Brice Corgnet
email: corgnet@em-lyon.com

Camille Cornand
email: cornand@gate.cnrs.fr

Nobuyuki Hanaki
email: nobuyuki.hanaki@iser.osaka-u.ac.jp

Emotional Markets: Competitive Arousal, Overbidding and Bubbles

Keywords: Competitive arousal, trading institutions, feedback, emotions and risk C91, C92, G41, D91. Emotional Markets: Competitive Arousal, Overbidding and Bubbles  Competitive arousal, trading institutions, feedback, emotions and risk. JEL codes: C91, C92, G41, D91

This paper investigates the influence of trading institutions on emotional arousal and bidding behavior through a series of behavioral and physiological experiments involving an investment task. In line with the competitive arousal hypothesis, we show that markets exacerbate the emotional arousal associated with winning bids, especially when buying the asset leads to substantial earnings. The Market treatment exhibits overbidding and bubble dynamics that are much less pronounced in the baseline treatments that use a BDM mechanism. These treatment differences disappear for investors who exhibit no base rate emotional arousal, suggesting that overbidding and bubbles might be eliminated by venting investors' excitement. Our study also suggests an alternative solution: designing trading institutions that mitigate competitive arousal.

Introduction 1.Emotions in markets

Markets are the cornerstone of modern economies, being an essential tool for the allocation of resources. Yet, they are complex systems whose behavior has been difficult to decipher.

Markets are unexpectedly volatile (Shiller 1981a;1981b;[START_REF] Ekman | Are There Basic Emotions?[END_REF], and tend to repeatedly produce mispricing patterns in the form of bubbles and crashes [START_REF] Smith | Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets[END_REF][START_REF] Sornette | Why Stock Markets Crash[END_REF][START_REF] Aliber | Manias, Panics, and Crashes[END_REF][START_REF] Greenwood | Bubbles for Fama[END_REF]. Although economists and finance scholars continue to debate the extent of these anomalies [START_REF] Fama | Two Pillars of Asset Pricing[END_REF], few would oppose that market prices can, at times, fail to reflect the available information. However, if market prices do not perfectly reflect fundamental information, what factors are they responding to?

To answer this question, numerous works have emphasized investors' biases in assimilating new information. These biases have been modeled as failures to apply Bayesian updating, relying instead on heuristics that give an excessive weight to small samples [START_REF] Rabin | Inference by Believers in the Law of Small Numbers[END_REF] and to any information confirming one's prior beliefs [START_REF] Daniel | Investor Psychology and Security Market Under-and Overreactions[END_REF]. Other papers have also emphasized people's inability to extract information from market signals [START_REF] Hong | A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets[END_REF][START_REF] Corgnet | What Makes a Good Trader? On the Role of Intuition and Reflection on Trader Performance[END_REF][START_REF] Eyster | Financial Markets Where Traders Neglect the Informational Content of Prices[END_REF].

Although previous research has emphasized cognitive limitations as the main driver of mispricing, a few exceptions in the literature have considered the role of emotions. For example, [START_REF] Andrade | Bubbling with Excitement: An Experiment[END_REF] have shown that inducing certain emotions, like excitement, can increase mispricing in experimental markets. In a similar setup, [START_REF] Breaban | Emotional State and Market Behavior[END_REF] have assessed traders' emotional state using face-reading software and found that price levels were lower (higher) when people entered the market in a more fearful (happier) emotional state. The literature on emotions in markets remains scant because of the technical challenges associated with measuring emotions. Yet, a recent study by [START_REF] Bossaerts | Emotional Engagement and Trading Performance[END_REF] has used physiological recordings to assess traders' arousal over the course of a market experiment. They have shown that anticipatory reactions measured using heart-rate recordings can predict traders' earnings. These findings extend prior empirical research using nonexperimental methods (see e.g., [START_REF] Lo | The Psychophysiology of Real-Time Financial Risk Processing[END_REF][START_REF] Shefrin | Beyond Greed and Fear[END_REF][START_REF] Lo | Fear and Greed in Financial Markets: A Clinical Study of Day-Traders[END_REF].

The research on emotions in financial markets has thus far focused on understanding the impact of emotions on market outcomes and traders' performance. However, we contend that it is critical to ask ourselves the reverse question: What is the impact of the trading institution on emotions? This new focus is insightful because trading institutions, unlike emotions that are deeply rooted in our revolutionary past [START_REF] Damasio | The Nature of Feelings: Evolutionary and Neurobiological Origins[END_REF][START_REF] Plutchik | The Nature of Emotions: Human Emotions Have Deep Evolutionary Roots, a Fact That May Explain Their Complexity and Provide Tools for Clinical Practice[END_REF], can be more readily reshaped. It follows that if certain emotions induce excessive mispricing and negatively impact traders' wealth, it would be most effective to design a new trading institution that tames these emotions, rather than attempting to control the emotional response of individual investors.

Our physiological study

Our aim is to compare trading institutions and assess whether markets hinder or exacerbate traders' emotional reactions and subsequent bidding behavior. To compare trading institutions, we employ a between-subject experimental design with three treatments: Baseline, Baseline-Feedback and Market. Inspired from Corgnet, Cornand, and Hanaki (2024) (CCH henceforth), the Baseline features an individual investment task, in which investors use a standard BDM mechanism to bid over 300 periods for a financial asset that delivers a small positive reward (either 10¢, 20¢, 30¢, 40¢ or 50¢) in more than 99% of the cases and a large loss (1,000 ¢) otherwise. The Baseline-Feedback treatment is the same as the Baseline except that investors can observe others' bids in the previous period. The Baseline-Feedback treatment is used as a control treatment that can be directly compared to the Market treatment as both give investors the same information about others' bids. The Market treatment thus only differs from Baseline-Feedback in the institution used to purchase the asset. In the Market treatment, we replace the uniform draw of the BDM mechanism by a uniform draw over the bids of the participants as in a random n th -price auction (see [START_REF] Shogren | A Random Nth-Price Auction[END_REF], thus endogenizing the price of the asset.

According to standard expected utility predictions, the Baseline and Market treatments should lead to the same bidding behavior. In contrast with this prediction, relying on the behavioral literature, we develop three hypotheses stating that treatment differences will emerge due to the presence of feedback and the competitive nature of markets.

In Hypothesis 1, we posit that bids in the Baseline-Feedback treatment will tend to be higher than in the Baseline treatment due to the peer effects associated with social feedback commonly observed in the literature [START_REF] Corazzini | Herding, Social Preferences and (Non-)Conformity[END_REF][START_REF] Kirchler | Rankings and Risk-Taking in the Finance Industry[END_REF][START_REF] Lahno | Peer Effects in Risk Taking: Envy or Conformity?[END_REF][START_REF] Kuziemko | Last-Place Aversion': Evidence and Redistributive Implications[END_REF][START_REF] Dijk | Rank Matters-The Impact of Social Competition on Portfolio Choice[END_REF][START_REF] Fafchamps | Keep up with the Winners: Experimental Evidence on Risk Taking, Asset Integration, and Peer Effects[END_REF][START_REF] Lindner | Social Motives and Risk-Taking in Investment Decisions[END_REF][START_REF] Gortner | Peer Effects and Risk Sharing in Experimental Asset Markets[END_REF].

In Hypothesis 2, we predict differences between Market and the two baseline treatments due to the impact of market competition on the emotional arousal of bidders. Competition, defined as the pursuit of scarce and contested assets [START_REF] Malhotra | The Desire to Win: The Effects of Competitive Arousal on Motivation and Behavior[END_REF][START_REF] Deutsch | A Theory of Co-Operation and Competition[END_REF], is a distinctive feature of markets compared to individual decision making in the baseline treatments. In line with the competitive arousal hypothesis [START_REF] Ku | Towards a Competitive Arousal Model of Decision-Making: A Study of Auction Fever in Live and Internet Auctions[END_REF][START_REF] Malhotra | The Desire to Win: The Effects of Competitive Arousal on Motivation and Behavior[END_REF][START_REF] Adam | Auction Fever! How Time Pressure and Social Competition Affect Bidders' Arousal and Bids in Retail Auctions[END_REF], we posit that competition will exacerbate emotional arousal associated with buying the asset and foster overbidding. Because in our experiment, competitive cues are, by design, more pronounced in the Market treatment than in the two baseline treatments, the competitive arousal hypothesis suggests bids will be higher in Market than in Baseline and Baseline-Feedback.

As a direct test of the competitive arousal hypothesis, we tested an additional hypothesis.

Hypothesis 3i states that emotional arousal will be higher in Market after a winning bid than in the two baseline treatments. Hypothesis 3ii posits that investors who exhibit a high level of base rate emotional arousal at the start of the experiment will bid higher in Market than in the baseline treatments whereas no treatment differences will be observed for investors who exhibit a low level of base rate arousal.

In a series of experiments conducted with 560 participants, we find that feedback has only a minimal impact on bidding behavior thus leading us to reject Hypothesis 1. We find support for Hypothesis 2 as overbidding is more pronounced in Market than in the baseline treatments.

In line with Hypothesis 3i, we find that emotional arousal, as measured using electrodermal activity (as in CCH) is substantially higher in Market than in the baseline treatments after winning bids while no differences are observed after non-winning bids. Exploring the competitive arousal hypothesis further, we also show that the increase in arousal due to winning bids in Market is more pronounced when the asset payoff was high. This shows that trading in a market rather than in a non-market institution can exacerbate the emotional arousal associated with material gain, which can be interpreted as a physiological manifestation of greed [START_REF] Seuntjens | Defining Greed[END_REF][START_REF] Frydman | What Drives Peer Effects in Financial Decision-Making? Neural and Behavioral Evidence[END_REF]. In line with Hypothesis 3ii, treatment differences in bidding behavior are only observed for investors who exhibit a high level of base rate arousal. In a final exploratory analysis, we show that investors exhibiting high base rate emotional arousal earn less and are more likely to go bankrupt.

Contributions

Overbidding in auctions

Our study of the causal impact of markets on emotional arousal contributes to several strands of the literature. In the auction literature, our approach sheds new light on the overbidding anomaly (see [START_REF] Cooper | Understanding Overbidding in Second Price Auctions: An Experimental Study[END_REF][START_REF] Kagel | 9. Auctions A Survey of Experimental Research[END_REF] Levin 2016 for a review). Our findings show that overbidding is likely due to competitive arousal, which is inherent to auctions. Exacerbated emotions in auctions could also, if associated with higher stress levels, impair cognitive functions [START_REF] Shields | The Effects of Acute Stress on Core Executive Functions: A Meta-Analysis and Comparison with Cortisol[END_REF] and mediate the effect of inattention that has been identified by [START_REF] Malmendier | The Bidder's Curse[END_REF] as a main driver of overbidding. Unlike competition, social feedback alone does not explain overbidding. We indeed show that in the absence of competition, bids are more in line with the risk-neutral valuation of the asset, regardless of the presence of social feedback (Baseline-Feedback) or not (Baseline).

Emotions in markets

The finance literature on emotions is scant and has ignored the impact of trading institutions on emotional reaction. 1 Our work demonstrates that trading institutions play an important role in explaining investors' emotional arousal. In particular, our results show that emotions play a critical role in explaining overbidding in markets. Importantly, traders who display reduced base rate emotional arousal do not exhibit substantial overbidding in Market. These findings relate to models of overbidding that rely on emotions such as regret [START_REF] Engelbrecht-Wiggans | Regret in Auctions: Theory and Evidence[END_REF]2008), spite [START_REF] Kirchkamp | Spite vs. Risk: Explaining Overbidding in the Second-Price All-Pay Auction[END_REF][START_REF] Mill | Competition between Friends and Foes[END_REF] and loss contemplation [START_REF] Delgado | Understanding Overbidding: Using the Neural Circuitry of Reward to Design Economic Auctions[END_REF]. However, the type of emotion we identify differs from that considered in previous works as it specifically relates to the joy of winning.

Financial bubbles

Our results also provide insights to another prevalent mispricing phenomenon: financial bubbles. Interestingly, we show that, even in the absence of retrading and speculative motives, bubbles and crashes patterns can be observed in markets. Unlike the dominant speculative hypothesis, our results show that it is the competitive nature of the market itself along with the 1 One exception is the work of [START_REF] Breaban | Emotional Differences between Isomorphic Auctions[END_REF] comparing first price and Dutch auctions. However, they do not consider non-auction institutions thus not analyzing the distinct effect of markets. competitive arousal exhibited by human traders that trigger bubbles and crashes. 2 Our findings thus show that exacerbated emotional arousal inherent to market competition can generate mispricing.

Competitive arousal

Finally, our study provides a direct test of the competitive arousal hypothesis by exogenously manipulating competition and measuring physiological arousal directly. Our results demonstrate the moderating role of emotional arousal on bidding behavior and the relationship between arousal and the "joy of winning" [START_REF] Cox | Theory and Individual Behavior of First-Price Auctions[END_REF]. Furthermore, we reveal that the "joy of winning" may also relate to greed [START_REF] Seuntjens | Defining Greed[END_REF][START_REF] Frydman | What Drives Peer Effects in Financial Decision-Making? Neural and Behavioral Evidence[END_REF] because the emotional arousal triggered by winning bids in Market is more pronounced when asset payoffs were high. To our knowledge, the only other direct tests of the competitive arousal hypothesis in the literature are due to [START_REF] Adam | Auction Fever! How Time Pressure and Social Competition Affect Bidders' Arousal and Bids in Retail Auctions[END_REF] and [START_REF] Teubner | The Impact of Computerized Agents on Immediate Emotions, Overall Arousal and Bidding Behavior in Electronic Auctions[END_REF] who manipulated rivalry by replacing human bidders with computerized bidders in experiments using English and first-price sealed-bid auctions, respectively. They found that prices were higher when bidders competed with other human bidders than when competing with computers, and their arousal, measured by heart rates and electrodermal activity, was higher when competing with other human bidders. However, unlike our study, the manipulations of [START_REF] Adam | Auction Fever! How Time Pressure and Social Competition Affect Bidders' Arousal and Bids in Retail Auctions[END_REF] and [START_REF] Teubner | The Impact of Computerized Agents on Immediate Emotions, Overall Arousal and Bidding Behavior in Electronic Auctions[END_REF] did not directly impact competition but the social dimension of competition as the authors rightly acknowledged.

Design

We designed an incentivized experiment that allows us to observe participants' behavioral and physiological reaction to the realization of financial gains and losses in an investment task across trading institutions. We design three treatments (Baseline, Baseline-Feedback and Market treatments) so as to disentangle the effect of social information and competition on investors' bidding behavior. The experiment consisted in two parts. In Part 1, participants earned money by responding to a survey eliciting various psychological and cognitive characteristics (Section 2.4). In Part 2, participants played a repeated investment task under the 2 Although dominant, the speculative hypothesis had been challenged by [START_REF] Lei | Nonspeculative Bubbles in Experimental Asset Markets: Lack of Common Knowledge of Rationality vs. Actual Irrationality[END_REF] who showed that bubbles could be observed even in the absence of retrading of shares using an experimental setup à la [START_REF] Smith | Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets[END_REF]. However, the follow-up studies of Tucker and Xu (2024a;2024b) show that carefully removing speculative motives indeed eliminates bubbles. three treatments in a between-subject design (Sections 2.1 and 2.2). In addition, in order to capture the role of emotions across trading institutions, we recorded physiological measures while participants played the investment task for half of the sessions (called Baseline physio, Baseline-Feedback physio and Market physio treatments, see Section 2.3). The protocol is described in Section 2.5.

