Information Ambiguity, Market Institutions And Asset .

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Information Ambiguity, Market Institutions andAsset Prices: Experimental Evidence Te Baoa , John Duffyb , and Jiahua ZhucaSchool of Social Sciences, Nanyang Technological University48 Nanyang Ave, 639818, SingaporebDepartment of Economics, University of California IrvineIrvine, CA 92697, USAcMa Yinchu School of Economics, Tianjin University92 Weijin Road, Tianjin, 300072, ChinaSeptember 29, 2021AbstractWe explore how information ambiguity and traders’ attitudes toward ambiguity affect expectations and asset prices under three different market institutions. Specifically, we test the prediction of Epstein and Schneider (2008) that information ambiguity will lead market prices to overreact to bad news and to underreact to goodnews. We find that such an asymmetric reaction exists and is strongest in individual prediction markets. It occurs to a lesser extent in single price call markets. Itis weakest of all in double auction markets, where buyers’ asymmetric reaction togood/bad news is cancelled out by the opposite asymmetric reaction of sellers.Keywords: Ambiguity Aversion, Information Ambiguity, Asset Bubbles, ExperimentalFinance, Signal ExtractionJEL Classification: C91, C92, D81, D83, G12, G40 We thank Soo Hong Chew, Ernan Haruvy, Daniel Houser, Shaowei Ke, Juanjuan Meng, RosemarieNagel, Ronald Peeters, Songfa Zhong and participants of 2019 SHUFE Behavioral and ExperimentalEconomics Workshop, Shanghai, 2019 D-TEA China Conference, Chengdu, 2019 Society for ExperimentalFinance Asia-Pacific Annual Regional Conference, Singapore and 2020 ESA Global Conference, 2020Virtual Experimental Finance Workshop and the seminar at Peking University for stimulating discussion.Financial support from Tier 1 Grant from MOE of Singapore (RG 69/19) and NTU-WeBank JRC (NWJ2020-003) and National Science Foundation of China (No. 71803201, No. 71773013, and No. 71873149)is gratefully acknowledged. This study is approved by the IRB of NTU Singapore under the approvalnumber IRB-2018-01-035.1

1IntroductionParticipants in financial markets confront many signals about market fundamentals on adaily basis. How should they process these signals? According to Epstein & Schneider(2008), agents take the quality of these signals into account. They assign more weight tosignals from a reliable, high quality source and less weight to signals from obscure, lowquality sources. The variance of a signal serves as a measure of signal quality. The qualityof a signal is viewed as high (low) when the variance of that signal is small (large). Whilethe variance of a signal can be considered as known when it comes from a source witha track record (e.g., earnings reports), there are also ambiguous signals from previouslyunknown sources for which the variance may be unknown, (e.g., social media, blogposts).Epstein & Schneider (2008) suggest that when faced with such information ambiguity,investors who are ambiguity averse behave as if they maximize expected utility under aworst-case belief as in Gilboa & Schmeidler (1989) about the quality of the ambiguoussignals that they receive. Thus, if there are ambiguity averse investors, there will bean asymmetric reaction to ambiguous signals: bad signals that convey information thatthe realized dividend is lower than the prior will be treated as if they are more accurate(have smaller variance) than good signals conveying that the realized dividend is higherthan the prior and ambiguity-averse agents will allocate higher weight to the bad signalswhen making decisions.1 Since signals matter for asset price determination, if there areambiguity averse investors then the volatility of asset prices should be greater underambiguous signals.The theory that Epstein & Schneider (2008) develop makes use of a representative agentasset pricing model. In this paper, we report on the results of an experiment that tests theimplications of Epstein and Schneider’s theory under three different market institutions:an individual prediction market, a single price call market, and a continuous double auction market. We consider three different market institutions for asset price determination,since the particulars of the market structure are not considered in Epstein & Schneider(2008)’s representative agent framework. Nevertheless, as we show, the market institu1Note that in Epstein & Schneider (2008), agents are modeled as net buyers of the asset. Therefore,a higher (lower) dividend is good (bad) news for them. However, a higher (lower) dividend is bad (good)news for a net seller, e.g., in a double auction market. We will discuss this difference in further detail inthe section on the double auction treatment.2

