Article

Corporate Prediction Markets: Evidence from Google, Ford, and Firm X1

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Abstract

Despite the popularity of prediction, markets among economists, businesses, and policymakers have been slow to adopt them in decision-making. Most studies of prediction markets outside the lab are from public markets with large trading populations. Corporate prediction markets face additional issues, such as thinness, weak incentives, limited entry, and the potential for traders with biases or ulterior motives—raising questions about how well these markets will perform. We examine data from prediction markets run by Google, Ford Motor Company, and an anonymous basic materials conglomerate (Firm X). Despite theoretically adverse conditions, we find these markets are relatively efficient, and improve upon the forecasts of experts at all three firms by as much as a 25% reduction in mean-squared error. The most notable inefficiency is an optimism bias in the markets at Google. The inefficiencies that do exist generally become smaller over time. More experienced traders and those with higher past performance trade against the identified inefficiencies, suggesting that the markets' efficiency improves because traders gain experience and less skilled traders exit the market.

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... It is possible that they actively searched for more information confirming their decisions, and therefore could have made more extreme (and often likely wrong) forecasts. Explorers who learn more about prospects may develop optimism bias (tendency to overestimate favorable or pleasing outcomes: Baron, 2007) or pessimism bias (when they exaggerate the likelihood of negative outcomes: Alexander and Lohr, 1998;Johns et al., 1998) leading to poorer exploration forecasts (Cowgill and Zitzewitz, 2015). Overconfidence effect is a bias in which a person's subjective confidence in his judgment is excessive (Hoffrage, 2004) leading to poorer judgements, and that may result from more time spent with the prospect. ...
... Interestingly, several survey participants indicated that they were not comfortable making exploration forecasts based on the limited provided information ("one-pagers") and without in-depth evaluation of plays, seismic data, well-logs and other subsurface information. However, some studies show that people use less information than they think to make judgements (Klein and O'Brien, 2018) and that the accuracy of judgements does not increase much when the amount of information increases (Slovic, 1973;Tsai et al., 2008;Cowgill and Zitzewitz, 2015). Even though explorers think that they will consider more information before making conclusions, they may not do this and instead rely heavily on the first impressions such as provided in one-pagers. ...
... This can and should be tested in non-anonymous studies, for example, inside exploration companies. Such studies with revealed identity of the participants and perhaps some built-in competition and incentives may also result in better forecasts than documented in this study (Hesthammer and Gebauer, 2014;Cowgill and Zitzewitz, 2015;Tetlock and Gardner, 2015). ...
Article
Petroleum explorers regularly make numerous forecasts for prospects and wells, but the quality of their predictions has inadequate documentation. Here I discuss the forecasting abilities of individual explorers inferred from a survey about future exploration wells, some of which were drilled in late 2018 and 2019. A total of 104 petroleum explorers provided 7,068 answers about eleven wells and about themselves, and subsets from this dataset were used to study their predictions. The survey participants were diverse, and most had M.Sc. or Ph.D. degrees with >16 years of industry experience working as individual contributors and/or middle managers for oil companies. Assessments of the geological probability of success (PoS) by different explorers for the same well were highly variable, and average assessments poorly discriminated between future discoveries and dry holes. Point (binary or multiple choice) forecasts by explorers participating in the survey were, on average, only slightly better than random guessing. The participants' highest academic degree had little apparent influence on the quality of the point forecasts. Years of experience resulted in only slightly better correlation with increasing quality of forecasts. Survey participants more familiar with the prospects (those who generated them or evaluated proprietary company data) made, on average, worse exploration forecasts than those who studied only the limited publicly available information provided with the survey. The duration of time spent on the survey did not affect the quality of forecasts. The aggregated opinion of all explorers (wisdom of the crowd) may be beneficial in assessments of the geological PoS, but perhaps not so when forecasted outcomes have binary or multiple possibilities. The generally poor forecasting abilities of individual petroleum explorers may surprise some decision‐makers and investors. However, many of the study results are based on relatively small datasets and should be treated as preliminary. Further research with larger datasets is necessary to replicate, validate and explain the findings of this study.
... An internal corporate market could be used to predict the launch date of a new product or the product's eventual success. Among the first companies to experiment with internal markets were Hewlett-Packard, which implemented real-money markets, and Google, which ran markets using its own internal currency that could be exchanged for raffle tickets or prizes [Plott andChen, 2002, Cowgill andZitzewitz, 2015]. More recently, Microsoft, Intel, Ford, GE, Siemens, and others have engaged in similar experiments [Berg and Proebsting, 2009, Charette, 2007, Cowgill and Zitzewitz, 2015. ...
... Among the first companies to experiment with internal markets were Hewlett-Packard, which implemented real-money markets, and Google, which ran markets using its own internal currency that could be exchanged for raffle tickets or prizes [Plott andChen, 2002, Cowgill andZitzewitz, 2015]. More recently, Microsoft, Intel, Ford, GE, Siemens, and others have engaged in similar experiments [Berg and Proebsting, 2009, Charette, 2007, Cowgill and Zitzewitz, 2015. ...
... However, even with a formal market structure in place, an employee might be hesitant to bet against the success of their team for fear of insulting her coworkers or angering management. If an employee has information that is unfavorable to the company, she might choose not to report it, leading to predictions that are overly optimistic for the company and ultimately contributing to an "optimism bias" in the market similar to the bias in Google's corporate markets discovered by Cowgill and Zitzewitz [2015]. ...
Conference Paper
We consider the design of private prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants' actions or beliefs. Our goal is to design market mechanisms in which participants' trades or wagers influence the market's behavior in a way that leads to accurate predictions, yet no single participant has too much influence over what others are able to observe. We study the possibilities and limitations of such mechanisms using tools from differential privacy. We begin by designing a private one-shot wagering mechanism in which bettors specify a belief about the likelihood of a future event and a corresponding monetary wager. Wagers are redistributed among bettors in a way that more highly rewards those with accurate predictions. We provide a class of wagering mechanisms that are guaranteed to satisfy truthfulness, budget balance in expectation, and other desirable properties while additionally guaranteeing epsilon-joint differential privacy in the bettors' reported beliefs, and analyze the trade-off between the achievable level of privacy and the sensitivity of a bettor's payment to her own report. We then ask whether it is possible to obtain privacy in dynamic prediction markets, focusing our attention on the popular cost-function framework in which securities with payments linked to future events are bought and sold by an automated market maker. We show that under general conditions, it is impossible for such a market maker to simultaneously achieve bounded worst-case loss and epsilon-differential privacy without allowing the privacy guarantee to degrade extremely quickly as the number of trades grows, making such markets impractical in settings in which privacy is valued. We conclude by suggesting several avenues for potentially circumventing this lower bound.
... Kollektiv intelligens er i høj grad et begreb, der er ved at udbredes i en digitaliseret samtid. Nyere forskningslitteratur påviser, at "crowds" har kollektiv intelligens, der kan bidrage med nye kreative og innovative løsninger, og endda forudsige fremtiden for virksomheder ganske praecist (Cowgill & Zitzewitz, 2015;Dahan, Soukhoroukova, & Spann, 2010;Hong & Page, 2001;2004;Surowiecki, 2004). ...