Investment Task (Baseline)

The design of the investment task of the Baseline (Part 2) is taken from CCH. We elicited participants' willingness to pay for an asset using the BDM method. At the beginning of each of the 300 periods, participants had to bid for a financial asset that delivered a small positive reward (either 10¢, 20¢, 30¢, 40¢ or 50¢) in 99.33% of the cases and a very large loss (1,000¢) otherwise. While rare, very large losses represent a standard feature of assets in financial markets. Primarily, we introduce this feature to maintain participant engagement throughout the 300 periods, ensuring the validity of our physiological measurements over the course of the experiment (see Section 2.3). The expected value of the asset each period was 23.1¢. The bid (any integer between 0 and 50) in each period was compared to a price (also an integer) randomly drawn from a uniform distribution between 1 and 50. If the bid of a participant was greater than or equal to the price, they paid the price and purchased the asset, otherwise they did not purchase the asset.

At the end of each period, a feedback screen informed participants about the reward of the financial asset, the earnings for the current period as well as cumulated earnings, which were equal to the initial endowment, composed of Part 1 fixed wage (1,200¢), plus the gains and losses from buying the asset in previous periods.

To make the potential monetary loss associated with investing in an asset meaningful, we asked participants to invest the fixed wage they earned in Part 1 during the investment task. In addition, participants were given a loan of 1,000¢ for liquidity reasons, which had to be repaid at the end of the experiment (as in [START_REF] Lei | Nonspeculative Bubbles in Experimental Asset Markets: Lack of Common Knowledge of Rationality vs. Actual Irrationality[END_REF]Sunder, 1982, 1988). 3 If the current wealth of participants (including the loan) was no longer sufficient to repay the loan, they would go bankrupt. 4 In that case, participants were not able to purchase the asset anymore and had to wait until the end of the session (while provided with Internet access). Investors who went bankrupt lost the fixed wage they earned on Part 1 and were only rewarded a 5-euro show-up fee. 5Because participants can lose all their endowment when they face very large losses, they might believe the experimenter is purposefully engineering the draws to ensure very large losses would occur, thus reducing participants' earnings and lowering the cost of the experiment. To make it clear to the participants that the sequence of draws was random and thus unpredictable, we adopted the following hand-run procedure. Before participants read the instructions, we showed them a transparent box containing 302 tokens of 6 different colors, each of which was associated with a potential return from the asset (blue token = 10¢, red token = 20¢, orange token = 30¢, green token = 40¢, purple token = 50¢, yellow token = -1,000¢). There were 60 tokens of each color, except for two yellow tokens. Once everyone had seen the tokens, we told participants we were taking a picture of the box that would be displayed on their screens during the experiment. 6 By observing this picture during the experiment, participants could form an estimate of the frequency of occurrence of each token. The distribution of tokens was thus not fully known by participants so as to allow for learning during the experiment.

For the first 15 sessions we conducted (Baseline physio of CCH), one participant, the picker, was randomly selected and escorted to a separate room. We asked the picker to put all the tokens in the transparent box into an opaque bag and draw the tokens with replacement. The picker entered the token draws on a computer and on a separate sheet of paper in real time. The picker signed this sheet of paper upon completion of the task, and it was then shown to all other participants at the end of the experiment to ensure the credibility of the procedure. The picker did not know the instructions for the investment task to avoid any cheating attempts or any retaliation by peers. 7 The picker was paid a fixed amount of 15, but incurred a 5-euro penalty if the task was not completed within one hour to ensure timely completion of the experiment. 8During the task, one of the experimenters closely monitored the picker to ensure they followed the procedure. For all the other sessions, instead, we explained that 15 participants, called pickers, had been randomly selected in 15 previous experimental sessions. We precisely explained their role to participants. At the beginning of each of these other sessions, a participant was randomly selected to choose a number between 1 and 15 in order to select the sequence of draws from one of the 15 previous experimental sessions. For each treatment, we ensured that a sequence of draws could not be selected in more than one session, thus facilitating comparability across treatments.

Treatment conditions (Baseline-Feedback and Market)

In addition to the Baseline described above, we implemented a Baseline-Feedback treatment and a Market treatment. The aim of the Baseline-Feedback treatment was to capture the potential effect of social information. In the Baseline-Feedback treatment, participants observed the individual bids set by all the other investors in the group after they made their decision and before receiving any feedback regarding their earnings in a period. The only feature that differed between the Baseline and Baseline-Feedback treatments was the additional feedback screen displayed to participants.

In the Market treatment, the same feedback screen (information on others' bids) as in the Baseline-Feedback treatment was displayed. The only difference between the Market and Baseline-Feedback treatments was the pricing mechanism. In the Market treatment, the uniform random draw over [START_REF]You have entered a price of 23 at which you[END_REF]50] of the BDM mechanism was replaced by a uniform random draw over the bids set by the participants to determine the price. Those with a bid strictly higher than the price bought the asset. This is similar to a random n th -price auction. 9,10 The random n th -price auction resembles a BDM mechanism because both the price and the number of buyers will vary across iterations. According to [START_REF] Shogren | A Random Nth-Price Auction[END_REF], similarly to the BDM mechanism, truth-telling is the dominant strategy in a private value n th -price auction.

Comparing the Baseline-Feedback treatment to the Market treatment allows us to capture the 9 The reason for not using the k th -price auction for the Market treatment (with the number of assets being sold to be (k-1)) is to avoid making the number of assets being sold constant across periods. Note that the number of assets bought in the baseline treatments is not fixed. By picking one bid at random, we introduce randomness in the number of assets being sold, with the maximum number of sold assets being equal to the number of players in the market minus one, and the minimum being zero. 10 Although the Market treatment was made as comparable as possible to the Baseline-Feedback treatment, two adjustments were made with respect to the BDM mechanism used in the Baseline and Baseline-Feedback treatments. First, the condition for a participant to buy the lottery was conditional on setting a price strictly higher than the selected price. Second, a participant could only enter a price between 1 and 50 (rather than between 0 and 50). This ensured that a participant could always decide not to participate in the auction by setting a price equal to one. impact of competition. The Market treatment is designed so that under expected utility theory, we expect no differences in bids across treatments. Using simulations of the time series of bids for Market and Baseline, we confirm this claim, and show that it holds true for a broader class of models studied in CCH (see Online Appendix II).

An English translation of the instructions for the investment task for each of the three treatments is reported in Internal Appendix A.1.

Measurement of emotions

For our three treatments, we conducted some sessions during which participants played the investment task while physiological measures were recorded (Baseline physio, Baseline-Feedback physio and Market physio treatments). This experimental design feature allowed us to precisely assess the emotional arousal (i.e., the magnitude of an emotional response) of participants using physiological tools measuring electrodermal activity during the investment task (Critchley et al., 2000;Boucsein, 2012;Christopoulos et al., 2019). This emotional arousal is a manifestation of the basic emotion of surprise (Ekman, 1999) and as such is deprived of positive or negative valence. The Baseline physio data are those collected by CCH and serve as a benchmark, while the Baseline-Feedback physio and Market physio data have been collected for the purpose of studying the impact of emotions in a market context.

From a practical point of view, following CCH, one of the experimenters placed electrodes on each participant's second phalanx (palmar surface) of the index and middle fingers of the nondominant hand using a Velcro strap and isotonic gel. Another experimenter checked the quality of the recordings before the experiment could start. Setting up the physiological equipment took about 20 minutes on average.

In our setup, we recorded electrodermal responses to two types of stimuli:11 i) a decision is made (referred to as decision arousal) 12 and ii) the earnings for the period are shown on the screen (referred to as feedback arousal). Because our focus is on the emotional arousal associated with buying or not buying the asset, we focus on feedback arousal in our analyses.

To ensure sufficient time elapsed between stimuli, we inserted a four-second waiting screen after a decision was made and after the receipt of the end-of-period feedback. A timer on the screen indicated the time participants had to enter a price using a cursor. If participants did not enter a price on the screen and validate their decision on time, the number indicated by the cursor was selected. To prompt participants to make a decision each period, the default value on the cursor was 50. Our metric of interest is the amplitude of the signal as computed using the Matlab routine developed in Joffily's (2018) electrodermal activity toolbox, which is equal to the peak of the physiological response measured in microsiemens. 13 In the Baseline-Feedback physio sessions, to get a sense of the valence of the emotions involved, we included a post-experimental survey (see Internal Appendix A.2) in which we asked participants about the emotion (anger, fear, joy, and sadness) they felt when they faced a financial asset that delivered a 10 ¢ reward and a 50¢ reward (see Internal Appendix A.3).

Survey

In Part 1, we collected extensive individual information regarding risk, loss and ambiguity attitudes as well as personality traits, cognitive skills and demographic data. We also elicited estimations of the percentage of yellow and orange tokens in the photograph of the box of tokens that was displayed on participants' screens during the investment task in Part 2. An English translation of the 8 blocks of tests that we performed is reported in Online Appendix I.1. 14

Protocol

Between May 2019 and October 2023, we invited a total of 560 participants from a participant pool of more than 2,500 students at a major university, where 44% of the participants were 13 As explained in CCH, in contrast to Breaban and Noussair (2018) and Kunreuther and Pauly (2018), we did not use face-reading software or survey measures to elicit the valence of emotions during the investment task. Although very appealing in identifying emotions, face-reading techniques have been challenged by emotion scholars (e.g., Keltner and Cordaro, 2017;Barrett et al., 2019;Martinez, 2019;Pollack et al., 2019). Moreover, we wanted to avoid that the elicitation of emotional valence interfered with the behavior in the investment task and with our physiological recordings. Finally, eliciting emotional valence throughout the investment task would have lengthened an already long experiment. 14 In sessions implementing Baseline, Baseline-Feedback and Market treatments, we conducted the tests of Blocks 1 to 8. In half of Baseline physio sessions, we conducted 12 blocks (see CCH). In Baseline-Feedback physio sessions, Market physio sessions and half of Baseline physio sessions, for Part 1 to be implementable online within a reasonable duration of 20 minutes, we only conducted the following tests: risk aversion in the gain domain (Block 2), estimation of tokens (Block 5), loss aversion (Block 8), availability heuristic 1 & 2 (Blocks 7 and 10), questions 5, 11, 17, 21, 23, 29 35, 41, 45, 47, 53, 59, 69, 93 of the personality test (Block 3), and demographic data (Block 12); we also added a gambling fallacy test (Block 13). males and their average age was 21.8 years old. All the tasks were computerized. We conducted a total of 62 sessions (see Table 1). Between November 2018 and June 2019, sessions were all conducted in the laboratory. For the Baseline physio sessions, the two parts of the experiment took place on two different days. To limit attrition, participants were only paid the show-up fee (5 euros) at the end of the first part and thus needed to come back on another day to collect their earnings, which consisted of a fixed wage of 12 euros and a small variable (either positive or negative) amount of pay depending on their decisions in some of the tests, after completion of the repeated investment Overall, these sessions lasted for 3.5 hours on average. Earnings were similar to Baseline physio sessions except that participants did not receive a second show-up fee for their presence on a second day.

Hypotheses

In our design, comparisons across treatments allow us to study the two main features of the market institution: social feedback and competition. Based on the existing literature, we derive three pre-registered hypotheses regarding the impact of these dimensions on bidding behavior. 18

Social feedback (Hypothesis 1)

Regarding the social feedback dimension, a rapidly growing number of experiments have shown evidence of peer effects in risk-taking in financial decisions. 19 Part of this literature focuses on rank incentives, which are non-monetary incentives related to one's relative position. For example, [START_REF] Kirchler | Rankings and Risk-Taking in the Finance Industry[END_REF] show that rank incentives increase risk-taking among underperforming professionals, but not among students, when they invest for themselves. 20 Corazzini and Greiner (2007) do not find any effect of others' information in sequential risky decisions in line with [START_REF] Kirchler | Rankings and Risk-Taking in the Finance Industry[END_REF] results with students. [START_REF] Lindner | Social Motives and Risk-Taking in Investment Decisions[END_REF] extend the analysis of [START_REF] Kirchler | Rankings and Risk-Taking in the Finance Industry[END_REF] on rank incentives by separating the effects of self-image and status motives on risk-17 Physiological experiments were not possible during Covid times, which explains the time gap between experiments. 18 In the pre-registration document, Hypothesis 1 encompasses Hypotheses 1 and 2, as described in this section. Hypothesis 3 corresponds to Hypotheses 2 and 3 in this section. 19 See [START_REF] Trautmann | Social Influences on Risk Attitudes: Applications in Economics[END_REF] for an overview of these effects in decisions under risk. 20 Kirchler, Lindner, and Weitzel (2020) show that the same result is obtained when they invest on behalf of third parties.

taking. They show that risk-taking among students is higher when the winner or the loser is publicly announced. However, they do not observe these effects for the case of professionals.

Finally, they observe that underperforming investors take more risks than outperformers when rankings are displayed and the winner or loser is publicly announced. In the same vein, [START_REF] Kuziemko | Last-Place Aversion': Evidence and Redistributive Implications[END_REF] show that individuals take more risks when they are at the very bottom of a performance ranking because of a phenomenon they refer to as 'last-place aversion'. [START_REF] Dijk | Rank Matters-The Impact of Social Competition on Portfolio Choice[END_REF] and [START_REF] Fafchamps | Keep up with the Winners: Experimental Evidence on Risk Taking, Asset Integration, and Peer Effects[END_REF] also find that underperformers take more risks to catch up with top performers. [START_REF] Schwerter | Social Reference Points and Risk Taking[END_REF] finds that portfolio choices depend on a social reference point such as another participant's income. They

show that decision makers make less risk-averse choices when peers' earnings are high. Lahno

and Serra-Garcia ( 2015) demonstrate that both social learning and income comparisons play an important role in understanding peer effects, where social learning occurs when one obtains critical information about the value of an investment by observing others' decisions. 21 In a portfolio choice experiment, Gortner and van der Weele (2019) find that peer information lowers within-group variation in peer earnings and increases diversification, thus reducing risktaking. Beyond individual portfolio choices, [START_REF] Schoenberg | Relative Performance Information in Asset Markets: An Experimental Approach[END_REF] study the impact of social information in an experimental asset market. They find that observing the earnings of the top performer increases the likelihood of bubbles.

