Securities Trading of Concepts (STOC) (Running title: SECURITIES TRADING OF CONCEPTS (STOC))

Market prices are well known to efficiently collect and aggregate diverse information regarding the economic value of goods and services, particularly for financial securities. We propose a novel application of the price discovery mechanism in the context of marketing research: to use pseudo-securities markets to measure consumer preferences over new product concepts. A securities-trading approach may yield significant advantages over traditional methods for measuring consumer preferences such as surveys, focus groups, concept tests, and conjoint studies, which are costly to implement, time-consuming, and often biased. Our approach differs from prior research on simulated markets and experimental economics in that we do not require any exogenous, objective “truth” such as election outcomes or box office receipts on which to base our securities market. Our trading experiments show that the market prices of securities designed to represent product attributes and features are remarkably efficient and accurate measures of preferences, even with relatively few traders in the market. The STOC method may offer a particularly efficient screening mechanism for firms developing new products and services, and deciding where to invest additional product-development dollars.

[1]  T. Palfrey,et al.  Asset Valuation in an Experimental Market , 1982 .

[2]  Sanford J. Grossman An Introduction to the Theory of Rational Expectations Under Asymmetric Information , 1981 .

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

[4]  John R. Hauser,et al.  Prelaunch forecasting of new automobiles : models and implementation , 1986 .

[5]  Michael T. Maloney,et al.  The complexity of price discovery in an efficient market: the stock market reaction to the Challenger crash , 2003 .

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

[7]  Charles R. Plott,et al.  Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem , 2002 .

[8]  V. Mahajan,et al.  New product models: Practice, shortcomings and desired improvements , 1992 .

[9]  Sanjay Srivastava,et al.  Dynamic Stock Markets with Multiple Assets: An Experimental Analysis , 1991 .

[10]  Russell J. Lundholm,et al.  Information Aggregation in an Experimental Market. , 1990 .

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

[12]  S. Sunder MARKET FOR INFORMATION: EXPERIMENTAL EVIDENCE' , 1992 .

[13]  Bobby J. Calder,et al.  Focus groups and the nature of qualitative marketing research. , 1977 .

[14]  Tomaso Poggio,et al.  Experimental Markets for Product Concepts , 2001 .

[15]  E. Dahan,et al.  The predictive power of internet-based product concept testing using visual depiction and animation , 2000 .

[16]  J. Hauser,et al.  The virtual customer , 2002 .

[17]  E. Fama,et al.  Efficient Capital Markets : II , 2007 .

[18]  V. Srinivasan,et al.  The Predictive Power of Internet‐Based Product Concept Testing Using Visual Depiction and Animation , 2000 .

[19]  J. Wolfers,et al.  Prediction Markets , 2003 .

[20]  J. Farmer Market Force, Ecology, and Evolution , 1998, adap-org/9812005.

[21]  M. Casson The Market for Information , 2003 .

[22]  C. Plott,et al.  Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets , 1988 .

[23]  C. Plott,et al.  Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models , 1982, Journal of Political Economy.

[24]  V. Smith Microeconomic Systems as an Experimental Science , 1982 .

[25]  H. Barger The General Theory of Employment, Interest and Money , 1936, Nature.

[26]  张谷 实验经济学(Experimental Economics)研究思路及成果应用简述 , 1994 .

[27]  Edward F. Fern The use of Focus Groups for Idea Generation: The Effects of Group Size, Acquaintanceship, and Moderator on Response Quantity and Quality , 1982 .

[28]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[29]  John R. Hauser,et al.  Fast Polyhedral Adaptive Conjoint Estimation , 2002 .

[30]  A. Lo,et al.  Frontiers of finance: evolution and efficient markets. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[31]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[32]  Hersh Shefrin,et al.  A Behavioral Approach to Asset Pricing , 2005 .

[33]  Allan D. Shocker,et al.  Estimating the weights for multiple attributes in a composite criterion using pairwise judgments , 1973 .

[34]  Michael P. Wellman,et al.  Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market , 1997, UAI.

[35]  Martin Spann,et al.  Internet-Based Virtual Stock Markets for Business Forecasting , 2003, Manag. Sci..

[36]  D. MacKenzie,et al.  The use of knowledge about society , 2008 .

[37]  David M. Pennock,et al.  Prediction Markets: Does Money Matter? , 2004, Electron. Mark..

[38]  R. Hanson,et al.  Information aggregation and manipulation in an experimental market , 2006 .