... Vi vil i det kommende afsnit give et overblik af resultaterne praesenteret i dette kapitel og samtidig diskutere implikationerne af resultaterne for Danmarks største virksomheder. Nyere prediction crowd sourcing studier i virksomheder (uden markeder) viser at frontlinje ansatte kan forudsige KPI'er og andre performance measures for virksomheder ganske praecist (Cowgill & Zitzewitz, 2015). Et senere studie af crowd forudsigelser uden markeder blandt medarbejdere i globale forretningsenheder viser at de frontlinjeansatte kollektivt kan identificere udviklingen i helt specifikke operationelle mål (Hallin & Lind, 2016). ...
... Danmarks største virksomheder står overfor et anseligt potentiale de kommende år i deres arbejde med at skabe konkurrencedygtighed i takt med, at den digitale transformation gør alle aspekter af forretningen afhaengig af data på realtid. Kollektiv intelligens og relaterede crowdsourcing-metoder kan bidrage med nye kreative og innovative løsninger i realtid, og endda forudsige fremtiden for virksomheder ganske nøjagtig så virksomhederne kan vaere i front af udviklingen (Hong & Page, 2001;2004;Landemore, 2012;Cowgill & Zitzewitz, 2015;Hallin et al., 2012;Hallin 2016). Gruppers såkaldte kollektive intelligens kan aggregeres ved brug af hardware og software og anses for en ny ressource for at udvikle konkurrencedygtighed for virksomheder. ...
Book
Full-text available
Dansk erhvervsliv er anerkendt som et af de mest innovative i verden, men i disse år møder dansk erhvervsliv nye store udfordringer. Danske virksomheder står overfor at tilpasse sig til den fjerde industrielle revolution, som bygger på den teknologiske udvikling og ikke mindst digitalisering (Erhvervs og Vækstministeriet, 2016). Samlet set vil den fjerde industrielle revolution medføre nye krav til virksomheder som vil blive udfordret på effektivitet, omkostning, kreativitet og innovation samt evnen til at forudsige nye markedstrends og operationelle ændringer bedre end konkurrenterne. Nyere forskningslitteratur påviser, at ”crowds” har kollektiv intelligens, der kan bidrage med nye kreative og innovative løsninger, og endda forudsige fremtiden for virksomheder ganske præcist (Hong & Page, 2001; 2004; Landemore, 2012; Surowiecki, 2004). Crowds såkaldte kollektive intelligens kan aggregeres i form af crowdsourcing ved brug af hardware og software og udgør en ny ressource for at udvikle konkurrencedygtighed for virksomheder.
... First, individual users react differently to true and false content [20]. Second, the collective opinions and actions of users can be aggregated to produce accurate forecasts, for example in prediction markets [5]. Third, in expectation, cascades surrounding true and false content differ in their patterns of propagation [29]. ...
... Granted access to the full Twitter historical archive, they began by searching for any tweet whose replies include a link to a fact-checking article from one of six websites. 5 They confirmed that the text of the fact-checking article was related to the content of the tweet by embedding both in a vector space and measuring their cosine similarity. They then collected all the retweets of the rumor, building the full cascade while discarding tweets that may have referenced the fact-checking article or were determined by a bot-detection algorithm [28] to have been written automatically. ...
... Second, the dataset was provided to us with permission from Twitter, with whom we are not affiliated. Researchers interested in gaining access to the data should contact either Vosoughi et al.[29] or Twitter directly.5 These websites are snopes.com, ...
Preprint
Full-text available
Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.
... A regulator may learn about the financial health of a bank from bond prices, and use this information for regulatory purposes (e.g., contingent capital with market trigger, Sundaresan and Wang, 2015). Or a company could use internal prediction markets-where asset values depend on the launch date of a new product-to predict whether deadlines can be met, and react if forecasts indicate major delays (e.g., Cowgill and Zitzewitz, 2015). ...
... To illustrate the self-defeating prophecy problem, consider the 'deadline securities' in corporate prediction markets, which are designed to forecast whether a project will be completed on time (e.g., Cowgill and Zitzewitz, 2015). In the applications, I show these provide improper incentives for traders to share information about the project if management reacts to these forecasts. ...
... Thus, the pricing problem is a self-defeating prophecy. In contrast, another type of corporate prediction market-which generates sales forecasts based on asset prices-provides proper incentives for traders to reveal their information, which might explain why these markets work well in practice (e.g., Plott and Chen, 2002;Cowgill and Zitzewitz, 2015). ...
... A regulator may learn about the financial health of a bank from bond prices, and use this information for regulatory purposes (e.g., contingent capital with market trigger, Sundaresan and Wang 2015). Or a company could use internal prediction markets-where asset values depend on the launch date of a new product-to predict whether deadlines can be met, and react if forecasts indicate major delays (e.g., Cowgill and Zitzewitz 2015). ...
... The condition is simple and requires invertibility of the expected asset value given the optimal policy reaction to trader information in a sufficient statistic of trader information. If the condition is not fulfilled, then traders anticipate that informative prices would trigger a policymaker reaction that leads to trader losses, thus revelation of trader information is not incentive compatible. 2 To illustrate the self-defeating prophecy problem, consider the "deadline securities" in corporate prediction markets, which are designed to forecast whether a project will be completed on time (e.g., Cowgill and Zitzewitz 2015). In the applications, I show these provide improper incentives for traders to share information about the project if management reacts to these forecasts. ...
... One example where these prediction markets affect company policy is mentioned in Cowgill and Zitzewitz (2015). Ford decided against introducing several new products after prediction market forecasts revealed that these would not be popular among consumers. ...
Article
Full-text available
I analyze a general setting where a policymaker needs information that financial market traders have in order to implement optimal policy, and prices can potentially reveal this information. Policy decisions, in turn, affect asset values. I derive a condition for the existence of fully revealing equilibria in competitive financial markets, which identifies all situations where learning from prices for policy purposes works. I discuss the possibility of using market information for banking supervision and central banking, and the general problem of asset design. I also demonstrate that some corporate prediction markets are ill‐designed, and show how to fix it.
... In the strong form of the efficient markets hypothesis, no additional information should be able to improve the accuracy of the last price. This form of crowdsourcing is used in many organizations, bridging the gap between economic theory and business practices (Cowgill and Zitzewitz 2015, Spann and Skiera 2003, Surowiecki 2005. ...
... Prices did not exhibit the typical favorite long-shot bias, implying that ranking system did not lead to excessive risk taking. The lack of distortions may be reassuring to companies that run prediction markets and rely on leaderboards and reputational incentives (Cowgill and Zitzewitz 2015). ...
Article
We report the results of the first large-scale, long-term, experimental test between two crowdsourcing methods: prediction markets and prediction polls. More than 2,400 participants made forecasts on 261 events over two seasons of a geopolitical prediction tournament. Forecasters were randomly assigned to either prediction markets (continuous double auction markets) in which they were ranked based on earnings, or prediction polls in which they submitted probability judgments, independently or in teams, and were ranked based on Brier scores. In both seasons of the tournament, prices from the prediction market were more accurate than the simple mean of forecasts from prediction polls. However, team prediction polls outperformed prediction markets when forecasts were statistically aggregated using temporal decay, differential weighting based on past performance, and recalibration. The biggest advantage of prediction polls was atthe beginning of long-duration questions. Results suggest that prediction polls with proper scoring feedback, collaboration features, and statistical aggregation are an attractive alternative to prediction markets for distilling the wisdom of crowds.