In our experimental design, the previously-studied peer effects are likely to be weak because we do not provide information about peers' earnings and rankings. Because we do not display other participants' wealth, we prevent investors from imitating high performers' behavior. Yet, we can still observe conformist behaviors in which investors place bids that converge over time toward the median or mode of the previous period (see [START_REF] Gortner | Peer Effects and Risk Sharing in Experimental Asset Markets[END_REF].

However, it is unclear how this conformist behavior would affect investors' risk-taking.

Previous works have also used field data to assess peer effects in financial decisions such as stock market participation [START_REF] Hong | Social Interaction and Stock-Market Participation[END_REF][START_REF] Kaustia | Peer Performance and Stock Market Entry[END_REF] and trading decisions [START_REF] Kelly | Market Contagion: Evidence from the Panics of 1854 and 1857[END_REF][START_REF] Hong | Thy Neighbor's Portfolio: Word-of-Mouth Effects in the Holdings and Trades of Money Managers[END_REF][START_REF] Shive | An Epidemic Model of Investor Behavior[END_REF]). For example, [START_REF] Simon | Facebook Finance: How Social Interaction Propagates Active Investing[END_REF] provide evidence that social interactions contribute to the use of active investment strategies. Using a high-stakes field experiment conducted with a brokerage firm, [START_REF] Bursztyn | Understanding Mechanisms Underlying Peer Effects: Evidence from a Field Experiment on Financial Decisions[END_REF] study two different channels by which peer effects might operate: social learning and social utility, which is the utility one gets from holding the same asset as others. Both social learning and social utility channels are found to have statistically significant effects on investment decisions.

In our setup, the Baseline-Feedback treatment introduces social feedback by showing other traders' bids to all participants. This provides bidders with all the information they need to calculate and understand their payoff in a given period. Yet, we purposefully left aside many of the ingredients which have been shown to trigger peer effects such as ranking incentives and social interactions. These other types of social feedback might have increased participants'

competitive drive, making it harder to isolate the distinct impact of the Market treatment on competition and bidding behavior. It follows that in our design the two main channels for peer effects, social learning and social utility, are limited. Social learning is restricted because there are no social interactions and all the information about the asset is publicly available to investors. In addition, our investment decision is simple and does not require uncovering the solution to an intricate optimization problem. Participants are thus less likely to imitate others' decisions than in a complex investment task with multiple assets (Gortner and van der Weele 2019; Apesteguia, Oechssler, and Weidenholzer 2020). Furthermore, participants are not assigned fixed identification numbers so that no one can track others' strategies over time.

Social utility is also limited because our participants are anonymous and social interactions are absent.

By design, we thus expect the difference in bidding behavior between the Baseline and Baseline-Feedback to be of limited magnitude. That said, in line with the existing literature on peer effects and risk-taking, we expect feedback to promote rather than hinder risk taking thus leading to higher bids in Baseline-Feedback than in Baseline. We summarize this conjecture in Hypothesis 1.

Hypothesis 1 (Social feedback)

Bids in Baseline-Feedback will be higher than in Baseline.

Competition

Beyond social feedback, the Market treatment differs from Baseline because of the presence of competition [START_REF] Deutsch | A Theory of Co-Operation and Competition[END_REF][START_REF] Malhotra | The Desire to Win: The Effects of Competitive Arousal on Motivation and Behavior[END_REF]). In the Market treatment, traders compete for the purchase of the asset in an auction so that not all bidders can buy the asset. It follows that if traders are unable to purchase the asset, it is because others have outbid them. In contrast, in the Baseline and Baseline-Feedback treatments, all traders might be able to buy the asset in a given period if the random BDM number is low enough. In the baseline treatments, when traders fail to purchase the asset, they can attribute it to an unusually high random BDM number and not, unlike the Market treatment, to the bidding behavior of other traders.

In line with the competitive arousal hypothesis [START_REF] Ku | Towards a Competitive Arousal Model of Decision-Making: A Study of Auction Fever in Live and Internet Auctions[END_REF][START_REF] Malhotra | The Desire to Win: The Effects of Competitive Arousal on Motivation and Behavior[END_REF]), we posit that competition will exacerbate emotional arousal associated with buying the asset and magnify the "joy of winning" (see [START_REF] Cooper | Understanding Overbidding in Second Price Auctions: An Experimental Study[END_REF][START_REF] Bos | The Value of Victory: Social Origins of the Winner's Curse in Common Value Auctions[END_REF][START_REF] Wells | Review of Social Psychology by Floyd Allport[END_REF][START_REF] Cox | Theory and Individual Behavior of First-Price Auctions[END_REF]. [START_REF] Ku | Towards a Competitive Arousal Model of Decision-Making: A Study of Auction Fever in Live and Internet Auctions[END_REF] emphasize that the desire to win is magnified in the presence of competition, thus leading to higher bids in the Market treatment than in the two baseline treatments. We summarize this prediction as follows.

Hypothesis 2 (Competition)

Bids in Market will be higher than in Baseline and Baseline-Feedback.

We note that the literature isolating the competition dimension of markets is scant, as we could only identify one paper [START_REF] Mengel | Do Markets Encourage Risk-Seeking Behaviour?[END_REF] directly comparing a market mechanism with an individual investment task. [START_REF] Mengel | Do Markets Encourage Risk-Seeking Behaviour?[END_REF] aim at studying the causal impact of markets on risk-taking. To that end, they compare a market treatment implemented using a call auction with a non-market treatment implemented using a BDM. In both treatments, people could trade two assets that varied in their riskiness. When the bids and asks of other participants were displayed in both treatments, Mengel and Peeters report that the risk premium on the riskier asset was larger in the market than in the non-market treatment toward the end of the experiment. However, their study uses a complex environment with private information, uncertainty and multiple assets, making it difficult to directly compare their setup with ours.

Emotions and financial decisions

Numerous works have emphasized how emotions can alter expected utility calculations, as put forth by the proponents of the 'risk-as-feelings' hypothesis [START_REF] Loewenstein | Risk as Feelings[END_REF] or the 'affect heuristic' [START_REF] Slovic | The Affect Heuristic[END_REF]). In the finance literature, scholars have increasingly recognized the relevance of emotions in markets [START_REF] Shefrin | Beyond Greed and Fear[END_REF][START_REF] Lo | Adaptive Markets: Financial Evolution at the Speed of Thought[END_REF], showing that induced excitement can produce higher bids [START_REF] Andrade | Bubbling with Excitement: An Experiment[END_REF].

In particular, the competitive arousal hypothesis posits that markets will produce "an adrenaline-laden emotion state that can arise during competitive interaction" (Malhotra 2010, p. 140). The physiological arousal triggered by winning a competitive auction has long been recognized as is illustrated by the "calor licitantis" ("bidder's heat"), which under Roman law, protected a bidder who had excessively paid due to bidder's fever (Corpus Juris Civilis, D.

39,4,9 pr.) (see [START_REF] Malmendier | The Bidder's Curse[END_REF].

To test the competitive arousal hypothesis further, we assess the impact of the Market treatment on emotional arousal, and the moderating role of emotional arousal on bidding behavior. In line with the competitive arousal hypothesis, we expect that emotional arousal associated with winning bids will be higher in Market than in the two baseline treatments whereas no differences will be observed for non-winning bids. Because the arousing effect of winning bids will not be observed for people who do not exhibit a base rate emotional response to bidding outcomes, we expect no treatment differences for these traders. In contrast, we expect bids to be higher in Market than in the baseline treatments for traders who exhibit a high base rate level of emotional arousal. We summarize our predictions regarding emotional arousal in Hypothesis 3.

Hypothesis 3 (Arousal and Markets)

i) Emotional arousal will be higher in Market than in Baseline and Baseline-Feedback for winning bids but no differences will be observed for non-winning bids.

ii) Bidders with a high base rate of emotional arousal will bid higher in Market than in Baseline and Baseline-Feedback whereas no treatment differences will be observed for those with a low base rate.

Results

As pre-registered and in line with CCH, our model specification uses panel regressions with random effects and robust standard errors clustered at the session level, with and without all the individual controls collected for all treatments.

Hypotheses 1 & 2 (Bids across treatments)

We first study the dynamics of bids across treatments and show that bids started at similar levels before diverging around period 100 (see Figure 1). Even though our setup is one in which all periods are independent so that one cannot retrade the asset in future periods, the Market treatment exhibits a common bubble-crash pattern often found in the experimental bubbles literature [START_REF] Noussair | A Collection of Surveys on Market Experiments[END_REF][START_REF] Palan | A Review of Bubbles and Crashes in Experimental Asset Markets[END_REF][START_REF] Smith | Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets[END_REF]. In Overall, bids were 3.0% higher on average in Baseline-Feedback (Mean = 24.35¢, SD = 10.05¢) than in Baseline (Mean = 23.63¢, SD = 10.62¢) but these differences were not statistically significant (see Figure 1, and the non-significant variable 'Treatment Feedback' in Table 2). This leads us to reject Hypothesis 1. In line with Hypothesis 2, bids were 9.1% higher in Market (Mean = 26.11¢, SD = 12.39¢) than in Baseline and Baseline-Feedback combined (Mean = 23.93¢, SD = 10.40¢). In Table 2, we show that the difference in bids between Market and Baseline is significant (see 'Treatment Market'). The difference between Market and Baseline-Feedback does not reach significance in regressions ( 1), ( 2) and (3) (see Coefficient tests: Market = Baseline Feedback, lower part of the table) but does so in regression (4).

However, the increase in bids in Market compared to both Baseline and Baseline-Feedback treatments combined is significant (see Market = Baseline Combined, lower part of Table 2).

Table 2. Bids and treatment effects. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4 In Figure 1, we observe that Market is the only treatment in which average bids per period are significantly above the expected value of the asset in the middle of the experiment (see blue To study dynamics, we fit bids using a quadratic trend in Table B1 (Internal Appendix B.1).

We find evidence of a bubble-crash quadratic trend given that 'Period' ('Period 2 ') is positively (negatively) significant in each treatment taken separately (see regressions ( 1) to ( 3)). As suggested in Figure 1, the anatomy of the bubble-crash pattern is more pronounced for Market than for the two baseline treatments (see Quadratic Trend Features at the bottom of the table ).

Indeed, given the estimated quadratic trends for each treatment, a peak value of 25.76 (26.18) [27.85] is achieved in period 67 (96) [133] in Baseline, Baseline-Feedback and Market. At its peak, overpricing was thus equal to 20.1% of the expected value of the asset in Market compared to 11.2% and 13.0% in Baseline and Baseline-Feedback. These results indicated that the amplitude of the bubble is more pronounced in Market than in the two baselines.

Result 1. (Bids, bubbles and Competition) i) Bids were significantly higher in Market than in the two baseline treatments.

ii) Bids exhibited a more pronounced bubble pattern in Market than in the two baseline treatments.

Hypothesis 3 (Emotional markets)

The observed difference in bids between Market and Baseline treatments cannot be explained by standard models as shown in Online Appendix II. As expressed in Hypothesis 3, one potential explanation relates to emotional arousal and the fact that bidding higher is highly rewarding to investors in markets in line with the competitive arousal hypothesis [START_REF] Ku | Towards a Competitive Arousal Model of Decision-Making: A Study of Auction Fever in Live and Internet Auctions[END_REF] according to which bidders tend to increase their bids due to the arousing effect of rivalry and the "joy of winning" [START_REF] Malhotra | The Desire to Win: The Effects of Competitive Arousal on Motivation and Behavior[END_REF]. The "joy of winning" is also a key argument in explaining overbidding in the contest literature (see [START_REF] Cooper | Understanding Overbidding in Second Price Auctions: An Experimental Study[END_REF]. In line with [START_REF] Malhotra | The Desire to Win: The Effects of Competitive Arousal on Motivation and Behavior[END_REF] competitive arousal hypothesis, we hypothesized in Section 3.3 that bids would be higher in auctions because they triggered higher levels of arousal than BDM.

The higher level of arousal can be due to the increased rivalry associated with a market environment compared to a BDM in line with the competitive arousal hypothesis (see Kilduff et al., (2010) andTo et al., (2018)). To our knowledge, this is the first time this hypothesis is tested using physiological measurements and a control treatment in which only competition is altered.

Our work allows us to provide a quantitative physiological measure of 'competitive arousal' by comparing the difference in physiological arousal across treatments when buying the asset.

To assess our third hypothesis, we leverage a unique dataset of 347 participants in 300 periods across three treatments amounting to 97,710 physio recordings.22

Hypothesis 3i (Arousal and joy of winning in markets)

We start our study of the role of emotions by testing Hypothesis 3i according to which winning bids that lead to buying the asset will trigger emotional arousal that will be more pronounced in Market than in the two baseline treatments. In Figure 2, we show that emotional arousal is consistently higher after a winning bid in Market, while this is not the case in the two baseline Figure 2. Proportion of investors who showed emotional arousal after observing feedback in a given period and across treatments. Red (blue) curves correspond to cases in which the investor bought (did not buy) the asset. We show the periods in which the payoff of the asset was positive (99.2% of the data). 23In Table 3, we show that these differences in emotional reaction to winning bids were significantly higher in Market than in Baseline. This is the case because the coefficient for 'Market × Win' is positive and significant across all specifications. In contrast, the coefficient for 'Baseline-Feedback × Win' is positive yet non-significant and about ten times smaller than the coefficient for 'Market × Win'. The higher emotional reaction due to winning in Market than in Baseline-Feedback is significant in all regressions (see Coefficient tests: Market × Win = Baseline-Feedback × Win, lower part of the table). In contrast, 'Market' and 'Baseline-Feedback' are not significant thus showing that no differences in arousal exist for non-winning bids between these two treatments and Baseline. Furthermore, the coefficient test for 'Market = Baseline-Feedback' cannot be rejected so that there is no significant difference in arousal between these two treatments for non-winning bids.

We note that the regressions shown in Table 3 suffer from endogeneity issues given that 'Win' is positively and significantly correlated with 'Bid' (ρ = 0.395, p-value < 0.001). It follows that the effect of winning captured in Table 3 might reflect differences in bidding behavior which, in turn, might be linked to individual differences among investors. On a positive note, our results continue to hold in regressions ( 2) and ( 4) when we control for gender, risk-aversion and loss-aversion which are the main drivers of differences in bids across participants.

Following CCH, we treat endogeneity issues in bids using observed prices. In Baseline and Baseline-Feedback, prices are determined using a BDM mechanism so that they are orthogonal to bids. In Market, prices are determined by an auction and are thus a function of bids in a given period so that, unsurprisingly, individual bids correlate positively with prices (ρ = 0.326, p-value < 0.001). To alleviate this issue, we thus constructed an alternative variable 'Price ⊥ ' that is orthogonal to bids regardless of the treatment. 24 This variable is thus uncorrelated with bids while correlating significantly with 'Win' (ρ = 0.687, p-value < 0.001). In Table B2 in Internal Appendix B.2, we use this variable as an instrument for 'Win' and replicate our findings.

