Prediction Markets

Wolfers.fm Page 37 Monday, June 8, 2009 3:12 PM

Prediction Markets: The Collective Knowledge of Market Participants Justin Wolfers Associate Professor of Business and Public Policy Wharton School, University of Pennsylvania Philadelphia

Prediction markets provide an information-aggregation technology applicable to a variety of topics, including political and financial risk. Because of the human idiosyncrasies identified by behavioral finance, prediction markets can fail, but historical data show them to be as accurate as traditional polling methodologies and far less expensive to establish and maintain.

n this discussion, I will make three substantive Sports Prediction Markets. Interestingly, I claims about prediction markets. First, if mar- sports betting markets manifest many of the charac- kets really are efficient, as the efficient market teristics expected of an efficient market, and they hypothesis asserts, then the prices that come out of now allow participants to trade stock during the any market contain valuable information. To make game on the likelihood of a particular team winning. the case for this claim, I will use data from sports For example, Figure 1 shows real-time betting dur- betting markets. Second, prediction markets can be ing Game 6 of the 2003 National League Champion- used to track political risk, which can be a key factor ship Series (NLCS). This prediction market from driving investment performance. Third, prediction Intrade pays $1 if and only if the Chicago Cubs win markets can fail, so I will conclude my discussion this particular game. At the beginning of the game, by describing why prediction markets work and it looks as if the Cubs have about a 75 percent chance what causes them to fail. of winning. In the top of the eighth inning, the Cubs go ahead of the Florida Marlins 3–0, and the chance Prediction Markets and the of the Cubs winning the game and going to the World Series reaches 95 percent. Unfortunately, in Efficient Market Hypothesis the bottom of that same inning, one of the great The efficient market hypothesis asserts that market collapses in sporting history occurred. A fan reaches prices fully reflect available information. But the out from the stands and prevents a catch by Cubs degree to which information is available determines outfielder Moises Alou. The Marlins proceed to the form in which the hypothesis reveals itself. In its score eight runs in that inning, and the market strong form, prices reflect all information; in its responds immediately to this new information. In a semi-strong form, prices reflect all publicly avail- matter of minutes, the market price declines from 95 able information; in its weak form, prices reflect cents to about 5 cents. only past data. In any one of these forms, however, The question more important than whether pre- prices can reflect, summarize, and aggregate a huge diction markets respond quickly to news, though, is amount of information, but the unfortunate impli- whether these market prices are accurate. To answer cation of the hypothesis is that excess returns are that question, I have charted data from all 21,885 unpredictable. Thus, prices can be said to follow a major league baseball games played from 1991 random walk. through 2000, and the data show that the predic- tions are quite close to the actual outcomes. For This presentation comes from the 2008 CFA Institute Annual Confer- instance, if the market says a team has a 33 percent ence held in Vancouver, British Columbia, Canada, on 11–14 May 2008. chance of winning, it actually wins 31 percent of the

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Figure 1. Real-Time Betting on the Chicago Cubs: 2003 NLCS, Game 6

Price ($) 1.00 Fan Spoils Alou’s Catch

0.80 Top of the 8th Inning 0.60

0.40

0.20

0 8:05 p.m. 9:00 p.m. 10:00 p.m. 11:00 p.m.

Note: Time is given in central (Chicago) time. Source: Based on data from Intrade.