... creative and innovative solutions, given the right circumstances. Crowdsourcing can also involve the collective predictions of stakeholders (Cowgill & Zitzewitz, 2015;Hallin, Andersen & Tveterås, 2017)-that there is a potential advantage to allowing a group of individuals, whatever their abilities, to predict an outcome. The new prediction trend is seen in the form of the use of 'prediction markets' (Cowgill & Zitzewitz, 2015;Wolfers & Zitzewitz, 2004) or 'crowd predictions' (predictions without markets) (Hallin, 2016;Hallin et al. 2017). ...
... Crowdsourcing can also involve the collective predictions of stakeholders (Cowgill & Zitzewitz, 2015;Hallin, Andersen & Tveterås, 2017)-that there is a potential advantage to allowing a group of individuals, whatever their abilities, to predict an outcome. The new prediction trend is seen in the form of the use of 'prediction markets' (Cowgill & Zitzewitz, 2015;Wolfers & Zitzewitz, 2004) or 'crowd predictions' (predictions without markets) (Hallin, 2016;Hallin et al. 2017). ...
Conference Paper
Full-text available
Danish corporations face challenges in adapting to the fourth industrial revolution resulting from the evolution in information technology and digitalization. Overall, the fourth industrial revolution has led to new requirements for Danish companies that are challenged in terms of efficiency, cost, quality in creativity and innovation, and the ability to predict new market trends and operational changes better than the competition. The purpose of this research is to map the state of collective intelligence behavior and related crowdsourcing methods applied by CEOs, directors and managers of Denmark's leading corporations across different sectors. As the first quantitative and qualitative research on the status of collective intelligence behavior and practices of top Danish corporations, we investigate the corporations’ use of collective intelligence, crowdsourcing methods (including crowdsourcing of creative and innovative solutions), prediction markets, and crowd predictions without markets. In addition, we examine various factors that can determine the usage of crowdsourcing within the individual company.
... Given the increasing prevalence of using teams, committees, and ad hoc groups for decision making in modern organizations, questions of how to achieve effective information sharing and collective learning to boost organizational performance have captured the interests of academics and practitioners (Morgeson et al. 2010). Recently, this drive to improve information flow and learning has led to the use of internal information markets in a variety of organizations, such as Ford, Google (Cowgill and Zitzewitz 2015), Hewlett-Packard (Gillen et al. 2013), and Nokia (Hankins and Lee 2011). ...
... The performance of these markets is generally encouraging. An internal prediction market at Ford outperformed expert forecasts of weekly vehicle sales, achieving a 25% lower mean squared error (Cowgill and Zitzewitz 2015). An internal prediction market at Intel outperformed Intel's official forecast, and the performance of the market was particularly impressive over short forecast horizons (Gillen et al. 2013). ...
Article
A crucial challenge for organizations is to pool and aggregate information effectively. Traditionally, organizations have relied on committees and teams, but recently many organizations have explored the use of information markets. In this paper, the authors compared groups and markets in their ability to pool and aggregate information in a hidden-profiles task. In Study 1, groups outperformed markets when there were no conflicts of interest among participants, whereas markets outperformed groups when conflicts of interest were present. Also, participants had more trust in groups to uncover hidden profiles than in markets. Study 2 generalized these findings to a simple prediction task, confirming that people had more trust in groups than in markets. These results were not qualified by conflicts of interest. Drawing on experienced forecasters from Good Judgment Open, Study 3 found that familiarity and experience with markets increased the endorsement and use of markets relative to traditional committees.
... The wisdom of the crowds principle constitutes the basis of prediction markets (or predictive markets), forums where participants can trade the probability of the outcomes of uncertain events (Arrow et al. 2008). The aggregate information in such markets can outperform other traditional forecasting methods, and can be applied to a limitless array of fields, from National Security (Wolfers and Zitzewitz 2004) to health care industry (Polgreen et al. 2007), multinational corporations' strategy (Cowgill and Zitzewitz 2015), and even to evaluate scientific research reproducibility (Dreber et al. 2015). To understand how prediction markets work, we can imagine the hypothetical presidential election in a random State. ...
Chapter
The way we perceive time has changed across the centuries. Some authors suggest that the dynamic complexity of the current world and its intrinsic uncertainty have loosened and modified the bonds among past, present and future. The present has extended and has become ‘thick’ (Poli 2015), while we seem to lack the necessary social skills to face the future from non-deterministic perspectives. Analysing the relationship between future and present, I claim that an active attitude toward the present can help us to foresight the future. Three domains may offer useful hints to evade from our short-sighted view: mindfulness, aesthetic and spontaneity. Moreover, several studies show that we can improve our forecasting ability by applying specific precautions and changing our mindset. The paper will then discuss further strategies that can enhance future awareness and influence the ’living futures’ (Adam and Groves 2007, p.198) embedded in the present.
... For discussions of the success of prediction markets in aggregating information seePennock et al. (2001),Chen and Plott (2002),Tetlock (2004),Gürkaynak and Wolfers (2006),Berg et al. (2008),Cowgill et al. (2009),Cowgill and Zitzewitz (2015), andAtanasov et al. (2017).3 In naturally occurring prediction markets, aggregate information is unlikely to identify an outcome with certainty and in such cases differential risk attitudes may provide a motive to trade. ...
Article
The efficient market hypothesis predicts that asset prices reflect all available information. A seminal experiment reported that contingent claim markets could yield market outcomes consistent with information aggregation when traders hold heterogeneous state-contingent values. However, a recent experiment found the rational expectation model outperformed the prior information and maxi-min models in contingent claim markets when traders hold homogeneous values despite the no trade equilibrium in that setting. But that same study failed to replicate the original result calling into question when, if ever, prices reliably reflect the aggregate information of traders with heterogeneous values. In this paper, we show contingent claim markets can robustly yield prices consistent with the efficient market hypothesis when traders hold heterogeneous values in certain circumstances. The key distinction between our environment and that of the previous studies is that we consider trader values that are correlated and not too dissimilar.
... The methodology could be easily applied to other prediction markets with high-frequency data and the clean arrival of major news, beyond sports, such as those run within major companies among employees (e.g. Cowgill and Zitzewitz, 2015) and public markets on political or financial events (e.g. The Iowa Electronic Markets (IEM), PredictIt and the now defunct Intrade.com). ...
Article
Full-text available
Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win. These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.
... 71 The accuracy of prediction markets for outcomes involving geopolitical or sporting events has spurred growing interest in using corporate prediction markets to engage employees, customers, regulators, stockholders, and suppliers in making predictions about project outcomes, sales forecasts, product features, and other types of strategic decisions. 72 There are challenges associated with using prediction markets to address known unknowns. First, markets require large numbers of active and informed participants. ...
Article
This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.
... When participants of the market hesitate to participate a market, the performance of market would be decreased because of reduction of transactions. Therefore, on a prediction market deployed in an enterprise organization [4], [5], it is crucial to consider the ways to preserve privacy to protect participants of the market as well as to keep the performance of the prediction done in the market. ...
... 1 Predictions based on asset prices in such markets overwhelmingly outperform conventional forecasting methods (e.g., Wolfers and Zitzewitz 2004, Arrow et al. 2008, Palan et al. 2020). It is not surprising, then, that governments and private corporations are increasingly using prediction markets as a basis for policy decisions (e.g, Chen and Plott 2002, Cowgill and Zitzewitz 2015, Gillen et al. 2017, Dianat and Siemroth 2021. Moreover, trading prices in natural financial markets can be used to inform policymaking if it is difficult, or even impossible, to design a dedicated artificial prediction market. ...
