Foundations of information markets

Information markets provide predictions for outcomes of future events. After dening prediction targets (e.g. the market share of an innovative product in a specic target market), the market's initiator creates derivatives called information-market stocks.

The inherent values of these stocks are tied to the predictions' outcomes. A stock's value derives from the market share of the innovative product at time point t. Stocks are paid out according to the outcome immediately after t. For example, if the market share of the innovation is 40% at the moment the market closes, the stocks may be cashed out at 40 units of market currency.

Before the information market starts, participants acquire or are endowed with a port-folio of information market stocks and information market currency to trade the stocks.

They are informed that their remuneration for participating will depend on their nal portfolio value after the information market stocks have been paid out; the higher their nal portfolio value, the higher their expected remuneration will be. Accordingly, if market participants hold dierent beliefs about the outcome of an innovation evaluation

issue, such as its likelihood of market success, they have an incentive to engage in trad-ing. The person who believes that the innovation holds high market success potential allocates higher value to acquiring stocks that derive from the innovation's market suc-cess than the other participants. Accordingly, this person will try to buy stocks from the participants in exchange for information currency as long the person believes that the stock is undervalued and has sucient amounts of cash. Over time, individual expecta-tions regarding the prediction task's outcome are aggregated via the trading mechanism.

Whenever participants acquire new information, they may form new expectations, which should incline them to buy or sell stocks and thus alter the information market's current prediction. Thus, incentives for participants are compatible with the initiator's goal of having market prices reect participants' expectations regarding the information market stock's true underlying value.

Figure 3.1 presents a simple example to illustrate the basic principles of information markets. One should be aware that a real information market would very likely feature a larger number of traders and prediction targets.

1. At the beginning, the future events in question require precise denition so that their underlying prediction object is fully understood by potential participants.

Participants' and initiators' expectations must reect the same underlying pre-diction object prior to, during, and after the market, in order for incentives to align.

2. Next, participants with heterogeneous expectations and information decide to join the market. Because they believe that they possess superior and exclusive knowl-edge regarding the underlying prediction task, they expect to benet from revealing personal information via their trading.

3. Before the information market starts, participants are endowed with an initial portfolio that consists of stocks and information market currency. In our example, there is one stock type and the currency is called virtual Dollar (v$).

4. In the illustrated example, the expectations of the two participants dier. This opens the possibility for trading. The participants engage in trading via a con-tinuous double auction mechanism (CDA): Participant 2 places a sell-oer at 35 virtual Dollars, which is absorbed by Participant 1's oer to buy stocks at prices up to 40 virtual Dollars. Accordingly, Participant 2 sells all her stock to Participant 1 for 35 virtual Dollars, from which the new portfolios and a market prediction

2. Individuals sign up for participation.

1. Information market stocks (or event derivatives) are defined.

Information market stock

“Share of the Apple iPhone 5 among newly sold smart phones

in December 2013 in Germany”

3. The information market is initiated and participants are endowed with stocks and currency.

Participant 1 Participant 2 2 stocks 2 stocks

100 v$ 100 v$

4. Participants start trading based on their expectations. Stock price and individual portfolios change accordingly.

Participant 1: I offer to buy stocks for up to 40

Participant 1 Participant 2 2+2 = 4 stocks 2-2=0 stocks

100-70=30 v$ 100+70=170 v$

5. The market is closed, the true underlying value becomes known and stocks are paid out.

“According to valid market data, the share of the Apple iPhone 5 among newly sold

smartphones in December 2013 in Germany was 37%”

Participant 1 Participant 2 4 x 37 v$ dividend

+ 30 v$ cash = 178 v$

170 v$ cash Last quoted stock price = 35 virtual Dollar

Figure 3.1.: Information market process example, from event denition to stock payout (Source: Own depiction)

result. Based on the given trade and assuming that no further trading will occur, the market prediction for the share of iPhone5 among newly sold smart phones in December 2013 in Germany would remain at 35%.

5. After December 2013, the true underlying value becomes known and stocks can be paid out accordingly. Assuming a true market share of 37%, we can observe that Participant 1 yields a higher overall portfolio value. His expectations are closer to the true underlying value, which allows him to generate larger prots than Partici-pant 2. Assuming a dierent outcome in which Apple had withdrawn the iPhone5 from the German market altogether before December 2013, the resulting share among newly sold devices would be 0%. In this case, Participant 2's expectations would be closer to the true underlying value and she would accordingly generate higher trading prots than Participant 1 (170 virtual $ vs. 30 virtual $).

