Abstract
Traders in global markets operate at different local times-of-day. This implies heterogeneity in circadian timing and likely sleepiness or alertness of those traders operating at less or more optimal times of the day, respectively. This, in turn, may lead to differences in both individual-level trader behavior as well as market level outcomes. We examined these factors by administering single-location and global sessions of an online asset market experiment that regularly produces mispricing and valuation bubbles. Global sessions involved real time trades between subjects in New Zealand and the U.S (i.e., “global” markets) with varied local times of day for each location. Individual traders at suboptimal times of day (or, “circadian mismatched” traders) engaged in riskier trading strategies, such as holding shares (the riskier asset) in later trading rounds and mispricing shares to a greater degree. These strategies resulted in lower earnings for circadian mismatched traders, especially in heterogeneous markets that also included traders at more optimal times-of-day. These differences were also reflected in market level outcomes. Markets with higher circadian mismatch heterogeneity across traders were more likely to exhibit longer lasting asset bubbles and greater share turnover volume. Overall, our results draw attention to a unique, but underappreciated, factor present across traders in global market environments, namely, differences in sleepiness across traders. Thus, this study hopes to highlight the role of circadian mismatch in attempting to understand trader behavior and, ultimately, market volatility.
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Notes
A recent paper by Hanaki et al. (2017) finds a significant increase in asset mispricing in experimental asset markets with both high and low cognitive ability subjects, which may also implicate temporary variations in available cognitive resources as a source of asset mispricing and market bubbles.
An American depositary (or depository) receipt (ADR) represents securities of a foreign company that trade in U.S. financial markets. These are denominated and pay dividends in U.S. dollars and may be traded like regular shares of stock. They are traded during U.S. trading hours.
A recent paper by Corgnet et al. (2018) also concludes that standard cognitive skills are not necessarily what make traders successful, but rather display of behavioral biases such as overconfidence may be a stronger indicator of poor performance in asset markets. However, they also conclude that ToM skills have only a marginal effect, which is different than what others have concluded (e.g., Bruguier et al. 2010).
Others have found that overconfidence in equity trading data may result in higher frequency trading activity (Grinblatt and Keloharju 2009).
More specifically, to the extent that sleep deprivation may selectively increase activation in portions of the prefrontal cortex (i.e., the ventro-medial PFC), the evidence suggests that such increased activation represents the decision maker’s increased focus on potential monetary gains. In other words, in the context of our task where monetary gains and losses are at stake, the increased PFC activation that may result from sleepy traders would suggest an increased optimism bias as opposed to an increase in decision quality (see Venkatraman et al. 2007, 2009, 2011).
Altered mood states may also be a factor to consider, as previous authors have hypothesized that mood may explain morning sunshine impacts on stock market returns, which may otherwise be difficult to reconcile with a rational expectations model (Hirshleifer and Shumway 2003). Yet another study in experimental asset markets suggests irrationality of traders may fuel speculative bubbles (Lei et al. 2001), though we appeal to a recent asset bundle choice experiment that found mild circadian mismatch may impact preferred asset bundles with no measurable change in rationality of choice (see Castillo et al. 2017).
Some studies such as Toplak et al. (2011), which rely on the Frederick (2005) Cognitive Reflection Task (CRT), suggest that behavioral differences are driven primarily by differences in cognitive skills. Therefore, we include a control for math abilities in our analysis. While we do not measure CRT in the present study, a related study (Castillo et al, 2017) measures CRT in an individual decision asset bundle choice task under conditions of circadian match and mismatch and found no significant difference in CRT scores across these randomly assigned treatment groups.
Rates of subjects who signed up but were not online for the experiment (i.e., virtual no-shows) were as follows: 4 am—20%; 8 am—12.5%; noon—15.6%; 4 pm—20%; 8 pm—11%; midnight—24%.
The experimenter conducted the experiments online and was in the same time zone as the east coast US subjects.
The various experiment options for the Veconlab Internet-based platform for experiments can be accessed at http://veconlab.econ.virginia.edu/admin.htm. The specific experiment used is the “Limit Order Market” option under the “Asset Market” submenu of the “Finance/Macro” experiment section.