tion matters for whether we observe an asymmetric reaction to bad or good news underambiguous signals.In our view, the theoretical predictions of Epstein & Schneider (2008) are three-fold. (1)Ambiguity averse participants’ perceived variance of an ambiguous signal is smaller whenit conveys bad news than when it conveys good news. Therefore, (2) ambiguity averseparticipants allocate a larger weight to signals that convey bad news than to signals thatconvey the good news. It follows that (3) the volatility of prices is greater when signalsare ambiguous than when they are unambiguous.To test these predictions, we design a two-stage experiment. In the first stage, participants’ attitudes toward ambiguity are measured along with measures for their riskaversion. Then, in the second stage, they participate in one of three different types ofexperimental asset markets as discussed above where they receive noisy (un)ambiguoussignals about the fundamental value of an asset, enabling us to see how they weight suchinformation and how traded prices vary with the ambiguity of the information received.In the first stage, we measure the participants’ ambiguity attitudes using a classic twocolor urn choice task following Ellsberg (1961), that is widely used in the literature, e.g.,Trautmann et al. (2008), Kocher & Trautmann (2013), Trautmann & Van De Kuilen(2015). Specifically, participants are asked to make a number of choices between pairs ofboxes (urns). The "K" or "known" box in each pair has known numbers (or fractions) ofpurple and orange balls. The "U" or "unknown" box in each pair has unknown numbers(or fractions) of purple and orange balls. Participants are instructed that if a purple ballis drawn from the box they chose, they will win a positive money amount; otherwisethey will earn 0. Using this task, we find that more than 66% of our participants can belabeled as "ambiguity averse", around 23% are "ambiguity neutral" and the remainingapproximately 10% are "ambiguity seeking". Thus, the degree of ambiguity aversion isheterogeneous across participants in our experiment.In the second stage, depending on the treatment, participants need to predict the fundamental value of an asset based on two signals, a public signal and a private signal andthen possibly trade the asset with other participants under a given market institution.The public signal is the known-to-all information that the fundamental value of the asset3

(more precisely, the dividend realization) is a random variable drawn from a particularnormal distribution. The private signal, s, is equal to the actual (but unknown) realization of the fundamental value of the asset plus some mean zero, normally distributednoise. Thus, the private signal is normally distributed with a mean equal to the realization of the fundamental value in each period and a variance that is known to changeevery 5 periods. The private signal is unambiguous in the first 15 periods. The varianceof the private signal is 1 in periods 1 5, then 0.25 in periods 6 10, and 4 in periods11 15. In the last 5 periods, the signal becomes ambiguous. In those final five periods,16 20, the variance of the signal lies somewhere between 0.25 and 4, but the actual valueof the variance and its distribution is unknown to market participants. Subjects in ourexperiment start out facing unambiguous private signals in the first 15 rounds becauseforming expectations with unambiguous signals is easier, and also provides subjects withthe opportunity to learn about the different variances that are possible in the final 5 periods, where signals are ambiguous. Further, the first 15 rounds allow practice with how toform predictions using the two signals (the public and the private signals). After subjectssubmit their predictions and make their trading decisions, the fundamental value of theasset is revealed. Subjects’ payoffs are calculated based on their predictions or tradingdecisions and the true fundamental value of the asset.Differently from Bleaney & Humphrey (2006), Halevy (2007), Bossaerts et al. (2010),etc., our experiment uses the variance of the private signal to characterize the ambiguityof the signal information rather than different probabilities in the returns to the asset.We understand that in the literature on risk and uncertainty, situations with unknownvariances are sometimes viewed as a compound lottery or a lottery with higher-order risk,e.g., Machina (1989), Miao & Zhong (2012), Noussair et al. (2014), Huang et al. (2020)etc. We nevertheless stick with the terminology "ambiguous signals" and "informationambiguity" in our paper, following the same language used by Epstein & Schneider (2008).To the best of our knowledge, this is also the first work on financial ambiguity in termsof the variance of signals instead of the probabilities of outcomes.Our experimental results confirm most of the theoretical predictions of Epstein & Schneider (2008). We find that ambiguity averse subjects predict a higher variance for theambiguous signal. Additionally, ambiguity averse individuals overestimate the variance of4