time. When the market says a team has a 67 percent ing take, which are, again, quite accurate. Therefore, chance of winning, it wins 65 percent of the time, an analyst covering the movie industry may find and when the market says a team has a 90 percent these forecasts quite useful, which means that such chance of winning, it wins 88 percent of the time. an exchange moves us toward the realm of socially The difference is how Las Vegas makes money. But useful prediction markets. the real point is that if a person wants accurate For instance, Deutsche Bank and Goldman information on the likely outcomes of baseball Sachs briefly started a new market (now folded, games, the betting odds do an excellent job of pro- unfortunately) that they called the “economic viding that information. derivatives market.” It allowed investors to trade The same point holds true for other sports. For on the future outcomes of particular economic indi- example, based on market expectations and game outcomes for the National Football League from cators, such as retail sales and business confidence. 1984 to 2000 (i.e., for 3,791 games), a regression The economic derivatives market provided fore- analysis shows a slope of almost 1.0 (0.997 to be casts for nonfarm payrolls, initial unemployment exact). Thus, when the market says that a team is claims, retail sales, and business confidence, and likely to win and gives a margin of a prespecified based on data that Refet Gürkaynak and I gathered, number of points (the “point spread”), that team, on these forecasts were more accurate than an average average, wins by the point spread. or consensus forecast of 35 economists. Do not, however, think of these findings as mere My conclusion, therefore, is that rather than conversation topics. Rather, think of prediction averaging the forecasts of 35 economists, those same markets as a technology that can be used to aggre- economists should be put in a room and told to bet gate opinions about anything of importance. or trade against each other. Taking the market- Socially Useful Prediction Markets. Consider aggregated price from this trading would provide a the firm called the Hollywood Stock Exchange. It more efficient way of aggregating forecasts. The runs prediction markets on the opening weekend task, then, is to harness this technology to more box office take of various movies. Essentially, it challenging issues. One such issue is politics. In fact, allows people to trade, or bet, on how well each prediction markets have been used quite exten- movie will perform. Data from that market can sively to predict political risk, which is a key factor show the aggregated forecast and the actual open- driving investment performance.

Prediction Markets

Prediction Markets and Political therefore, represent a valuable gain in accuracy and a significant reduction in costs. Risk ■ Results from recent cycles. In the 2004 U.S. In 2003, a recall election was held to remove Califor- election cycle, investors could trade not only which nia Governor Gray Davis from office and replace candidate would become president but also who him with one of several contenders, one of whom would win the Electoral College votes of each state. was Arnold Schwarzenegger. Prediction markets Based on data from Intrade, the prediction market allowed people to trade on the question of whether price accurately predicted every state that went for Schwarzenegger would win the governorship. A George W. Bush and every state that went against contract paying $100 if Schwarzenegger won could him. For the 2004 U.S. Senate races, the Intrade price be traded on two exchanges—Intrade, which is was correct for every state but Alaska. In the 2006 based in Ireland, or the World Sports Exchange, election cycle, most knowledgeable observers and which is based in the Caribbean. Attempts to recall many Republicans did not expect the Democrats to other governors had occurred in California, but win enough seats to gain the majority in the Senate. none had been successful. Nevertheless, I set my But every single Senate race went exactly the way computer to look for arbitrage opportunities the election-eve prediction market said it would. between Intrade and the World Sports Exchange. ■ History of political prediction markets. Such Every four hours, my computer checked these two prediction markets seem like a marvelous new tech- websites and searched for potential profits. By the nology for forecasting election results, but as it turns end of the exercise, I had made no money, but I had out, betting on politics is as old as politics itself. Two learned an important research lesson. When I fol- enterprising economic historians—Rhode and lowed the chart of the two exchange prices, the bid Strumpf—have gathered data on the amount of bet- price in one market was never above the asking ting that has occurred on U.S. presidential elections price in the other, which means that the two markets from 1884 through 1928. Opinion polling did not are closely linked. The price pattern that we are so exist in any systematic or nationwide manner at the familiar with in other financial markets appears to time, but betting on elections occurred regularly on hold true for political prediction markets. the curb where the American Stock Exchange now stands. In fact, large sums of money were traded. Accuracy of Political Prediction Markets. For the 1884–1928 period, an average of $37 million These markets have a tremendous historical record. (in 2002 dollars) was bet per election, which turns In 1988, faculty from the University of Iowa created out to be a $2.28 bet per vote and the equivalent of a trading market now called the Iowa Electronic 54 percent of total campaign spending. By using Markets. This market has followed hundreds of archival data, one can follow the performance of elections in the United States and elsewhere, so an political prediction markets for well over 100 years. extensive record exists to chart its predictive accu- In fact, in a comparison of the election-eve pre- racy. Except for a few misses, the predicted outcome diction markets for 1880 through 2004 with the actual and the actual outcome of most elections converge results, once again, the prediction markets accu- along the same line. rately indicated—with two exceptions—whether a Consider in particular the U.S. presidential elec- Republican would win the election. In 2000, the pre- tions. When I imagine trying to predict the winner diction markets picked George Bush to win, which of the next election, I typically think of two methods. he did, but he did not win the popular vote as The first is to create a traditional polling operation. expected. The other exception occurred in the 1948 I would hire hundreds of staff members, set up election and is, I think, the most famous exception in massive phone banks, call thousands of Americans U.S. political history—the election in which Harry around the country, and then have top-notch statis- Truman unexpectedly defeated Thomas Dewey. ticians analyze the resulting data to come up with a Those two results aside, however, the prediction forecast. The second method is to set up a website markets have been decidedly accurate. and let people log on and trade to their heart’s content. I would then use the resulting prices to Utility of Prediction Markets. Based on the establish a prediction. The first method is quite historical evidence, the prediction market seems to accurate. It tends to predict U.S. presidential elec- be a wonderful tool, if for no other reason than it tions by roughly  2.0 percentage points, but it is makes all of us political forecasters. The efficient also expensive to operate. The second method—that market hypothesis says that all available information is, a political prediction market—is far less expen- is embodied in the market price. Prediction markets, sive, and it forecasts U.S. presidential elections to such as Intrade or the Iowa Electronic Markets, offer within  1.4 percentage points. Prediction markets, access to that very market price. Therefore, all I have