Our findings are consistent with Hypothesis 3i and more generally with the conjecture that the "joy of winning" is exacerbated in Market. We examine our hypothesis in more detail by inquiring on the valence of emotion, thus complementing our physiological arousal measure.

Table 3. Arousal and winning bids. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4 In Table 3, we excluded the 0.8% of observations associated with negative payoffs. 25 This was done because these large negative payoffs trigger a specific emotional reaction unrelated to competitive arousal and that was studied in CCH for the case of Baseline. 26 The authors showed that arousal was linked to anger when investors bought the asset, and the payoff was -1,000.

Unsurprisingly, using data collected for Baseline-Feedback, we found that people did not report an anger response when buying the asset when payoffs were positive (see Table B3 in Internal Appendix B.2). 27 Out of the four basic valanced emotions (fear, anger, joy and sadness, Ekman 1992), only joy was reported as a prominent emotion in the case of buying the asset and when the payoff was equal to its maximum possible value of 50. 28 In that case, the participants reported feeling "Very much" joy and "Not at all" anger, fear or sadness. The "joy" emotion was thus more intense when buying the asset than any of the other emotions (Sign Rank Tests, all p-values < 0.001). When not buying the asset, all reported emotions were of low intensity and significantly lower than the midpoint in the Likert scale. Yet, "sadness" was the most intense emotion (Sign Rank Tests, all p-values < 0.005). This indicates a "pain of losing" effect associated with non-winning bids, which is the counterpart of the "joy of winning" effect. However, the "pain of losing" effect was of lower magnitude than the "joy of winning" (Sign Rank Test for the difference in difference, p-value < 0.001). Overall, people reported being more joyful and less sad after a winning than after a non-winning bid. Interestingly, the selfreported emotional intensity associated with the end-of-experiment questionnaire was overall in line with physiological arousal (see Figure B1 in Internal Appendix B.2).

The case of a payoff of 50 is one in which winning bids cannot be associated with monetary loss given that the maximum bid is 50. In contrast, winning bids were most of the time (65.1%) associated with a monetary loss when the asset payoff was equal to the minimum possible value of 10. In that case, we expect mixed feelings as the "joy of winning" is mitigated by the pain associated with losing money. In line with that claim, participants reported low-intensity emotions that were significantly below moderate whether they had placed a winning or a nonwinning bid (Sign Rank Tests, all p-values < 0.001) (see right panel of Table B3 in Internal Appendix B.2). In contrast with the case in which the asset payoff was 50, sadness (joy) was more (less) pronounced after a winning (non-winning) bid (Sign Rank Tests, all p-values < 0.001) when the payoff was 10. We refer to this effect as the "pain of winning". However, the impact of the "pain of winning" in our results should not be overstated. First, the "joy of winning" effect is of significantly higher magnitude than the "pain of winning" (Sign Rank

Test for the difference in difference, p-value < 0.001). Second, although winning bids were most of the time (65.1%) associated with a monetary loss when the asset payoff was 10, they were overall mostly associated with monetary gains (76.4%).

In Figure B2 in Internal Appendix B.2, we show that arousal was higher in Market than in the two baseline treatments when asset payoffs were high (40 or 50) whereas it was not necessarily the case for low payoffs (10 or 20). In Table B4 in Internal Appendix B.2, we show that the increase in arousal in Market is indeed exacerbated for high asset payoffs (40 or 50) compared to low asset payoffs (10 or 20) as shown by the positive and significant interaction term for 'Market × Win × High Payoff'. Beyond the pure "joy of winning", the increase in arousal in Market after winning bids thus also captures the "joy of money", which could reflect heightened greed in markets [START_REF] Seuntjens | Defining Greed[END_REF][START_REF] Frydman | What Drives Peer Effects in Financial Decision-Making? Neural and Behavioral Evidence[END_REF]. This is confirmed in Table B5 (Internal Appendix B.2) in which we show that the term 'Treatment Market × Win × Payoff' is consistently positive and significant.

One could ask if there is a "joy of winning" at all beyond the "joy of money". In Table B4, we

show that low payoffs (10 or 20) defined as being below the median payoff of 30 lead to a winning bid arousal as the coefficient 'Market × Win' is positive and significant. This occurs even though investors make monetary losses in half the cases (49.0%) compared to 0.7% for high payoffs (40 or 50). Yet, one notices that the coefficient for the impact of winning bids with high payoffs is twice higher than under low payoffs (Coefficient tests: 'Market × Win × High Payoff = Market × Win', all regressions p-values > 0.1). Yet, Table B6 shows that there exists an emotional reaction due to winning bids in the absence of monetary gains, which is captured by the interaction term 'Market × Win' that is positive and significant. The share of the increase in emotional arousal that is captured by the "joy of winning" rather than by the "joy of money" can be estimated as

Market × Wi

Market × Wi + Market × Wi ×M ey Gai , which ranges between 62.2% and 65.1% in the regressions in Table B6. To alleviate endogeneity issues in this estimation, we proceed in Table B7 as we did in Table B2 by using 'Price ⊥ ' as an instrument for 'Win'. In Table B6, 'Money Gain' is also endogenous because it depends on participants' bids so we use an instrument for this variable by creating a 'Money Gain Payoff' dummy variable that takes value one when asset payoffs are at least equal to 20. This variable is such that monetary gains occur 88.7% of the times after a winning bid when it takes value one compared to 32.2% when it takes value 0. This alternative estimation method led to similar results regarding the relative importance of the "joy of winning", which ranges between 58.8% and 60.5%.

In Result 2, we summarize our findings regarding our test of Hypothesis 3i.

Result 2. (Arousal across treatments)

i) Investors were substantially more aroused after winning bids in Market than in the two baseline treatments.

ii) No treatment differences in arousal were observed after non-winning bids.

iii) Only a small fraction of the arousal associated with winning bids can be attributed to monetary gains.

Hypothesis 3ii (Base rate arousal)

We proceed to testing Hypothesis 3ii according to which bids are higher in Market than in the two baseline treatments due to an increased level of arousal in this treatment. To avoid endogeneity issues when measuring the impact of emotional arousal on bidding behavior, we use a measure of base rate arousal, defined as the number of times a person was aroused (Arousal Dummy equals one) in the first five periods of each session following the definition in CCH. We then assess the impact of base rate arousal levels on bidding behavior. 29 In line with Hypothesis 3ii, we observe that investors who had a level of base rate arousal below the median, which was equal to 2, exhibited limited differences in bidding behavior across treatments (see left panel in Figure 3). In contrast, we observe treatment differences for investors who had a level of base rate arousal above the median (see right panel in Figure 3). 29 The cited authors used a 5-period cutoff because the first negative payoff across all series employed in their experiment occurs in period 7. This means the base rate arousal measure is not impacted by negative payoffs events, which we know from CCH produce a substantially higher level of arousal than normal positive payoffs.

Low base rate arousal investors

High base rate arousal investors In Tables B8 andB9, we replicate the regression analyses in Table 2 for below-and abovemedian base rate arousal investors. In line with Figure 3, we find that the significant increase in bids in Market compared to Baseline only emerges for investors with an above-median base rate arousal (see 'Market' in Table B9). This finding is consistent with Hypothesis 3ii and is summarized in Result 3.

As shown in Figure 3, bids in Baseline-Feedback for above-median base rate arousal are in between Baseline and Market. Actually, bids in Baseline-Feedback are significantly higher than in Baseline in regressions ( 1) and ( 3) while not being significantly lower than Market in any of the regressions (see 'Baseline-Feedback' in Table B9). This suggests feedback about others' bids might also have increased the "joy of winning" for investors who have a high base rate arousal level. This result shows that investors who have a high base rate level of arousal are also likely to respond to the presence of feedback by increasing their bids.

Result 3. (Bids and base rate arousal)

i) Bids were not significantly different across treatments for those who exhibited below-median base rate arousal.

ii) Bids were significantly higher in Market than in Baseline for investors who exhibited abovemedian base rate arousal but not significantly higher in Market than in Baseline-Feedback.

Exploratory analysis: earnings and bankruptcy rates

We have already shown that Market tends to produce higher bids than the two baseline treatments, especially for those investors who exhibit a high level of base rate arousal. This implies that average bids are also higher than the expected value of the asset in Market for investors with a high base rate arousal level. Because placing bids that are equal to the expected value would maximize one's expected earnings in all three treatments, it follows that investors with a high base rate arousal level will earn less in Market than in the baseline treatments (see Figure 4, upper panel).

Figure 4.

Average earnings (bankruptcy rates) across treatments for low (high) base rate arousal investors on the left (right) panel for sessions in which bankruptcies were possible, that is characterized by the occurrence of at least two negative payoffs. 95% confidence intervals included for earnings (not included for bankruptcy rates which are simple proportions).

Higher bids in Market are also associated with a higher risk of facing a large negative payoff during the experiment. In sessions in which investors faced two of these negative payoffs, they could go bankrupt. Focusing on these sessions (68.9% of the data), we observe that for high base rate arousal investors the frequency of bankruptcies was higher in Market (31.3%) than in Baseline (14.0%) and Baseline-Feedback (23.3%) (see Figure 4, lower panel). Interestingly, the reverse ordering of treatments was observed for low base rate arousal investors. This implies exhibiting low base rate arousal especially protects investors from bankruptcy when in a market setup.

We show the statistical significance of these differences in Tables B10 andB11 in Internal Appendix B.3. In Table B10, we show that the interaction term 'Market × Base rate arousal' is negative and significant for regressions ( 3) and ( 4) showing that base rate arousal hurts participants earnings in Market compared to Baseline. These regressions consider the sessions where bankruptcies were possible, which are characterized by the occurrence of at least two negative payoffs. The interaction term 'Baseline-Feedback × Base rate arousal' does not reach statistical significance so that base rate arousal is not detrimental in Baseline-Feedback compared to Baseline. Although the magnitude of the coefficient for 'Market × Base rate arousal' is about two to three times larger, depending on the regression, than 'Baseline-Feedback × Base rate arousal', we report no significant differences between these two interaction terms (see Coefficient tests at the bottom of Table B10). In Market, the estimates in Table B10 show that for an investor with a median level of base rate arousal, equal to 2, the decrease in earnings ranges from to 3.29 to 5.58 euros for a 3-hour experiment, which corresponds to about 10% of their earnings for the experimental session.30

In Table B11, similar results are obtained when considering bankruptcy rates so that base rate arousal was associated with more frequent bankruptcies in Market compared to Baseline (see 'Market × Base rate arousal'). Furthermore, the coefficient test comparison between 'Market × Base rate arousal' and 'Baseline-Feedback × Base rate arousal' is significant in sessions where bankruptcies were possible (see Coefficient tests at the bottom of Table B11 for regressions ( 3) and ( 4)). This shows that, when considering bankruptcies, base rate arousal was also detrimental to traders in Market when compared to Baseline-Feedback.

We summarize our findings regarding earnings and bankruptcies in Result 4.

Result 4 (exploratory). (Bankruptcy, earnings and arousal)

In Market, investors with a high base rate arousal were more likely to go bankrupt and earned less in sessions where bankruptcy was possible than those with a low base rate arousal. No differences between these two groups were observed for the baseline treatments.

Discussion

Our study is the first to provide causal evidence that markets trigger specific emotions. These emotional responses, unique to the market institution, are characterized by investors experiencing heightened emotional arousal when outcompeting other traders. Interestingly, the exacerbated emotional response to winning bids in markets was more pronounced when asset payoffs were high. This suggests competitive arousal made investors more sensitive to cash earnings. This phenomenon can be viewed as a physiological measure of greed, which is heightened in markets. Overall, our results indicate that the distinctive feature of markets, namely competition, only produces behavioral differences due to the associated emotional arousal. Indeed, bids did not differ between market and non-market institutions for investors who exhibited low base rate emotional arousal. For investors exhibiting high base rate arousal, the market institution may be particularly detrimental as it exacerbates emotions and induces lower gains due to a higher risk of bankruptcy.

Interestingly, competitive arousal induces aroused traders to bid at higher levels while not impacting their unaroused counterparts thus creating heterogeneity in bidding behavior. These heterogenous reactions could, in a market in which speculation is possible, further facilitate the development of bubbles since even the less aroused traders will be willing to bid high to resell to aroused traders. This mechanism relates to behavioral models of speculative bubbles in which traders are assumed to hold heterogenous beliefs due to overconfidence over the asset value [START_REF] Abreu | Bubbles and Crashes[END_REF][START_REF] Scheinkman | Overconfidence and Speculative Bubbles[END_REF][START_REF] Hong | Asset Float and Speculative Bubbles[END_REF]. Our study can provide a physiological foundation for the persistence of heterogenous beliefs ingrained in traders' varying arousal responses to market outcomes.

Our findings imply that overbidding could be mitigated using venting techniques (e.g., Bushman, 2002;Xiao and Houser, 2005;Bolle et al., 2014;Dickinson and Masclet, 2015;Steenbarger, 2015) and other emotional regulation strategies relying on biofeedback [START_REF] Kandasamy | Interoceptive Ability Predicts Survival on a London Trading Floor[END_REF][START_REF] Astor | Integrating Biosignals into Information Systems: A NeuroIS Tool for Improving Emotion Regulation[END_REF]. In markets, venting could be achieved by delaying the feedback regarding auction winners and more drastically by deploying circuit breakers and trading halts [START_REF] Abad | Holding Back Volatility: Circuit Breakers, Price Limits, and Trading Halts[END_REF][START_REF] Magnani | Dynamic Runs and Circuit Breakers: An Experiment[END_REF][START_REF] Lauterbach | Stock Market Crashes and the Performance of Circuit Breakers: Empirical Evidence[END_REF]. However, emotional regulation is unlikely to be effective for all investors [START_REF] Kandasamy | Interoceptive Ability Predicts Survival on a London Trading Floor[END_REF][START_REF] Astor | Integrating Biosignals into Information Systems: A NeuroIS Tool for Improving Emotion Regulation[END_REF] as marked differences exist in the effectiveness of these strategies [START_REF] Bonanno | Regulatory Flexibility: An Individual Differences Perspective on Coping and Emotion Regulation[END_REF][START_REF] Cox | Theory and Individual Behavior of First-Price Auctions[END_REF][START_REF] Gross | Individual Differences in Two Emotion Regulation Processes: Implications for Affect, Relationships, and Well-Being[END_REF].

Furthermore, mitigating emotional arousal could have the unintended consequence of quieting emotional responses related to fear and anxiety that can help traders avoid excessive risk [START_REF] Bechara | Deciding Advantageously Before Knowing the Advantageous Strategy[END_REF][START_REF] Bossaerts | Emotional Engagement and Trading Performance[END_REF].

Given the limitations of the emotional regulation approach [START_REF] Raio | Cognitive Emotion Regulation Fails the Stress Test[END_REF], our paper offers an appealing alternative that consists in redesigning existing institutions. Doing so will reduce competitive arousal without necessarily impacting other emotional responses that are necessary to make successful investment decisions.

already drawn in other sessions identical to yours or in this session by other participants in another group than yours. This number will be communicated on your screen.