good news relative to bad news when the signal is ambiguous. This asymmetric reactionis strongest in the individual prediction market design (Treatment I), less present in thecall market design (Treatment C) and weakest in the double auction markets (TreatmentDA). Indeed, the asymmetric reaction at the individual trader level also prevails in thedouble auction markets. The absence of an aggregate asymmetric response to bad or goodnews in the double auction market is due to the fact that the asymmetric reaction on thebuyers’ side cancels the (opposite) asymmetric reaction on the sellers’ side. Finally, wealso find a larger mispricing of the asset and greater price volatility when the signal isambiguous than when it is unambiguous in Treatments I and C, but not in TreatmentDA.Our results provide strong support for the notion that information ambiguity and ambiguity attitudes play an important role in financial market decision-making. The comparisonbetween the three market institutions in our experiment also provides useful insights as tohow information ambiguity will influence the market quality and informational efficiencyof different market settings and provides useful implications for market regulators anddesigners of market institutions.The remainder of the paper is organized as follows. Section 2 reviews the related literature.Section 3 presents the experimental design. Section 4 discusses the experimental results.Section 5 concludes.2Literature ReviewThis study complements and extends previous research on the role of ambiguity andinformation ambiguity in financial markets in a number of ways.There have been several seminal studies since the 1990s, e.g., Sarin & Weber (1993),Chen & Epstein (2002), that have addressed the role of ambiguity for assessments ofthe fundamental value of an asset that have used both decision-theoretic and marketbased approaches. A general conclusion from this literature is that ambiguity robustlyleads to a lower price of the asset (the “ambiguity premium”) in individual decisionmaking experiments. In market experiments, the evidence is mixed. While ambiguity5

unambiguously leads to lower prices under a single price call market mechanism, somestudies find that ambiguity also leads to lower prices under a continuous double auctiontrading mechanism while other studies do not.Investigations of the role of information ambiguity for asset pricing, the subject of thispaper, are more recent, and most of these studies employ an individual decision-makingrather than a market-based framework. A general conclusion from these studies is that,as Epstein & Schneider (2008) predict, subjects overreact to bad news and underreactto good news. To our knowledge, Corgnet et al. (2012) is the only other study thatinvestigates information ambiguity in a market setting. They use the double auctionmarket institution and find that information ambiguity does not seem to lead to lowerasset prices.Table 1 summarizes the studies in the literature that are most closely related to thispaper. Our findings complement these prior studies in several ways: (1) in terms of therole of information ambiguity in individual decision-making asset pricing experiments,we find that the theoretical prediction of Epstein & Schneider (2008) also holds in ourprediction market institution (using a learning-to-forecast experimental (LtFE) design)where individuals make only a point prediction for the asset price. (2) The Epstein &Schneider (2008) prediction is weaker under the single price call market institution andis weakest of all in the continuous double auction market, which is consistent with theabsence of an effect of information ambiguity in such markets as reported by Corgnet etal. (2012)Our double auction treatment differs from Corgnet et al. (2012) in several importantways. First, the data generating process in our paper is the same as that of Epstein &Schneider (2008) while theirs departs from Epstein and Schneider in several ways. Second,they study the role of public signals while we focus on ambiguous private signals. Third,they focus on double auction markets while we study the role of information ambiguityin double auction markets, call markets and individual prediction markets. Our resultsshow that information ambiguity leads to a bias in belief updating in individual decisionmaking problems and to a lesser extent in the call market, while the role of ambiguousinformation is very limited in double auction markets. Together with the findings fromthe literature on ambiguity, it seems that the impact of both ambiguity and information6

