Timely Information from Prediction Markets

Journal of Economic Theory, 2016

A three-stage market-game mechanism is devised that is simple (actions are quantities and outcomes are determined by arithmetic operations that do not depend on details of the economy) and achieves efficiency in a two-divisible-good, pure-exchange setting with potential information-aggregation. After an entry stage, agents make offers which are provisional for all but a small, randomly selected group. Then, those offers are announced, and everyone else makes new offers with payoffs determined by a Shapley-Shubik market game. For a finite and large number of players, there exists an almost ex post efficient equilibrium. Conditions for uniqueness are also provided.

Economics: The Open-Access, Open-Assessment E-Journal, 2010

We explore how disclosure requirements that regulate the release of new information may affect the dynamics of financial markets. Our analysis is based on three agent-based financial market models that are able to produce realistic financial market dynamics. We discover that the average deviation between market prices and fundamental values increases if new information is released with a delay, while the average price volatility is virtually unaffected by such regulations. Interestingly, the tails of the distribution of returns become fatter if fundamental data is released less continuously, indicating an increase in financial market risk.

Information Systems Research, 2010

Geographically dispersed sellers in electronic reverse-marketplaces such as those hosted by Freemar-kets are uncertain about the number of others sellers in any given market session. Over the course of several market sessions, they learn about the competitive structure of their market. How sellers learn to reduce the level of market structure uncertainty, is dependent on the market transparency scheme (revelation policy) adopted. The revelation policies differ in terms of the level of competitive information revealed. Thus, they determine what sellers learn, how they bid in future, and, in general how the consumer surplus generated changes. In this paper, using game-theory, we compare a set of revelation policies commonly used in electronic reverse marketplaces on consumer surplus. Based on our analysis, we find that the policy that generates the least amount of market structure uncertainty for the sellers should be chosen to maximize consumer surplus. This contradicts the traditional view that under uncertainty sellers are worse off and the buyer better off. This paper provides insights into this apparently anomalous outcome.

2012

instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these "first-generation" (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, "second-generation" (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior.

1997

This paper presents a simple model of auction equilibrium. The distinctive feature of the model is that each bidder may discover the value that the item represents for herself, provided she spends some amount in order to be well informed. For each agent, the decision of whether or not to acquire information depends on a private cost of information acquisition and on her conjectures regarding the behavior of other bidders. A rational expectations equilibrium is characterized. (*) Department of Economics, University of Illinois at Urbana-Champaign and Department of Economics, University of California at Santa Barbara. Research supported in part by NSF Grant SBR-9496303. The authors appreciate comments from Richard Engelbrecht-Wiggans, Charles Kahn, and Steven Williams on an earlier version of this paper. A helpful suggestion from Adolf Hildenbrand is also acknowledged. None of them bears responsibilities for shortcomings or omissions.

2004

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. We 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. * The authors thank Manuela Abbate for research assistance and an anonymous referee for helpful comments. We gratefully acknowledge the financial support of the International Foundation for Research in Experimental Economics.

2010

D ouble auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings.

How should institutions convey relevant information to the public? Should they schedule their communications or release information as it becomes available? What are the welfare effects of an unanticipated information release? We model a decentralized economy and show that a credible schedule delays trade towards the information release date and unanticipated information arrivals entail a loss of insurance opportunities. We apply these findings to the scheduling of monetary policy decisions following the Federal Open Market Committee meetings from 1995 till 2010 and its effects on the dynamics of trade on the Federal Funds Futures market. We use the model to empirically identify periods of credible (prior to 2001) and non-credible scheduling (after 2001). Finally we measure the loss in risk-trading activity due to off schedule announcements.

Journal of Economic Dynamics and Control, 2019

Apparently contradictory evidence has accumulated regarding the extent to which financial markets are informationally efficient. Shedding new light on this old debate, we show that differences in the distribution of private information may explain why informational efficiency can vary greatly across markets. We find that markets are informationally efficient when complete information is concentrated in the hands of competing insiders whereas they are less efficient when private information is dispersed across traders. A learning model helps to illustrate why inferring others' private information from prices takes more time when information is more dispersed. We discuss the implications of our findings for understanding the potential consequences of lowering the cost of information on the informational efficiency of markets.

American Economic Journal: Microeconomics, 2017

We analyze the welfare implications of information aggregation in a trading model where traders have both idiosyncratic endowment risk and asymmetric information about security payoffs. The optimal market size balances two forces: (i) the risk-sharing role of markets, which creates a positive externality amongst traders, against (ii) the information-aggregation role of prices, which leads to prices that are more correlated with security payoffs, thereby undermining the hedging function of markets. Our analysis indicates that a market with infinitely many traders may not be the right welfare benchmark in the presence of risk aversion and information aggregation. (JEL D43, D62, D82, D83)