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to do is look up one price and I become a political chances of winning the nomination did not change. forecaster. I do not need to read the Wall Street Journal, The primaries in May led to a split decision—Indiana the Washington Post, or the New York Times. In fact, I for Clinton, North Carolina for Obama—so both have begun acting on this very idea. I am now writ- could claim a victory. But at this point, the political ing political commentary on the current election prediction markets decided that splitting victories cycle for the Wall Street Journal, and the only data I was better news for Obama than for Clinton, so his am allowed to use are prediction market data. If the stock price climbed dramatically. political prediction market actually aggregates all The key point is that prediction markets pro- available information, that is the only information vide an excellent way to track political risk. But the source any of us should be watching. We can ignore ability to track political risk is not an end in itself; all the other commentary. its ultimate purpose, in this case at least, is its utility Figure 2 illustrates the sort of narrative I have as a valuation factor. been presenting so far. It shows the fluctuating price of a security during the 2008 Democratic nomina- Political Risks and Financial Markets. As tion race that pays $100 if Barack Obama becomes a social scientist, if I wanted to find out whether the nominee. Until his surprising victory in the Iowa Democrats or Republicans are good for stocks, I caucuses, the price on the security was only $20, but would like to do the same thing a physical scientist it quickly moved upward, to around $70 at the time would do: Run an experiment in which I randomly of the New Hampshire primary. At that point, how- assign the presidency half the time to Republicans ever, Hillary Clinton’s dramatic comeback caused and half the time to Democrats and then measure Obama’s stock to fall but not to pre-Iowa levels. The how stocks performed under each party. Fortu- Michigan primary turned out to be no news for nately for me, such an experiment actually occurred anyone, but Super Tuesday proved to be good news for Obama, and he got more good news with the on Election Day 2004. Potomac primary. After Obama lost Ohio and Texas By following the changing probability of who— to Clinton, Obama’s stock declined again slightly. George Bush or John Kerry—would become presi- Even after Obama lost the Pennsylvania pri- dent and comparing that probability with the mary, his stock value remained high. According to simultaneous rise and fall of the S&P 500 Index, I the New York Times, Clinton’s victory in Pennsylva- was able to perceive the likely effect of either pres- nia was clearly important; however, seen through idency on the stock market. For example, on 2 the lens of political prediction markets, she won November 2004, from noon (when the probability Pennsylvania by the margin expected in a state with of Bush retaining the presidency was 60 percent) to Pennsylvania’s characteristics. As a result, her 6:00 p.m.—when exit polls put Bush’s fortunes at

