FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS

Transcription

1 The Journal of Prediction Markets (2008) 2 3, 1 14 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS Charles de los Reyes and Lawrence J. Raifman CR@jhu.edu Department of Psychological and Brain Studies The Johns Hopkins University ljraifman@comcast.net Over the past 20 years, predictive markets have risen from relative obscurity to prominence. Relevant across several fields of study, these markets have utilized collective wisdom in order to better ascertain outcomes of future events. This paper look at the mechanics of trading and finds enduring pricing anomalies based on errant behavior on behalf of both buyers and sellers. Specifically, trading behavior seems to ignore the time value of money associated with buying and selling contracts even when the interest forgone is non-trivial. As such, the possibility for arbitrage presents itself in some of these cases. Finally, we examine the associated consequences of this behavior in regards to the mechanics of these markets and possible strengthening reforms in hopes of developing deeper, more robust predictions markets. 1 INTRODUCTION Beginning in 1988 a group of professors at the Tippie College of Business at the University of Iowa initiated a specialized marketplace designed to predict political election outcomes. It is known as the Iowa Elections Markets (IEM), and the market has endured and thrived over the ensuing years. Unlike the typical financial marketplace, with its dual function 1 as source of capital allocation to firms and asset valuation via the application of expectations theory, 2 the IEM successfully recognized the inherent utility of applying the financial markets to obtain accurate forecasts or predictions about uncertain events. Predictions markets are markets where participants trade contracts whose payoff is determined by currently unknown future events. By the 1990 s, the use of predictive markets to forecast the Federal Funds rates set by the Federal Open Market Committee every six weeks as the center piece of US Government monetary policy, 3 political elections, and sporting events on intrade. com, 4 for example, provided a relatively liquid marketplace for the buying and selling of these contract. With the dawn of the new millennium, for-profit firms, including main-street Wall Street companies like Intrade.com, Tradesport.com, Betfair.com have sprung up 5 offering 6 opportunities to make money on forecasts concerning commitments by countries to counter climate change, probability of an avian flu outbreak, or outbreak of war in specific locales. The University of Buckingham Press (2008)

2 2008, 2 3 THE JOURNAL OF PREDICTION MARKETS Also markets have been established in which participants trade virtual currency, known as pseudo-markets, such as markets featured on Newsfutures. com and Ideosphere.com. 7 Predictions markets have been touted as being more accurate in setting an expected probability than is polling results or other expert derived opinions about uncertain future events. 8 Underlying support for the concept of predictions markets comes from the wisdom of markets, recently made popular by James Surowiecki s book, The Wisdom of Crowds. To illustrate the point that the market is smarter than either experts or opinion polls, Mr. Surowiecki pointed to the ability of the stock market to identify Morton Thiokol as responsible for the Challenger Disaster within a few minutes of the ill fated explosion, where as the New York Times mention other sources. 9 Likewise, in the 2004 election, on August 8, 2004, the New York Times Daniel Gross, article was titled, Polls say Kerry. Futures Say Bush. 10 Researchers Berg, Forsythe, Nelson, and Reitz noted that for prediction markets to be accurate, they require liquidity (enough traders), and market mechanisms that facilitate aggregation of information to provide a forecast. Predictions market mechanics are different from traditional finance markets. A prediction market contract is defined by a specific horizon period not usually found in finance markets; a trade s payoff is linked to the outcome of a event occurring in the future, such as the Super bowl winner (first Sunday in February), or winner of a presidential election (first Tuesday in November every four years). Among the various trading approaches, 11 a typical winner take all contract is characterized by a payoff of $100 to be paid if and only if the event occurs, like a certain team winning the Super bowl. The price paid, here denoted as c represents the market s expectations of the probability that an event will occur. 12 For contracts that are dichotomous in payoff, the payoff of $100 to the holder of the contract if the event occurs as specified in the rules, or zero if not. If a contract is currently selling for about $75, then it is assumed that there is about a 75% chance of the event occurring as specified in the contract. Buyers and sellers set bid and ask prices for the contracts to advertise willingness to buy and sell contracts at a decided price. As with any market free of undue regulation, the sale is made when someone is willing and able to take another s price. The development of predictions markets to be used to forecast has come with some controversy. Justin Wolfers and Eric Zitzewitz recent article 13 summarized developments in 2003 when the Department of Defense in response to the direction from Deputy Secretary of Defense Paul Wolfowitz set up a predictions market with taxpayers money to help him set policy based upon forecasts of future terrorist attacks upon this country, referred to as geopolitical risk contract futures. As the authors noted, a political uproar followed. Critics savaged DARPA, which was called the Futures Markets Applied to Prediction (Future MAP) for proposing terrorism futures, and rather than spend political capital defending a tiny program, the proposal was dropped. 14 This moral hazard associated with prediction markets has been dubbed the 2