One of the sessions (Market 25, Appalachian State, 4:00 am) inadvertently used a dividend draw in the Low Returns treatment that allowed for a 1/10 chance of a zero dividend in just round 1 of the treatment—dividends were explained to participants as based on the roll of a 10-sided die. Rather than rolls 1–5 producing a $.40 dividend and rolls 6–10 producing a $1.00 dividend, the table showing the dividend for each possible die outcome in round 1 listed a $0.00 dividend if the die roll was a 1. The realized dividend in that round was, in fact, the $1.00 high dividend, but this implies fundamental share value in the first round of that treatment for that one market was closer to $6.70 for round 1 and mechanically rising to $6.96 in round 10. The subjects correctly saw the intended dividend outcomes that would result from the die outcome as $.40 for rolls 1–5 and $1.00 for rolls 6–10 in rounds 2–10 and there is no evidence this error, which was never realized in the dividend draw, affected behavior in any way.
At the times the experiments were carried out the exchange rate was between US $1 = NZ $1.38-$1.47.
The validity of this methodology is documented in Sect. 5 of this paper. There may be an additional concern regarding selection of subjects into the different time slots of the experiment. While we do not have all measures that might be desired on which to assess sample selection, we formally tested for differences in observable characteristics of our participants across the HighMM = 0 and HighMM = 1 participants. We found no significant differences in gender, age, race, math level, Epworth sleepiness, anxiety risk, depression risk, or self-reported recent sleep deprivation across these two samples of participants (p > .10 in all instances: Mann–Whitney tests of medians).
In order to make sure that our results are not an artefact of this dichotomous classification of subjects into alert or sleepy categories, we also undertake a parallel analysis with the underlying continuous variable MMLevel (bounded by zero and one) as the regressor of interest. We say more on this issue below in Footnote 18, when we present our results.
The scoring of MathGood = 1 accounted for differences in the average math levels of New Zealand versus U.S. students. Specifically, we asked subjects to self-report their grade in the last high school math course they took. Taking into account the different grading standards, we scored 45% of the U.S. subjects (n = 85 of 187 total US participants) as MathGood = 1 and 55% of the New Zealand subjects (n = 62 of 116 total NZ participants) as MathGood = 1.
The Epworth Sleepiness Scale (Johns 1991) is often used in sleep studies.
Unfortunately, we did not elicit beliefs from traders regarding expected dividend outcomes or asset prices, which would have helped to discriminate between the different potential mechanisms that could all contribute to tired subjects holding more shares in later rounds. For example, though tired subjects are predicted to be both more overconfident in holding shares and also less able to anticipate a market downturn, our data may not be sufficient to distinguish between these two mechanisms. However, earnings analysis we conduct later in this section may help identify whether risk taking plays an important role independent of anticipation and overconfidence.
As noted earlier, we also replicate all our results replacing the HighMM dummy with the underlying continuous variable MMLevel ∈[0,1]. These results are similar to those in Table 3, except that for markets with heterogeneity above the median, we get significant differences in the High Returns treatment when using HighMM in Table 3 but we do not get significant differences in the High Returns treatment when we use MMLevel. We still get similar significant differences in the Low Returns treatment. As in Table 3 we do not find any significant differences in the markets below the median if we use MMLevel. In the interest of parsimony, we do not present these results in detail, but they are available from the corresponding author upon request.
The earnings regressions we report contain data for 294 participants rather that 303. This is because, as noted in Table 3 above, the very first session, a local session with 9 participants had 20 rounds in the High Returns treatment as opposed to the required 15. We have taken out this session in looking at final earnings resulting in dropping 9 participants, thereby reducing the number of observations from 303 to 294.
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Acknowledgements
The authors thank the Walker College of Business and the University Research Council at Appalachian State University, as well as the University of Auckland Faculty Research Development Fund for funding this research. Valuable research assistance was provided by Ananrita Chaudhuri, James Jones, Sherry Li, Ryan Mills and Tony So. We are grateful to Brice Corgnet and Henk Berkman for providing valuable comments on multiple occasions. We have benefited greatly from feedback provided by participants in many conferences including those at the Economic Science Association Annual Meetings (San Diego, CA), Economic Science Association North American Meetings (Tucson, AZ and Richmond, VA), Economic Growth and Development Conference (New Delhi, India) as well as seminar participants at Monash University, Middlesex University, Utah State University, and Appalachian State University. We sincerely appreciate the feedback provided by Charles Noussair, the editor in charge of handing the paper, as well as two anonymous reviewers.
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Dickinson, D.L., Chaudhuri, A. & Greenaway-McGrevy, R. Trading while sleepy? Circadian mismatch and mispricing in a global experimental asset market. Exp Econ 23, 526–553 (2020). https://doi.org/10.1007/s10683-019-09623-0
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DOI: https://doi.org/10.1007/s10683-019-09623-0