Figure 2. Probability of Obama Winning the 2008 Democratic Nomination, 1 January 2008 to 12 May 2008

Probability (%) 100 IA NH MI Super Potomac OH, TX PA IN, NC Tuesday Primary 80

0 1/Jan/08 1/Feb/08 1/Mar/08 1/Apr/08 1/May/08

Prediction Markets

their lowest point (i.e., a 30 percent probability that The challenge is to find creative ways to deploy he would retain the presidency)—the S&P 500 fell this technology to better forecast our future. By 0.7 percent. Thus, if reducing the likelihood of a using prediction markets, qualitative issues that are Bush presidency by 30 percentage points causes hard to value can now be quantified and aggregated stocks to decline 0.7 percent, I can infer that remov- in a useful way. ing George Bush entirely would cause stocks to decline 2.3 percent (–0.7 percent/–30 percentage Why Prediction Markets Work and points). As the actual vote counting began, however, the probability of Bush retaining the presi- What Causes Them to Fail dency rose 65 percentage points in just a few hours. When it comes to large social issues—such as war Everyone had already voted, and the S&P 500 rose or global warming—media commentators may err 1.3 percent, thus suggesting that stocks would be by taking popular rather than accurate positions. worth perhaps 2 percent more (1.3 percent/65 per- After all, they are not taking positions with their centage points) under Bush than under Kerry. own assets. But that is exactly what people in the Republicans, I am sure, will be reassured to learn prediction markets, like those in the financial mar- that their candidate is good for the stock market, kets, do every day. So, prediction markets are quite and Democrats will be reassured to see that the effective at removing incentives to be popular difference between the two is actually rather small. instead of accurate. Similarly, prediction markets, Equally interesting is the issue of which compa- like financial markets, encourage participants to nies are expected to do well under which candi- seek out better information and act on that informa- dates. According to a paper by Knight (2006), a tion, thus increasing their chances of outperforming George Bush stock index includes such businesses other participants. as big oil, big tobacco, and big pharmaceuticals. By If no useful information is available to be aggre- contrast, a John Kerry index includes environmen- gated, however, prediction markets are not espe- tally friendly manufacturing companies and generic cially helpful. After all, they are nothing more than pharmaceuticals. From March to October 2004, as an efficient means of aggregating available informa- each nominee’s fortunes rose and fell, so did their tion. For example, when the prediction markets indices. As Bush’s fortunes rose, so did his index, were asked whether weapons of mass destruction and when his fortunes fell, his stock index also fell. would be discovered in Iraq by May 2003, June 2003, July 2003, or September 2003, they were no more Prediction Markets and Broad Political accurate than the rest of us. Issues. Prediction markets can be used to measure other forms of political risk. For example, before the Behavioral Finance and Long-Shot Bias. United States went to war in Iraq, Intrade had a Besides the question of available information, we security that was worth $100 if Saddam Hussein must also keep in mind recent research in behavioral was no longer the leader of Iraq by June 2003. finance. Such research demonstrates that people do Because the United States appeared less likely from not always act in their apparent best interests. September through November 2002 to go to war, the Therefore, human behavior can prevent prediction Saddam Hussein security dropped from a value of markets from being as efficient as possible. Consider about $80 to about $40. Then, as the likelihood of one of the best-documented mistakes that individ- war rose for the next several months, so did the uals make in markets. The difference between a Saddam Hussein security, reaching a value of about small probability and a tiny probability can be dif- $75 in February 2003, a couple of months before the ficult to perceive, even in situations like horse rac- war actually began. ing, where reasonably reliable odds are known in In contrast, the S&P 500 moved largely in the advance. Despite this knowledge, individuals tend opposite direction of the Saddam Hussein to bet too much on an extremely low-probability security—a clearly negative correlation. One could event relative to what they are prepared to wager on interpret this movement as a warning from the an outcome with better odds. Anyone who follows markets, in contrast to the views expressed by most the markets should be aware of this reality. political and economic commentators, that going to war in Iraq would be bad for the U.S. economy. Behavioral Finance and Human Pre- The data suggest that the difference between peace conceptions. Another problem in markets is that and war was worth as many as 15 percentage we all bring our preconceived notions to the mar- points for U.S. stocks. Based on these and similar kets. To help people understand this problem, I results, it seems clear that political risk is a key sometimes conduct a little thought experiment. factor in investment valuations. When I ask people to imagine a great CEO, most

