risk attitudes over gains and losses.

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A 5,000 95 105,000 5

B 5,000 50 15,000 50

C 1,000 10 11,000 90

D 1,000 90 91,000 10

E 2,000 50 18,000 50

F 0 50 20,000 50

G -2,000 90 118,000 10

H -5,000 50 25,000 50

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How Do You Choose An Analyst?

The idea that financial analysts play an important role in financial markets is rather consensual (Cowles, 1933; O’Brien, 1990). Yet there is some debate on whether following the advice of analysts brings value to investors after transaction costs (Womack, 1996; Mikhail, Walther, and Willis, 2004; Li, 2005). Related to this is the difficulty in identifying the analysts with superior stock picking skills. In a paper Aiguzhinov et al. (2015) show that the rankings of financial analysts are useful to investors because strategies based upon these rankings yield positive abnormal returns.

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Distortions In Deriving Preferences