At the end of the experiment, if you wish, you will be able to consult the sheet of paper signed by the participant who drew the tokens and who was randomly selected by this procedure. You will be able to check that this sheet of paper is the correct one to be selected by matching its number with the one given to you on the screen and to verify that the sequence of tokens drawn is correct.

Your task:

You will play for 300 periods.

At each period, your task is to decide how much you are willing to pay for a lottery that gives you the following payoffs (which may be negative) depending on the color of the token drawn by the randomly selected participant:

• Blue: 10 cents The outcome of the lottery in one period is independent of the outcome of the lottery in another period: in each period a new token is drawn into the bag which has strictly the same content in each period.

To make your decisions, you will use the fixed amount of 12 euros (1200 cents) that you were attributed to answer the tests during the first part of this experimental session. This initial endowment is intended both to allow you to pay the lottery and to deal with the possibility of a yellow token being drawn. The earnings for each period are added to this initial endowment.

In addition, we make you a loan of 10 euros (1000 cents) for liquidity reasons, which you will repay at the end of the experiment.

If your endowment is no longer sufficient to cover the actual occurrence of a yellow token, you will no longer be able to participate in the experiment and you will only earn your variable payoffs acquired during the tests as well as 5 euros for showing-up.

You can select on your screen any price between 0 and 50 cents up to which you would be willing to buy the lottery.

The computer randomly selects an integer from 1 to 50. If the price you indicate is greater than or equal to the number selected by the computer, then you buy the lottery for the price equal to the number selected by the computer.

If the price you indicate is strictly lower than the number selected by the computer, then you keep your endowment and do not buy the lottery. At each period, your payoff, if you actually buy the lottery, is given by: Lottery payoff -price paid to purchase the lottery Your total earnings over the 300 periods are given by: 1200 cents of fixed test earnings + (lottery payoff -price you paid to buy the lottery) × 300 periods + variable test earnings + 5 euros of show-up fee.

----------------------------------------------------------------------------------------------------------------

In the case of MARKET TREATMENT, this screen was:

Your task:

You can select on your screen any price between 1 and 50 cents up to which you would be willing to buy the lottery.

The computer randomly selects the price proposed by one of the participants.

If the price you indicate is strictly greater than the price selected by the computer, then you buy the lottery for the price equal to the number selected by the computer.

If the price you indicate is lower than or equal to the number selected by the computer, then you keep your endowment and do not buy the lottery. At each period, your payoff, if you actually buy the lottery, is given by: Lottery payoff -price paid to purchase the lottery Your total earnings over the 300 periods are given by: 1200 cents of fixed test earnings + (lottery payoff -price you paid to buy the lottery) × 300 periods + variable test earnings + 5 euros of show-up fee.

-

---------------------------------------------------------------------------------------------------------------

Example 1

You have entered a price of 28 at which you are ready to buy the lottery.

The computer randomly selects between 1 and 50 the number 12. In this case, the price you have indicated is higher than the selected number, so you buy the lottery for 12 cents that corresponds to the number selected by the computer. This lottery will give you:

• 10 cents if the token drawn is blue, in which case your payoff for this period is -2 cents (10-12). • 20 cents if it is red, in which case your payoff for this period is 8 cents (20-12).

• 30 cents if it is orange, in which case your payoff for this period is 18 cents (30-12).

• 40 cents if it is green, in which case your payoff for this period is 28 cents (40-12).

• 50 cents if it is purple, in which case your payoff for this period is 38 cents (50-12).

• -1,000 cents if it is yellow, in which case your payoff for this period is -1012 cents (-1000-12).

- ---------------------------------------------------------------------------------------------------------------In the case of MARKET TREATMENT, this screen was:

Example 1

You have entered a price of 28 at which you are ready to buy the lottery. The computer randomly selects the price proposed by one of the participants which is equal to 12.

In this case, the price you have indicated is strictly higher than the selected number, so you buy the lottery for 12 cents that corresponds to the number selected by the computer. This lottery will give you:

• 10 cents if the token drawn is blue, in which case your payoff for this period is -2 cents (10-12). • 20 cents if it is red, in which case your payoff for this period is 8 cents (20-12).

• 30 cents if it is orange, in which case your payoff for this period is 18 cents (30-12).

• 40 cents if it is green, in which case your payoff for this period is 28 cents (40-12).

• 50 cents if it is purple, in which case your payoff for this period is 38 cents (50-12).

• -1,000 cents if it is yellow, in which case your payoff for this period is -1012 cents (-1000-12). ----------------------------------------------------------------------------------------------------------------

Example 2

You have entered a price of 21 and the computer randomly selects between 1 and 50 the number 43. In this case, the price you have indicated is lower than the selected number, so you will not buy the lottery. In this case, your payoff is 0 for this period.

- ---------------------------------------------------------------------------------------------------------------In the case of MARKET TREATMENT, this screen was:

Example 2

You have entered a price of 21 and the computer randomly selects the price proposed by one of the participants which is equal to 43. In this case, the price you have indicated is lower than the selected number, so you will not buy the lottery. In this case, your payoff is 0 for this period. ----------------------------------------------------------------------------------------------------------------Information: After each period, you will be informed about the token that has been drawn, your payoff for the lottery, as well as your available cash which is equal to your initial endowment (2200 cents) plus or minus the accumulated gains and losses for buying (or not) the lottery. You will also be able to see this information at the bottom of your screen for all periods before the current period.

At the end of the experiment, if you wish, you can have a look at the sheet of paper signed by the participant who drew the tokens. This will allow you to check that the sequence of drawn tokens is correct.

- --------------------------------------------------------------------------------------------------------------

-

In the case of FEEDBACK AND MARKET TREATMENTS, this screen was:

Information:

After each period, you will be able to observe for 4 seconds the prices offered by the other participants and their relative position in relation to your own price proposal by means of a simple graph. An example is shown below where you have proposed a price of 24 while the other five participants have proposed the following prices: 12, 28, 28, 30 and 41.

On the next screen, you will be informed about the token that has been drawn, your payoff for the lottery, the price that you have offered, the average price offered by the other participants as well as your available cash which is equal to your initial allocation (2200 cents) plus or minus the accumulated earnings and losses.

You will also be able to see this information at the bottom of your screen for all periods prior to the current period. ----------------------------------------------------------------------------------------------------------------

Decision-making time:

To ensure that the experiment is completed on time, we expect you to make your decision within 10 seconds in each period. Note that you can take a little more time at the beginning of the experiment and that you are expected to make your decisions more quickly over time. You are given 30 seconds in the first period and 20 seconds in the second period. From the third period onwards, you will have 10 seconds to make your decision. A timer on the screen will indicate the time you have to enter a price using the cursor and validate your decision. If you do not enter a price on the screen and validate your decision in time, the number indicated by the cursor will be selected.

A.2. POST-EXPERIMENTAL QUESTIONNAIRE ABOUT EMOTIONS FOR Market physio, Baseline-Feedback physio AND HALF OF DATA OF Baseline physio

The order of the two following questions was randomized.

(Q1) When the yellow token was drawn during the experiment and you suffered a loss of 1,000 euro cents, how much did you feel the following emotion? Anger, Fear, Joy, Sadness (1-Not at all 2-A little 3-Moderately 4-Very much 5-Very much) (Q2) When the yellow token was drawn during the experiment, but you did not suffer a loss of 1,000 euro cents, how much did you feel the following emotion? Anger, Fear, Joy, Sadness (1-Not at all 2-A little 3-Moderately 4-Very much 5-Extremely)

We also randomized the order of presentation of each emotion. The order of the following questions was randomized two by two on (Q3) and (Q4) on the one hand and ( Q5) and (Q6) on the other.

(Q3) When the blue token (payment of 10 cents) was drawn during the experiment and you bought the lottery, did you feel the following emotion? Anger, Fear, Joy, Sadness (1-Not at all 2-A little 3-Moderately 4-Very much 5-Very much) (Q4) When the blue token (payment of 10 cents) was drawn during the experiment and you did not buy the lottery, did you feel the following emotion? Anger, Fear, Joy, Sadness (1-Not at all 2-A little 3-Moderately 4-Very much 5-Very much) (Q5) When the purple token (payment of 50 cents) was drawn during the experiment and you bought the lottery, did you feel the following emotion? Anger, Fear, Joy, Sadness (1-Not at all 2-A little 3-Moderately 4-Very much 5-Very much) (Q6) When the purple token (payment of 50 cents) was drawn during the experiment and you did not buy the lottery, did you feel the following emotion? Anger, Fear, Joy, Sadness (1-Not at all 2-A little 3-Moderately 4-Very much 5-Very much)

We also randomized the order of presentation of each emotion.

B.2. Hypothesis 3

Table B2. Arousal dummy and winning bids (IV regression). Instrumental variable panel regressions with random effects along with robust standard errors in parentheses. Instrument used for 'Win' is 'Price ⊥ '. (std) stands for standardize variables.

(

Figure B1

. Arousal Dummy as a function self-reported emotional intensity for the case of a payoff of 50 and a winning bid. Self-reported emotional intensity was calculated as the average Likert scale answer on the four basic emotions for the case of winning bid and a payoff of 50.

Table B3. Emotion reported by participants at the end of the experiment (Baseline-Feedback treatment) for the four basic valanced emotions after a winning and a non-winning bid. Only participants who effectively placed a winning (non-winning) bid for a given payoff were asked to report their emotion. Mean (median) responses for a Likert scale ranging from 1 "Not at all" to 5 "Extremely". SD stands for standard deviation. In brackets, we also report Sign Rank Tests p-values [p] for the hypothesis that the reported emotion is moderate (Answer 3 in the 5-point Likert Scale).

All p-values inequalities continue to hold using the Holm-Bonferroni procedure given that we report the results of 16 tests in the table.

Figure B2.

Emotional arousal for winning bids across treatments and asset payoffs. "Baseline-F" stands for Baseline-Feedback. B4. Arousal and winning bids with low vs high payoff interaction effect. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4), AR(1) autoregressive errors are used, and no period variable and period fixed effects are included. (std) stands for standardize variables. DEPENDENT Table B5. Arousal and winning bids with payoff interaction effect. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4), AR [START_REF]You have entered a price of 23 at which you[END_REF] Table B6. Arousal and winning bids with money gain interaction effect. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4), AR [START_REF]You have entered a price of 23 at which you[END_REF] Table B8. Bids and treatment effects for below-median base rate arousal investors. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4), AR [START_REF]You have entered a price of 23 at which you[END_REF] Table B9. Bids and treatment effects for above-median base rate arousal investors. Linear panel regressions with random effects and period fixed effects along with robust standard errors clustered at the session level in parentheses (( 1) and ( 2)). In ( 3) and ( 4

B.3. Earnings and bankruptcy rates

Table B10. Final earnings in cents as a function of base rate arousal, treatment dummies and individual controls. OLS regressions with robust standard errors clustered at the individual level. Regressions ( 1) and ( 2) consider all physio sessions and regressions ( 3) and ( 4) consider sessions in which at least two negative payoffs were drawn (68.9% of the data). (std) stands for standardize variables.

( option A or option B. To finalize the computation of your payoffs, a second number between 1 and 10 will also be randomly selected by the computer.

• For example, if the first number selected by the computer is 3, this indicates that line 3 will be chosen for the computation of your payoffs. If for this line you have chosen option A, you will earn 2 euros if the second number randomly selected by the computer is 1, 2 or 3. If the second selected number is 4, 5, 6, 7, 8, 9 or 10, you will earn 1.60 euros.

• For example, if the first number selected by the computer is 5, this indicates that line 5 will be chosen for the computation of your payoffs.

Personality test

Basic information and materials for the HEXACO Personality Inventory-Revised (Ashton and Lee, 2009), an Instrument that assesses the six major dimensions of personality (Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger)

53

Even in an emergency I wouldn't feel like panicking.

58

When I'm in a group of people, I'm often the one who speaks on behalf of the group.

59

I remain unemotional even in situations where most people get very sentimental.

Block 3 Extended Cognitive Reflection Test (CRT)

We administered the extended (seven-question) version of the CRT in which the original three questions (Frederick, 2005) are augmented with four additional questions recently developed and validated by Toplak, West and Stanovich (2014). Our measure of cognitive reflection is given by the total number of correct answers (from 0 to 7). Participants had 15 minutes in total to complete the CRT.

computer. This number will determine which line will be used to calculate your gains or losses. The computer will randomly select a second number between 1 and 6 to determine the exact amount of your winnings or losses.

Example:

You have chosen one option (A or B) for each of the ten rows in the table. Next, the computer randomly selects row to be used to calculate your gains and losses.

-If you selected option A for line 7, then you will lose 2.40 euros if the second number chosen by the computer at random is 1, 2 or 3. If the second number chosen by the computer is 4, 5 or 6 you will win 5.00 euros.

-If you selected option B for line 7, then you will lose 1.00 euro if the second number chosen by the computer at random is 1, 2 or 3. If the second number chosen by the computer is 4, 5 or 6 you will win 1.00 euro.

Block 8 Demographic data

We asked participants about a few demographic questions: age, gender, diploma, baccalauréat grade, socio-professional category, color blindness, number of previous participations in experimental sessions, mother tongue.

I.2. SPECIFIC INSTRUCTIONS FOR THE RANDOMLY SELECTED PARTICIPANT (PICKER) (see CCH)

You are the selected participant. You have been selected to perform a different task from the other 6 participants. Your task is to: -put all the chips in an opaque bag; -pick a token from the bag, tick the color of the token on your computer screen, tick the color of the token on the sheet of paper in front of you, put the token back into the opaque bag (so that the contents of the bag always remains the same), mix the tokens, and again pick a token from the bag, tick the color of the token on your computer screen, tick the color of the token on the sheet of paper, put the token back in the bag, mix the tokens, and so on until you have drawn a total of 300 tokens; -to sign the sheet of paper at the end of your task.

Your earnings consist of a fixed amount of 15 euros. If you do not complete your task in an hour, or if you make a mistake (i.e. tick the wrong color), you will only be paid 10 euros. You will be under the supervision of an experimenter at all times. It is expected that you will take an average of 10 seconds to pull a token, tick the color of the token on your computer, tick the color on the sheet of paper, mix the tokens. A timer on the computer will tell you if you are on time.

If you have any questions, please raise your hand and we will come to answer your questions.

Each set of parameter values represents a simulated trader. In each period, these six simulated traders submit bids. Bids evolve over time based on the past realization of bids, prices, and payoffs. In the Baseline, the price in each period is determined by a BDM mechanism, applying the same randomly generated price to all the six players as in the experimental design. In the Market, the price in each period is randomly chosen from the bids, excluding bankrupt traders as in our experimental design. The asset payoff over 300 periods is the same as the one used in the experiment, where there are 15 different series of draws (see CCH). We used the same six sets of best fit parameter values as well as the payoffs series in the two treatments (Baseline and Market) under comparison.