ambiguity tend to be more pronounced in individual decision problems, and less so inlarge, decentralized markets like double auction markets.In addition, our paper is also related to several strands of the theoretical and empiricalliterature on the role of ambiguity in asset markets. Theoretical research has investigatedhow ambiguity aversion leads to asymmetric market reactions to different kinds of information. Zhang (2006) finds that greater information uncertainty leads to higher expectedreturns following good news and lower expected returns following bad news. Caskey(2008) shows that ambiguity averse investors can result in persistent mispricing of assets.Ambiguity averse investors work to reduce ambiguity at the expense of information loss,which can explain underreaction and overreaction to accounting accruals. Li & Janssen(2018) find that the disposition effect, the reluctance to realize losses and the eagernessto realize gains, can lead investors to underreact to private signal realizations about anambiguous asset. There is a lot of empirical and theoretical research on ambiguity andasset pricing, e.g., Chen & Epstein (2002), Cao et al. (2005), Gollier (2011), Illeditsch(2011), Jeong et al. (2015), Gallant et al. (2015), Bianchi & Tallon (2018), Brenner &Izhakian (2018). Much of this literature argues that ambiguity aversion leads to a higherequity premium in asset markets. In addition, some studies have shown that ambiguityhas an impact on asset prices and volatility.Finally, our paper contributes to the literature on belief updating about public signalsand private signals, e.g., Heinemann et al. (2004), Boswijk et al. (2007), Eil & Rao (2011),De Filippis et al. (2017), Duffy et al. (2018), Enke & Zimmermann (2019), Diks et al.(2019), Hommes et al. (2020), and to the literature on belief updating under compounduncertainty and ambiguity, e.g., Klibanoff et al. (2009), Corgnet et al. (2012), Ert &Trautmann (2014), Moreno & Rosokha (2016), Hanany & Klibanoff (2019), Huang et al.(2020). Our work is distinguished from these papers by allowing belief updating of thevariance of signals rather than the mean of signals.7

8This paperPrediction MarketCall MarketDouble AuctionMarket ExperimentMarket ExperimentDouble AuctionBDM MechanismSealed Bid AuctionDouble AuctionPortfolio ChoiceSealed Bid AuctionIndividual DecisionMarket ExperimentCorgnet et al. (2012)Market ExperimentIndividual DecisionMarket ExperimentFüllbrunn et al. (2014)Liang (2019)Market ExperimentBossaerts et al. (2010)Individual DecisionDouble AuctionIndividual DecisionAhn et al. (2014)Epstein & Halevy (2019)Call MarketIndividual DecisionChen & Epstein (2002)Sealed Bid AuctionIndividual DecisionKocher & Trautmann (2013)Market DesignSealed Bid AuctionDouble AuctionSetupIndividual DecisionMarket ExperimentAuthorsSarin & Weber (1993)Table 1A summary of the literature related to this paper.Private SignalPrivate SignalPrivate SignalInformation AmbiguityInformation AmbiguityPublic SignalInformation AmbiguityInformation AmbiguityPrivate SignalInformation yPrivate SignalInformation naryBinaryAmbiguous yAmbiguityType of AmbiguityAmbiguityAmbiguityResultambiguity leads to lower pricesambiguity leads to lower pricesambiguity leads to entry to ambiguous market, but noimpact on transaction priceambiguity leads to lower pricesmost subjects seem to be expected utility maximisers while few exhibit high level of ambiguity seeking/aversion in an individual portfolio choice experimentambiguity does influence portfolio holding by individual investors and the market priceambiguity leads to lower asset pricesambiguity leads to lower assetpricescompared to risky signals, subjects have more difficulty updating their beliefs based on ambiguous erreaction to bad/good newsinformation ambiguity does not lead to over- or underreaction to signalsinformation ambiguity leads to strong overreaction/underreaction to bad/good newsinformation ambiguity leads to mild overreaction/underreaction to bad/good newsinformation ambiguity does not lead to over- or underreaction to signals

