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Stock Prices, Prediction Markets, and Information Efficiency: Evidence

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Stock Prices, Prediction Markets, and Information Efficiency: Evidence Stock Prices, Prediction Markets, and Information Efficiency: Evidence from Health Care Reform By Joshua Bell April 1, 2011 I would like to thank Professor William Evans for his advice and counsel throughout the year on this research project. A. INTRODUCTION Prediction markets are online trading forums where individuals buy and sell contracts based on the uncertain outcomes for future events. These types of betting exchanges began with academic research in U.S. political elections, but as prediction markets have grown in size and design, the types of events have similarly expanded to predicting anything from the recent NFL Lockout to the amount of winter snowfall in New York City or whether a terrorist attack will occur somewhere in the world before 2013.1 Prediction markets, as an industry, are still relatively small, but rapidly growing in popularity and full of promise for economics. Research thus far has examined prediction markets as tools for forecasting, risk management, and information aggregation, focusing largely on how the efficient market hypothesis (EMH) applies to the functional mechanics of these betting exchanges. The EMH stipulates that securities markets are rather effective in processing and incorporating information into stock prices. For example, good news from a company‘s quarterly earnings report will drive up the price of its stock. As a type of (albeit smaller) financial market, prediction markets can serve as a test of the efficient market hypothesis. If the EMH applies to prediction markets, the following should be true: First, news and other information surrounding a particular event should change the price of a prediction market contract as the news becomes publicly available. Second, the most efficient prediction markets should aggregate news and other information rather quickly into their contract prices, thus preventing opportunities for arbitrage and other market manipulations. Lastly, if prediction markets are efficient, as is the stock market, information surrounding a particular event, say health care reform, should be reflected in both contracts traded in prediction markets and in stock prices for companies affected by that particular event (health care firms). 1 Intrade, the self-proclaimed World‘s Leading Prediction Market, has garnered significant press attention in the past few weeks for this last event (terrorist attack before 31 Dec 2013). Many people have questioned the ethical dimensions of such a market, while the website holds that it may in fact prove a tool in assessing the potential risk of attack. The US Department of Defense similarly garnered criticism for trying to create its own prediction market for terrorist attacks in 2003. 2 This paper explores U.S. health care reform in 2009-2010 as a measure of the information efficiency in prediction markets. Barack Obama championed universal health care as a key part of his presidential platform in 2008, and by the following summer, debate had picked up in the House of Representatives. During the year long reform effort, Intrade.com, a leading prediction market website, hosted two different contracts (referred to in this paper as the Public Option Contract and the ObamaCare Contract) which covered the event window of health care reform.2 In looking at how price movements in these contracts correlate with health care stock prices, prediction markets can be utilized as a tool to potentially tap into the rich information embedded in security prices to reveal market expectations of health care reform. This analysis furthers a growing body of empirical research regarding prediction markets in events studies for financial economics. The conclusions in this paper suggest an important future for prediction markets in economic research. The findings show through a variety of tests that the ObamaCare Contract and the Public Option Contract are indeed informationally efficient, quickly pricing news into the probability that health care reform would pass through Congress. Regressing these contract prices on their immediate lags reveals that only the first lag is statistically significant and slightly negative, indicating a small bid-ask bounce.3 These findings are consistent with other economic literature concerning how quickly information is processed in prediction markets. Correlation tests, adjusting for the change in the overall market as measured by the S&P 500, suggest that news and other information which impact stock prices for health care firms also appear to drive the prices for the ObamaCare and Public Option prediction markets. However, the statistical significance for these correlation coefficients depends strongly on the 2 A distinction must be made. The Public Option Contract ran from June 16, 2009 until December 31, 2009. This contract asked whether a public option would be included in the final reform bill, if a reform bill were passed at all. The ObamaCare Contract ran from January 21, 2010 until President Obama signed the bill into law on March 23, 2010. This contract asked whether Congress would pass ObamaCare. 