We have conducted 100 simulation runs in both Baseline and Market. We are interested in the dynamics of the average bids these two treatments generate. Below are two examples of the times series of average bids (thin lines) and the corresponding 10-period moving average (thick lines) for a group of six simulated traders in the Market (blue) and Baseline (green) treatments. In computing average bids, we exclude traders who went bankrupt as we do in our analysis of experimental data. By taking the average over 100 simulation runs, we obtain Figure O2, which shows no noticeable difference between Market and Baseline whether we consider the model parameters in CCH (left panel) or only expected utility (right panel). The expected utility model simulations is a simplification of the CCH model simulations that assume ( , 𝑅 ) = max , 𝛼 𝑖 with risk aversion parameters 𝛼 ∈{0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4}, recency parameters (in subject belief updating) 𝜌 ∈{0.0, 0.2, 0.4, 0.6, 0.8, 1.0} and the noise in the bid choice is chosen to be very low (𝜏 = . . We consider all possible combinations of these two parameter values (amounting to 42 combinations), and for each simulated trader, we randomly pick one. In each simulation, we generate six simulated traders.

  task. Part 1 lasted for one hour and Part 2 for 3.5 hours. Average earnings were 39 euros approximately. For Baseline, Baseline-Feedback and Market treatments, Part 1 was shortened, lasted for half an hour and was performed on the same day, during the same session as Part 2.

  line with previous research on experimental market bubbles, in the Market treatment, bids start below the fundamental value before peaking in the middle of the experiment and crashing toward the end. In contrast, bids decline over time in the Baseline and Baseline-Feedback treatments.

Figure 1 .

 1 Figure 1. Average bids per period along with 10-period moving averages (thick lines) across treatments. Colored bands show average bids per period that deviate significantly (at a 1% significance level, Sign Rank Tests) from the expected value for each treatment. Note that expected value (EV = 23.1) is very close to the median bid prediction (23.0) using model simulations from CCH (see Online Appendix II).

  colored bands in Periods 100 to 200). Overall, average bids in a given session were significantly above the expected value in 74.0% of the periods in Market compared to 25.3% and 36.7% for Baseline and Baseline-Feedback (Proportion tests comparing Market and each of the two baseline treatments, p-values < 0.001).

  treatments. In Market, investors showed on average an emotional reaction to winning bids in 27.8% of the cases compared to 20.2% for other bids. For Baseline [Baseline-Feedback], the difference in emotional reaction was less pronounced (23.3% vs 21.8%) [25.0% vs 20.9%].

Figure 3 .

 3 Figure 3. Average bids per period along with 10-period moving averages (thick lines) across treatments. Left panel. Investors who had a level of base rate arousal below the median. Right panel. Investors who had a level of base rate arousal above the median. Colored bands show average bids per period that deviate significantly (at a 1% significance level, Sign Rank Tests) from the expected value (EV = 23.1) for each treatment.

Figure O1 .

 O1 Figure O1. Average bids for two simulation runs (CCH model) over 300 periods.

Table 1 . Treatment sessions

 1

	Treatment	Dates	Number of	Number of	Composition of
			sessions	participants	groups
	Baseline	Between May and June 2019	6	70	9 groups of 6 4 groups of 4 15
		Between November			2 groups of 7
	Baseline physio	2018 and February 2019	30	171	17 groups of 6
		and in April 2022			11 groups of 5
	Baseline-Feedback	Between	5	72	12 groups of 6
	Market	May and June 2019	4	71	11 groups of 6 1 group of 5
	Market physio	Between May and December 2022	9	81	6 groups of 6 9 groups of 5
	Baseline-Feedback physio	Between September and October 2023	8	95	15 groups of 6, 1 group of 5 16

  ), AR[START_REF]You have entered a price of 23 at which you[END_REF] autoregressive errors are used, and no period variable and period fixed effects are included. (std) stands for standardize variables. Baseline Combined is a dummy variable taking value one if a person is assigned to one of the two baseline treatments. This test reports the result for the regression in which the only treatment dummy is 'Baseline-Combined'. Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

		(1)	(2)	(3)	(4)
	DEPENDENT VARIABLE			Bid
	Market	2.2844**	2.0292**	2.2581***	1.9930***
		(1.0273)	(0.9738)	(0.7610)	(0.7546)
	Baseline-Feedback	0.6398	0.3477	0.7063	0.4072
		(1.0462)	(1.0176)	(0.7367)	(0.7300)
	Physio Dummy	0.6430	0.3249	0.8249	0.5204
		(0.9496)	(0.9074)	(0.6409)	(0.6402)
	Number of large losses up to t-2	0.3542	0.3742	-1.1492****	-1.1358****
		(0.2907)	(0.2923)	(0.0451)	(0.0451)
	Asset Payoff in t-1	0.0115****	0.0114****	0.0143****	0.0141****
		(0.0024)	(0.0024)	(0.0011)	(0.0011)
	Large Loss Dummy in t-1	12.7159****	12.5760****	14.3144****	14.1659****
		(2.4410)	(2.4399)	(1.1522)	(1.1543)
	Period	-0.0391****	-0.0392****
		(0.0034)	(0.0034)
	Male Dummy (std)		-0.8489***		-0.8860***
			(0.3224)		(0.3069)
	Risk Aversion (std)		-0.7811**		-0.7606**
			(0.3421)		(0.3140)
	Loss Aversion (std)		-0.7992**		-0.7990**
			(0.3855)		(0.3153)
	Constant	24.1851****	24.5298****	24.1629****	24.4908****
		(1.0039)	(0.9763)	(0.6543)	(0.6522)
	Coefficient Tests
	Market = Baseline Feedback Market = Baseline Combined †	0.1916 0.0450	0.1645 0.0510	0.0568 0.0050	0.0483 0.0080
	R 2	0.0388	0.0538	0.0313	0.0459
	Observations	157,318	156,291	157,318	156,291
	Number of investors	560	556	560	556

**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. †

  ), AR[START_REF]You have entered a price of 23 at which you[END_REF] autoregressive errors are used, and no period variable and period fixed effects are included. Negative payoffs periods are excluded from the analysis (99.2% of the data included). (std) stands for standardize variables.

	DEPENDENT	(1)	(2)	(3)	(4)
	VARIABLE		Arousal Dummy
	Market × Win	0.0485****	0.0481****	0.0490****	0.0485****
		(0.0101)	(0.0103)	(0.0068)	(0.0068)
	Market	0.0033	0.0024	0.0033	0.0022
		(0.0189)	(0.0176)	(0.0188)	(0.0187)
	Baseline-Feedback × Win	0.0097	0.0094	0.0084	0.0080
		(0.0084)	(0.0085)	(0.0063)	(0.0063)
	Baseline-Feedback	0.0041	0.0004	0.0056	0.0018
		(0.0197)	(0.0193)	(0.0179)	(0.0179)
	Win	0.0276****	0.0279****	0.0281****	0.0285****
		(0.0043)	(0.0044)	(0.0038)	(0.0038)
	Number of large losses up to t-2 0.0109*	0.0117*	-0.0042***	-0.0039**
		(0.0065)	(0.0066)	(0.0016)	(0.0016)
	Asset Payoff	0.0004****	0.0004****	0.0005****	0.0005****
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Asset Payoff in t-1	0.0001	0.0001	0.0001	0.0001
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Large Loss Dummy in t-1	0.0924	0.0812	0.0533	0.0440
		(0.1005)	(0.1015)	(0.0953)	(0.0958)
	Period	-0.0003**	-0.0003**
		(0.0001)	(0.0001)
	Male Dummy (std)		0.0236***		0.0236***
			(0.0076)		(0.0075)
	Risk Aversion (std)		0.0062		0.0069
			(0.0062)		(0.0073)
	Loss Aversion (std)		0.0015		0.0011
			(0.0090)		(0.0074)
	Constant	0.3411****	0.3416****	0.1945****	0.1957****
		(0.0244)	(0.0244)	(0.0115)	(0.0115)
	Coefficient Tests
	Market × Win = Baseline-Feedback × Win	0.0011	0.0012	<0.001	<0.001
	Market = Baseline-Feedback	0.9683	0.9176	0.9151	0.9827
	R 2	0.0086	0.0112	0.0029	0.0057
	Observations	96,264	95,243	96,264	95,243
	Number of investors	344	340	344	340
	**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a
	dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. Win is a
	dummy that takes value one if a participant bought the asset in a given period. Number of large losses up to t-2

equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

  a participant was assigned to the Market (Baseline-Feedback) treatment. Win is a dummy that takes value one if a participant bought the asset in a given period. Negative payoffs periods are excluded from the analysis (99.2% of the data included). Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period.

		)	(2)
	DEPENDENT VARIABLE	Arousal Dummy
	Market × Win	0.0498****	0.0495****
		(0.0104)	(0.0105)
	Market	0.0027	0.0018
		(0.0187)	(0.0186)
	Baseline-Feedback × Win	0.0093	0.0088
		(0.0075)	(0.0076)
	Baseline-Feedback	0.0037	-0.0001
		(0.0166)	(0.0169)
	Win	0.0272****	0.0276****
		(0.0045)	(0.0046)
	Large losses up to t-2	0.0121**	0.0129***
		(0.0048)	(0.0048)
	Asset Payoff	-0.0003****	-0.0003****
		(0.0000)	(0.0000)
	Asset Payoff in t-1	0.0001	0.0001
		(0.0001)	(0.0001)
	Large Loss Dummy in t-1	0.0976	0.0890
		(0.0940)	(0.0946)
	Period	-0.0002****	-0.0002****
		(0.0001)	(0.0001)
	Male Dummy (std)		0.0239***
			(0.0079)
	Risk Aversion (std)		0.0060
			(0.0064)
	Loss Aversion (std)		0.0015
			(0.0086)
	Constant	0.2357****	0.2370****
		(0.0128)	(0.0126)
	Coefficient Tests
	Market × Win = Baseline-Feedback × Win	<0.0001	<0.0001
	R 2	0.0075	0.0101
	Observations	97,022	95,995
	Number of investors	344	340
	**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes
	value one if

Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

  Feedback) treatment. Win is a dummy that takes value one if a participant bought the asset in a given period. † High Payoff is a dummy taking value one when payoffs were above the median payoff of 30, that is 40 or 50. Median payoff of 30 not included in the regression so that directly compare low (10 or 20) and high (40 or 50) payoffs. Negative payoffs periods are excluded from the analysis (99.2% of the data included). Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

					(4)
	VARIABLE		Arousal Dummy
	Market × Win × High Payoff †	0.0337**	0.0337*	0.0290***	0.0292***
		(0.0170)	(0.0172)	(0.0099)	(0.0099)
	Market × Win	0.0369****	0.0371****	0.0428****	0.0430****
		(0.0104)	(0.0106)	(0.0090)	(0.0091)
	Market	0.0018	0.0005	-0.0007	-0.0023
		(0.0192)	(0.0179)	(0.0192)	(0.0191)
	Baseline-Feedback ×	0.0070	0.0065	0.0071	0.0066
	Win
		(0.0082)	(0.0083)	(0.0070)	(0.0070)
	Baseline-Feedback	0.0045	0.0006	0.0051	0.0011
		(0.0198)	(0.0194)	(0.0182)	(0.0182)
	Win	0.0262****	0.0265****	0.0257****	0.0261****
		(0.0045)	(0.0045)	(0.0042)	(0.0043)
	Large losses up to t-2	0.0121*	0.0129*	-0.0026	-0.0023
		(0.0067)	(0.0068)	(0.0022)	(0.0022)
	Asset Payoff	0.0004***	0.0004***	0.0004****	0.0004****
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Asset Payoff in t-1	0.0000	0.0000	0.0000	0.0000
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Large Loss Dummy in t-	0.0332	0.0193	0.0013	-0.0101
	1
		(0.1154)	(0.1163)	(0.1080)	(0.1086)
	Period	-0.0005***	-0.0005***
		(0.0002)	(0.0002)
	Male Dummy (std)		0.0231***		0.0229***
			(0.0076)		(0.0077)
	Risk Aversion (std)		0.0064		0.0071
			(0.0061)		(0.0074)
	Loss Aversion (std)		0.0017		0.0014
			(0.0092)		(0.0075)
	Constant	0.3619****	0.3645****	0.2012****	0.2026****
		(0.0349)	(0.0350)	(0.0121)	(0.0120)
	R 2	0.0100	0.0124	0.0032	0.0058
	Observations	76,162	75,358	76,162	75,358
	Number of investors	344	340	344	340
	**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value
	one if a participant was assigned to the Market (Baseline-

  autoregressive errors are used, and no period variable and period fixed effects are included. (std) stands for standardize variables. Feedback) treatment. Win is a dummy that takes value one if a participant bought the asset in a given period. Negative payoffs periods are excluded from the analysis (99.2% of the data included). Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

	DEPENDENT	(1)	(2)	(3)	(4)
	VARIABLE		Arousal Dummy
	Market × Win × Payoff	0.0011**	0.0011**	0.0010****	0.0010***
		(0.0005)	(0.0005)	(0.0003)	(0.0003)
	Market × Win	0.0166	0.0167	0.0180	0.0179
		(0.0129)	(0.0131)	(0.0115)	(0.0116)
	Market	0.0032	0.0023	0.0033	0.0022
		(0.0189)	(0.0176)	(0.0188)	(0.0187)
	Baseline-Feedback × Win	0.0097	0.0093	0.0084	0.0079
		(0.0084)	(0.0086)	(0.0063)	(0.0063)
	Baseline-Feedback	0.0041	0.0004	0.0056	0.0018
		(0.0197)	(0.0193)	(0.0179)	(0.0179)
	Win	0.0275****	0.0278****	0.0280****	0.0285****
		(0.0043)	(0.0044)	(0.0038)	(0.0038)
	Large losses up to t-2	0.0109*	0.0117*	-0.0042***	-0.0039**
		(0.0065)	(0.0066)	(0.0016)	(0.0016)
	Asset Payoff	0.0003***	0.0003***	0.0004****	0.0004****
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Asset Payoff in t-1	0.0001	0.0001	0.0001	0.0001
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Large Loss Dummy in t-1	0.0916	0.0803	0.0528	0.0434
		(0.1004)	(0.1013)	(0.0953)	(0.0958)
	Period	-0.0003**	-0.0003**
		(0.0001)	(0.0001)
	Male Dummy (std)		0.0236***		0.0236***
			(0.0076)		(0.0075)
	Risk Aversion (std)		0.0062		0.0069
			(0.0062)		(0.0073)
	Loss Aversion (std)		0.0015		0.0011
			(0.0090)		(0.0074)
	Constant	0.3443****	0.3447****	0.1974****	0.1986****
		(0.0245)	(0.0245)	(0.0116)	(0.0115)
	R 2	0.0088	0.0113	0.0031	0.0058
	Observations	96,264	95,243	96,264	95,243
	Number of investors	344	340	344	340
	**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value
	one if a participant was assigned to the Market (Baseline-

  autoregressive errors are used, and no period variable and period fixed effects are included. (std) stands for standardize variables. Money Gain is a dummy taking value one when investors obtain a monetary gain in a given period. Negative payoffs periods are excluded from the analysis (99.2% of the data included). Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

