At the end of the experiment, subjects learn about their payoffs from all parts of the experiment. We ask them to fill in a short questionnaire concerning demographics and background data. We also ask participants how tired they feel after the experiment and how strenuous they have perceived the entire experiment on a 6-point Likert scale.
Then, all earnings are paid out in private and the subjects are dismissed from the laboratory. Hence, we obtained 16 independent observations, eight for each of our treatment conditions. We only invited students who had never participated in an asset market experiment before. We also excluded students potentially familiar with the CRT or the Stroop task.
These were read aloud to ensure common knowledge. Remaining questions were answered in private. The former is an average price per asset, whereas the latter is an average price per trade. Our results remain unaffected by the choice of adjustment; in line with the literature, we mainly display results based on volume-adjusted prices in the following. In order to quantify the tendency of markets to exhibit irrational exuberance we compare trading prices with the fundamental value of the asset.
We asked for it in the post-experimental questionnaire in order to make sure that it is not a common phenomenon among our participants. All tests reported in this paper are two-sided unless stated otherwise. RAD is constructed as the ratio of the average absolute difference of mean market price and funda- mental value relative to the average fundamental value of the asset.
RD is the ratio of the average difference between mean market price and fundamental value relative to the average fundamental value. The difference between the two measures is how the difference between mean market price and fundamental value enters the calculation: For RAD the difference enters in absolute terms, thus all deviations from the fundamental value — either overpricing or underpricing — increase RAD, making RAD a measure of average mispricing. For RD the wedge between market price and fundamental value retains its sign, thus periods with overpricing and underpricing can cancel each other out.
Hence, RD provides the dominant direction of mispricing, making it, in effect, a measure of average overpricing. Both measures are straightforward to interpret: A RAD of. Both measures are independent of the number of periods and the fundamental value. In both conditions, average market prices start out at a similar level, displaying a moderate level of underpricing. However, from the third period onwards, average prices in both conditions exceed the fundamental value. Eventually, average market prices drop sharply, but do not drop below the fundamental value again.
The most conservative comparisons between the two treatments are based on market averages over all traders and over all ten periods. This is the approach we apply for all non-parametric tests regarding aggregate market outcomes. A Wilcoxon signed- ranks test confirms the impression from eyeballing, i. Thus, trade among individuals with low self-control leads to overpricing which is more than twice as high as in the baseline HIGHSC. Figure 3 displays the price evolution of single markets in the two conditions.
There is a high degree of path-dependence and endogeneity in price evolution in the markets and a lot of heterogeneity among markets in the same condition. Therefore, finding a significant difference between the two conditions is quite striking. The emergence of overpricing can oftentimes be attributed to constant prices despite decreasing fundamental values Huber and Kirchler, ; Kirchler et al. Value Mean Price Fund.
Detailed descriptive results on the variables considered in this section can be found in sections A. The average number of correct answers in the CRT was 1. We conclude that the Stroop task did not have an impact on our incentivized version of the CRT.
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Like the literature exploring the effect of reduced self-control on risk attitude that has come to inconclusive results e. Bruyneel et al. People low in self-control have been reported to become more passive Baumeister et al. Increased passivity and thus a thinner market in LOWSC, where few trades could drive overpricing, could be responsible for our results. Thus we compare the number of shares traded in the two conditions.
Figure 4 illustrates the evolution of average shares traded per period. We therefore run regressions, including controls as indepedent variables. To avoid endogeneity problems across trading periods and between subjects, respectively, we aggregate overpricing measures over all periods on the individual level and use robust standard errors clustered at the market level. We do this separately for sales and 18 If we include the observations from our second experiment, the CRT scores of the two groups become 1.
Similar results hold for the other tests in this section.
We define measures for individual overpricing for purchases and sales, which we call IndRDpurchases and IndRDsales , respectively. We report results on IndRDpurchases as the dependent variable in the regressions in Table 1. In appendix A.
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In specification 2 , our measure of risk attitude is not significant, but if we also include interactions with our treatments in specifications 3 and 4 , relative risk seeking is correlated with lower individual overpricing when self-control capabilities are reduced. Performance on the CRT has the expected effect of reducing the tendency of buying at prices above fundamental value in all specifications where it is included, and its effect does not significantly differ between participants in LOWSC and HIGHSC markets.
