Sunday, September 17, 2017

Francis Larson, John A. List, and Robert D. Metcalfe, “Can Myopic Loss Aversion Explain the Equity Premium Puzzle? Evidence from a Natural Field Experiment with Professional Traders.” August 31, 2016; available here.

  • The puzzle: the real return on US equities is about 8% per annum, versus about 1% for riskless assets. This spread cannot easily be explained as a risk premium. 
  • One hypothesis: traders display myopic loss aversion (MLA), and hence the frequent downticks (short-term declines in asset value) are psychologically costly – people will only put up with these costs if there is an offsetting premium in the monetary return. If MLA can explain the equity premium puzzle, then it must be present in the “marginal” trader. 
  •  Laboratory experiments have found that myopic loss aversion is common. The standard design involves varying the rate at which price information is delivered to traders. Those who receive information at high frequency are exposed to more revelations of downticks, and hence, if they display MLA, they will underinvest in the risky asset, the one that is subject to lots of downticks. 
  • The Larson, List, and Metcalfe paper employs a (natural?) field experiment, where traders do not know they are taking part in an experiment; they think they are beta-testing a new online trading platform. 
  • The traders' recompense is to be paid eventually in-kind based on the profits that they accrue during their two weeks of testing. They can “buy” a risky asset whose return is tied (in a not-fully-obvious way) to the US dollar exchange rate. The tying is such as to bias the return to the asset to be positive. 
  • The experiment reveals MLA – traders who are given infrequent (once per 4 hours) price updates keep more of their stake in the risky asset, and earn considerably more, than those traders who receive second-by-second updates. 
  • Traders tend to desire more frequent price updates, but perhaps that information degrades their performance. 
  • Since both the Frequent (n=73) and Infrequent (n=78) groups of traders can trade at any time, this experiment avoids a confounding feature of past laboratory experiments, that both information and trade opportunities are altered among conditions.

Friday, September 8, 2017

Bartling, Brandes, and Schunk (2015) on Soccer and Reference Points

Bj√∂rn Bartling, Leif Brandes, Daniel Schunk, “Expectations as Reference Points: Field Evidence from Professional Soccer.” Management Science 61(11): 2646-2661, 2015; working paper version (pdf) here.
  • Do soccer teams play differently, and less “rationally,” when they are in the loss domain relative to expectations?
  • Betting odds give a measure of expectations for match outcomes in professional soccer; so, we can test if teams play differently, and less rationally, when they are in the loss domain (performing worse than expected).
  • Indicators are the numbers of yellow cards and red cards, as well as substitution patterns.
  • The rationality of any changed behaviors in the loss domain can be checked by how final outcomes are influenced.
  • If a team that is favored is behind, they are in the loss domain. These teams receive more cards (by 14%), and their coaches make more offensively-minded substitutions (by a large margin), than if the game situation were the same but the team was not in the loss domain. 
  • Both the additional cards and the increase in offensive substitutions seem to lead to worse outcomes for teams. 
  • The additional cards when a team is in the loss domain tend to be related to frustration-style events, such as dissent or violent conduct. The loss domain is psychologically more challenging and this leads to worsened decision making.
For more on reference points and soccer, see the BEO post on Dickson, Jennings, and Koop (2016) on Domestic Violence and Glaswegian Football. 