Article
Markets are increasingly used as information aggregation mechanisms to predict future events. If policymakers and managers use markets to guide policy and managerial decisions, interested parties may attempt to manipulate the market in order to influence decisions. We study experimentally the willingness of managers to base decisions on market information under the shadow of manipulation. We find that when there are manipulators in the market, managers under-utilize the information revealed in prices. Furthermore, mere suspicion of manipulation erodes trust in the market, leading to the implementation of suboptimal policies—even without actual manipulation. This paper was accepted by Yan Chen, behavioral economics and decision analysis.
... On balance, research suggests that the Delphi method tends to improve forecast accuracy over simple averages of group members' forecasts if it is correctly applied (Rowe and Wright 1999;Wright and Rowe 2011;Lin, Goodwin, and Song 2014). Prediction markets have also been found to yield greater accuracy than individual experts in company forecasting (Cowgill and Zitzewitz 2015). However, there is as yet no clear evidence that any one of the approaches always results in a more accurate forecast (e.g. ...
Article
Demand forecasts are the key input to many crucial planning tasks, yet such forecasts are strongly and frequently influenced by human judgment. This chapter reviews and summarizes research on behavioral aspects of forecasting, including aspects of both individual decision‐making and organizational decision‐making. In particular, this chapter highlights some of the difficulties and assumptions inherent to the task. It discusses point forecasting, estimating uncertainty, and forecasting within and between organizations. It provides an overview of research on improving judgmental forecasting, including providing feedback and guidance to forecasters, specific elicitation methods, group‐based forecasting methods, and interaction with statistical forecasts. Future research opportunities include methods for incorporating promotions into forecasts, mitigating algorithm aversion, and including organizational stakeholder perspectives and practices such as sales and operations planning.
... For example, prediction markets have been brought into enterprise settings, typically designed to capture information broadly available from the crowd (e.g. Berg & Rietz, 2003;Cowgill & Zitzewitz, 2013;O'Leary, 2015), in contrast to gathering information from experts (e.g. O'Leary, 1993). ...
... A growing strand of the literature highlights the value of collective judgments, often referred to as the 'Wisdom of Crowds', for assessing the probability of future events (e.g., Surowiecki, 2004). Researchers usually elicit such wisdom by relying on controlled experiments (e.g., Herzog & Hertwig, 2011;Lorenz, Rauhut, Schweitzer, & Helbing, 2011;Simmons, Nelson, Galak, & Frederick, 2011), prediction markets (e.g., Cowgill & Zitzewitz, 2015, Forsythe, Rietz, & Ross, 1999, Wolfers & Zitzewitz, 2004 or even prediction polls (e.g., Atanasov, Rescober, Stone, Swift, Servan-Schreiber, & Tetlock, 2016). These settings allow researchers retain varying degrees of control over the mechanism through which collective judgments arise. ...
Article
This paper investigates the value of collective judgments which stem from settings that have not been designed explicitly to elicit the ‘Wisdom of Crowds’. In particular, I investigate information obtained from transfermarkt.de, an online platform where a crowd of registered users assess the value of professional soccer players. I show that forecasts of international soccer results based on the crowd's valuations are more accurate than those based on standard predictors, such as the FIFA ranking and the ELO rating. When this improvement in forecasting performance is applied to betting strategies, it leads to sizable monetary gains. I further exploit information on the preferences of individual crowd members in order to investigate whether wishful thinking hampers the accuracy of crowd valuations, but fail to find evidence that such is the case.
... This is a high bar, as prediction markets have been found to outperform tipsters in the context of sports (Spann and Skiera, 2009), and outperform polls and experts in the context of political races (Vaughan Williams and Reade, 2015). Prediction markets have even performed well when illiquid, as was the case in the corporate prediction markets studied by Cowgill and Zitzewitz (2015), and have also performed well when attempts have been made to manipulate prices, as was the case in the presidential betting markets studied by Rhode and Strumpf (2004) and Rothschild and Sethi (2015). In addition, and of particular relevance to our setting, prediction/betting markets have been found to accurately digest information on events (goals) almost immediately (Croxson and Reade, 2014). ...
Article
Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? We source 13.8 million posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. Using a microblogging dictionary to analyze the content of Tweets, we find that the aggregate tone of Tweets contains significant information not in betting prices, particularly in the immediate aftermath of goals and red cards. (JEL G14, G17)
... This is a high bar, as prediction markets have been found to outperform tipsters in the context of sports (Spann and Skiera 2009), and outperform polls and experts in the context of political races (Vaughan Williams and Reade 2016a). Prediction markets have even performed well when illiquid, as was the case in the corporate prediction markets studied by Cowgill and Zitzewitz (2015), and have also performed well when attempts have been made to manipulate prices, as was the case in the presidential betting markets studied by Rhode and Strumpf (2004) and Rothschild and Sethi (2016). In addition, and of particular relevance to our setting, prediction/betting markets have been found to accurately digest information on events (goals) almost immediately (Croxson and Reade 2014). ...
Article
Social media is now used as a forecasting tool by a variety of firms and agencies. But how useful are such data in forecasting outcomes? Can social media add any information to that produced by a prediction/betting market? In this paper we source 13.8m posts from Twitter, and combine them with contemporaneous Betfair betting prices, to forecast the outcomes of English Premier League soccer matches as they unfold. We find that the Tweets of certain journalists, and the tone of all Tweets, contain information not revealed in betting prices. In particular, Tweets aid in the interpretation of news during matches.
... Hu and Wallace (2013) present a two-stage mechanism for aggregating information that evokes features similar to dynamic pari-mutuel betting systems and, especially, the staged price increases in the IAM. 3 The Iowa Electronic Markets constituted the first "prediction markets" in the sense that the price of a binary security can be viewed as a probability and used to predict elections (see Berg et al. [2008] for a survey of these applications). Internal corporate prediction markets were broadly deployed at Google and other firms (Cowgill, Wolfers, and Zitzewitz 2009;Cowgill and Zitzewitz 2014) to gauge employees' sentiments on everything from a company's performance to general industry issues. pending on the realized value of the outcome of interest. ...
Article
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A new information aggregation mechanism (IAM), developed via laboratory experimental methods, is implemented inside Intel Corporation in a long-running field test. The IAM, incorporating features of pari-mutuel betting, is uniquely designed to collect and quantize as probability distributions dispersed, subjectively held information. IAM participants’ incentives support timely information revelation and the emergence of consensus beliefs over future outcomes. Empirical tests demonstrate the robustness of experimental results and the IAM’s practical usefulness in addressing real-world problems. The IAM’s predictive distributions forecasting sales are very accurate, especially for short horizons and direct sales channels, often proving more accurate than Intel’s internal forecast.
... For instance, the prediction accuracy of participants who have high prediction ability (at the high end of the distribution of prediction accuracy) may tend to be less affected by audience size or online endorsement, because these participants would have already put efforts and made predictions carefully even in the absence of treatments, and therefore there would be limited room for further improving their prediction accuracy. Cowgill and Zitzewitz (2015) empirically find that prediction market participants differ significantly in their prediction skill levels. ...
