Manipulation
Though the creator of a prediction market cannot, absent fraud, guarantee a preferred result, there is a danger that individual traders in a prediction market might be able to manipulate forecasts. One way to do so would be to disseminate false information. To the extent that this approach is successful, it works because it changes individual probability assessments. Prediction markets sometimes might fail to filter out misinformation, but the more troublesome possibility is that someone might be able to manipulate the market by engaging in trades. Perhaps attempts at manipulation have been relatively uncommon in prediction markets because there is little to be gained from such attempts. A Yankees fan might like to manipulate a game to ensure a Yankees victory, but manipulating a prediction market to predict a Yankees victory seems unlikely to have any real-world consequences. Moreover, buying shares and pushing prices above their fundamental values is costly. There might be prediction markets, however, that some individuals would like to manipulate.49 Suppose, for example, that some voters decide whom to vote for in part based on whom they expect to win.50 Further, suppose that some of these same voters base their predictions of who will win either directly or indirectly on prediction markets. It might then be in the financial or at least the ideological interest of some wealthy individuals to seek to manipulate prediction markets that forecast election returns. A significant investment in prediction markets might have the direct effect of causing financial loss, at least if prices immediately push back in the original direction. Such an investment, however, conceivably could affect the election if the results were readily available to the public. This scenario admittedly seems unlikely today given the lack of attention to prediction market forecasts, but the question remains whether prediction markets will remain effective if intense pressures exist to manipulate them. There are theoretical and empirical reasons to believe that prediction markets can be manipulated in the short term. The theoretical point is simply that the number of orders on the bid and ask queues at any time is finite. Rational traders will limit their exposure to trading on bid and ask queues, since leaving offers on such queues opens traders to losses in the event that new developments make substantial changes in probability assessments. So, if I buy enough shares, I can buy not only all the shares available at the lowest quoted price but also some shares at the next higher price. At least at the moment the trade is completed, the last traded price will likely be different from the previous price, and the midpoint of the bid-ask spread will also rise. Indeed, for enough money, I should be able temporarily to drive the price to whatever level I please. The empirical point is that there have been efforts to manipulate prediction markets. Such an attempt presumably explains the early October blip in Kerry shares visible in figure 1.7. Other attempts have been documented. For example, speculators briefly increased the value of Pat Buchanan shares in an Iowa Electronic Markets contest.51 In addition, Paul Rhode and Koleman Strumpf document and analyze two sustained attempts to manipulate the TradeSports 2004 election markets on September 13 and October 15, 2004, leading to large price changes that do not appear to have been based on any information.52 Rhode and Strumpf show that such attempts at manipulation were nothing new. Precursors to modern election markets existed between 1880 and 1940,53 and Rhode and Strumpf identify apparent attempts at manipulation in these early markets.54 In all cases, the authors show that the effects of manipulation attempts on prices decreased over time. Rhode and Strumpf also ran an interesting field experiment on the Iowa Electronic Markets during the 2000 presidential campaign.55 At predetermined times, they made a total of eleven planned trades, determining at random the side in which to invest. On some occasions, Rhode and Strumpf bet only on one of the election markets (either vote share or winner take all), to determine whether the trade on one market contaminated the other. Such contamination indeed may have occurred in the minutes after the initial trade, though it was not statistically significant. On other occasions, Rhode and Strumpf attacked both election markets simultaneously. That approach might be expected to be more likely to convince other market participants that the trades were based on real information, because someone seeking to profit on new information is likely to bet on both markets. In all cases, Rhode and Strumpf demonstrate significant changes in the short term on the level of other participants’ trades, but as hours passed, there was no effect. Planning random trades would appear to have no effect at all on prices twenty-four hours later. These results are not surprising. There is relatively little “private” information about presidential elections, or at least relatively little that one could keep secret for very long. Someone playing the markets, therefore, would place relatively little emphasis in deciding what to do about trades made twenty-four hours earlier. Over time, the effects of trades should be expected to dissipate. But this is not entirely reassuring for two reasons. The first is John Maynard Keynes’s observation that in the long run we will all be dead. The long run in this context is very short, but the short run is still long enough that manipulation conceivably could have some effect. This is particularly true if a market is expected to end at a particular time, so manipulation immediately prior to the