Monday, September 4, 2017

Liu and Onculer (2017) on Ambiguity Attitudes

Yuanyuan Liu and Ayse Onculer, “Ambiguity Attitudes over Time.” Journal of Behavioral Decision Making 30: 80-88, 2017. 
  • Ambiguity aversion is not uncommon when dealing with relatively high-probability gains; ambiguity neutrality or even ambiguity seeking comes into play for low-probability gains. (Ambiguity attitudes can be quite different for loss settings or for mixed gain-loss scenarios.)
  • This article investigates whether delayed resolution of uncertainty changes attitudes towards ambiguity. The authors find that a one-year delay tends to undermine ambiguity aversion in those high-probability gains scenarios. 
  •  Liu and Onculer propose that for immediate prospects, the affective system (Kahneman’s system 1) makes the call, but that in asking people to think about risks with future resolution, the cognitive (system 2) tends to take over, and dissipates the ambiguity aversion that appears in the high-probability condition. 
  • For low-probability gains, system 2 is in charge even for immediate prospects, so delay has no effect on ambiguity attitudes.
  • Affect can influence decision making. Likely events tend to be psychologically closer than unlikely ones, increasing reliance upon affective decision making. Likewise, immediacy also triggers affective decision making. Temporal distance, alternatively, will privilege cognitive decision making.
  • The authors posit that for immediate decisions concerning high-probability gains, ambiguity aversion is present, but for low-probability gains, ambiguity aversion or a weak preference for ambiguity emerges. If the resolution of the prospects is delayed, then ambiguity aversion towards high-probability gains will be reduced, with no effect on low-probability gains.
  • Priming subjects to adopt a cognitive decision-making style will reduce ambiguity aversion for high-probability prospects. Alternatively, for temporally distant prospects, priming subjects to use an affective style will increase ambiguity aversion for the high-probability prospects. 
  •  In a series of three web-based urn-problem surveys, the authors find support for their hypotheses.

Professor Thaler’s American Economic Association Presidential Address

Richard H. Thaler, “Behavioral Economics: Past, Present, and Future.” American Economic Review 106(7): 1577–1600, 2016 (working paper pdf available here).

• Economics provides an approach to optimal decision making -- and that is well and good. But we should not let that model distract us from how people actually make decisions.

• When people make decisions, they make them as fallible Humans, not as textbook Econs. They remain fallible Humans irrespective of how often we are told that: (1) their decisions will look "as if" they are Econs; (2) their departures from the full Econ will be unsystematic; (3) when the stakes are high they will convert into Econs; (4) with time they will learn to be Econ; and, (4) the special magic of market settings will see to it that only Econs survive.

• Does the market "get prices right"? Consider the closed-end mutual fund with ticket symbol CUBA. Typically, CUBA is priced at about a 10-to-15 percent discount relative to its underlying assets. But after December 18, 2014, CUBA started to trade at a 70% premium over the value of its underlying securities, and premium pricing continued for about a year.

• Why? On December 18, 2014, President Obama announced that the US would normalize diplomatic relations with Cuba. The CUBA mutual fund has nothing to do with the country of Cuba.

• When Humans make decisions under uncertainty, the sort of preferences they display are not those of expected utility theory. Rather, many decisions seem to involve "prospect theory"-style preferences: (1) utility is based on changes in wealth from some reference point; (2) people are loss averse; and (3) people do not weight potential outcomes according to the objective probabilities.

• For intertemporal preferences, people often display a present bias, a taste for instantaneous gratification, and in many ways, do not exhibit exponential discounting.

• As with preferences, people also do not seem to hold fully rational beliefs. In particular, people display excessive optimism and excessive confidence in their beliefs.

• Actual choices are influenced by "supposedly irrelevant factors [p. 1595]," where the supposition of irrelevance is made within standard economic models. For instance, default settings tend to influence ultimate choices, even in high-stakes situations (such as retirement planning) where the defaults are less-than-optimal and easy to override.

• In the future, economic models will incorporate those behavioral features that best improve their predictive accuracy without imposing high costs in terms of complexity; "behavioral" will disappear as an adjective for a subset of economics, as all economics will be as behavioral as necessary.

Saturday, May 6, 2017

Huck, Szech, and Wenner (2015) on Information Avoidance; or, Am I Getting Paid Enough to Finish this Outline?

Steffen Huck, Nora Szech, and Lukas M. Wenner, “More Effort with Less Pay: On Information Avoidance, Belief Design and Performance.” CESifo Working Paper No. 5542, October, 2015 (pdf).

• If information can influence your expectations and motivations, you might want to avoid acquiring information. For instance, knowing that there are high stakes riding on your performance might lead you to choke under the pressure. This paper reports on an experiment that tests whether people might choose to avoid information about their precise (piece-rate) wage. 

• The experiment is of the “real effort” variety, that is, subjects are asked to engage in a tedious, painstaking task: typing 60-character nonsensical alphanumeric strings, mixed uppercase and lowercase, backwards into a computer. For each correctly typed string, a piece rate is paid, and the subjects have one hour in which to type (or not, as they wish). In case they want to not work, they have a magazine at hand to help pass the hour. 

