Securities trading of concepts (STOC)
Name
Poggio_Securities trading.pdf
Size
7.7 MB
Format
Adobe PDF
Checksum (MD5)
3538b7347d593103a8e0e696f12f76ca
Author(s)
Dahan, Ely
Kim, Adlar J.
Lo, Andrew W.
Poggio, Tomaso A.
Chan, Nicholas
Date Issued
January 2012
Journal
Journal of Marketing Research
Publisher
American Marketing Association
Citation
Dahan, Ely et al. “Securities Trading of Concepts (STOC).” Journal of Marketing Research 48.3 (2011): 497–517.
Version
Author's final manuscript
Abstract
Identifying winning new product concepts can be a challenging process that requires insight into private consumer preferences. To measure consumer preferences for new product concepts, the authors apply a “securities trading of concepts,” or STOC, approach, in which new product concepts are traded as financial securities. The authors apply this method because market prices are known to efficiently collect and aggregate private information regarding the economic value of goods, services, and firms, particularly when trading financial securities. This research compares the STOC approach against stated-choice, conjoint, constant-sum, and longitudinal revealed-preference data. The authors also place STOC in the context of previous research on prediction markets and experimental economics. Across multiple product categories, the authors test whether STOC (1) is more cost efficient than other methods, (2) passes validity tests, (3) measures expectations of others, and (4) reveals individual preferences, not just those of the crowd. The results show that traders exhibit a self-preference bias when trading. Ultimately, STOC offers two key advantages over traditional market research methods: cost efficiency and scalability. For new product development teams deciding how to invest resources, this scalability may be especially important in the Web 2.0 world.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Sloan School of Management
Sloan School of Management. Laboratory for Financial Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1509/jmkr.48.3.497
