Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings

Multi-outcome and Multidimensional Market Scoring Rules

Hanson's market scoring rules allow us to design a prediction market that still gives useful information even if we have an illiquid market with a limited number of budget-constrained agents. Each agent can "move" the current price of a market towards their prediction. While this movement still occurs in multi-outcome or multidimensional markets we show that no market-scoring rule, under reasonable conditions, always moves the price directly towards beliefs of the agents. We present a modified version of a market scoring rule for budget-limited traders, and show that it does have the property that, from any starting position, optimal trade by a budget-limited trader will result in the market being moved towards the trader's true belief. This mechanism also retains several attractive strategic properties of the market scoring rule

Five Open Questions About Prediction Markets

Interest in prediction markets has increased in the last decade, driven in part by the hope that these markets will prove to be valuable tools in forecasting, decision-making and risk management -- in both the public and private sectors. This paper outlines five open questions in the literature, and we argue that resolving these questions is crucial to determining whether current optimism about prediction markets will be realized.

Five open questions about prediction markets

Interest in prediction markets has increased in the last decade, driven in part by the hope that these markets will prove to be valuable tools in forecasting, decisionmaking and risk management--in both the public and private sectors. This paper outlines five open questions in the literature, and we argue that resolving these questions is crucial to determining whether current optimism about prediction markets will be realized.Forecasting ; Financial markets ; Econometric models

Essays in Corporate Prediction Markets

Personal subjective opinions are one of the most important assets in management. Prediction markets are mechanisms that can be deployed to elicit and aggregate a group of people’s opinions regarding the outcome of future events at any point in time. Prediction markets are exchange-traded markets where security values are tied to the outcome of future events. Prediction markets are systematically designed in a way that their market prices capture the crowd’s consensus about the probability of a future event. Corporations harness internal prediction markets for managerial decision making and business forecasting. Prediction markets are traditionally designed for large and diverse populations, two properties that are not often displayed in corporate settings. Therefore special considerations must be given to prediction markets used in corporations. Our first contribution in this thesis is in addressing the issue of diversity, in the sense of risk preferences, in corporate prediction markets. We study prediction markets in the presence of risk averse or risk seeking agents that have unknown risk preferences. We show that such agents’ behavior is not desirable for the purpose of information aggregation. We then characterize the agents’ behavior with respect to prediction market parameters and offer a systematic method to market organizers that fine tunes market parameters so at to best mitigate the impact of a pool agents’ risk-preferences. Our Second contribution in this thesis is in recommending prediction market mechanisms in different settings. There are many prediction market mechanisms with various advantages and weaknesses. The choice of a market mechanism can heavily affect the market accuracy and in turn, the success of a managerial decision, or a forecast based on prediction markets’ prices. Using trade data from two real-world prediction markets, we study the two main types of prediction markets mechanism and provide the much-needed insight as to what market mechanism to choose in various situations

Information Procurement and Delivery: Robustness in Prediction Markets and Network Routing.

In this dissertation we address current problems in information procurement and delivery. Uncertainty commonly reduces the efficacy of information procurement systems, such as prediction markets, and information delivery systems, such as Internet backbone networks. We address the problems of uncertainty by designing robust algorithms and protocols that function well under uncertainty. Telecommunication backbone networks are used for delivering information across the Internet. Current backbone networks mostly employ protocols that include sender-receiver based congestion control. However, as protocols that do not have congestion control available become more prevalent, the network routers themselves must perform congestion control. In order to maximize network throughput, routing policies for backbone networks that take into account router based congestion control must be devised. We propose a mathematical model that can be used to design improved routing policies, while also taking into account existing flow management methods. Our model incorporates current active congestion control methods, and takes into account demand uncertainty when creating routing policies. The resulting routing policies tended to be at least 20% better than those currently used in a real world network in our experiments. Prediction markets are information aggregation tools in which participants trade on the outcome of a future event. One commonly used form of prediction market, the market scoring rule market, accurately aggregate the beliefs of traders assuming the traders are myopic, meaning they do not consider future payoffs, and are risk neutral. In currently deployed prediction markets neither of these assumptions typically holds. Therefore, in order to analyze the effectiveness of such markets, we look at the impact of non-myopic risk neutral traders, as well as risk averse traders on prediction markets. We identify a setting where non-myopic risk neutral traders may bluff, and propose a modified prediction market to disincentivize such behavior. Current prediction markets do not accurately aggregate all risk averse traders' beliefs. Therefore, we propose a new prediction market that does. The resulting market exponentially reduces the reward given to traders as the number of traders increases; we show that this exponential reduction is necessary for any prediction market that aggregates the beliefs of risk averse traders.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75962/1/sdimitro_1.pd

