Abstract

http://ssrn.com/abstract=1163442
 
 

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Securities Trading of Concepts (STOC)


Ely Dahan


UCLA Medical School Urology Research

Adlar J. Kim


Massachusetts Institute of Technology (MIT)

Andrew W. Lo


Massachusetts Institute of Technology (MIT) - Sloan School of Management; Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); National Bureau of Economic Research (NBER)

Tomaso Poggio


Massachusetts Institute of Technology (MIT) - Brain and Cognitive Sciences

Nicholas T. Chan


AlphaSimplex Group, LLC

July 20, 2008


Abstract:     
Market prices are well known to efficiently collect and aggregate diverse information regarding the economic value of goods, services, and firms, particularly when trading 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 for new product concepts. This is the first research to test potential new product concepts using virtual markets and the first to validate such an approach using eight years of stated-choice and longitudinal revealed preference data. We directly address the challenge of validating simulated market results in which actual outcomes cannot be observed. A securities-trading approach may yield significant advantages over traditional methods - such as surveys, focus groups, concept tests, and conjoint analysis studies - for measuring consumer preferences. These traditional methodologies can be more costly to implement, more time-consuming, and susceptible to potential bias. Our approach differs from prior research on prediction markets and experimental economics in that we do not require any exogenous, objective "truth" such as election outcomes or movie box office receipts on which to base our securities market. We also differ by demonstrating that in this context, metrics summarizing all prior trades are more informative than closing prices alone. In fact, STOC markets are seen to resemble traditional market research more than they resemble prediction markets.

As a measure of internal validity, each of three product categories is tested in independent STOC markets. In the context of new product development, exogenous truth may not be available as the majority of potential new product concepts are never launched, and actual demand may never be revealed for many concepts. To address the need for external validity we empirically test three approaches comparing STOC trading results against preferences measured through: (1) virtual concept testing (of bicycle pumps and crossover vehicles), (2) stated-choices (of crossover vehicles) and (3) actual sales of the subset of product concepts that are launched in a simulated store (laptop bags) and in the real marketplace (crossover vehicles). These experiments reveal that the market prices of securities designed to represent product concepts are remarkably efficient, accurate, and internally consistent measures of preferences, even when conducted with relatively few traders. We also note that while STOC prices do measure preferences, they do not necessarily predict actual sales. Because the number of stocks tested can scale in the number of traders, the STOC method is particularly efficient at screening promising new products and services from a large universe of possibilities. For new product development (NPD) teams deciding where to invest product-development resources, this scalability may be especially important in the Web 2.0 world in which customers interact with firms and with each other in suggesting numerous product design possibilities.

Number of Pages in PDF File: 58

Keywords: prediction markets, experimental markets, new product development, consumer preferences

JEL Classification: M30, M31, C91, C92, D12, D84

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Date posted: July 23, 2008 ; Last revised: September 20, 2010

Suggested Citation

Dahan, Ely and Kim, Adlar J. and Lo, Andrew W. and Poggio, Tomaso and Chan, Nicholas T., Securities Trading of Concepts (STOC) (July 20, 2008). Available at SSRN: http://ssrn.com/abstract=1163442 or http://dx.doi.org/10.2139/ssrn.1163442

Contact Information

Ely Dahan
UCLA Medical School Urology Research ( email )
924 Westwood Boulevard
Suite 1000
Los Angeles, CA 90095
United States
310-985-9703 (Phone)
Adlar J. Kim
Massachusetts Institute of Technology (MIT) ( email )
77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States
Andrew W. Lo (Contact Author)
Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )
100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)
HOME PAGE: http://web.mit.edu/alo/www
Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)
Stata Center
Cambridge, MA 02142
United States
National Bureau of Economic Research (NBER)
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Tomaso Poggio
Massachusetts Institute of Technology (MIT) - Brain and Cognitive Sciences ( email )
Artificial Intelligence Labratory
Cambridge, MA 02139
United States
(617) 253-5230 (Phone)
Nicholas Tung Chan
AlphaSimplex Group, LLC ( email )
One Cambridge Center
Cambridge, MA 02142
United States
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References:  46
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