• The piece rate is random; with probability .5, it is quite low – .1 euro per string – and with probability .5 it is significant: 1 euro per string. The different conditions of the experiment turn on: whether the workers know their wage (Full Info); whether they choose to be or not to be informed of their wage until after the work hour is over (Info Choice); and whether they have no option but to remain uninformed (No Info). A fourth condition is where workers know that their wage is .55 euro per string, which is the weighted average of the high and low piece rates, and hence might serve as the expected wage for workers who do not know their wage. 

• Not surprisingly, if you know your wage, you work harder if the wage is high than if it is low (and you work at an in-between rate if you know you earn the medium wage). If you get to decide whether or not to be told your wage, well, 30 out of 95 subjects choose not to be told. These uninformed folks work like machines, even outpacing subjects who know they have a high piece rate.

• Perhaps those subjects who choose not to learn their wage rate happen to be really productive workers, that is, maybe the option of not knowing the wage within the Info Choice group selects for the best workers. This possibility is tested by the No Info treatment, where subjects don’t know their piece rate in advance, but not by choice: they are not given the option of learning their wage. But these subjects also perform at a very high level, so selection effects don’t seem to drive the result: not possessing wage information tends to bolster productivity. 

• When asked about their decisions, those subjects who choose to learn their piece rate in advance tend to explicitly note their interest in tailoring their effort to the rate. Those who choose not to learn their piece rate tend to mention the effect that a low rate would have on their motivation, and/or a fear that a high rate might lead to anxiety. 

• The authors adapt a model of Brunnermeier and Parker (2005; pdf here) to indicate that a high piece rate can both increase the revenue from effort, and also increase the costs of supplying effort: a high rate might induce anxiety, for instance. Further, agents receive utility not just from what happens today but also some utility (today) from anticipation of what is likely to happen next period. The anticipation utility provides a channel for utility-maximizing agents to hold biased beliefs, overoptimistic views of what the future will bring. With a high enough anticipatory utility, agents will prefer to avoid knowledge of their wage: they are information avoiders.

Bobadilla-Suarez, Sunstein, and Sharot (2016) on Under-Delegation

Sebastian Bobadilla-Suarez, Cass R. Sunstein, and Tali Sharot, “Are Choosers Losers? The Propensity to Under-Delegate in the Face of Potential Gains and Losses.” February 15, 2016 (pdf, perhaps somewhat updated, available here).

• People seem to value control over decisions, and are willing to sacrifice (in terms of expected returns) to make a decision themselves rather than to delegate. Perhaps for decisions involving losses, however, people might prefer to delegate, to insulate themselves a bit from painful choices. 

• This paper reports on two laboratory experiments that are conducted to look at delegation versus control. Sample sizes are rather small: 26 subjects for the first experiment, and 25 for the second. In some trials, participants can delegate the choice to an advisor, after being informed of the reliability and cost of the advisor. (In reality, the advisor is a computer player programmed with the appropriate reliabilities.) 

• Both experiments start with a learning phase. Participants are shown two geometrical shapes, and are asked to pick the better one. They are not told what criteria go into “better,” but if they pick correctly, they receive a higher payoff (or avoid a bigger loss) than if they choose incorrectly. Unbeknownst to the players, there is no underlying rule for “better,” the computer just designates, randomly, one of the two shapes as better. But humans are good at finding patterns in random events!

• In the delegation phase of experiment one, participants are given the option, prior to being shown the shapes, to delegate their decision to an advisor (actually, a chooser) whose probability of choosing correctly is revealed in advance, along with the cost of using the advisor. After the delegate-or-not decision, the two shapes are revealed, a choice is made (although, if made by an advisor, not revealed), and onto the next round (60 in total). Subjects do not learn the outcome of individual trials, but are compensated at the end on a random subset of ten of the trials. 

• Given that the subjects themselves have a 50% chance of choosing correctly, it is easy to determine when the expected monetary payoff to the subject is higher by delegating the decision to the advisor (thanks to those known probabilities and costs; incidentally, the price of using the advisor is only imposed if the advisor is used, and if the advisor chooses correctly). 

