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The structure of simExchange game stocks

Brian Shiau

Draft: January 27, 2007

Contracts on most prediction markets are often binary contracts that pay depending on whether the event described by the contract occurs or does not occur. This structure is often referred as a binary option [1]. However, a prediction market is not restricted in solving yes or no questions. Contracts can be created to pay a scaling amount so that a prediction market can attempt to ascertain a quantity, such as how much a movie will gross in box office receipts.

Prediction markets have used contracts similar to futures to answer such questions. These contracts have some value that corresponds to a prediction and expire at a certain point, such as four weekends after a movie is released. At expiration, the contract holder cashes out the contract at the spot price (or the sum of box office receipts after four weekends) [2]. However, the problem with video games is that a game can continue to sell for years to come so any arbitrary expiration is not indicative of how well a game will sell. In creating a prediction market for video games, the simExchange required a structure that would accommodate the nature of the video game industry.

There are other quirks to the video game business. One particular problem that has been the ire of many analysts is the lack of comprehensive sales data [3]. Unlike Hollywood movies, video games do not have official sales figures every weekend. Instead, the industry relies on point-of-sale studies, surveys, and intelligent extrapolations from companies like NPD to get an estimate of how many copies a game has sold. The number of copies a game has sold will vary from source to source, although NPD is considered the standard by many in North America as it is the most comprehensive for North American sales.

Given these two problems, video games can continue to sell for years and the lack of data with absolute truth, the simExchange could not easily adopt the structure of most prediction markets already in existence. Instead, it sought a time-tested structure that has been used to answer a similarly mirky question: how much is a company that may last for decades really worth?

No one knows with absolute certainty how much a company is actually worth. That is one reason why the stock market exists for people to trade shares of a company. The stock market aggregates the information of all the traders to hopefully ascertain an accurate valuation for the company (this concept is known as the Efficient Market Hypothesis [4]). Due to the similarity of the issues, stocks on the simExchange function very similarly.

A stock's price on the simExchange corresponds to the lifetime worldwide sales of a game, in which 1 DKP corresponds to 10,000 copies sold. These stock prices will climb or fall with monthly sales reports, just like a company's stock price will climb or fall with quarterly earnings reports. A stock on the simExchange will also increase or decrease as a result of news on the product, just as a company's stock will increase or decrease as a result of news on their products. If people believe a stock is underpriced given the data, people will bid it up and vice versa [5]. There is no automated function by the New York Stock Exchange to cash out a stock and pay shareholders a lump sum of cash depending on how the quarterly earnings for the company fared.

Eventually, a game will stop selling, just like eventually a company will stop growing. In this case the stock price will merely stagnate. Investors of game stocks can cash out just like they would with company stocks by selling their shares (or covering if they are short the stocks). The simExchange market makers will supply the liquidity to close those positions.

Due to this structure, in an efficient state where a diverse pool of traders are participating in the simExchange, game stock prices should become a strong predictor of the lifetime worldwide sales of video game titles [5].

References:

[1] Wolfers, Justin & Zitzewitz, Eric. "Prediction Markets in Theory and Practice." March 2006. (PDF)
[2] Hollywood Stock Exchange Frequently Asked Questions.
[3] Electronic Gaming Business, October 6, 2004.
[4] Shleifer, Andrei. Inefficient Markets: An Introduction to Behavioral Finance. New York: Oxford University Press, Inc. 2000.
[5] Chen, Kay-Yut & Plott, Charles R. "Information Aggregation Mechanisms: Concept, Design, and Implementation for a Sales Forecasting Problem." Hewlett Packard Laboratories and California Institute of Technology. March 2002.


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