Abstract. We study the equilibrium behavior of informed traders interacting with two types of automated market makers: market scoring rules (MSR) and dynamic parimutuel markets (DPM). Although both MSR and DPM subsidize trade to encourage information aggregation, and MSR is myopically incentive compatible, neither mechanism is incentive compatible in general. That is, there exist circumstances when traders can benefit by either hiding information (reticence) or lying about information (bluffing). We examine what information structures lead to straightforward play by traders, meaning that traders reveal all of their information truthfully as soon as they are able. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a finite-period dynamic game. We employ two different information structures for the logarithmic market scoring rule (LMSR): conditionally independent signals and conditionally dependent signals. When signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE) for LMSR. However, when signals are conditionally dependent, there exist joint probability distributions on signals such that at a PBE in LMSR traders have an incentive to bet against their own information—strategically misleading other traders in order to later profit by correcting their errors. In DPM, we show that when traders anticipate sufficiently better-informed traders entering the market in the future, they have incentive to partially withhold their information by moving the market probability only partway toward their beliefs, or in some cases not participating in the market at all. 1

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)

Citing recent successes in forecasting elections, movies, products, and other outcomes, prediction market advocates call for widespread use of market-based methods for government and corporate decision making. Though theoretical and empirical evidence suggests that markets do often outperform alternative mechanisms, less attention has been paid to the magnitude of improvement. Here we compare the performance of prediction markets to conventional methods of prediction, namely polls and statistical models. Examining thousands of sporting and movie events, we find that the relative advantage of prediction markets is surprisingly small, as measured by squared error, calibration, and discrimination. Moreover, these domains also exhibit remarkably steep diminishing returns to information, with nearly all the predictive power captured by only two or three parameters. As policy makers consider adoption of prediction markets, costs should be weighed against potentially modest benefits.

Abstract. R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy and the design issues to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process.

R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies, there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process. PREDICTION MARKETS AS AN INNOVATIVE WAY TO MANAGE R&D PORTFOLIOS 1

The main objective of this research is to use prediction markets as negotiation agents, for supporting R&D portfolio management. To support this research, we iteratively designed, developed, operated and evaluated several prototypes. We start by presenting the weaknesses of the current techniques for managing R&D portfolio. Then, we intend to demonstrate that prediction markets correct these weaknesses in R&D portfolio management. Furthermore, following a design science paradigm, we illustrate the design of our artifacts using build-andevaluate loops supported with a field study, which consisted in operating the prediction markets in different settings. 1.

Abstract We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting

In prediction markets, assets are created whose final cash value is tied to a particular event (e.g., Will a new factory be opened by the end of the quarter?) or a parameter (e.g., How many units of a product will be sold during the next quarter?). Prediction markets are based on the idea that the equilibrium price should reflect the information possessed by the market participants. Essentially, prediction markets are particularly simple financial markets that are created with the purpose of collecting information, but serve no liquidity purposes. Despite the hype in the press, there is a limited amount of theoretical analysis in this area. In this paper, we present a modeling framework that can guide practitioners and researchers to understand the role and improve the design of corporate prediction markets. As Wolfers and Zitzewitz (2006) stress, prediction markets must overcome a number of challenges in order to be used as effective prediction tools. In a corporate setting, market designers are often concerned about the possibility of outcome manipulation. Also being members of the organization, market participants are often in a position to take actions that directly influence

Documented results indicate prediction markets effectively aggregate information and form accurate predictions. This has led to a proliferation of markets predicting everything from the results of elections to a company’s sales to movie box office receipts. Recent research suggests prediction markets are robust to manipulation attacks and resulting market outcomes improve forecast accuracy. However, we present evidence from the lab indicating that well funded, single minded manipulators can in fact destroy a prediction market’s ability to aggregate information. Our results clearly indicate that the usefulness of prediction markets as inputs to decision making may be limited.

Event studies have been used to address a variety of political questions—from the economic effects of party control of government to the importance of complex rules in congressional committees. However, the results of event studies are notoriously sensitive to both choices made by researchers and external events. Specifically, event studies will generally produce different results depending on three interrelated things: which event window is chosen, the prior probability assigned to an event at the beginning of the event window, and the presence or absence of other events during the event window. In this paper we show how each of these may bias the results of event studies, and how prediction markets can mitigate these biases. Event studies have been used in political science to study the cost of regulation (Schwert, 1981), the value of political connections (Roberts, 1990a; Fisman, 2001), the effect of political parties on defense spending (Roberts, 1990b), the importance of rules in congressional committees (Gilligan and Krehbiel, 1988), the reaction of different interests to trade legislation