Abstract:
Various problems currently exist in the operation of using prediction markets for corporate forecasting. One of these problems we address is the problem related to the “consistency and consolidation of metrics”. We hierarchically structure and link sets of markets traded using an automated market maker and dynamically balance markets.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 12/172,140, filed on Jul. 11, 2008 for “Prediction Market Roll Up” on behalf of inventor Matthew John Fogarty; in turn, U.S. patent application Ser. No. 12/172,140 claims priority under 35 USC 119(e) to provisional application No. 60/955,826 filed Aug. 14, 2007 and to provisional application No. 60/950,700 filed Jul. 19, 2007. Each of U.S. patent application Ser. No. 12/172,140 and the aforementioned provisional applications are hereby incorporated by reference in their entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     No federal funds have been used for research and development. 
     BACKGROUND 
     We address various fundamental problems in the use of prediction markets for corporate forecasting. 
     Prediction Markets 
     Prediction markets are speculative markets created for the purpose of making predictions. Assets are created whose final value is tied to a particular event (e.g., will the next US president be a Republican) or metric (e.g., total sales next quarter). The current market prices (in the prediction market) can then be interpreted as predictions of the probability of the event or the expected value of the metric. Other names for prediction markets include information markets, decision markets, idea futures, event derivatives, and virtual markets. 
     Prediction markets can be traded using a continuous double auction (matching of buy and sell orders) or an automated market maker. 
     Corporate Forecasting 
     Many corporations and organizations forecast their future financial and operational results. These forecasts are typically structured using bottom-up assumptions and inputs. For example, when forecasting sales revenue for the next fiscal year, the forecast could be constructed using revenue per product per region per month. A typical multinational organization may forecast sales on 100 products in 50 countries over 12 months, this would equate to approximately 60,000 inputs (100×50×12). Forecasts are then consolidated to more summary levels and retain consistency through simple math (e.g. Worldwide annual sales of product A=sum of sales for product A across all months across all countries). 
     Depending on the requirements and the analysis, this forecast can be reported at multiple levels—product A, in Region B in Month 4, or total sales for the year in Region B, or total sales of product D over the first half of the year for example. For these types of analysis, the data should be consistent, for example, the sum of all regions equals the world wide total. In this way, summary data can be “drilled down” to show bottom level data. For example,  FIG. 1  shows how worldwide summary data can be drilled down to show regional summary data, and regional summary data can be drilled down to show bottom level product data for that region. 
     SUMMARY 
     Various problems currently exist in the operation of using prediction markets for corporate forecasting. One of these problems we address is the problem related to the “consistency and consolidation of metrics”. 
     We address this problem by hierarchically structuring and linking sets of markets traded using an automated market maker and dynamically balancing markets using an algorithm. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows how worldwide summary data can be drilled down to show regional summary data, and regional summary data can be drilled down to show bottom level product data for that region. 
         FIG. 2  shows data of a set of five discrete prediction markets. 
         FIG. 3  illustrates an example data set, where each data item represents a forecast sales figure in dollars. 
         FIG. 4  is an example of data on which a trade may be made, and  FIG. 5  illustrates a result of the trade. 
         FIG. 6  illustrates an example in which the bottom level metrics are adjusted, and the summary metrics related to the adjusted bottom level metrics are adjusted to maintain consistency. 
         FIG. 7  is a flowchart illustrating an example of how the prediction market rollup methodology may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The Problem 
     To date, prediction markets used for corporate forecasting have been based on sets of discrete forecasted metrics. For example, a set of prediction markets may have a market forecasting a particular region&#39;s revenue in a particular time period, or a market forecasting world wide revenue. However, because each of these markets is traded separately from the other markets, it is unlikely that the set of markets will be consistent. Without this consistency, it is challenging to interpret the data. For example,  FIG. 2  below shows a set of 5 discrete markets. To be consistent, the result for the first 4 markets should sum to the result for the last market (Total revenue should equal the sum of the revenue per category). However, because the markets are separate, it is not clear if the total should equal 114 (sum of product markets) or 120. 