		(1)	(2)	(3)	(4)
	DEPENDENT VARIABLE		Arousal Dummy
	Market × Win × Money Gain †	0.0210*	0.0197	0.0206**	0.0192**
		(0.0124)	(0.0127)	(0.0093)	(0.0093)
	Market × Win	0.0346***	0.0351***	0.0353****	0.0358****
		(0.0121)	(0.0124)	(0.0092)	(0.0092)
	Market	0.0032	0.0023	0.0032	0.0021
		(0.0189)	(0.0176)	(0.0188)	(0.0187)
	Baseline-Feedback × Win	0.0097	0.0093	0.0084	0.0080
		(0.0084)	(0.0086)	(0.0063)	(0.0063)
	Baseline-Feedback	0.0041	0.0004	0.0056	0.0018
		(0.0197)	(0.0193)	(0.0179)	(0.0179)
	Win	0.0276****	0.0279****	0.0280****	0.0285****
		(0.0043)	(0.0044)	(0.0038)	(0.0038)
	Large losses up to t-2	0.0110*	0.0117*	-0.0043***	-0.0039**
		(0.0065)	(0.0066)	(0.0016)	(0.0016)
	Asset Payoff	0.0004****	0.0004****	0.0005****	0.0005****
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Asset Payoff in t-1	0.0001	0.0001	0.0001	0.0001
		(0.0001)	(0.0001)	(0.0001)	(0.0001)
	Large Loss Dummy in t-1	0.0946	0.0833	0.0555	0.0460
		(0.1007)	(0.1016)	(0.0953)	(0.0958)
	Period	-0.0003**	-0.0003**
		(0.0001)	(0.0001)
	Male Dummy (std)		0.0235***		0.0236***
			(0.0076)		(0.0075)
	Risk Aversion (std)		0.0061		0.0069
			(0.0062)		(0.0073)
	Loss Aversion (std)		0.0015		0.0011
			(0.0090)		(0.0074)
	Constant	0.3423****	0.3427****	0.1958****	0.1969****
		(0.0242)	(0.0242)	(0.0115)	(0.0115)
	R 2	0.0087	0.0112	0.0030	0.0057
	Observations	96,264	95,243	96,264	95,243
	Number of investors	344	340	344	340
	**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value
	one if a participant was assigned to the Market (Baseline-Feedback) treatment. Win is a dummy that takes value one if a participant bought the asset in a given period.

†

Table B7 .

 B7 Arousal dummy and winning bids (IV regression). Instrumental variable panel regressions with random effects along with robust standard errors in parentheses. Instrument used for 'Win' is 'Price ⊥ ' and 'Money Gain Payoff' for 'Money Gain'. (std) stands for standardize variables. *** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. Win is a dummy that takes value one if a participant bought the asset in a given period. † Money Gain is a dummy taking value one when investors obtain a monetary gain in a given period. Money Gain Payoff is a dummy variable that takes value one when asset payoffs are at least equal to 20. Negative payoffs periods are excluded from the analysis (99.2% of the data included). Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

		(1)		(2)
	DEPENDENT VARIABLE Market × Win × Money Gain †	0.0235*	Arousal Dummy	0.0222*
		(0.0126)		(0.0127)
	Market × Win	0.0335**		0.0340**
		(0.0135)		(0.0137)
	Market	0.0038		0.0030
		(0.0187)		(0.0186)
	Baseline-Feedback × Win	0.0085		0.0081
		(0.0076)		(0.0077)
	Baseline-Feedback	0.0040		0.0003
		(0.0166)		(0.0169)
	Win	0.0273****		0.0277****
		(0.0046)		(0.0046)
	Large losses up to t-2	0.0126***		0.0133***
		(0.0048)		(0.0049)
	Asset Payoff	0.0005****		0.0004****
		(0.0001)		(0.0001)
	Asset Payoff in t-1	0.0001		0.0000
		(0.0001)		(0.0001)
	Large Loss Dummy in t-1	0.0581		0.0482
		(0.0940)		(0.0946)
	Period	-0.0002****		-0.0002****
		(0.0001)		(0.0001)
	Male Dummy (std)			0.0238***
				(0.0079)
	Risk Aversion (std)			0.0059
				(0.0064)
	Loss Aversion (std)			0.0015
				(0.0086)
	Constant	0.2133****		0.2146****
		(0.0127)		(0.0126)
	R 2	0.0032		0.0056
	Observations	96,264		95,243
	Number of investors	344		340

*

  autoregressive errors are used, and no period variable and period fixed effects are included. (std) stands for standardize variables. *** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. Physio Dummy is a dummy that takes value one if a participant was assigned to a session in which physiological recording were used. † Baseline Combined is a dummy variable taking value one if a person is assigned to one of the two baseline treatments. This test reports the result for the regression in which the only treatment dummy is 'Baseline-Combined'. Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

	Below-median base rate arousal	(1)	(2)	(3)	(4)
	investors
	DEPENDENT VARIABLE			Bid
	Market	0.7022	0.9786	0.7547	1.0039
		(1.5562)	(1.5591)	(1.3683)	(1.3265)
	Baseline-Feedback	-0.8700	-0.6674	-0.4781	-0.2734
		(1.4497)	(1.4624)	(1.4631)	(1.4437)
	Physio Dummy	27.4513****	26.9178****
		(1.2196)	(1.1898)
	Number of large losses up to t-2	0.7819*	0.8437*	-1.1019****	-1.1003****
		(0.4709)	(0.4675)	(0.0970)	(0.0972)
	Asset Payoff in t-1	0.0199****	0.0203****	0.0205****	0.0209****
		(0.0047)	(0.0047)	(0.0026)	(0.0026)
	Large Loss Dummy in t-1	20.5932****	21.0393****	20.0471****	20.4427****
		(4.9850)	(4.9710)	(2.7298)	(2.7496)
	Period	-0.0434****	-0.0442****
		(0.0070)	(0.0070)
	Male Dummy (std)		-0.5870		-0.7172
			(0.6778)		(0.5913)
	Risk Aversion (std)		-1.4495***		-1.4872***
			(0.5182)		(0.5267)
	Loss Aversion (std)		0.3705		0.2638
			(0.4118)		(0.5701)
	Constant			26.1283****	25.6132****
				(0.8286)	(0.8197)
	Coefficient Tests
	Market = Baseline Feedback Market = Baseline Combined †	0.3809 0.5140	0.3320 0.4130	0.4503 0.4820	0.4176 0.3780
	R 2	0.0440	0.0687	0.0098	0.0325
	Observations	33,548	33,253	33,548	33,253
	Number of investors	119	118	119	118

*

  ), AR[START_REF]You have entered a price of 23 at which you[END_REF] autoregressive errors are used, and no period variable and period fixed effects are included. (std) stands for standardize variables. *** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. Physio Dummy is a dummy that takes value one if a participant was assigned to a session in which physiological recording were used. † Baseline Combined is a dummy variable taking value one if a person is assigned to one of the two baseline treatments. This test reports the result for the regression in which the only treatment dummy is 'Baseline-Combined'. Number of large losses up to t-2 equals the number of times a participant faced an asset paying off a large loss up to period t-2. Large Loss Dummy in t-1 takes value 1 if a participant faced an asset paying off a large loss in the previous period. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

	Above-median base rate arousal	(1)	(2)	(3)	(4)
	investors
	DEPENDENT VARIABLE			Bid
	Market	4.1266*	3.9826**	3.9745***	3.8011***
		(2.1478)	(1.8450)	(1.4876)	(1.4597)
	Baseline-Feedback	3.1379**	2.0405	3.3029***	2.2776*
		(1.2857)	(1.3666)	(1.2691)	(1.2750)
	Physio Dummy	31.0256****	26.6468****
		(1.6045)	(1.3048)
	Number of large losses up to t-2	0.1478	0.1449	-1.2898****	-1.2783****
		(0.4391)	(0.4405)	(0.0864)	(0.0867)
	Asset Payoff in t-1	0.0107**	0.0104**	0.0141****	0.0137****
		(0.0052)	(0.0052)	(0.0019)	(0.0019)
	Large Loss Dummy in t-1	11.7568**	11.4180**	14.1714****	13.7152****
		(5.1801)	(5.2320)	(1.9742)	(1.9836)
	Period	-0.0617****	-0.0454****
		(0.0084)	(0.0072)
	Male Dummy (std)		-0.9458*		-0.9376*
			(0.5541)		(0.5655)
	Risk Aversion (std)		-1.6962**		-1.4860***
			(0.6841)		(0.5605)
	Loss Aversion (std)		-0.4356		-0.3980
			(0.5173)		(0.5359)
	Constant			23.4128****	23.8290****
				(0.7826)	(0.7816)
	Coefficient Tests
	Market = Baseline Feedback Market = Baseline Combined †	0.6537 0.1580	0.3131 0.0620	0.6782 0.0580	0.3418 0.0340
	R 2	0.0663	0.0975	0.0831	0.1065
	Observations	41,591	41,166	41,591	41,166
	Number of investors	152	150	152	150

*

  *** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Market (Baseline-Feedback) is a dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

		)	(2)	(3)	(4)
	DEPENDENT VARIABLE		Earnings (¢)
	Base rate arousal	39.3103	45.0626	98.4853	97.3822
		(51.4263)	(44.9425)	(62.4423)	(65.6939)
	Market	-191.6862	-184.1349	381.0387	356.3781
		(243.5186)	(280.5862)	(321.6154)	(325.0031)
	Market × Base rate arousal	-165.5089*	-169.0087*	-280.2235**	-272.2101**
		(88.7706)	(86.9218)	(124.2218)	(124.3335)
	Baseline-Feedback	372.9449	322.2874	475.3355	402.1810
		(278.3205)	(359.0928)	(472.2676)	(477.5135)
	Baseline-Feedback	-79.8332	-50.4491	-114.8014	-76.0486
	× Base rate arousal	(97.3599)	(72.5050)	(107.5761)	(96.4366)
	Number of negative payoffs	-409.3336**** -402.6040****	-190.2332	-192.1182
	in a session	(50.5046)	(99.9947)	(189.0979)	(189.5547)
	Male Dummy (std)		7.6228		12.4411
			(71.7152)		(92.3680)
	Risk Aversion (std)		-23.1100		-27.2819
			(59.1271)		(81.6115)
	Loss Aversion (std)		-36.4742		-47.5107
			(56.5094)		(77.8082)
	Constant	3,903.6759**** 3,870.2122**** 2,988.0134**** 3,000.0339****
		(171.9186)	(241.3569)	(540.4861)	(552.1176)
	Coefficient Tests
	Market × Base rate arousal = Baseline-Feedback × Base rate arousal	0.4338	0.2624	0.2251	0.1452
	R 2	0.2000	0.2011	0.0458	0.0527
	Observations	338	334	232	230

*

Table B11 .

 B11 Bankruptcy as a function of base rate arousal, treatment dummies and individual controls. Probit regressions with robust standard errors clustered at the individual level. Regressions (1) and (2) consider all physio sessions and regressions (3) and (4) consider sessions in which at least two negative payoffs were drawn (68.9% of the data). (std) stands for standardize variables.

		(1)	(2)	(3)	(4)
	DEPENDENT VARIABLE		Bankruptcy Dummy
	Base rate arousal	-0.1434*	-0.1561*	-0.1667*	-0.1888**
		(0.0813)	(0.0838)	(0.0854)	(0.0881)
	Market	-0.9991**	-1.0139**	-1.8015****	-1.8260****
		(0.4692)	(0.4701)	(0.4301)	(0.4102)
	Market × Base rate arousal	0.3712**	0.3819**	0.6037****	0.6156****
		(0.1678)	(0.1678)	(0.1545)	(0.1482)
	Baseline-Feedback	-0.6185	-0.5561	-0.5783	-0.5446
		(0.4093)	(0.4098)	(0.4193)	(0.4274)
	Baseline-Feedback	0.2455*	0.1976	0.2420*	0.2109
	× Base rate arousal	(0.1457)	(0.1488)	(0.1470)	(0.1525)
	Number of negative payoffs	0.1906***	0.1979***	-0.1670	-0.1521
	in a session	(0.0616)	(0.0631)	(0.1138)	(0.1154)
	Male Dummy (std)		0.0640		0.1219
			(0.0968)		(0.1074)
	Risk Aversion (std)		-0.0490		-0.0302
			(0.1038)		(0.1155)
	Loss Aversion (std)		0.0486		0.0631
			(0.0910)		(0.1045)
	Constant	-1.2912****	-1.2858****	-0.0628	-0.0609
		(0.2332)	(0.2419)	(0.3820)	(0.3982)
	Coefficient Tests
	Market × Base rate arousal = Baseline-Feedback × Base rate arousal	0.5025	0.3213	0.0387	0.0193
	Pseudo-R 2	0.0528	0.0557	0.0712	0.0751
	Observations	338	334	232	230

**** p-value < 0.001, *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1. Bankruptcy Dummy takes value one if a participant went bankrupt during the experiment. Market (Baseline-Feedback) is a dummy that takes value one if a participant was assigned to the Market (Baseline-Feedback) treatment. Risk Aversion{Loss Aversion} is measured as the switching point in Holt and Laury (2002) (Online Appendix I, Block 1) {Brink and Rankin, 2013, Block 4}.

  If for this line you have chosen option B, you will earn 3.85 euros if the second number randomly selected by the computer is 1, 2, 3, 4 or 5. If the second selected number is 6, 7, 8, 9 or 10, you will earn 0.10 euros.Once in front of the decision table, you can always come back to the present instruction screen by clicking on Instructions.