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people will choose a tall, white male wearing a prices often provide efficient forecasts, including pinstripe suit. Thus, female CEOs do not fit the forecasts of political risks. Making money in the typical representation of a great CEO, and investors markets may be difficult, but prices offer a useful may be likely to undervalue companies directed by summary statistic of available information. Second, a female CEO. To investigate this hypothesis, I gath- prediction market prices can be useful inputs for ered all the earnings announcements in the United decision making in finance, management, and pub- States during the last decade and organized them by lic policy. Third, the success of prediction markets is the size of the earnings surprise that occurred. context specific, and the idiosyncrasies of human Because women represent about 1 percent of all behavior found in behavioral finance may provide CEOs, they also represent about 1 percent of all hints of profit opportunities. Finally, prediction earnings announcements. But when I look at the markets offer two particularly interesting uses to largest positive earnings surprises, women are dra- investment professionals: (1) providing market- matically overrepresented, and when I look at the aggregated forecasts of political risk as an input to largest negative earnings surprises, women are dra- the investment process and (2) providing a tool for matically underrepresented. This finding implies to information aggregation within investment firms me that market participants systematically underes- themselves. Most companies have an information- timate the value of companies directed by women. aggregation mechanism that includes neither Not only that, but further analysis of the data shows accountability nor a reward system. It is called a that female CEOs have a tremendous propensity to “staff meeting.” A prediction market within a com- surprise male analysts. pany could be used to aggregate information from staff in a far more efficient manner. Conclusion Editor’s Note: Justin Wolfers provides consulting services to sev- A few things need to be kept in mind when dealing eral prediction markets. with prediction markets. First, if one assumes that the efficient market hypothesis is correct, market This article qualifies for 0.5 CE credits.

REFERENCES

Gürkaynak, Refet, and Justin Wolfers. 2006. “Macroeconomic Election.” Journal of Public Economics, vol. 90, no. 4–5 (May): Derivatives: An Initial Analysis of Market-Based Macro Forecasts, 751–773. Uncertainty, and Risk.” NBER Working Paper 11929 (January). Rhode, Paul, and Koleman Strumpf. 2004. “Historical Presiden- Knight, Brian. 2006. “Are Policy Platforms Capitalized into tial Betting Markets.” Journal of Economic Perspectives, vol. 18, Equity Prices? Evidence from the Bush/Gore 2000 Presidential no. 2 (Spring):127–141.