Article
The performance of prediction markets depends crucially on the quality of user contribution. A social-media-based prediction market can utilize aspects of social effects to improve users’ contribution quality. In this study, we examine the causal effect of social audience size and online endorsement on prediction market participants’ prediction accuracy through a randomized field experiment. By conducting a comprehensive treatment effect analysis, we estimate both the average treatment effect (ATE) and the quantile treatment effect using the difference-in-differences method. Our empirical results on ATE show that an increase in audience size leads to an improvement in prediction accuracy, and that a higher level of online endorsement also leads to prediction improvements. Interestingly, we find that the quantile treatment effects are heterogeneous: users of intermediate prediction ability respond most positively to an increase in social audience size and online endorsement. These findings suggest that prediction markets can target people of intermediate abilities to obtain the most significant prediction improvement. The online appendix is available at https://doi.org/10.1287/isre.2016.0679.
... Is this to be the swan song for corporate prediction markets? While academia -the theorists -has touted the success of the principle, adoption by the business community has been slow (Cowgill & Zitzewitz, 2013). Successors like big data and predictive analytics as well as scenario planning might now supersede prediction markets. ...
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Despite great acclaim for the potential and use of prediction markets as an efficient tool for aggregating individual judgments, corporate adoption has been limited. To find out why, Thomas Wolfram examined existing research and interviewed several dozen key business executives. He reports that problems with overall acceptance of the corporate prediction market (CPM) often stem from a lack of trust by management as well as a greater business focus on big data and social-media content. However, hope for the CPM persists, if certain obstacles can be finessed.
... For example, prediction markets have been brought into enterprise settings, typically designed to capture information broadly available from the crowd (e.g. Berg & Rietz, 2003;Cowgill & Zitzewitz, 2013;O'Leary, 2015), in contrast to gathering information from experts (e.g. O'Leary, 1993). ...
This paper investigates alternative configurations of different blockchain architectures that can be used for gathering and processing transactions in a range of different settings, including accounting, auditing, supply chain and other types of transaction information. Although there has been substantial focus on the peer-to-peer and public versions of blockchain, this paper focuses primarily on cloud-based and private configuration versions of blockchains and investigates use configurations, advantages and limitations as firms bring blockchain-based market mechanisms into their organizations. In addition, this paper investigates some emerging issues associated with blockchain use in consortium settings. Finally, this paper relates some proposed uses of blockchain for transaction processing to other technologies, such as data warehouses and databases.
... 204-207). (Polgreen et al. 2007), multinational corporations' strategy (Cowgill and Zitzewitz 2015), and even to evaluate scientific research reproducibility (Dreber et al. 2015). ...
Thesis
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This thesis seeks to analyse how we relate to the future in a complex and uncertain world, our attempts to predict it and what cognitive strategies may influence our judgment and decision. It will also try to develop a better understanding of what elements can help us to improve our prospection, both as individuals, in personal choices, and as a society, to plan and address our actions. The first chapter will describe how the relationship between humans and the future has evolved in history. The second chapter will introduce some of the main concepts of futures studies, and the attempts to give a structure to the discipline. The third chapter gathers psychology contributions to future thinking, the proposed paradigm shift from the classic past perspective to a new approach that considers the future, and some of the mechanisms that deceive our perception of the future. The fourth chapter discusses how we deal with complexity and uncertainty, and examines the concept of Black Swan and the strategies to predict the future. Finally, the fifth chapter reflects on the previously mentioned aspects and underlines the role of present attitudes to improve prospection.
... The wisdom of the crowd has been observed in a wide range of settings, in estimating weights of objects (Galton 1907, Wagner andSuh 2014), in political forecasts (Sjöberg 2009, Murr 2015, climate-related events (Hueffer et al. 2013), physician diagnostics (Kurvers et al. 2016), and economic forecasts (Kelley and Tetlock 2013, Budescu and Chen 2014, Nofer and Hinz 2014. The principle has been applied by practitioners across fields (Surowiecki 2004) and used in electronic technologies and online platforms that collect and aggregate opinions, for example, for organizational decision making (Spann and Skiera 2003, Armstrong 2006, Cowgill and Zitzewitz 2015. ...
Article
Teams, juries, electorates, and committees must often select from various alternative courses of action what they judge to be the best option. The phenomenon that the central tendency of many independent estimates is often quite accurate—“the wisdom of the crowd”—suggests that group decisions based on plurality voting can be surprisingly wise. Recent experimental studies demonstrate that the wisdom of the crowd is further enhanced if individuals have the opportunity to revise their votes in response to the independent votes of others. We argue that this positive effect of social information turns negative if group members do not first contribute an independent vote but instead cast their votes sequentially such that early mistakes can cascade across strings of decision makers. Results from a laboratory experiment confirm that when subjects sequentially state which of two answers they deem correct, majorities are more often wrong when subjects can see how often the two answers have been chosen by previous subjects than when they cannot. As predicted by our theoretical model, this happens even though subjects’ use of social information improves the accuracy of their individual votes. A second experiment conducted over the internet involving larger groups indicates that although early mistakes on easy tasks are eventually corrected in long enough choice sequences, for difficult tasks wrong majorities perpetuate themselves, showing no tendency to self-correct. This paper was accepted by Yuval Rottenstreich, decision analysis.
... The methodology could be easily applied to other prediction markets with high-frequency data and the clean arrival of major news, beyond sports, such as those run within major companies among employees (e.g. Cowgill and Zitzewitz, 2015) and public markets on political or financial events (e.g. The Iowa Electronic Markets (IEM), PredictIt and the now defunct Intrade.com). ...
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Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.
... This effort involves a sufficiently powered replication of P&S as well as the inclusion of an 3 The positive results of P&S have been highly influential. There has been a wave of field studies discussing the apparent successful use of prediction markets at major companies such as Hewlett-Packard, Ford, Google, Microsoft, Yahoo and IBM (Chen and Plott, 2002;Cowgill, Wolfers and Zitzewitz 2009;Cowgill and Zitzewitz, 2015). Pennock, Lawrence, Lee and Nielsen (2001) and Gürkaynak and Wolfers (2006) find that prediction markets are better at forecasting Oscar winners and certain economic variables than experts in those areas. ...
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We attempt to replicate a seminal paper that offered support for the rational expectations hypothesis and reported evidence that markets with certain features aggregate dispersed information. The original results are based on only a few observations, and our attempt to replicate the key findings with an appropriately powered experiment largely fails. The resulting poststudy probability that market performance is better described by rational expectations than the prior information (Walrasian) model under the conditions specified in the original paper is very low. As a result of our failure to replicate, we investigate an alternate set of market features that combines aspects of the original experimental design. For these markets, which include both contingent claims and homogeneous dividend payments (as in many prediction markets), we do find robust evidence of information aggregation in support of the rational expectations model. In total, our results indicate that information aggregation in asset markets is fragile and should only be expected in limited circumstances. This paper was accepted by Bruno Biais, finance.
... The details of the implementation are described in this paper's working-paper version.22 The Wikipedia entry Prediction Market andCowgill and Zitzewitz (2015) contains further examples and evidence on the efficacy of internal auctions. ...
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We study a normative model of an internal capital market, used by a company to choose between its two divisions’ pet projects. Each project’s value is initially unknown to all but can be dynamically learned by the corresponding division. Learning can be suspended or resumed at any time and is costly. We characterize an internal capital market that maximizes the company’s expected cash flow. This market has indicative bidding by the two divisions, followed by a spell of learning and then firm bidding, which occurs at an endogenous deadline or as soon as either division requests it.