3 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS problem of the Assassination Market, where a person could personally profit by affecting some unfavorable outcome. Fears exist that a market could not only gauge likelihoods, but also directly affect the likelihoods of certain outcomes. Following a less controversial path has been the contribution of predictions markets to corporate policy planning decisions. 15 The next frontier for the use of predictions markets is in the corporate setting. Internal prediction markets, utilized in corporations employees, who buy and sell contracts, is fast becoming a source for planning and forecasting. Based on the probabilities that employees believe accurately reflect how a certain product will sell in the marketplace, these markets can enhance decisions regarding inventory, allocation of resources, and other important corporate finance decisions. 16 More recently, Google has gotten into the act. Its internal predictive market has been the largest in operation to date. 17 According to a New York Times article, In the last two and a half years, 1,463 employees have made wagers with play money (Goobles, as in rubles) on questions like: Will Google open a Russia office? Will Apple release an Intel-based Mac? How many users will Gmail have at the end of the quarter? 18. Google s markets revealed a number of interesting correlations including the importance of microgeography, or how people interact in the workplace, and an optimism bias in new employees PRICING AND THE OVERLOOKED TIME VALUE OF MONEY Initially, when a new contract is made open to trade for participants, there are no initial or opening prices. Traders interested in buying or selling contracts enter, and the market settles on a price. As new information becomes available, price changes occur. As noted previously, predictive markets are important to policy analysts like politicians, sports betters, corporate policy wonks, and advisors in general because the prediction market s equilibrium price is assumed to be the market s best approximation of the true underlying probability of the forecasted future event occurring. Trades occurring in a predictions market carry with them an important distinction not found in a traditional finance market. In a predications market, for the seller to write contracts, he or she must maintain a reserve requirement with the market host, to be held in the case that the contract closes at $100. Of course this requirement only applies to the seller, not the buyer. $100 must be held for each contract sold, but some of that can be covered by the proceeds from the sale itself. If the contract has a possible payoff of $100, the seller receives price c dollars, say for example $40. Then the seller receives the $40 from the buyer, but needs to maintain $100 for that contract until expiry. The seller is obligated to add an amount equal to ($100 - c ) of his own money to fulfill the reserve requirement. If, as in the example, the buyer pays $40 to the seller for a contract possibly paying $100 then the seller must also pay $60 out-of-pocket in order to have $100 held in his account. This insures against 3

4 2008, 2 3 THE JOURNAL OF PREDICTION MARKETS any default after the closing of the contract. When the contracts for an event close, those buyers who own contracts referring to the winning candidate will receive $100 on each contract, while other buyers who punted a losing candidate will receive no payout. Sellers of winning contracts lose their reserve requirement, while the sellers of non-occurring contracts keep the sale price and also any money paid out-of-pocket as part of a reserve obligation. Sellers must accept forgone interest on the money held as reserve in the case that they do not receive interest on their accounts. In such a case, the seller should adjust his ask price higher to compensate for this forgone interest, although empirically it seems not to occur. One could check to see, for a set of mutually exclusive events, whether the sum of the ask prices was 100[(1+r)^t], where r is the risk-less rate of investment, and t is the time until the closing out of the contract, measured in years. However, for a heavily traded event (which has a low bid-ask spread regardless of r and t ), the sum of the ask prices sums up to only a tad over 100 (often or 100.2, because of the bid-ask spread). Similarly for buyers, prices should be adjusted to account for forgone interest. Usually, there is little or no time inconsistency between payment and payout. But with predictive markets, bets can be made years in advance of the payout. For example, a contract paying if Hillary Clinton wins the 2008 Presidential Election has been offered by intrade.com since