Publication:
Understanding the Role of Information in the Evolution of Prices in Markets With Emerging Technology

No Thumbnail Available

Date

2017-05-11

Published Version

Published Version

Journal Title

Journal ISSN

Volume Title

Publisher

The Harvard community has made this article openly available. Please share how this access benefits you.

Research Projects

Organizational Units

Journal Issue

Citation

Research Data

Abstract

Each of the following essays explores economic or strategic issues in a market with emerging technology. In each chapter, we study the dynamic evolution of prices – daily adjustments in used-car prices, price evolution of Bitcoin during its adoption, and price evolution of biotech drug candidates as they go through the drug-development process. In each case, major players’ information plays an essential role in shaping the price dynamics, which in turn drive other important strategic variables such as the timing of technology adoption and technology transfer. Chapter 1 develops a model of dynamic pricing with seller learning in the online used-car market. The model is estimated using novel panel data of a leading used-car dealership. We quantify that, while dealer's average net profit per car in the estimation sample is around $1,150, the initial assessment is worth around $101 and the subsequent learning in the selling process helps improve the dealer's profit by at least $269. These estimates suggest a potentially high return to taking the information-based approach to pricing idiosyncratic products. Chapter 2 studies the evolvement of Bitcoin prices during its adoption and how Bitcoin prices can be explained by market fundamentals such as Bitcoin transaction volume when used for payments and the evolution of beliefs about the likelihood that the technology will survive. Empirical evidence shows that as of mid-2015, active usage was not growing quickly, and that investors held the majority of Bitcoins. This implies that Bitcoin prices are likely to be more sensitive to investor beliefs than to current transaction volume. Chapter 3 develops a model to show that, when there is asymmetric information in the market, biotech companies are more likely delay transfer of drug candidates when they have plenty of financial resources. We test this hypothesis with a novel panel dataset that combines drug-development history, licensing and acquisitions, and funding events. Overall, empirical evidence suggests that the market for ideas in the biopharmaceutical industry is operating reasonably well in the clinical-trial stage.

Description

Other Available Sources

Keywords

Economics, General

Terms of Use

This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service

Endorsement

Review

Supplemented By

Referenced By

Related Stories