This short example illustrates that the revelation of valid expectations via trading bene-ts the revealer through higher trading probene-ts. At the same time, the market's initiator benets by gaining more accurate predictions, thus showing the information market's incentive compatibility. It also reveals that the quality of the information market out-comes depends mainly on the expectations and actions of its participants.

The ecacy of information markets builds upon the famous proposition by Nobel Prize winner Friedrich von Hayek that markets are a relatively eective instrument (compared to central planning) to pass on essential information (Hayek 1945). The ecient market hypothesis posits that prices on markets eciently and fully reect all available information held by market participants via the markets' trading mechanisms (Fama 1970). On a micro-level, deviations between market prices and individual expec-tations will prompt participants to reveal their expecexpec-tations via trading. Participants will try to buy or sell information market stocks as long as they believe that current market prices do not reect the future state of the underlying event. Empirical data from laboratory experiments and nancial markets support the notion that prices in asset markets can eciently reect all available information (Plott and Sunder 1982;

Plott and Sunder 1988; Fama and French 1992).

Historically, information markets have been used in the U.S. for betting on voting out-comes in presidential elections as early as 1868. These early markets focused on the promise of individual gains over the aspect of information aggregation, but their price quotes were still widely used by newspapers and campaigners as sources of valid forecasts.

Figure 3.2.: Prediction quality in global political information markets between 1988 and 2000 (Source: Berg et al. (2008))

They reached their high point in 1916, when spending on electoral betting markets was twice the total spending on the election campaigns, and before legal restrictions forced them to cease (Rhode and Strumpf 2004).

Current scientic publicity on information markets was triggered when political markets were revived for academic purposes at the University of Iowa in 1988 (Forsythe et al.

1992). In fact, the Iowa Electronics Market has signicantly outperformed major U.S polls in all presidential elections since the market's inception. Figure 3.2 displays the absolute accuracy of 237 political information markets at midnight on the evening before the actual election. We observe considerable accuracy, as almost all predictions are very closely lined up on the 45-degree line (Berg et al. 2008).

Further results demonstrate the superiority of information markets over opinion polls for predicting election outcomes. In the same article, Berg et al. (2008) compare the accuracy of 964 opinion polls with the results of the corresponding information markets.

Information markets signicantly outperformed opinion polls in the large

majority of pair-wise comparisons. The information markets provided signicantly more accurate predictions at all time points leading up to the elections.

After their success in the political domain, information markets were identied by man-agement scholars as promising tools for business evaluation and forecasting. In 1999, the Hollywood Stock Exchange (HSX) was founded as a pioneering public information market to forecast new movie success (Keiser and Burns 1998). Movie success has his-torically been considered very dicult to predict. Movies are complex and gestalt-like innovative products, which inhibits valid prediction by stochastic methods (Vany and Walls 1999). Information markets have been able to absorb new movies' inherent com-plexity. The expectations of market participants about a movie's future success are derived from its gestalt (Karniouchina 2011). Participants include potential moviego-ers and lm critics, and hence incorporate ultimately sound information sources. The HSX has been very successful at predicting box oce success since its introduction, sig-nicantly outperforming alternatively established methods like expert panels or opinion polls (Spann and Skiera 2003b). The authors emphasize that in contrast to expert panels like Box Oce Mojo, information markets provide access to a larger pool of potentially valid information sources, compatible incentives for valid self-selection of participants, and the revelation of participants' true beliefs. Compared to traditional opinion polls among moviegoers, information markets foster learning from other participants via the trading mechanism and emphasize information from better-informed participants, since these are more likely to prot from the market and continue to participate (Spann and Skiera 2003a).

Ortner pioneered business applications when he applied information markets to evaluate project durations at Siemens (Ortner 1998). More corporate applications eventually fol-lowed. Information markets outperformed quantitative forecasting methods for existing product lines at HP (Chen and Plott 2002), and also outperformed Delphi methods for predicting the impact of technological change at a major German telecommunications provider (Spann and Skiera 2003a). Large technology companies such as Google, IBM, Microsoft or BestBuy have applied information markets to gather intelligence for de-mand forecasting and product development processes (Cowgill et al. 2008; Lacomb et al.

2007; Dye 2008).

3.2. Applications of information markets for