On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules

We study market scoring rule (MSR) prediction markets in the presence of risk-averse or risk-seeking agents that have unknown yet bounded risk preferences. It is well known that if agents can be prescreened, then MSRs can be corrected to elicit agents’ beliefs. However, agents cannot always be screened, and instead, an online MSR mechanism is needed. We show that agents’ submitted reports always deviate from their beliefs, unless their beliefs are identical to the current market estimate. This means, in most cases it is impossible for a MSR prediction market to elicit an individual agent’s exact belief. To analyze this issue, we introduce a measure to calculate the deviation between an agent’s reported belief and personal belief. We further derive the necessary and sufficient conditions for a MSR to yield a lower deviation relative to another MSR. We find that the deviation of a MSR prediction market is related to the liquidity provided in the MSR’s corresponding cost-function prediction market. We use the relation between deviation and liquidity to present a systematic approach to help determine the amount of liquidity required for cost-function prediction markets, an activity that up to this point has been described as “art” in the literature.Natural Sciences and Engineering Research Council of Canad

PRIVATE PREDICTION MARKETS AND THE LAW

This paper analyses the legality of private prediction markets under U.S. law, describing both the legal risks they raise and how to manage those risks.  As the label "private" suggests, such markets offer trading not to the public but rather only to members of a particular firm.  The use of private prediction markets has grown in recent years because they can efficiently collect and quantify information that firms find useful in making management decisions.  Along with that considerable benefit, however, comes a worrisome cost:  the risk that running a private prediction market might violate U.S. state or federal laws.  The ends and means of private prediction markets differ materially from those of futures, securities, or gambling markets.  Laws written for those latter three institutions nonetheless threaten to limit or even outlaw private prediction markets.  As the paper details, however, careful legal engineering can protect private prediction markets from violating U.S. laws or suffering crushing regulatory burdens.  The paper concludes with a prediction about the likely form of potential CFTC regulations and a long-term strategy for ensuring the success of private prediction markets under U.S. law

Why the Law Hates Speculators: Regulation and Private Ordering in the Market for OTC Derivatives

A wide variety of statutory and common law doctrines in American law evidence hostility towards speculation. Conventional economic theory, however, generally views speculation as an efficient form of trading that shifts risk to those who can bear it most easily and improves the accuracy of market prices. This Article reconciles the apparent conflict between legal tradition and economic theory by explaining why some forms of speculative trading may be inefficient. It presents a heterogeneous expectations model of speculative trading that offers important insights into antispeculation laws in general, and the ongoing debate concerning over-the-counter (OTC) derivatives in particular. Although trading in OTC derivatives is presently largely unregulated, the Commodity Futures Trading Commission recently announced its intention to consider substantively regulating OTC derivatives under the Commodity Exchange Act (CEA). Because the CEA is at heart an antispeculation law, the heterogeneous expectations model of speculation offers policy support for the CFTC\u27s claim of regulatory jurisdiction. This model also, however, suggests an alternative to the apparently binary choice now available to lawmakers (i. e., either regulate OTC derivatives under the CEA, or exempt them). That alternative would be to regulate OTC derivatives in the same manner that the common law traditionally regulated speculative contracts: as permitted, but legally unenforceable, agreements. By requiring derivatives traders to rely on private ordering to ensure the performance of their agreements, this strategy may offer significant advantages in discouraging welfare-reducing speculation based on heterogeneous expectations while protecting more beneficial forms of derivatives trading

Middleman margins and asymmetric information: an experiment with potato farmers in West Bengal

West Bengal potato farmers cannot directly access wholesale markets and do not know wholesale prices. Local middlemen earn large margins; pass-through from wholesale to farm-gate prices is negligible. When we informed farmers in randomly chosen villages about wholesale prices, average farm-gate sales and priceswere unaffected, but pass-through to farm-gate prices increased. These results can be explained by a model where farmers bargain ex post with village middlemen, with the outside option of selling to middlemen outside the village. They are inconsistent with standard oligopolistic models of pass-through, search frictions or risk-sharing contracts.Accepted manuscrip