We conclude that neither changes in cognitive skills nor in risk preferences after self-control depletion can explain our main result of excess overpricing after self-control depletion. The motivation for this additional experiment is twofold. First, asset market experiments are zero sum games and behavior is highly path-dependent and endogenous to market prices, which makes it technically impossible to analyze differences in behavior resulting from reduced self-control in our homogeneous markets.
Therefore, we wanted a condition in which traders under both conditions are active at the same time. Second, since in real-world settings — either due to dispositional differences or due to differential previous demands on self-control resources — it is likely that individuals high and low in self-control interact, we want so see whether the effect of reduced self-control observed in LOWSC markets can be replicated with a smaller share of depleted traders in MIXED markets.
We conducted eight additional sessions with 16 markets in April and November We were interested whether our variation of self-control had taken effect via changes in emotional states. In order to reduce experimenter demand effects and as is common in experiments analyzing emotions, we confronted subjects with several emotions of which some were not relevant at all to our question of interest.
Apart from the assignment to the respective version of the Stroop task within a market and the additional questions in the questionnaires of the last four sessions, the experimental protocol remained exactly the same as in Experiment I. Experimental participants were not aware of the different versions of the Stroop task, i. Interestingly, the effect of reduced self-control on mispricing and overpricing does not seem to be changed if only part of the trader population is self-control depleted.
That is, in some of these markets prices exhibit a hump-shaped development, initially increasing and peaking in some intermediate period. Thus already the presence of a moderate share of traders with depleted self-control abilities 20 The results of these comparisons also hold when looking at quantity- or trade-adjusted mean prices. However, when analyzing trading behavior, distinguishing cause and effect is particularly difficult, as already mentioned earlier.
A particular deviation in behavior by some traders in the early phases of a market might shift behavior of other non-depleted traders. We therefore start by focusing on the very first trading period, where dependencies are less relevant than in later periods. Remember that we conduct all statistical tests based on the most conservative definition of independence the market level , and hence significant effects are usually associated with large absolute differences. After period one, these differences vanish, suggesting that non-depleted traders start imitating the behavior of self-control depleted traders.
From trading period two on, however, their behavior has incited non-depleted traders to behave similarly and hence set many markets on an entirely different trajectory. While 21 Results for period two are not reported, but available upon request from the authors. We consider this as evidence that inhibited self-control abilities affect overpricing, but that depleted traders are not necessarily driven out of the market.
Instead, as shown previously, they might goad non-depleted traders into speculative behavior, making everyone end up with similar profits. In particular, we asked participants to rate how strongly they felt a number of emotions at the beginning of the first period and at the end of the last period, respectively.
We asked participants to recollect there emotions. Note that we collapsed all the emotional measures on the treatment group level within each market and test for differences with Wilcoxon signed-rank tests. Remember that Lahav and Meer found that inducing positive mood before trading leads to higher deviations from fundamental values and thus larger levels of overpricing and that correlational studies also suggest such a relationship Breaban and Noussair, ; Hargreaves Heap and Zizzo, The average responses to all the emotion-related questions and the test statistics can be found in Table 9 of the appendix.
Average values for changes in emotions over time can be found in Table Even though the difference in averages goes in the expected direction, given the responses to the questions on experienced emotions, they fail to reach significance on conventional levels. The results indicate that the behavior of the traders with depleted self-control abilities might have been driven by emotional factors to a larger degree than they were aware of themselves. Toplak et al. Thus, we can interpret the CRT score as a measure of cognitive control.
In order to check whether the effect of CRT performance on profits is similar here, we ran additional regressions which we report in table 5. Note that we excluded participants who had indicated that they knew at least one of the CRT questions at the end of the experiment. Together with the results indicating higher emotional valence and reactivity, this suggests an interpretation of trading behavior of MIXLO participants as relatively more relying on impulsive system 1 processes than on reflective system 2 processes Kahneman, The difference in overpricing cannot be explained by a change in risk attitudes or a simple change in cognitive abilities.
Experiment II gives us additional ways to assess potential explanations for the excess overpricing after self-control depletion. First, there are differences in trading behavior. Self-control depleted traders trade slightly less, and their initial trading behavior shows patterns of speculative trading.