July of If a buyer pays price c for a contract, he gives up the ability to earn some riskless rate of return on c dollars. It would be expected that the sum of the bid prices would approach 100/[(1+r)^t] for a set of mutually exclusive events. Regardless of whether or not interest is paid on sellers reserve requirements, the buyer is certainly foregoing interest when purchasing a contract to be held for a long period of time. However, we see that once a market becomes relatively active, the sum of the bid prices for a set of mutually exclusive events approaches just under 100 (oftentimes 99.9, such as in the case of the specified contracts for which party will win the 2008 U.S. Presidential Election). Empirical evidence points generally to an ignorance of going market rates for traders in predictive markets. In other asset markets, most notably the stock markets, a change by the Federal Reserve in interest rates affects immediate and drastic changes in prices. There s no reason why sale prices, bid-ask prices, and volume of sales shouldn t also change in predictions markets. Changes in interest rates affect the going risk-less rate of return, and the amount of forgone interest by buyers and sellers. An increase in interest rates should have a cascading effect on the savings rates, and change traders amount of interest forgone when entering into contractual obligations. Yet, prices in general appear unaffected. 3 ARBITRAGE & PROFIT OPPORTUNITIES Given that prices are not currently in line with their true discounted expected future value, there begs the question of whether or not arbitrage op- 4

5 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS portunities are available to someone entering a predictive market. The main obstacle is of course the reserve requirement. Without such an obligation, a person could simply sell a bundle of contracts comprised of one of each of the possible outcomes. The seller could then take the money from the sales, invest it for some risk-free rate of return r, and keep whatever is left as profit. Because the contracts are mutually exclusive, the seller will have to pay exactly 100 with certainty. For a regular market participant, the opportunity for risk-less profit lies in gaining exemption from the reserve requirement. Some possibilities for this will be discussed later. For the market owner (i.e. the owners of intrade.com, or any other predictions market), the reserve requirement is a boon. When a transaction is made, a reserve requirement, for all practical purposes, becomes an interest-free loan to the owner until the contract expires (then the money must be available for payment should the contract finish in the money [i.e. event happens as specified]). One would presume that it is in the best interest of the market owner to encourage trading. Trading in which a high volume of new contracts, being bought and sold, will serve to pull more money into its reserve holdings. To that end, market operators such as intrade.com might gain more profit by entering their own market and acting as market makers. Such an entity could enter into lots of mutually exclusive contracts, which would serve to completely hedge against contract-specific risk. They could pull more money into the market which would serve as interest-free loans (as well as perhaps making some money off of a greater number of transaction costs). This money held as reserves can be invested in risk-free assets such as high yield certificates of deposit (with FDIC or something equivalent) or U.S. Treasury strips and bonds. 4 A MODEL OF ARBITRAGE IN PREDICTIONS MARKETS & DETERMINANTS OF PROFITABILITY In predictive markets, participants ignore the time-value of money. Contracts bought and sold in a predictive market frequently have horizons lasting for months. A contract ties up money for extended periods of time, at a cost to the buyers and sellers. Specifically, the return that an investor could otherwise have achieved through savings and investment is forgone. During and clear price deviations create an arbitrage opportunity for those able to accurately determine bid and ask prices for a set of mutually exclusive contracts. In the prediction marketplace, many mutually exclusive contracts are bought and sold. It is helpful to think of a bundle of contracts for a given event. This bundle consists of one contract for each possible of N outcomes outcome, and it is assumed that trading is allowed on each such possibility. For example, in the case of an election, a typical bundle might consist of the following: one contract for Democrats win, one contract for Republicans win, and one contract for Anyone else (Independent, etc.) Wins. A property 5