			Block 2
	Decision	Option A	Option B	Option choice
	1	10% of earning 2.0 euros	10% of earning 3.85 euros
		90% of earning 1.6 euros	90% of earning 0.1 euros	 Option A	 Option
				B
	2	20% of earning 2.0 euros	20% of earning 3.85 euros
		80% of earning 1.6 euros	80% of earning 0.1 euros	 Option A	 Option
				B
	3	30% of earning 2.0 euros	30% of earning 3.85 euros
		70% of earning 1.6 euros	70% of earning 0.1 euros	 Option A	 Option
				B
	4	40% of earning 2.0 euros	40% of earning 3.85 euros
		60% of earning 1.6 euros	60% of earning 0.1 euros	 Option A	 Option
				B
	5	50% of earning 2.0 euros	50% of earning 3.85 euros
		50% of earning 1.6 euros	50% of earning 0.1 euros	 Option A	 Option
				B
	6	60% of earning 2.0 euros	60% of earning 3.85 euros
		40% of earning 1.6 euros	40% of earning 0.1 euros	 Option A	 Option
				B
	7	70% of earning 2.0 euros	70% of earning 3.85 euros
		30% of earning 1.6 euros	30% of earning 0.1 euros	 Option A	 Option
				B
	8	80% of earning 2.0 euros	80% of earning 3.85 euros
		20% of earning 1.6 euros	20% of earning 0.1 euros	 Option A	 Option
				B
	9	90% of earning 2.0 euros	90% of earning 3.85 euros
		10% of earning 1.6 euros	10% of earning 0.1 euros	 Option A	 Option
				B
	10	100% of earning 2.0 euros	100% of earning 3.85 euros
		0% of earning 1.6 euros	0% of earning 0.1 euros	 Option A	 Option
				B

  , Conscientiousness, Openness to Experience) is made available by Kibeom Lee and Michael C. Ashton at http://hexaco.org/hexaco-inventory. We used 20 of the 60 item self-reported version of the test. that involve active social interaction to those that involve working alone. 17When I suffer from a painful experience, I need someone to make me feel comfortable.22On most days, I feel cheerful and optimistic.23I feel like crying when I see other people crying.28 I feel that I am an unpopular person.29When it comes to physical danger, I am very fearful.34In social situations, I'm usually the one who makes the first move.

	4	I feel reasonably satisfied with myself overall.
	5	I would feel afraid if I had to travel in bad weather conditions.
	10	I rarely express my opinions in group meetings.
	11	I sometimes can't help worrying about little things.
	16 I prefer jobs 35 I worry a lot less than most people do.
	40	The first thing that I always do in a new place is to make friends.
	41	I can handle difficult situations without needing emotional support from anyone else.
	46	Most people are more upbeat and dynamic than I generally am.
	47	I feel strong emotions when someone close to me is going away for a long time.
	52	I sometimes feel that I am a worthless person.

This loan ensured that participants would have enough cash to bid for the asset even after a very large loss.

Participants would typically go bankrupt when suffering two very large losses.

There is limited liability in our experiment because bankrupt participants did not repay the loan in full. On average, they repaid 73.8% of the loan.

Actually, a photograph of the box was taken prior to the first experimental session so that the picture displayed on participants' screens was exactly the same in all sessions.

The other participants knew the picker did not know the instructions for the investment task. An English translation of the instructions for the picker is reported in Online Appendix I.2.

This penalty was never implemented. After the picker started his or her task, one of the experimenters installed the physiological tool on the remaining participants who then read the instructions for the investment task. Because the picker started his or her task before the investment task, the potential issue of the picker drawing tokens too slowly never occurred.

After a stimulus is observed, the electrodermal activity needs time to rise and this is referred to as latency. Latency is on average about 4 seconds. In the following seconds, the signal rises until it reaches a peak. In the absence of further stimulation, the signal recovers its baseline (pre-stimulus) level.

This relates to what[START_REF] Bechara | Deciding Advantageously Before Knowing the Advantageous Strategy[END_REF] refer to as anticipatory arousal.

The number of participants in each group was irrelevant in the two baseline treatments because only the information about one's own bids were shown on the screen.

One session (n = 12) crashed in period 269. That is why we collected one more session than intended (n = 95, instead of 80).

[START_REF] Bault | Medial Prefrontal Cortex and Striatum Mediate the Influence of Social Comparison on the Decision Process[END_REF][START_REF] Frydman | What Drives Peer Effects in Financial Decision-Making? Neural and Behavioral Evidence[END_REF] present similar results along with neurological evidence.

Absent bankruptcies and the two cases of deficient electrodes, we would have 104,100 recordings.

In CCH, we study the physiological reaction of traders to large losses in the Baseline treatment only. In that case, the emotional reaction to buying the asset is unambiguously negative.

We construct this variable using the orthog command in Stata 17.0.

Unsurprisingly, because these observations constitute a small fraction of the data, our results are largely unaltered when including these periods in the analysis.

Not only is the valence of the emotion, as explained in the text, different but also the magnitude of the response. The average for the 'Arousal Dummy' was 56.6% compared to 22.7% for the other payoffs with no major differences across treatments.

We study arousal as a function of payoffs rather than monetary gains and losses because the latter are endogenous since they depend on participants' bids.

The only non-valanced basic emotion is surprise. Surprise tends to accompany the other emotions and is captured with our physiological measurement.

This decrease in earnings in Market for a base rate arousal equal to 2 is calculated as 2×'Base rate arousal' + 2×'Market×Base rate arousal', which leads to the following estimates of-3.29, -3.36,-5.58 and -5.42 in regressions (1) to[START_REF]POST-EXPERIMENTAL COMPREHENSION QUIZ FOR Market physio, Baseline-Feedback physio AND HALF OF[END_REF].

We selected 11 statements out of the 14 proposed byHong and Faedda (1996).

 An earlier version circulated under the title "Risk-taking and tail events across trading institutions." The present version is based on newly collected physiological data. This research has benefited from the financial supports of the Japanese Society for Promotion of Sciences (Project number 18K19954, 20H05631, 23H00055), of the French National Research Agency (ANR) under an ORA-Plus project "BEAM" (ANR-15-ORAR-0004) and under the French-German project "ASUR" (ANR-18-FRAL-0 0 08-01 ASUR), and of IDEXLYON from Université de Lyon (project INDEPTH) within the Programme Investissements d'Avenir (ANR-16-IDEX-0005), of emlyon business school, and it was performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program Investissements d'Avenir (ANR-11-IDEX-007) operated by the French National Research Agency, and UCA JEDI (ANR-15-IDEX-01) of Université Côte d'Azur (project UCAinACTION). We would like to thank Mateus Joffily for useful discussions and assistance with physiological recordings, and Quentin Thévenet for the design of the computer software and for his assistance during experimental sessions. The design of the experiment reported in this paper has been approved by the IRB of INSERM (#18-493) in May 2018.

Appendices INTERNAL APPENDIX

The Internal Appendix is organized as follows:

A. Instructions (Part 2, investment task) and Post-experimental Questionnaire on Emotions

B. Robustness of results

Appendix A: Instructions (Part 2 investment task) and questionnaire on emotions

A.1. INSTRUCTIONS FOR PART 2 (COMMON TO THE 3 TREATMENTS; UNLESS OTHERWISE STATED INSTRUCTIONS ARE FOR THE BASELINE TREATMENT)

Oral instructions; Sentences in italics are for the readers and not shown to participants.

In this second part, we are carrying out the experiment itself.

Here is a bow of tokens. After the experimenter has shown the box, he tells the participants: We will take a picture that will be reported on your screen to remind you the contents of the box.

The experimenter takes a photograph that is supposed to resemble the one depicted on participants' screens later on.

Instructions -PART 2 ON SCREEN

During preceding experimental sessions, 15 participants have been randomly selected to perform the following task: -put all the chips in an opaque bag; -pick a token from the bag, tick the color of the token on his/her computer screen; -tick the color of the token on the sheet of paper in front of him/her; -put the token back into the opaque bag (so that the contents of the bag always remains the same), mix the tokens; -and again pick a token from the bag, tick the color of the token on his computer screen, tick the color of the token on the sheet of paper; -put the token back in the bag, mix the tokens, and so on until you have drawn a total of 300 tokens; -sign the sheet of paper at the end of his/her task. Each selected participant was paid a fixed amount of 15 euros to complete this task in an hour. None of these participants knew your own task of the current experiment. 15 draws of 300 tokens have thus been realized in total and one of these draws will be randomly selected for the current experiment.

Instructions

You are divided into groups of 6 participants.

We will now proceed to the selection of one of these 15 draws for each group of 6 participants.

We assigned a number to each of the 15 draws, written on the back of the sheets signed by the participants who picked up the tokens.

For each group of 6 participants, a randomly selected participant will have to choose a number between 1 and 15 on his screen. All numbers between 1 and 15 can be chosen, except those

ONLINE APPENDIX

The Online Appendix is organized as follows:

I. Instructions (Part 1, picker task), program, screens and understanding questionnaire II.

Simulations for hypotheses

Appendix I: Instructions (Part 1, picker task), screen, program and comprehension quiz

I.1. INSTRUCTIONS FOR PART 1 (on screen) (COMMON TO THE 3 TREATMENTS; UNLESS OTHERWISE STATED, INSTRUCTIONS ARE FOR THE BASELINE TREATMENT)

Welcome.

Thank you for participating in this experiment.

Please turn off your phone. It is forbidden to talk to other participants throughout the session.

If at any time during the session you need help, press the red button on the left of your desk or raise your hand, we will immediately come to answer your questions in private.

Instructions

This experimental session is composed of two parts.

In the first part, you will perform a series of tests on the computer, for which you will receive a fix amount of 12 euros (1200 cents) and a variable payoff which will depend on your decisions in some of the tests.

The fix amount of 12 euros will be used in the second part of this experimental session as an initial endowment. The task you will complete in this second part will be described in details once the first part ends.

The variable payoffs that you will earn will be added to the total payoffs and will be paid in cash at the end of the experiment.

Instructions -PART 1

In this first part, you will answer 8 blocks of questions.

Please answer the following questions as best as you can.

Calculators, paper and pen are not allowed.

Block 1

Risk aversion in the gain domain

Following Holt and Laury (2002) we use the following risk aversion test.

For each line of the table presented on the following screen, indicate whether you prefer option A or option B.

Note that there is a total of 10 lines in the table, but only one line will be randomly selected to compute your payoffs. As all lines are equally likely to be selected for the computation of your payoffs, you should attribute the same importance to each of your decisions.

At the end of the experiment, a number between 1 and 10 will be randomly selected by the computer. This number will determine which line will be used to compute your payoffs. Your payoff for the selected line thus depends on the option that you will have chosen for this line:

Taken from Frederick (2005):

A bat and a ball cost $1.10 in total. The bat costs a dollar more than the ball. How much does the ball cost? ____ cents [Correct answer: 5 cents; intuitive answer: 10 cents]

If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? ____ minutes [Correct answer: 5 minutes; intuitive answer: 100 minutes]

In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake? ____ days

Block 4 Loss aversion

Following Brink and Rankin (2013) we use the following loss aversion test.

For this task, your potential losses will be subtracted from your total gains in the various tests.

For each line in the table on the following screen: please indicate whether you prefer option A or option B. Even if the table has a total of 10 rows, only one row will be randomly selected for the calculation of your gains or losses. Since all lines are likely to be selected for the calculation of your gains or losses, you must give equal weight to each of your decisions. At the end of the experiment, a number between 1 and 10 will be randomly selected by the

Block 5 Reactance scale

Following Hong and Faedda (1996), we asked participants to evaluate on a scale from 1 to 5 (1 = Strongly agree, 2 = Agree, 3 = Neither agree nor disagree, 4 = Disagree, 5 = Strongly disagree), the extent to which they agree or disagree with the following 11 statements 31 : trigger a sense of resistance in me. I find contradicting others stimulating. When something is prohibited, I usually think that's exactly what I am going to do.

I consider advice from others to be an intrusion.

I become frustrated when I am unable to make free and independent decisions.

It irritates me when someone points out things which are obvious to me. I become angry when my freedom of choice is restricted.

Advice and recommendations induce me to do just the opposite.

I resist the attempts of others to influence me.

It makes me angry when another person is held up as a model to follow.

When someone forces me to do something, I feel like doing the opposite.

Block 6 Availability heuristic

Based on Tversky and Kahneman (1974), we administered an availability heuristic test.

We are going to show you a list of names. Please click OK to view this list. Please pay attention. Note that there is a total of 10 lines in the table, but only one line will be randomly selected to compute your payoffs. As all lines are equally likely to be selected for the computation of your payoffs, you should attribute the same importance to each of your decisions.

Mark

At the end of the experiment, a number between 1 and 10 will be randomly selected by the computer. This number will determine which line will be used to compute your payoffs. Your payoff for the selected line thus depends on the option that you will have chosen for this line: option A or option B. To finalize the computation of your payoffs, a second number between 1 and 10 will also be randomly selected by the computer.

• For example, if the first number selected by the computer is 3, this indicates that line 3 will be chosen for the computation of your payoffs. If for this line you have chosen option A, you will lose 2 euros if the second number randomly selected by the computer is 1, 2 or 3. If the second selected number is 4, 5, 6, 7, 8, 9 or 10, you will lose 1.60 euros.

• For example, if the first number selected by the computer is 5, this indicates that line 5 will be chosen for the computation of your payoffs. If for this line you have chosen option B, you will lose 3.85 euros if the second number randomly selected by the computer is 1, 2, 3, 4 or 5. If the second selected number is 6, 7, 8, 9 or 10, you will lose 0.10 euros.

Once in front of the decision table, you can always come back to the present instruction screen by clicking on Instructions.

II. SIMULATIONS

In one simulation run, we have simulated the extended model proposed by CCH by randomly drawing (with replacement) six sets out of 171 sets of best fit parameter values reported by CCH (see their Section 5.6.2). The model of CCH incorporates, risk aversion, loss aversion, recency bias, and asymmetric updating of reference wealth in response to a loss and a gain. Furthermore, the bid is determined stochastically based on the expected "utility". Namely, trader i submits bid b in period t according to Pr , 𝑖, = exp 𝜏 , 𝑖, ∑ exp 𝜏 𝑘, 𝑖, where , 𝑖, is the z-score normalized value of , 𝑖, and 𝜏 is the parameter governing the noise in the choice. The expected "utility" of submitting bid b in period t for trader i, , 𝑖, , is defined as follows:

, otherwise and where 𝛼 and 𝜆 are the degrees of risk and loss aversion, respectively, and and 𝑅 are the actual and reference wealth level, respectively, in period t. max{ , } appears instead of because of the limited liability in the experiment. is the return from color c. The subjective probability for trader i for color c in period t, , , is

We assume a conjugate prior for the multinominal distribution associated with the probability of occurrence of each color. The conjugate prior for the multinomial distribution is the Dirichlet distribution, 𝐷𝑖 𝜂 , 𝜂 , 𝜂 , 𝜂 , 𝜂 , 𝜂 , where 𝜂 represents the prior for the probability of occurrence of color c and 𝜂 = ∑ 𝜂 = captures the overall weight assigned to the prior probabilities. We denote the sample evidence regarding the probability of the occurrence of the respective colors until period t by = ∑ ∑ 𝜌 -, = =

, where , is a dummy variable that takes a value of one if color c is observed in period j and zero otherwise. 𝜌 captures the recency bias for trader i in updating this subjective probability.

Finally, the asymmetric updating of the reference wealth is captured by assuming 𝑅 = { 𝜔 -𝑅 -+ -𝜔 - if < 𝑅 - 𝜔 + 𝑅 -+ -𝜔 + 𝑜 ℎ𝑒 𝑖 𝑒 with 𝜔 -≥ 𝜔 + . This captures the idea that traders are more reluctant to adjust their reference wealth level to accumulated losses than gains. We refer to this approach as the CCH model throughout. We summarize the results calculated over all periods in