Q&A: Wolfers Question and Answer Session Justin Wolfers

Question: Are prediction mar- Wolfers: Because I am an econ- prediction market, a play money kets subject to manipulation? omist, I would lose my union card market tends to be more efficient if I did not say that real money and thus may approach the effi- Wolfers: Many attempts have always makes a difference. One of ciency of a real money market. been made to manipulate predic- the important things about pre- tion market prices. A good exam- Question: Why are some of diction markets is that they ask ple occurred when Pat Buchanan the prediction markets used for participants to take positions with was running for the Republican economic analysis no longer their own money. presidential nomination. He available? Having said that, I once spoke encouraged his supporters to buy with someone who had created a Wolfers: Which markets sur- Pat Buchanan stock. Their behav- prediction market company that vive and which do not is a function ior, however, did not change the uses play money, and he bet me of which markets are profitable to reality that Pat Buchanan had little that his play money market could run for those who run the market. chance of winning the nomina- do as well as a real money market. For example, Goldman Sachs and tion. Therefore, if someone says I So, we set up a competition in Deutsche Bank were charging am willing to buy stock in Pat which his market predicted the what I considered unbelievably Buchanan, someone in the market winner of Saturday football high fees, which proved to be a will sell. But the very nature of free games while I used Intrade, a real disincentive to trade. So, it was not entry and the supply response money market, to predict win- a profitable business for them to meant that Pat Buchanan’s sup- ners. By the end of the season, the stay in. Nevertheless, if such a porters could not affect the price two markets had amassed compa- market existed, many of us would for more than a couple of hours. rable prediction records. In that find it valuable. So, manipulation attempts instance, therefore, money did not Many governments around sometimes occur, but I am aware make a big difference. the world are thinking about run- of no attempt that has succeeded I can offer two explanations ning such markets within their for a sustained period. There were for this result. First, prediction bureaucracies so that, as I men- manipulation attempts again in markets—whether using play tioned earlier, their economists the 2008 presidential markets, but money or real money—do some- will have a more efficient aggre- these too ultimately were unsuc- thing useful that polls do not do. gation tool than staff meetings. I cessful, as prices reverted to fun- Prediction markets ask the right foresee such markets being used damentals over the course of a question. They do not ask, as polls within organizations more often couple of weeks. do, “Who would you like to win?” than between organizations. Question: Can widespread use or “Who do you plan to vote for?” Question: How far in advance of prediction markets influence Prediction markets ask, “Who do do prediction markets work? subsequent behavior in the sense you think will win?” Second, of persistence momentum? there is no way to get rich in play Wolfers: Let me make two money prediction markets. There- observations. First, the closer we Wolfers: At this point in the fore, there are no big players influ- come to any event, the easier it political cycle, prediction markets encing such markets, which is not is to predict the outcome. Second, are still a fringe phenomenon. The the case in real money markets. at any given horizon, prediction number of words devoted by the For example, Warren Buffett markets seem to do better than press to prediction markets com- has a big influence on U.S. equity the alternative. pared with those devoted to polls markets. That is probably a good is sufficiently small that we prob- Question: What impact do thing, and it makes the market ably have little need to worry tail events have on prediction more efficient. But there may also about persistence momentum. markets? be large pools of uninformed Question: Is there a difference money in real money markets, Wolfers: Every time a tail in the behavior of prediction mar- which may make the real market event happens, I get calls asking kets that use real money and those less efficient. Without unin- whether the prediction markets that use play money? formed money in a play money are wrong. This happened after

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the 2008 New Hampshire pri- the difference between a 5 per- Wolfers: Remember how well mary. The New Hampshire cent chance, a 2 percent chance, play money prediction markets primary markets said there was and a 1 percent chance. do with no money? Then you only a 7 percent chance Clinton should remember, too, that high Question: What are some of the would win; Clinton won. When volume times zero price equals better prediction markets? such things happen, we tend to zero dollar volume. Yet, play forget a truism about 7 percent Wolfers: Let me say up front that money markets do quite well probabilities, which is that they I have a conflict of interest here despite their zero dollar volume. will happen 7 times out of 100. because I advise many of these In fact, my observation—and If they happen less than that or companies. That said, Intrade has this is just an intuitive answer, more than that, then the markets a great deal of good data and offers not a scientific one—is that pre- are in trouble. perhaps the best U.S. political cov- diction markets do surprisingly But also keep in mind that erage. Another excellent one is Bet- well with only a small number prediction markets are not well fair, although it has more British of participants. calibrated over very small prob- than U.S. political coverage. The abilities. When markets are pre- I have seen within corpora- Iowa Electronic Markets is pre- tions perhaps a dozen people dicting between a 10 and 90 dominantly political, so it has a trade in a market and do quite percent chance of an event occur- narrower range than the others. ring, I take that as a fairly good well. And remember, the right assessment of probability. Nev- Question: How much volume is metric is the accuracy of the fore- ertheless, prediction markets required for the prediction mar- cast. Is it more accurate than you seem to be fairly bad at telling kets to be seen as accurate? would have had otherwise?