6 2008, 2 3 THE JOURNAL OF PREDICTION MARKETS of these bundles is that a bundle bought or sold is entirely risk-free. A bundle can be sold for some amount of money, and it is assured that one contract of the bundle will close at 100, and all others must close at zero. For the present, we will simply make the assumption that bundles can be sold for 100. An investor can borrow cash at some rate. Presumably, the loan can be contracted such that repayment of principal and interest just prior to the closing and expiration of the contracted event on which the arbitrage will be performed. After borrowing money, one can enter into a predictive market. In a liquid marketplace, the bid-prices tend to sum to 100 (often staying at 99.8 or 99.9 for very popular events such at the 2008 presidential election). When selling a contract (or bundle thereof), the seller receives the proceeds of the sale. In exchange, the seller has to maintain monies sufficient to cover all outstanding contracts, in the event that those contracted events all occur, and the seller must payout. The amount of money available to bear interest is dependant on the number of mutually exclusive contracts per bundle. Where N is the number of mutually exclusive contracts, and X is the amount of money borrowed, the amount of interest earned via reserve requirement would be: X(Rs) + [X(Rs)/(N-1)] X*Rs*[1 + 1/(N-)] Where Rs is the return per dollar on monies held in an account. Rs can be computed using continuously compounded interest: Rs = (1+r)^t Where t is the time until settlement of the contract, in years, and r is the annualized rate of return on accounts. The arbitrageur earns interest on the money he has borrowed, as well as money that has been brought in through sales of contracts (which are bundled together to hedge any risk). As N gets larger, less money can be brought in through sales due to the constraints of the reserve requirement. Due to the nature of the reserve requirement, a situation where there are fewer possible mutually exclusive events means that more money can be brought in from sales relative to the amount of money borrowed by the arbitrageur. If the savings rate offered by the market is sufficient, risk-free profit can be earned by selling bundles and simply earning interest during the (non-trivial) time in between the sale and the closing out of the contracts. Specifically, the rate that the arbitrageur borrows at, Rb, must fulfill the condition Rb > Rs(1+1/(N-1)), again assuming that bundles can be sold for

7 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS Contracts in predictive markets close at a pre-determined date. An arbitrageur can easily plan ahead in order to be sure that there will be money available to pay off each bundle at 100. Interest earned in excess is profit. For example, assuming a targeted event, such as the Presidential election, where there are a lot of candidates vying for the presidency (N is large). Selling contracts for each candidate would create a large reserve requirement. If bid prices sum to 100, then the average bid price will be 100/N, where a large N leads to relatively lower prices, lower sale prices, and higher out-of-pocket reserve obligations for the seller. The reserve requirement is inversely related to the price. After selling a contract, the seller must hold 100 in his account, to payout in the case where the contracted event occurs. When the seller sells a contract, part of the reserve requirement is covered by the proceeds from the sale. The rest must be covered out-of-pocket by the seller. So, the reserve requirements that the seller has to pay necessarily increases when there are a lot of alternative contract buyer. If one considers that a bundle consists of every possible outcome, selling a single bundle comes increasingly expensive (in terms of-out-of pocket contributions to the reserve requirement) as the number of contracts per bundle ( N ) increases. All else (borrowing, savings rates), a smaller N will always be better for the arbitrageur than a larger N. When bundles are sold, money is effectively borrowed from the buyers in between the sale and closing of the contracts. The function (1/N-1) yields the amount of money, in excess of the original loan, which is pulled into your account by selling the mutually exclusive contracts, assuming that bundles of contracts can be sold at 100. As an aside, this model takes an important liberty with the assumption that the bid prices will sum to 100. [In actuality, they sum to about 99.9 in environments of high liquidity]. That is to say: (From i=1 to N) S Bi = 100; Where Bi is the bid price of contract i, and there exist N unique mutually exclusive contracts. As the number of contracts per bundle increases, the obligation for a reserve requirement increases (more bets to cover) and so the benefit (arbitrage) is reduced. If a bundle were to be sold for 99, and not 100, then a sure loss (just on contracts) of 1 would be realized. This might still be overcome by interest earned on the remaining 99, if savings rates were high enough. Margins of profitability become generally thinner, however, as the markets become less liquid, and the bid-ask spread increases. An example is provided below, in keeping with the assumption in question. To continue, suppose that there is an event for which there are only two outcomes: Republicans win or Democrats win. Let s also suppose that the current bid prices are 40 and 60, respectively. Here, a bundle would be one contract for Democrats win ( Dem ) and one contract for Republican s 7