• The advisors are configured (costs and accuracy) in such a way that the subjects would do best (in expected monetary terms) by delegating on half of the trials. Instead, subjects choose to delegate only about 30% of the time, whether they are choosing for gains or to avoid losses. The departures from “optimal” play are almost all in the direction of failing to delegate the decision when expected returns would have been higher with delegation. 

• Subjects’ perceptions of their own abilities to choose are canvassed, and the aversion to delegation is not the result of excessive optimism with respect to the accuracy of their own choices. 

• The second experiment is nearly identical to the first one, except that the decision to choose or to delegate is now made each round after the shapes themselves have been revealed. Still, there is substantial, even slightly worse, under-delegation. 

• The fact that under-delegation also is present in the “loss” trials indicates that people, at least in this setting, don’t seem to want to shield themselves from painful decisions through delegation.

Thursday, May 4, 2017

Gilovich and Kumar (2015) on Experiences versus Material Goods

Thomas Gilovich and Amit Kumar, “We’ll Always Have Paris: The Hedonic Payoff from Experiential and Material Investments.” Advances in Experimental Social Psychology 51: 147-187, 2015. [The version outlined below has different page numbers (and perhaps other discrepancies) from the version available here.]

• Experiences provide long-lasting memories, at least relative to material goods; experiences are gifts that keep on giving. Perhaps subjective well-being (SWB) and income would be more closely related if people shifted their spending from material goods to experiential goods. 

• Vacations, concerts, dining out: these are examples of experiential goods. Electronic gadgetry and clothing fall into the material goods category. People consistently report higher happiness from experiential goods relative to material goods. 

• Experiences are better for SWB for a variety of reasons. First, they are less subject to the adaptation which, over times, erodes the pleasure boost given by a material acquisition. Experiences are more pleasurable (and interesting) to talk about, and can become embedded in our identity, our story. 

• Adaptation is helpful in allowing people to come to terms with negative events, so if bad experiences are not subject to adaptation, we could suffer long-term from unpleasant moments. But experiences tend to be malleable, so that bad experiences – the vacation where it rained the whole time – tend to be looked at more favorably, through rose-tinted glasses, over time. Indeed, the negative features of experiences can become, in retrospect, their chief virtues. 

• Even in anticipation of consumption, experiences bring more pleasure than material goods. Waiting in line is less unpleasant when it is to see a play than to buy a gadget. And after the fact, people tend to think of money spent on experiences as having been better spent than that expended on material objects. 

• The social element that is attached to many experiences is one of the sources of their SWB advantages. And talking about experiences after the fact is one of their social elements. Talking about material possessions is a bit of a snooze fest. 

• The endowment effect is connected to an unwillingness to part with something that we “own”; people tend to show larger endowment effects, greater attachments, to potential experiences than to material goods. 

• Material possessions like cars are relatively easy to compare along quality dimensions – your neighbor’s Ferrari is probably better than your Ford. But who had a better vacation, or a better time at the concert? These are harder to compare, and hence, experiences to some extent protect us against unfavorable social comparisons. And if we enjoy our concert, we are not much concerned about opportunity costs, those potentially better concerts we sacrificed to go to “ours”. 

• If you buy a bad car, you regret it, you have buyer’s remorse. But the more frequent regret in the experiential realm concerns foregone opportunities, not buyer’s remorse. And it is easy to find the good in a bad experience, but not so easy to find the upside of a malfunctioning computer. “Surprises” tend to be positive when they arise during experiences, but negative when they are connected to material goods! 

• Some material goods are purchased with the intention of using them to enhance experiences, such as watching movies with your friends and family in a home video center. But perhaps the actual consumption of these goods is less social and less experiential and even less common than anticipated. If people have the experiences in mind when they make the purchases, they may be systematically fooled, as it were, into overspending on what turn out to be merely material goods. 

• Perhaps connoisseurs are able to convert material goods (collectibles, say) into experiential goods, by harnessing the same social, identity, and even narrative advantages that other types of experiences present. 

• Can we design cities, and our lives, to maximize experience?