3 Bid-ask bounce is the price fluctuation between what a trader is willing to buy (bid) and the price a trader is willing to sell (ask), absent of other market movement. Bid-ask bounce is most noticeable in thinly traded securities. Prediction market securities have shown slight bid-ask bounce, but not enough to deviate away from the mean beliefs of market participants. 3 thickness or thinness of the prediction market. A prediction market with a trading volume less than 10,000 overall trades during the event window conveys much less about price drivers, and as a result, loses power as an information-aggregating tool relative to the stock market. Evidence from the ObamaCare Contract suggests that the winners of the reform effort are health care facilities (hospitals), managed care firms, and brand pharmaceuticals, while medical device distributors, health services, and generic pharmaceuticals are losers. Furthermore, the Public Option Contract indicates that managed care firms were largely expected to lose market capitalization should a government-run program be legislated in the final reform effort. The paper is divided into five remaining sections. First, a detailed history of prediction markets and corresponding research, as well as the timeline for health reform in 2009-2010. Second, a presentation of several empirical tests of the ObamaCare Contract and the Public Option Contract as appropriate proxies for the probabilities of health care reform passing through Congress. Third, a summary of the correlation tests between the daily change in Intrade prices and stock prices. Fourth, through OLS regression, a time series analysis of the expected market cap effect in the health care industry contingent on the inclusion of a public option. Fifth, a concluding discussion of the potential biases which may affect the findings of this paper. B. PREDICTION MARKETS Prediction markets are expressively created for the purpose of making predictions. Traders gamble money on uncertain events in the hopes of winning on a correct bet, similar to how a person in Vegas might put $20 down on red at a roulette table. However, there is a very clear and important distinction between a Vegas gambler and a trader in prediction market contracts: prediction markets offer the opportunity to buy and sell contracts conditional on available information regarding the event, rather than simply relying on probabilities alone. In 4 fact, interestingly enough, trading activity in prediction markets actually reveals the probability of an event.4 Since their inception in the late 1980‘s, these markets have evolved to include a variety of market designs, but the most common market is a winner-take-all, continuous double auction with a binary outcome. The final cash value for a contract is directly tied to whether a particular event occurs or whether a particular parameter is met. For example, individuals may trade on the expectation that The King’s Speech will win the Academy Award for Best Picture, or that the Dow Jones Industrial Average will close at month‘s end at or above 13,000. Prediction markets, also known as information markets or event futures, came to prominence with the emergence of the Iowa Electronic Markets (IEM). Created by the Tippie School of Business at the University of Iowa, the first markets collected bets on the expected presidential vote shares for the Republican and Democratic nominees in the 1988 election. As academic interest developed over the subsequent decade, the IEM expanded to host a wide array of prediction markets for U.S. political elections at the federal and state level. The basic structure is simple. A contract pays $1 if a candidate wins, or $0 otherwise. Before the election is over and the results are official, the contract trades for $p, a value that is determined through trading activity, which in turn is driven by information about the election as the news circulates publicly.5 The price of the share, as it fluctuates between $0 and $1, comes to represent the probability that a certain candidate will win the election. Economists have worked with this basic structure to discover the value prediction markets might hold for event studies. 4 See Wolfers and Zitzewitz (2005) for a comprehensive analysis of prediction market prices as the probability for event outcomes, analyzing data under several different utility functions. 5 A good contemporary example of this is Sarah Palin‘s ―blood libel‖ comment following the shootings in Tucson, Arizona. Sarah Palin, as a prominent conservative figure, is considered by many to be formulating a presidential bid in 2012. As such, Intrade has hosted a prediction market, asking ―Will Sarah Palin be the Republican nominee in 2012?‖ Prior to her first public comments regarding the tragedy, Palin‘s security was trading around 18 ($1.80), indicating that she had a chance (1 in 20) at garnering the nomination (Mitt Romney was the favorite, trading at 21).
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