8 2008, 2 3 THE JOURNAL OF PREDICTION MARKETS win ( Rep ). If one bundle is sold, what is actually happening is that one contract of each is being sold. The seller (arbitrageur) receives 40 and 60, or 100. Having sold two contracts, he is obligated to maintain 200 as a reserve requirement (100 for each contract). So, he must post 100 of his own (borrowed) money. In this case for each bundle he sells, he must post half the of total reserve requirement with his own (borrowed) money. N=2. Thus, if he posts half of the reserve requirement, or 100, he is effectively doubling the amount of interest available for him to receive. He will earn interest on 200. The following function defines how much of a reserve requirement is the seller subject to. Thus, when N=2, the function solves that the seller will earn two times the interest on the money that is borrowed. =1+1/N-1 =1+1/2-1 =1+1/1 =1+1 =2 Now, let s suppose that as was the case in 1980, there were candidates that ran for president of the US (Reagan, Carter, and Anderson); Rep, Dem, or Ind. Here, a bundle is comprised of three unique, mutually exclusive contracts. Let s say that the bid prices are 60, 30, and 10, respectively. Selling one bundle still earns 100 ( ), but now there is an obligation for 300 to be maintained as a reserve requirement. So, with the same X dollars, you will be able to sell fewer complete bundles. The function, solved for when N=3, yields =1+1/(N-1) =1+1/(3-1) =1+1/2 =1.5 Here, the arbitrageur earns interest on one and a half times the X dollars that he borrows. As the number of the contracts increases, the interest earned will dwindle until a certain point where arbitrage is no longer practical or feasible. To conclude, in spite of the reserve requirement, arbitrage can still be possible in a predictions market. Withdrawing reserve funds out of one s account is not necessary, so long as the market pays interest on account balances, and the rate is relatively competitive with borrowing rates offered elsewhere. Interest earned by the seller in a predictions market has to approximate the risk free rate found in the marketplace in general. In a typical predictions market the number of alternatives available to write contracts on is almost always more than two. For example, there are three possibilities offered for 8

9 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS trade (by intrade.com) for the 2008 Presidential Election: Republican, Democrat, and Independent. In these cases the interest rate necessary for the seller to earn from the market on his reserve requirement obligation would need to be greater than 2/3 rd of the rate money is borrowed. In some cases, say for example the contracts traded on what person will win the presidency, there exist so many possibilities that selling bundles quickly becomes prohibitively expensive for an arbitrageur. 5 RISKS TO ARBITRAGE IN A PREDICTIONS MARKET The overall level of interest rates in the market affect arbitrage. The amount earned is a function not only of the spread between interest rates, and the number of contracts offered for an event, but also on the nominal rates themselves. In a high interest setting, savings and borrowing rates should be higher. All else equal, such a setting will be more lucrative for an arbitrageur. For example, assume that the borrowing rate to the arbitrageur, Rb, is 10.75%. The arbitrageur (seller) of contracts in a predictions market can expect to earn an interest rate, Rs, on his or her borrowed money given to the predictions market as a reserve requirement at 9%, hypothetically. If N is small, an arbitrageur can expect a relatively large risk-free profit because interest rates are high general. In the case where there are three contracts to a bundle, N=3, the seller would be realizing a return of 13.5% on monies initially borrowed from the bank (9% * 1.5, or the function value when N=3), providing him or her with a profit of about 3% (after paying back interest on the initial loan). On the other hand, if rates are depressed, and the seller has a borrowing rate of 3.25%, and a return on reserve deposits of only 2.5%, then the expected return for the seller is 3.75% (2.5% * 1.5), providing the seller with a very thin profit margin of.25%. When other problems, such as bid-ask spread (discussed below) are taken into account, arbitrage might not be profitable even when prices are not at their true underlying values. Arbitrageurs incentive to target predictions markets is mediated by several factors, including the number of alternatives offered to a buyer and seller (the number of contracts that make a bundle), the interest rate return offered to the seller by placing obligated money into the reserve requirement account, and the risk free interest rate environment. Lastly, it would suffice to note possible problems with the arbitrage model presented, if actually tested. In the model, it has been assumed the bid prices of a bundle of contracts total 100. In actuality, the total of the bid prices made is less than 100 by a fraction. The spread below 100 is a reflection of a lack of adequate liquidity in the market, and must be accounted for by the arbitrageur as a necessary cost. In addition, the model assumes that a seller can find many buyers for each and every alternative choice in order that they buy contracts. It might be the case that while trying to leverage this arbitrage opportunity, there might lack enough willing buyers, especially for long-shot contracts. An arbitrageur could choose to ignore these problems and continue, but would then be 9

