A Product Concept Evaluation System Utilizing Preference Markets

Recently, prediction markets are also used for estimating preferences, whose correct answer will not be revealed even after the market is closed, and, when used for the purpose, they are called preference markets. In order to utilize a preference market for estimating the attractiveness of product concepts expressed as combinations of various attributes, two technical questions remain to be answered. Firstly, how to estimate the preference on every possible combination of the attributes under consideration based on the results from a preference market comparing only a limited number of concepts? Secondly, how to incentivize the participants in the preference market to provide their estimation truthfully? This paper, therefore, develops a new product concept evaluation system by combining preference markets with conjoint analysis, and proposes three guidelines for how to determine the payoff for prediction securities. They are (1) to determine the payoff not directly from the security prices but indirectly through a model; (2) to run multiple markets in parallel if possible and use the results from all of them when determining the payoff; and (3) to use smoothed values instead of the final values as the security prices for determining the payoff. Moreover, the proposed system is tested with a simple evolutionary game simulation.

[1]  Martin Spann,et al.  New Product Development 2.0: Preference Markets—How Scalable Securities Markets Identify Winning Product Concepts and Attributes* , 2010 .

[2]  Gregoris Mentzas,et al.  IDEM: A Prediction Market for Idea Management , 2008, WEB.

[3]  Thomas A. Rietz,et al.  Wishes, expectations and actions: a survey on price formation in election stock markets , 1999 .

[4]  Li Chen,et al.  Design and Use of Preference Markets for Evaluation of Early Stage Technologies , 2009, J. Manag. Inf. Syst..

[5]  Peter R. Winters,et al.  Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .

[6]  Kurt Matzler,et al.  Predicting New Product Success with Prediction Markets in Online Communities , 2013 .

[7]  Robert Forsythe,et al.  Anatomy of an Experimental Political Stock Market , 1992 .

[8]  Wolfgang Jank,et al.  Second-Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms , 2009 .

[9]  Li Chen,et al.  Reliability (or "lack thereof") of on-line preference revelation: A controlled experimental analysis , 2013, Decis. Support Syst..

[10]  C. Holt Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[11]  Henry G. Berg,et al.  Hanson's Automated Market Maker , 2012 .

[12]  B. Skiera,et al.  Sourcing, Filtering, and Evaluating New Product Ideas: An Empirical Exploration of the Performance of Idea Markets , 2012 .

[13]  Ekaterina V. Karniouchina Are Virtual Markets Efficient Predictors of New Product Success? The Case of the Hollywood Stock Exchange* , 2011 .

[14]  Tomaso Poggio,et al.  Securities Trading of Concepts (STOC) , 2011 .

[15]  Robin Hanson,et al.  Combinatorial Information Market Design , 2003, Inf. Syst. Frontiers.

[16]  Christina Ann LaComb,et al.  The imagination market , 2007, Inf. Syst. Frontiers.

[17]  Wolfgang Jank,et al.  Second-Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms: Second-Generation Prediction Markets , 2012 .

[18]  R. Hanson LOGARITHMIC MARKETS CORING RULES FOR MODULAR COMBINATORIAL INFORMATION AGGREGATION , 2012 .

[19]  Thomas S. Gruca,et al.  The Effect of Electronic Markets on Forecasts of New Product Success , 2003, Inf. Syst. Frontiers.

[20]  Kay-Yut Chen,et al.  New Product Blockbusters: The Magic and Science of Prediction Markets , 2007 .

[21]  C. Plott Markets as Information Gathering Tools , 2000 .

[22]  Anita Elberse,et al.  The Power of Stars: Do Star Actors Drive the Success of Movies? , 2007 .

[23]  David M. Pennock,et al.  The Real Power of Artificial Markets , 2001, Science.