We test whether the frequency of feedback information about the performance of an investment portfolio and the £exibility with which the investor can change the portfolio in£uence her risk attitude in markets. In line with the prediction of myopic loss aversion (Benartzi and Thaler (1995)), we ¢nd that more information and more £exibility result in less risk taking. Market prices of risky assets are signi¢cantly higher if feedback frequency and decision £exibility are reduced.This result supports the ¢ndings from individual decision making, and shows that market interactions do not eliminate such behavior or its consequences for prices. IN FEBRUARY 1999, BANK HAPOALIM, Israel’s largest mutual funds manager, announced that it intended to change its information policy towards its client^investors. The bank would send information about the performance of its funds not every month as it had in the past, but rather only once every three months. Clients would still be able to check the performance every day if they chose, but

We develop a model of how information flows into a market, and derive algorithms for automatically detecting and explaining relevant events. We analyze data from twenty-two "political stock markets" (i.e., betting markets on political outcomes) on the Iowa Electronic Market (IEM). We prove that, under certain eciency assumptions, prices in such betting markets will on average approach the correct outcomes over time, and show that IEM data conforms closely to the theory. We present a simple model of a betting market where information is revealed over time, and show a qualitative correspondence between the model and real market data. We also present an algorithm for automatically detecting significant events and generating semantic explanations of their origin. The algorithm operates by discovering significant changes in vocabulary on online news sources (using expected entropy loss) that align with major price spikes in related betting markets.

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

In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation. We leverage a unique data source of almost 2000 people’s subjective probability judgments on 2003 US National Football League games and compare with the “market probabilities ” given by two different information markets on exactly the same events. We combine assessments of multiple experts via linear and logarithmic aggregation functions to form pooled predictions. Prices in information markets are used to derive market predictions. Our results show that, at the same time point ahead of the game, information markets provide as accurate predictions as pooled expert assessments. In screening pooled expert predictions, we find that arithmetic average is a robust and efficient pooling function; weighting expert assessments according to their past performance does not improve accuracy of pooled predictions; and logarithmic aggregation functions offer bolder predictions than linear aggregation functions. The results provide insights into the predictive performance of information markets, and the relative merits of selecting among various opinion pooling methods.

We analyze the eciency and forecast accuracy of two market games on the World Wide Web: the Hollywood Stock Exchange (HSX) and the Foresight Exchange (FX). We quantify the degree of arbitrage available on HSX, and compare with a real-money market of a similar nature. We show that prices of HSX movie stocks provide good forecasts of actual box oce returns, and that prices of HSX securities in Oscar, Emmy, and Grammy award outcomes constitute accurate assessments of the actual likelihoods that nominees will win. Similar investigations reveal that FX securities prices serve as reliable indicators of uncertain future events. We argue that, in certain circumstances, market simulations can furnish some of the same societal benets as real markets, and can serve as acceptable substitute testbeds for conducting experiments that would otherwise be dicult or impossible.

Much evidence supports that financial markets have the ability to aggregate information. When tied to a random variable, a financial market can forecast the value of the random variable. It then becomes a prediction market. We establish a model of prediction markets with aggregate uncertainty, and theoretically characterize some fundamental properties of prediction markets. Specifically, we have shown that a prediction market is guaranteed to converge to an equilibrium, where traders have consensus on the forecast. The best possible prediction a prediction market can make is the direct communication equilibrium. However, prediction markets do not always converge to it. We have proved that a sufficient condition for the convergence to the direct communication equilibrium under our model is that the private information of each trader, conditioned on the state of the world, is identically and independently distributed. Furthermore, if this condition is satisfied, the prediction market converges in at most two rounds. 1

This paper looks at changes in financial values as reflections of historical events. More specifically, the historical events considered here refer to World War II and the period immediately preceding it. In particular, the time span between Hitlers rise to power (with his appointment as chancellor of the Reich on 30 January 1933) and the redevelopment of Europe after the war (with the two Marshall-Plan-Conferences in September 1947 held at Paris) is taken into consideration. We analyze the change in the values of national government bonds issued in Swiss Francs and traded on the Swiss bourse during the period 1933 to 1946. While all the nations directly or indirectly involved in WW II heavily interfered in, or closed, their stock and bond exchanges, the Swiss government, for reasons of neutrality, refrained from doing so (except for the two months following the German attack against the West in May and June 1940, when the Swiss bourse did close). The government bond market in Switzerland involved five countries: Germany, Austria, France and Belgium, as well as Switzerland itself. There was only very limited * The authors are professor of economics and research associate, respectively, at the Institute of Empirical Economic Research of the University of Zurich, Bl mlisalpstrasse 10, CH 8006, Zurich, Switzerland

Abstract. A dynamic pari-mutuel market (DPM) is a hybrid between a continuous double auction (CDA) and a pari-mutuel market. Like a CDA, a DPM incentivizes traders to reveal their information early. Like a pari-mutuel market, a DPM has infinite liquidity, allowing trades at any time. In this paper, we examine empirical questions related to DPMs: Do prices in DPMs predict events of interests? How do traders behave in DPMs? Leveraging a data set from the Yahoo!/O’Reilly Tech Buzz Game, a live system using the DPM, we show that prices offer informative forecasts of future event trends. At the agent level, we find that on average human traders outperform robot traders who randomly place trades. Those human traders who are seemingly more rational, buying when the implied market probability is low and selling when it is high, obtain higher profit on average than those who appear less rational. We examine other aspects of the game, including incentives to manipulate the market. 1

grateful for the help and comments of S.P. Kothari, Mike Lemmon, and especially an anonymous referee and Rick

The results of recent studies on prediction markets are encouraging. Prior experience demonstrates that markets with different incentive schemes predicted uncertain future events at a remarkable accuracy. In this paper, we study the impact of different monetary incentives on prediction accuracy in a field experiment. In order to do so, we compare three groups of traders, corresponding to three treatments with different payment schemes, in a prediction market for the FIFA World Cup 2006. Somewhat surprisingly, our results show that performance-related payment schemes do not necessarily increase the prediction accuracy. Due to the risk aversion of traders the competitive environment in a rank-order tournament leads to the best results in terms of prediction accuracy.