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Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting soft information (information that cannot be quantified) and making predictions in unknowable risk situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the gold standard for assessing soft signals and make use of intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.
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We initiate the study of incentive-compatible forecasting competitions in which multiple forecasters make predictions about one or more events and compete for a single prize. We have two objectives: (1) to incentivize forecasters to report truthfully and (2) to award the prize to the most accurate forecaster. Proper scoring rules incentivize truthful reporting if all forecasters are paid according to their scores. However, incentives become distorted if only the best-scoring forecaster wins a prize, since forecasters can often increase their probability of having the highest score by reporting more extreme beliefs. In this paper, we introduce two novel forecasting competition mechanisms. Our first mechanism is incentive compatible and guaranteed to select the most accurate forecaster with probability higher than any other forecaster. Moreover, we show that in the standard single-event, two-forecaster setting and under mild technical conditions, no other incentive-compatible mechanism selects the most accurate forecaster with higher probability. Our second mechanism is incentive compatible when forecasters’ beliefs are such that information about one event does not lead to belief updates on other events, and it selects the best forecaster with probability approaching one as the number of events grows. Our notion of incentive compatibility is more general than previous definitions of dominant strategy incentive compatibility in that it allows for reports to be correlated with the event outcomes. Moreover, our mechanisms are easy to implement and can be generalized to the related problems of outputting a ranking over forecasters and hiring a forecaster with high accuracy on future events. This paper was accepted by Yan Chen, behavioral economics and decision analysis.
Conference Paper
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Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting “soft” information (information that cannot be quantified) and making predictions in “unknowable risk” situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the “gold standard” for assessing “soft” signals and make use intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.
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Innovative forecasting methods using new data sources have been developed to address various problems in operations management, such as demand, sales, and event forecasts. One of the methods for forecasting events consists of prediction markets where participants can take financial positions that may generate returns depending on whether certain events occur or not. Results in experimental psychology and behavioral economics have shown that individuals, including experts, can be subject to judgment bias when making probability estimates for future events. We examine, in this study, whether prediction markets are immune to such bias in estimating event probability. We find that even when there are large numbers of transactions and high volumes of trades, probabilistic fallacies still occur. Moreover, when they occur, they tend to be persistent over a certain period of time, and they tend to happen in situations similar to the ones where individual probabilistic fallacies are reported to occur. Our results have implications for the design of prediction markets and at the same time call for caution when using forecasts generated this way.
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Prediction markets are a promising instrument for drawing on the “wisdom of the crowds”. For instance, in a corporate context they have been used successfully to forecast sales or project risks by tapping into the heterogeneous information of decentralized actors in and outside of companies. Among the main market mechanisms implemented so far in prediction markets are (1) the continuous double auction and (2) the logarithmic market scoring rule. However, it is not fully understood how this choice affects crucial variables like prediction market accuracy or price variation. Our paper uses an experiment-based and micro validated simulation model to improve the understanding of the mechanism-related effects and to inform further laboratory experiments. The results underline the impact of mechanism selection. Due to the higher number of trades and the lower standard deviation of the price, the logarithmic market scoring rule seems to have a clear advantage at a first glance. This changes when the accuracy level, which is the most important criterion from a practical perspective, is used as an independent variable; the effects become less straightforward and depend on the environment and actors. Besides these contributions, this work provides an example of how experimental data can be used to validate agent strategies on the micro level using statistical methods.
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Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting “soft” information (information that cannot be quantified) and making predictions in “unknowable risk” situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the “gold standard” for assessing “soft” signals and make use intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.
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We develop a model that shows that an overconfident manager, who sometimes makes value-destroying investments, has a higher likelihood than a rational manager of being deliberately promoted to CEO under value-maximizing corporate governance. Moreover, a risk-averse CEO's overconfidence enhances firm value up to a point, but the effect is nonmonotonic and differs from that of lower risk aversion. Overconfident CEOs also underinvest in information production. The board fires both excessively diffident and excessively overconfident CEOs. Finally, Sarbanes-Oxley is predicted to improve the precision of information provided to investors, but to reduce project investment.
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We conducted laboratory experiments for analyzing the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) relative to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) who were required to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While we did not find statistically significant differences in accuracy between the four methods overall, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets did not outperform FTF for any of the questions and were inferior for three questions. The relative performances of nominal groups and FTF were mixed and the differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants' prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value relative to a simple average of the forecasts. In addition, we examined participants' perceptions of the group and the group process. The participants rated personal communications more favorably than computer-mediated interactions. The group interactions in FTF and nominal groups were perceived as being highly cooperative and effective. Prediction markets were rated least favourably: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.
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We conduct laboratory experiments on variants of market scoring rule prediction markets, under different information distribution patterns, to evaluate the efficiency and speed of information aggregation, as well as test recent theoretical results on manipulative behavior by traders. We find that markets structured to have a fixed sequence of trades exhibit greater accuracy of information aggregation than the typical form that has unstructured trade. In comparing two commonly used mechanisms, we find no significant difference between the performance of the direct probability-report form and the indirect security-trading form of the market scoring rule. In the case of the markets with a structured order, we find evidence supporting the theoretical prediction that information aggregation is slower when information is complementary. In structured markets, the theoretical prediction that there will be more delayed trading in complementary markets is supported, but we find no support for the prediction that there will be more bluffing in complementary markets. However, the theoretical predictions are not borne out in the unstructured markets. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.
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Decision and risk analysts have considerable discretion in designing procedures for eliciting subjective probabilities. One of the most popular approaches is to specify a particular set of exclusive and exhaustive events for which the assessor provides such judgments. We show that assessed probabilities are systematically biased toward a uniform distribution over all events into which the relevant state space happens to be partitioned so that probabilities are "partition-dependent." We surmise that a typical assessor begins with an "ignorance prior" distribution that assigns equal probabilities to all specified events, then adjusts those probabilities insufficiently to reflect his or her beliefs concerning how the likelihoods of the events differ. In five studies, we demonstrate partition dependence for both discrete events and continuous variables (Studies 1 and 2), show that the bias decreases with increased domain knowledge (Studies 3 and 4), and that top experts in decision analysis are susceptible to this bias (Study 5). We relate our work to previous research on the "pruning bias" in fault-tree assessment (e.g., Fischhoff, Slovic, & Lichtenstein, 1978) and show that previous explanations of pruning bias (enhanced availability of events that are explicitly specified, ambiguity in interpreting event categories, demand effects) cannot fully account for partition dependence. We conclude by discussing implications for decision analysis practice.
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We analyze the value placed by rational agents on self-confidence, and the strategies employed in its pursuit. Confidence in one's abilities generally enhances motivation, making it a valuable asset for individuals with imperfect willpower. This demand for self-serving beliefs (which can also arise from hedonic or signaling motives) must be weighed against the risks of overconfidence. On the supply side, we develop a model of self-deception through endogenous memory that reconciles the motivated and rational features of human cognition. The resulting intrapersonal game of strategic communication typically leads to multiple equilibria. While “positive thinking” can improve welfare, it can also be self-defeating (and nonetheless pursued).