10 2008, 2 3 THE JOURNAL OF PREDICTION MARKETS accepting some risk (a violation of true arbitrage). Further, all markets have transaction costs, which although small, must be considered as a unavoidable cost to the arbitrageur. Each of these reservations to arbitrage in a predictions market represent obstacles that when discounted for permit the arbitrageur to achieve a risk free profit. 6 NEAR FUTURE FOR REGULATION IN PREDICTIONS MARKETS Looking ahead, we expect the Fortune 500 firms will adopt predictions market as an inhouse source of information to make policy. Furthermore, interest in public policy decision making and election forecasting will also increase participation in predictions markets. Increased participation in predictions markets is likely to impact arbitrage opportunities by those interested in risk free strategies. With current judgment towards pricing equilibrium, we propose that an increase in participation shall initially cause arbitrage to become more lucrative. Sophisticated traders will initially step in to take risk-free profits from naïve participants in predictions markets. Such action reflecting superior knowledge about how market pricing works shall likely lead to shifts in pricing that would extinguish arbitrage profitability. In the future, today s naïve traders will probably become more aware of the importance of slight mispricings as predictions markets are appreciated in the same way financial markets are. In the future, we anticipate predictions markets will come under close scrutiny by authorities like the Securities and Exchange Commission or the Futures Exchange Commission?, charged with protecting participants against fraud and manipulation. This inevitability will lead to calls for further regulation, not unlike the regulation experienced by the financial markets in response to the crisis of liquidity brought about by the recent sub prime meltdown. We expect regulation most adverse to arbitrageurs would come in the form of making a reserve requirement. That is, arbitrageurs (or sellers of bundles) would be required to invest the money received as would a fiduciary, that is, pay into an account, whose interest return is limited, rather than be free to invest the proceeds freely. The reserve requirement would dictate the interest rate offered by the account owner, which likely would be less than the market return the arbitrageur would achieve. The risk to the buyer of seller default would be reduced at the expense of the arbitrageur s profits. One consequence of reducing the incentive of arbitrageurs to enter the predictions markets would be a lack of liquidity. This likely would retard the growth of predictions markets, because buyers would not have opportunities to freely buy into options, assuming market makers are not properly encouraged to participate. However, given regulation is usually well intentioned, it would be rationalized on the basis that reports of buyers being defrauded by predictions markets would generate adverse public reaction, and thereby hurt the cause of these markets going forward. Striking a balance between preserv- 10

11 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS ing prediction market liquidity and protecting investor interests is necessary, but it must be done smartly. The regulation solution is likely to be found in the same format presently used in financial markets, where maintain margin accounts at brokerage houses protects against fraud while preserving liquidity. The true enemy of arbitrageurs in a predictions market is the same as found in any financial market: In financial markets arbitrageurs are well paid to ensure that markets maintain efficiency, that is, inefficiency is arbitraged away as soon as possible. For predictions markets, as in financial markets, arbitrageurs are needed to prevent more than momentary inefficiency. To the extent that arbitrage can be executed, prices will be driven to their true underlying values. Further, unlike a financial market, in a predictions market there will always be a small number of (naïve) participants who care more about the entertainment value of predictions markets, and ignore minor deviations in prices that arbitrageurs seek out. A second, equally important function for arbitrageurs is to provide liquidity, which in today s financial world, more than ever before, is recognized as crucial to the functioning of markets To the extent that regulators have recognized the importance of the dual function of regulation, that is, protecting against fraud, but also providing liquidity to make a market go, then arbitrageurs will find an important niche for themselves in the predictions market for some time to come. ACKNOWLEDGEMENTS The authors would like to thank Andrew de los Reyes and David Gordon for their help with research. In addition, we would like to thank Professor Michael Abramowicz for helpful comments. NOTES 1. Hayek (1945) was the first to recognize that markets both allocate resources, providing a source of capital to firms who offer optimal growth and investment return opportunities, but also have a secondary function, often unrecognized aggregating information in order to offer forecasts about future success. 2. Prices in futures and options markets aggregate information regarding the future values of commodities. It is widely assumed that futures prices are the best predictors of actual future spot prices, consistent with the widely recognized expectations hypothesis. Here, the commodities are not tangible goods, but rather the various possible outcomes of an unknown event. 