Securities Trading of Concepts (STOC)

58 Pages Posted: 23 Jul 2008 Last revised: 20 Sep 2010

See all articles by Ely Dahan

Ely Dahan

University of California, Los Angeles (UCLA) - Department of Urology

Adlar J. Kim

Massachusetts Institute of Technology (MIT)

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Laboratory for Financial Engineering; Santa Fe Institute

Tomaso Poggio

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

Nicholas T. Chan

AlphaSimplex Group, LLC

Date Written: July 20, 2008


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.

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

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

Suggested Citation

Dahan, Ely and Kim, Adlar J. and Lo, Andrew W. and Poggio, Tomaso and Chan, Nicholas Tung, Securities Trading of Concepts (STOC) (July 20, 2008). Available at SSRN: or

Ely Dahan

University of California, Los Angeles (UCLA) - Department of Urology ( email )

10833 Le Conte Avenue
Box 951738
Los Angeles, CA 90095-1738
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) - Laboratory for Financial Engineering ( email )

100 Main Street
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)


Santa Fe Institute

1399 Hyde Park Road
Santa Fe, NM 87501
United States

Tomaso Poggio

Massachusetts Institute of Technology (MIT) - Department of 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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