     One solution to this problem could be for the corporation to operate only discrete prediction markets on the metrics at the lowest levels. For the multinational in the example above (100 products in 50 countries over 12 months), this would mean running 60,000 discrete prediction markets on the metrics at the lowest level. For summary metrics, world wide revenue for example, the bottom level forecasts could be summed up. 
     However, for prediction markets to work well, there should be a sufficient number of traders (wisdom of crowds) and sufficient liquidity in each market. Having a very high number of markets, given a limited number of potential traders, would lead to low liquidity in each market, and poor quality forecasts. In addition, many potential participants (especially higher up in the organization) can judge the accuracy of a forecast better at the summary level (e.g. world wide revenue) and may find it difficult to judge accuracy at the bottom level (e.g. sales on product C in Region E in month 7), and so would be unlikely to trade. 
     In summary, with current methods, it is difficult to use prediction markets for corporate forecasting while maintaining consistency of data between bottom level and consolidated (summary) forecasted metrics. 
     The Prediction Market Roll Up Solution 
     We address some or all of these issues using a “Prediction Market Roll Up” (PMRU). 
     The essence of PMRU is to build a hierarchy of linked markets representing bottom level and summary forecasted metrics. Any metrics (bottom level and summary) can be traded using a prediction market using an automated market maker and all metrics related to the traded data point are adjusted to maintain data consistency. 
     Example of Intended Use 
     To explain an example of the PMRU solution, we show how a company can forecast revenue from various products in 3 regions over a single period. The company is interested in the bottom level forecasts (product sales by region) and various summary forecasts—the total revenue for a category of products (sports products), the worldwide revenue by product, total revenue by region and total revenue worldwide. 
     The example starts with a data set in  FIG. 3 . Each figure represents the forecast sales in dollars. 
     Markets are then set up on all the metrics. In this example there are 32 different metrics, 18 bottom level metrics (for a specific product and region) and 14 summary metrics. Therefore there are 32 different potential markets to trade. 
     Trading on a Bottom Level Forecast 
     If a trader believes that the forecast for Sports Product A in Reg 1 (10) was too low, the trader could trade (buy shares) in that market which would increase the value of that particular metric. In our example, this buying activity increases the metric from 10 to 11. 
     In the PMRU solution, the summary markets (boxed) related to this bottom level market adjust automatically by adding 1 to their price (representing the forecast). In this way the data set remains consistent. 
     Trading on a Summary Level Forecast 
     The PMRU solution also allows traders to trade in forecasts in summary metrics. To maintain consistency, PMRU method follows a 3 step process.
     1. Summary metric is traded, trading action causes metric to change   2. Bottom level metrics that sum to the traded summary metric are adjusted relative to (e.g. in proportion to) their share of the summary metric pre trading.   3. Other summary metrics related to the adjusted bottom level metrics are adjusted   

     For example, if based on the data in  FIG. 4 , a trader thought that the total sports products sales in Reg 1 should be higher than 31, this trader could trade that particular metric (buy the shares). This would cause the price (and the forecast metric) to rise. In this example ( FIG. 5 ) this buying action causes the metric to rise from 31 to 34 (increase of 3). 
     To maintain consistency of the bottom level metrics (sales by product in region 1) related to this summary metric, these bottom level metrics are adjusted relative to (e.g. in proportion to) their share of the summary metric before the trading event. For example, Sports Product A represented 35% of the Sports Products total before the trading. After the trade, Sports Product A will still represent 35% of the Sports Products total so will be adjusted from 11 (35% of 31) to 12.1 (35% of 34). 
     Once the bottom level metrics are adjusted, the summary metrics related to the adjusted bottom level metrics are adjusted ( FIG. 6 ) to maintain consistency. 
     In this way, although only one metric was traded, an additional 9 metrics were adjusted (3 bottom level and 6 summary) and the matrix of metrics remains consistent. 
     Note: while the examples illustrate a set of metrics on two dimensions (Regions and Products) in some examples, there may be more than two dimensions. In addition, the examples illustrate forecasts of sales revenue, in some examples the metric forecasted could include other important metrics such as costs or headcounts or profits etc.