3. Krueger and Kuttner (1996) has described the benefits of the market in Federal Funds futures contracts for use as a predictive policy tool, which accurately forecasts Federal Reserve target rates 4. Intrade.com is a public prediction market, based in Dublin, where participants can trade contracts based on the outcomes of future events. World politics, elections, and entertainment are just some of the many areas with contract-specific events trading. 5. See also, Wolfers and Zitewitz, page, 111, Recently, Goldman Sachs and Deutsche Bank have launched markets on the likely outcome of future readings of economic statistics, including employment, retail sales, industrial production and business confidence. 6. Typical examples of election contracts include forecasting a candidate s chances of winning an election, which party nominees will be involved in elections, who will control the houses of congress, whether particular bills or treaties will pass, or whether countries will join the European Union. Cited Berg, Joyce, Forsythe, Robert, Nelson, Forrest, and Thomas Reitz, (November 2000), Results from Dozen Years of Election Futures Markets Research, College of Business Administration, University of Iowa, page 6. 11

12 2008, 2 3 THE JOURNAL OF PREDICTION MARKETS 7. Ideospace.com is a predictions market in every sense, except that real monies are not exchanged. Instead, participants trade simply for the entertainment value of trying to beat the market. Hollywood Stock Exchange predicts opening weekend box office success, and these predictions have been quite accurate. Further, this market has been about as accurate at forecasting Oscar winners as an expert panel (Pennock, Lawrence, Giles and Nielsen, 2001). Jaems Surowiecki wrote in The Wisdom of Crowds, In 2002, the HSX picked 35 of 40 correctly. 8. Accuracy in predictions market footnote. Wolfers and Zitewitz cited Berg, Joyce, Forsythe, Robert, Nelson, Forrest, and Thomas Reitz, (November 2000), Results from Dozen Years of Election Futrues Markets Research, College of Business Administration, University of Iowa. These authors summarize the evidence from the Iowa Electronic Markets, documenting that the market has both yielded very accurate predictions and also outperformed large scale polling organizations. Their research found that in the week leading up to the presidential elections, predictions markets predicted results were accurate with an average absolute error of 1.5% points. Whereas the Gallup poll forecasts showed a standard error of 2.1% points. Researchers Berg, Forsythe, Nelson, and Reitz noted that for prediction markets to be accurate, they require liquidity (enough traders), and market mechanisms that facilitate aggregation of information to provide a forecast. According to a review by James Surowiecki in The Wisdom of Crowds, the IEM s performance in 49 different elections between 1988 and 2000 found that IEM election even prices were off by 1.37% in presidential elections, 3.43% in other elections, and 2.12% in foreign elections. These results outperformed the major national polls. Of the 596 different polls taken between 1988 and 2000, the IEM market price on the day each of those polls were released was more accurate. Polls tended to be very volatile. IEM forecasts less volatile and changed only in response to new information. They were more reliable. 9. See, James Surowiecki, The Wisdom of Crowds. The reliance upon the wisdom of market is dependant upon independence and diversity of market participants, decentralization, and aggregation. Specifically, if you ask a large enough group of diverse, independence people to make a predication, and then average those estimates, the errors each of them takes in coming up with an answer will cancel themselves out. Subtract the error, and you are left with the information. 10. Daniel Gross, Polls Say Kerry. Futures Say Bush, New York Times, August 8, He wrote, A comparison of 596 opinion polls with Iowa s presidential futures prices at the time the polls were conducted shows that the futures prices were closer to the actual result 76 percent of the time, according to Thomas A. Rietz, an associate professor of finance at the University of Iowa and a director of the market. As of Friday, Iowa traders thought that Mr. Bush had a 52 percent probability of winning. 11. Justin Wolfers and Eric Zitzewitz, Predictions Markets, Journal of Economic Perspectives Volume 18, Number 2 Spring 2004 Pages , at page 107. Mr. Wolfer and Mr. Zitzewitz refer to several other formats used by traders in prediction markets, including index contract trading (in which the contract paid varies based on the percentage of the vote received by a candidate as opposed to simply whether or not the candidate wins or loses), even-money bet (that is, winners double their money while losers receive zero), spread betting (where in football, where the bet is either that one team will win by at least a certain number of points or not.), etc. 12. Justin Wolfers and Eric Zitzewitz, Predictions Markets, Journal of Economic Perspectives Volume 18, Number 2 Spring 2004 Pages , at page Justin Wolfers and Eric Zitzewitz, Predictions Markets, Journal of Economic Perspectives Volume 18, Number 2 Spring 2004 Pages They described an program in which the Proposed contracts were based on indices of economic health, civil stability, military disposition, conflict indicators and potentially even specific events. For example, contracts might have been based on questions like How fast will the non-oil output of Egypt grow next year? or Will the U.S. military withdraw from country A in two years or less? Moreover, the exchange would have offered combinations of contracts, perhaps combining an economic event and a political event. The concept was to discover whether trading in such contracts could help to predict future events and how connections between events were perceived. The authors noted that the futures market successfully predicted its own demise. 