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.
Björn 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 the Dickson, Jennings, and Koop (2016) analysis of Domestic Violence and Glaswegian Football.
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.
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, (5) 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.