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Political stock markets (PSM) are sometimes seen as substitutes for opinion polls. On the bases of a behavioural model, specific preconditions were drawn out under which manipulation in PSM can weaken this argument. Evidence for manipulation is reported from the data of two separate PSM during the Berlin 1999 state elections.
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A central tenet of economics is that individuals respond to incentives. For psychologists and sociologists, in contrast, rewards and punishments are often counterproductive, because they undermine “intrinsic motivation”. We reconcile these two views, showing how performance incentives offered by an informed principal (manager, teacher, parent) can adversely impact an agent's (worker, child) perception of the task, or of his own abilities. Incentives are then only weak reinforcers in the short run, and negative reinforcers in the long run. We also study the effects of empowerment, help and excuses on motivation, as well as situations of ego bashing reflecting a battle for dominance within a relationship.
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Results from the Iowa Political Stock Market are analyzed to ascertain how well markets work as aggregators of information. The authors find that the market worked extremely well, dominating opinion polls in forecasting the outcome of the 1988 presidential election, even though traders in the market exhibited substantial amounts of judgment biases. Their explanation is that judgment bias refers to average behavior, while in markets it is marginal traders who influence price. They present evidence that in this market a sufficient number of traders were free of judgment bias so that the market was able to work well. Copyright 1992 by American Economic Association.
Article
This chapter examines a new class of markets at the intersection of traditional betting and traditional financial markets. We call these ‘prediction markets’. Like both financial and betting markets, prediction markets focus on uncertain outcomes and involve trading in risks. Prices from these markets establish forecasts about the probabilities, mean and median outcomes, and correlations among future events. These prices have been used to accurately predict vote shares in elections, the box office success of Hollywood movies and the probability that Saddam Hussein would be deposed by a certain date. Other names for these markets include ‘virtual stock markets’, ‘event futures’, and ‘information markets’. Financial economists have long known about the information-aggregating properties of markets. Indeed, the efficient markets hypothesis, a centrepiece of financial theory, can be stated simply as, ‘market prices incorporate all available information’. While financial instruments can be very complex, prediction markets tend to be analytically simple. Their current simplicity, however, belies their powerful potential future as a way to hedge against geopolitical and other forms of risk as envisioned by Athanasoulis, Shiller and van Wincoop (1999) and Shiller (2003). Currently, most prediction markets are quite small, with turnover ranging from a few thousand dollars on the early political markets run by the University of Iowa, to several million bet in the 2004 election cycle on TradeSports, to hundreds of millions bet on the announcement of economic indicators in Goldman Sachs and Deutsche Bank's ‘Economic Derivatives’ market.
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This paper analyzes how asset prices in a binary market react to information when traders have heterogeneous prior beliefs. We show that the competitive equilibrium price underreacts to information when there is a bound to the amount of money traders are allowed to invest. Underreaction is more pronounced when prior beliefs are more heterogeneous. Even in the absence of exogenous bounds on the amount that traders can invest, prices underreact to information provided that traders become less risk averse as their wealth increases. In a dynamic setting, underreaction results in initial momentum and then reversal in the long run.
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Financial market-based analysis of the expected effects of policy changes has traditionally been exclusively retrospective. In this paper, we demonstrate by example how prediction markets make it possible to use markets to prospectively estimate policy effects. We exploit data from a market trading in contracts tied to the ouster of Saddam Hussein as leader of Iraq to learn about financial market participants' expectations of the consequences of the 2003 Iraq war. We conducted an ex-ante analysis, which we disseminated before the war, finding that a 10% increase in the probability of war was accompanied by a $1 increase in spot oil prices that futures markets suggested was expected to dissipate quickly. Equity price movements implied that the same shock led to a 1.5% decline in the S&P 500. Further, the existence of widely-traded equity index options allows us to back out the entire distribution of market expectations of the war's near-term effects, finding that these large effects reflected a negatively skewed distribution, with a substantial probability of an extremely adverse outcome. The flow of war-related news through our sample explains a large proportion of daily oil and equity price movements. Subsequent analysis suggests that these relationships continued to hold out of sample. Our analysis also allows us to characterize which industries and countries were most sensitive to war news and when the immediate consequences of the war were better than ex-ante expectations, these sectors recovered, confirming these cross-sectional implications. We highlight the features of this case study that make it particularly amenable to this style of policy analysis and discuss some of the issues in applying this method to other policy contexts.
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Decision makers have a strong tendency to consider problems as unique. They isolate the current choice from future opportunities and neglect the statistics of the past in evaluating current plans. Overly cautious attitudes to risk result from a failure to appreciate the effects of statistical aggregation in mitigating relative risk. Overly optimistic forecasts result from the adoption of an inside view of the problem, which anchors predictions on plans and scenarios. The conflicting biases are documented in psychological research. Possible implications for decision making in organizations are examined.
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Many firms issue stock options to all employees. We consider three potential economic justifications for this practice: providing incentives to employees, inducing employees to sort, and employee retention. We gather data from three sources on firms’ stock option grants to middle managers. First, we directly calibrate models of incentives, sorting and retention, and ask whether observed magnitudes of option grants are consistent with each potential explanation. We also conduct a cross-sectional regression analysis of firms’ option-granting choices. We reject an incentives-based explanation for broad-based stock option plans, and conclude that sorting and retention explanations appear consistent with the data.
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Prediction markets are increasingly being considered as methods for gathering, summarizing and aggregating diffuse information by governments and businesses alike. Critics worry that these markets are susceptible to price manipulation by agents who wish to distort decision making. We study the effect of manipulators on an experimental market, and find that manipulators are unable to distort price accuracy. Subjects without manipulation incentives compensate for the bias in offers from manipulators by setting a different threshold at which they are willing to accept trades.
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The use of equity-based compensation for rank-and-file employees is a puzzle. We analyze whether the popularity of option compensation may be driven by employee optimism, and show that optimism by itself is insufficient to make option compensation optimal. The crucial insight is that firms compete with financial markets as suppliers of equity to employees and that employees’ access to the equity market restricts firms’ ability to profit from employee optimism. Firms must be able to extract some of the implied rents even though employees can purchase company equity in the financial markets. Such rent extraction becomes feasible if employees prefer the stock options offered by firms to the equity offered by the market, or if the traded equity is overvalued. We provide empirical evidence that firms use broad-based option compensation when boundedly rational employees are likely to be excessively optimistic about company stock, and when employees are likely to strictly prefer options over stock.
Article
Prediction markets are low volume speculative markets whose prices offer informative forecasts on particular policy topics. Observers worry that traders may attempt to mislead decision makers by manipulating prices. We adapt a Kyle-style market microstructure model to this case, adding a manipulator with an additional quadratic preference regarding the price. In this model, when other traders are uncertain about the manipulator's target price, the mean target price has no effect on prices, and raising the variance of the target price can "increase" average price accuracy, by boosting the returns to informed trading and thereby incentives for traders to become informed. Copyright (c) The London School of Economics and Political Science 2008.
Article
Analyses of the effects of election outcomes on the economy have been hampered by the problem that economic outcomes also influence elections. We sidestep these problems by analyzing movements in economic indicators caused by clearly exogenous changes in expectations about the likely winner during election day. Analyzing high frequency financial fluctuations following the release of flawed exit poll data on election day 2004, and then during the vote count we find that markets anticipated higher equity prices, interest rates and oil prices, and a stronger dollar under a George W. Bush presidency than under John Kerry. A similar Republican-Democrat differential was also observed for the 2000 BushGore contest. Prediction market based analyses of all presidential elections since 1880 also reveal a similar pattern of partisan impacts, suggesting that electing a Republican president raises equity valuations by 2–3 percent, and that since Ronald Reagan, Republican presidents have tended to raise bond yields.
