Abstract
Virtual Stock Markets (VSM) are a young, powerful and still evolving research tool. VSM were developed around 20 years ago as forecasting instrument of election outcomes, having delivered very precise results ever since. In recent years, various business applications of the given concept have been presented, namely forecast generation, decision support, product concept evaluation and the identification of lead users. This article explains the basic concept of VSM, describes the potential areas of application and shows examples of successful implementations in business practice. Directions for further research are identified.
TopBackground
VSM work similar to regular stock markets. However, the listed stocks do not represent the shares of companies but are tied to the outcomes of future events. Every stock has a fixed lifetime after which the actual outcome of the predicted event can be observed. The final value of the stock is determined accordingly. During the lifetime of the stock market, traders compare the current market prices with their individual expectations of the outcome and make trades accordingly. Supply and demand determine the prices of stocks.2
Following the logic of Hayek hypothesis (Hayek, 1945) and information efficiency hypothesis (Fama, 1970), the resulting market prices reflect the traders’ aggregate expectations of the future events, which the stocks are tied to. According to Hayek (1945), this mechanism to aggregate information works efficiently even in the extreme case of all market participants holding diverging information.
The first application of VSM was the Iowa Presidential Stock Market (Forsythe et al., 1992). In this example, virtual stocks were traded representing the vote shares of the different candidates in the 1988 Presidential elections. Actual outcomes could be predicted very precisely. Forecasts based on VSM outperformed every pollster’s forecast in terms of prediction accuracy and low fluctuation levels in forecasts prior to the election date. VSM have been able to repeat this remarkable performance in the subsequent implementations (e.g., Berg et al., 2008), sparking academics´ interest and laying the basis for different applications in related fields, most importantly in business research and practice.
Key Terms in this Chapter
Virtual Stock Market (VSM): a market that allows trading of stocks representing the outcomes of future events. Market prices reflect the aggregate expectations of participants and can be used as a forecasting tool.
Hayek Hypothesis: Market prices work as a quick and efficient means to aggregate information that are diversely held by individual market participants (Hayek, 1945).
Groupthink: a pattern of decision making by a group characterized by reaching consensus without sufficient discussion and consideration of alternative solutions to the problem at hand.
Decision Support Systems: a computer information systems that support decision-making activities.
Lead User: a user who faces needs months or years prior to the majority of users in a market and who benefits significantly from obtaining a solution to those needs (von Hippel, 1986).
Products Concept Testing: the process of estimating consumer responses to a product idea prior to its market launch. Product concepts are tested in order to improve the rate of successful new product introductions (Moore, 1982).
Marginal Trader: a very active and well informed market participant. Marginal traders do not suffer from any bias when evaluating stock prices. They frequently make trades close to the current market price, thereby adjusting it to the information they hold.
Information Efficiency Hypothesis: A capital market is called “efficient” if its prices always fully reflect all available information (Fama, 1970).