market end might go unchecked. The second is that some prediction markets might depend on a relatively small number of traders who have a fair amount of private information or private analysis of public information. In such a market, a trader might put considerable emphasis on preceding trades. Other experimental evidence, however, suggests that manipulation may be difficult even in a market in which private information exists, at least when market participants are aware of the incentives for manipulation. Robin Hanson, Ryan Oprea, and David Porter ran an experimental market in which subjects traded an asset and received different information relevant to determining the value of the asset. Some randomly chosen traders were given an incentive to manipulate the market: they were promised that the higher the final market price, the higher the payout. All traders were aware that some traders were given an incentive to manipulate the market and knew whether the incentive was to manipulate prices up or down. Those who were given manipulation incentives in fact submitted higher bids, but this ultimately had no effect on market prices. Individuals who were not given manipulation incentives bid against the manipulators and drove the market prices down. More generally, to the extent that traders know in advance that certain parties will have incentives to manipulate, they will have incentives to counteract such manipulation by seeking to push prices in the opposite direction. Indeed, Hanson et al. show that attempts to manipulate the market might increase market accuracy.56 In securities markets generally, “noise traders,” that is, traders who do not trade according to fundamentals, may increase market accuracy because informed traders can profit by trading against them. (In Chapter 7 we will encounter arguments that noise traders may decrease market accuracy.) Adding unidentifiable noise traders for any particular security may decrease accuracy, but noise traders as a whole should increase accuracy. In a prediction market, the larger the number of people who can be expected to trade in a market for reasons unrelated to fundamentals, the greater the incentives for others to enter the market, and the additional insight provided by these entrants should increase market accuracy. From the perspective of market efficiency, Hanson et al. argue, manipulators can be seen simply as noise traders. Manipulation is more likely to succeed when market participants are not informed of the incentive of some participants to manipulate a market. An experiment by Martin Strobel confirms this.57 The experiment used prediction markets to estimate the number of black balls and white balls in an urn. Different participants in the market were able to view different subsets of the balls. In some iterations of the experiment, a robot trader sought to manipulate the market in one direction. The results confirmed that the manipulation attempts did have a statistically significant effect in the expected direction. Market participants in this experiment cannot know whether someone with whom they are trading is trading on the basis of information or on the basis of a manipulation incentive. They thus ascribe some positive probability to the contingency that the trades reflect information, and this changes the assessment of market participants about the number of balls in the urn. Manipulation seems likely to have a long-term effect on markets only to the extent that market participants misestimate the extent of attempts at such manipulation. For example, if market participants believe that there are probably individuals who are seeking to bid up market prices, these participants will respond by seeking to push prices in the opposite direction. But if in fact there are no manipulators, then this error will cause prices to be too low. On the other hand, if market participants underestimate the extent of market manipulation, that manipulation will be somewhat successful. In order to manipulate a market successfully, one has to attempt manipulation in higher volumes than other parties expect. This analysis suggests that manipulation might affect prediction market prices, but only to the extent that neutral market participants are genuinely fooled into misestimating others’ private information. Prediction markets should continue to reflect consensus predictions, but misinformation through trading can have some effect. When the general level of manipulation is known but it is not known which tradable contracts have been targeted and in which direction, manipulation should increase overall average price accuracy while nonetheless succeeding at biasing the particular targeted results. Manipulation may be the most serious obstacle to widespread adoption of prediction markets, but it will not tend to bias prices when the degree of incentives of participants to manipulate is known. It seems especially unlikely to be a problem where there is little private information and thus little derivatively informed trading, that is, where individuals’ price assessments are not greatly affected by the trades of others. If manipulation is nonetheless deemed too serious a problem for some applications of prediction markets, there are two possible solutions. First, where there is a discrete group of potential manipulators, those individuals can be barred from participation. Of course, there is always a danger that these potential manipulators can pay off other market participants, but legal or contractual sanctions can reduce that possibility. Second, prediction markets might be limited to a group of authorized traders who are believed to have no incentive to manipulate the outcome.
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