14. Senator Barbara Boxer led the opposition to this form of predictions markets. She said, There is something very sick about it. I couldn t believe that we would actually commit $8 million to create a Web site that would encourage investors to bet on futures involving terrorist attacks and public assassinations, Senate Minority Leader Tom Daschle, D-South Dakota, said on the Senate floor.... I can t believe that anybody would seriously propose that we trade in death... How long would it be before you saw traders investing in a way that would bring about the desired result? Her objections harked back to the objections that were initially raised against the introductions of the options markets, when it was widely seen as being unpatriotic for investors to benefit at the failure of firms. The concern this time was that a terrorist organization might profit by betting a terrorist act will occur, and then be driven to make such an event occur. Justin Wolfers and Eric Zitzewitz, Predictions Markets, Journal of Economic Perspectives Volume 18, Number 2 Spring 2004 Page 119. If terrorists are sophisticated enough to lace bets in futures markets, surely they can do so with standard futures contracts 12

13 FORGONE INTEREST AND CONTRACT MISPRICING IN PREDICTIVE MARKETS on oil prices, by selling short stock in insurance companies or the entire stock market rumors have circulated widely that ther was unusual trading in option on United and American Airlines stock in the week prior to the attacks of September 11, See, Justin Lahart, A look at Google s Prediction Market, January 7, Justin Wolfers and Eric Zitzewitz reported on corporate activity at Hewlett-Packard and Seimens. They cited a number of studies (Chen, Kay-Yut and Charles Plott, Information Aggregation Mechanisms: Concept, Design, and implementation for sales forecasting problem, CalTech Social Science Working Paper No 1131, 2002; also, Ortner, Gerhard, Forecasting Markets An industrial application, Technical University of Vienna, 1998). that evaluated corporate internal predictions markets Other internal predictions markets have been established at Abbot Labs, Arcelor Mittal, Best Buy, Chrysler, Corning, Electronic arts, Eli Lilly, Fito Lay, GE, Intel, Intercontinental Hotels, Masterfoods, Microsoft, Motorola, Nokia, Pfizer, Qualcomm, and TNT. ability to forecast. Ortner (1998) described a successful predictions market established within Siemens who sought forecasts as to whether or not the firm would fail to deliver on a software project on time. As was the case with Hewlett-Packard, which focused on forecasting sales of printers, the predictions market succeeded whether traditional tools failed. Wolfers and Zitzewitz related that to succeed employees who served as traders needed to participate. They noted, In each case, the firms ran real money exchanges, with only a relatively small trading population (20 60 people), and subsidized participation in the market, by either endowing traders with a portfolio or matching initial deposits. The predictive performance of even these very thin markets was quite striking. 17. As of January 2008, Google s internal predictive market has been the largest compared with others in the corporate world. 18. Noam Cohen, New York Times, January 7, 2008, Google s Lunchtime Betting Game. See also, Bo Cowgill, Justin Wolfers, and Eric Zitzewitz, Using prediction markets to track information flows: Evidence from Google, January 6, 2008 paper presented to the AEA. These authors described the Google internal market as follows: In google s terminaology, a market asks a question, how many users will Gmail have? That has two to five possible mutually exclusive and completely exhaustive answers, e.g., fewer than x users, between x and y, and more than y. each answer corresponds to a security that is worth a unit of current called a Gooble, if the answer turns out to be correct and zero otherwise. Trade is conducted via a continuous double auction in each security. As on the IEM, short selling is not allowed, traders can instead exchange a Gooble for a complete setoff securities and then sell the ones they choose. (see page 4). 19. Bo Cowgill, The flow of information at the Googleplex, The Official Google Blog Insights from Googlers into our products, technology and the Google Culture, January 8, He concluded: (1) traders within a small radius of each other tend to make similar trades at the same time, (2) new employees are more optimistic and this bias was more pronounced when Google stock had appreciated. Additionally, Bo Cowgill, Justin Wolfers, and Eric Zitzewitz, Using prediction markets to track information flows: Evidence from Google, January 6, 2008 found that volume in fun and serious markets are positively correlated suggesting that the former might help create, rather than crowd out, liquidity for the latter. (see page 5). 13