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Subjective and estimated objective winning probabilities are obtained from 20,247 harness horse races. It is shown that subjectively a horse with a low winning probability is exaggerated and one with a high probability of winning is depressed. Various hypotheses characterizing the bettors' behavior to explain the observed subjective-objective probability relation are explored. Under some simplified assumptions, a utility of wealth function of a decision maker is derived, and a quantitative summary measure of his risk attitude is defined. Attitude toward risk of a representative bettor is examined. It is found that he is a risk lover and tends to take more risk as his capital dwindles.
Article
We present theory and evidence of stock price manipulation. Manipulators trade in the presence of other traders seeking information about the stock's true value. More information seekers imply greater competition for shares, making it easier for manipulators to trade and potentially worsening market efficiency. Data from SEC enforcement actions show that manipulators typically are plausibly informed parties (insiders, brokers, etc.). Manipulation increases volatility, liquidity, and returns. Prices rise throughout the manipulation period and fall postmanipulation. Prices and liquidity are higher when manipulators sell than when they buy. When manipulators sell, prices are higher when liquidity and volatility are greater.
Article
The informational content of different forecasts can be compared by regressing the actual change in a variable to be forecasted on forecasts of the change. We use the procedure in Fair and Shiller (1987) to examine the informational content of three sets of ex ante forecasts: the American Statistical Association and National Bureau of Economic Research Survey (ASA), Data Resources Incorporated (DRI), and Wharton Economic Forecasting Associates (WEFA). We compare these forecasts to each other and to "quasi ex ante" forecasts generated from a vector autoregressive model, an autoregressive components model, and a large-scale structural model (the Fair model). Copyright 1989 by MIT Press.
Article
This paper presents a framework for applying prediction markets to corporate decision-making. The analysis is motivated by the recent surge of interest in markets as information aggregation devices and their potential use within firms. We characterize the amount of outcome manipulation that results in equilibrium and the impact of this manipulation on market prices. (JEL: D71, D82, D83, D84) (c) 2007 by the European Economic Association.
Article
It is generally agreed that speculators can make profits from insider trading or from the release of false information. Both forms of stock-price manipulation have now been made illegal. In this article, we ask whether it is possible to make profits from a different kind of manipulation, in which an uninformed speculator simply buys and sells shares. We show that in a rational expectations framework, where all agents maximize expected utility, it is possible for an uninformed manipulator to make a profit, provided investors attach a positive probability to the manipulator being an informed trader.
Article
To understand the extent to which partisan majorities in Congress influence economic policy, we compare financial market responses in recent midterm elections to Presidential elections. We use prediction markets tracking election outcomes as a means of precisely timing and calibrating the arrival of news, allowing substantially more precise estimates than a traditional event study methodology. We find that equity values, oil prices, and Treasury yields are slightly higher with Republican majorities in Congress, and that a switch in the majority party in a chamber of Congress has an impact that is only 10-30 percent of that of the Presidency. We also find evidence inconsistent with the popular view that divided government is better for equities, finding instead that equity valuations increase monotonically, albeit slightly, with the degree of Republican control.
Article
It is commonly believed that prices in secondary financial markets play an important allocational role because they contain information that facilitates the efficient allocation of resources. This paper identifies a limitation inherent in this role of prices. It shows that the presence of a feedback effect from the financial market to the real value of a firm creates an incentive for an uninformed trader to sell the firm's stock. When this happens the informativeness of the stock price decreases, and the beneficial allocational role of the financial market weakens. The trader profits from this trading strategy, partly because his trading distorts the firm's investment. We therefore refer to this strategy as manipulation. We show that trading without information is profitable only with sell orders, driving a wedge between the allocational implications of buyer and seller initiated speculation, and providing justification for restrictions on short sales.
Article
This paper analyzes the large and often well-organized markets for betting on U.S. presidential elections that operated between 1868 and 1940. Four main points are addressed. First, we show that the market did a remarkable job forecasting elections in an era before scientific polling. Second, the market was fairly efficient, despite the limited information of participants and active attempts to manipulate the odds. Third, we argue political betting markets disappeared largely because of the rise of scientific polls and the increasing availability of other forms of gambling. Finally, we discuss lessons this experience provides for the present.
Article
Participants in prediction markets such as the Iowa Electronic Markets trade all-or-nothing contracts that pay a dollar if and only if specified future events occur. Researchers engaged in empirical study of prediction markets have argued broadly that equilibrium prices of the contracts traded are market probabilities' that the specified events will occur. This paper shows that if traders are risk-neutral price takers with heterogenous beliefs, the price of a contract in a prediction market reveals nothing about the dispersion of traders' beliefs and partially identifies the central tendency of beliefs. Most persons have beliefs higher than price when price is above 0.5, and most have beliefs lower than price when price is below 0.5. The mean belief of traders lies in an interval whose midpoint is the equilibrium price. These findings persist even if traders use price data to revise their beliefs in plausible ways.
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Analysis of decision making under risk has been dominated by expected utility theory, which generally accounts for people's actions. Presents a critique of expected utility theory as a descriptive model of decision making under risk, and argues that common forms of utility theory are not adequate, and proposes an alternative theory of choice under risk called prospect theory. In expected utility theory, utilities of outcomes are weighted by their probabilities. Considers results of responses to various hypothetical decision situations under risk and shows results that violate the tenets of expected utility theory. People overweight outcomes considered certain, relative to outcomes that are merely probable, a situation called the "certainty effect." This effect contributes to risk aversion in choices involving sure gains, and to risk seeking in choices involving sure losses. In choices where gains are replaced by losses, the pattern is called the "reflection effect." People discard components shared by all prospects under consideration, a tendency called the "isolation effect." Also shows that in choice situations, preferences may be altered by different representations of probabilities. Develops an alternative theory of individual decision making under risk, called prospect theory, developed for simple prospects with monetary outcomes and stated probabilities, in which value is given to gains and losses (i.e., changes in wealth or welfare) rather than to final assets, and probabilities are replaced by decision weights. The theory has two phases. The editing phase organizes and reformulates the options to simplify later evaluation and choice. The edited prospects are evaluated and the highest value prospect chosen. Discusses and models this theory, and offers directions for extending prospect theory are offered. (TNM)
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This paper explains why seemingly irrational overconfident behavior can persist. Information aggregation is poor in groups in which most individuals herd. By ignoring the herd, the actions of overconfident individuals ("entrepreneurs") convey their private information. However, entrepreneurs make mistakes and thus die more frequently. The socially optimal proportion of entrepreneurs trades o# the positive information externality against high attrition rates of entrepreneurs, and depends on the size of the group, on the degree of overconfidence, and on the accuracy of individuals' private information. The evolutionary stable proportion trades o# the survival of the group against the survival of overconfident individuals. # This paper (UCLA WP-9/97, May 16, 2000) is available from http://linux.anderson.ucla.edu/research.html. Common US mail address: Anderson Graduate School of Management, 110 Westwood Plaza, Box 951481, Los Angeles, CA 90095, Tel: (310) 825-2508. We are gratef...