3Experimental DesignOur experiment consists of three types of experimental markets, namely the individualprediction market (Treatment I), the single price call market (Treatment C), and thecontinuous double auction market (Treatment DA). We adopt a two-stage design foreach treatment. In the first stage (Part 1) participants’ attitudes towards ambiguity aremeasured along with measures for their attitudes toward risk. Since risk attitude is notthe central question of this study, we put the detailed information about the risk attitudeelicitation task in Appendix B. Then, in the second stage (Part 2), they participate inan experimental prediction or asset market. The design of Part 1 is the same for alltreatments, while the design of Part 2 is different for each treatment.We recruited 191 undergraduates from Nanyang Technological University as participantsin this experiment. Subjects were from various areas of study, but were primarily economics majors. Based on pre-experiment survey responses, all of our subjects reporthaving completed a basic course in statistics. Subjects were awarded a show-up fee of 3Singapore dollars (SGD) for participating and could earn additional earnings based ontheir performance in the experiment.Table 2 summarizes important characteristics of our experimental design including the sizeof each market in terms of the number of traders, the number of markets conducted pertreatment, the total number of subjects per treatment, the average duration of a session ofeach treatment, and the average payment that each subject received per treatment. Notethat in the two market treatments, C and DA, each market involves six participants.Table 2Characteristics of the experimental design.Treatment IMarket size1Number of markets41Number of subjects41Average session hours1.5 hoursAverage payoff23 SGDTreatment C615901.5 hours19 SGDTreatment DA610602 hours22 SGDUpon arrival, participants are randomly seated in the lab. The experimenter then makesa brief presentation about experimental procedures. After that, subjects are given 309

minutes to read the instructions, during which they are free to ask questions. Participants are required to successfully answer a number of control questions designed to checktheir comprehension of the instructions before they can start Part 1 and Part 2 of theexperiment.Part 1 elicits each participant’s ambiguity and risk attitudes, and Part 2 is a 20-periodindividual-decision making or market-trading game. Detailed information about Part 1is presented in Section 3.1, and details about Part 2 are described in Section 1We categorize participants using the same method used by Trautmann et al. (2011),Trautmann & Van De Kuilen (2015), into "ambiguity averse" types and "non-ambiguityaverse" types; the latter can be further divided up between "ambiguity neutral" and"ambiguity seeking" types, To ascertain ambiguity attitudes, subjects are asked to make10 choices between pairs of boxes, Box K and Box U. Each of the two boxes contains100 balls. The color of the balls is either purple or orange. The numbers (and hencethe fraction) of purple and orange balls are known in Box K, as the subjects can see thenumber of purple and orange balls (and hence the fraction of purple and orange balls)on their computer screen. The number (and hence the fraction) of purple and orangeballs are unknown in Box U. After the subject chooses a box (K or U), one ball is drawnrandomly from the selected box. Subjects are instructed that they will earn 3 SGD if apurple ball is drawn. Thus, the information about the probability of winning 3 SGD iscertain for Box K, while it is ambiguous for Box U. Each of the ten choices between BoxK and Box U appears in a single row on the subject’s decision screen. From the first rowto the final 10th row, the fraction of purple balls in Box K decreases from 100% to 0%with a step decrease of 10%. The participant must choose between Box K or Box U ineach of the ten rows. If the participant switches her/his choice from the known Box K tothe unknown Box U when the fraction of purple balls in box K is more than 50%, thens/he is ambiguity seeking. If the participant switches her/his choice from Box K to Box Uwhen the fraction of purple balls in Box K is exactly 50%, then s/he is ambiguity neutral.Finally, if the participant switches her/his choice from Box K to Box U when the fraction10

of purple balls in Box K is less than 50%, then s/he is ambiguity averse.2One row is randomly drawn from the ambiguity aversion elicitation task in Part 1. Thesubject’s choices for the selected row determine their payoff for Part 1. That is, theambiguity aversion elicitation task is incentivized.3.2Part 2The second part of our experiment is based on the theoretical framework of Epstein& Schneider (2008), which we briefly review here. Ex-ante, there is no ambiguity inthe information that each agent receives. In each period t, each agent is told that thedividend, θt , earned per unit of an asset held is a random variable that is drawn from anormal distribution with mean m and variance, σθ2 , that is, θt i.i.d.N (m, σθ2 ).Then, in the ambiguous information setting of Epstein & Schneider (2008), prior to tradein the asset, each agent gets a noisy private signal, st , about the likely value of θt in periodt. Specifically, each a

Binary prices Binary prices rautmann (2013 Binary no price Epstein (2002 Binary prices al. (2014 Binary maximis- seek- er- t al. (2010 Binary individ- price al. 2014 Binary prices Binary sset prices Halevy (2019 Auction y Binary diffi- sig- nals Liang (2019 sm y Binary erreac- news al. (2012 Auction y Binary under- signals et y Gaussian erreac .

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