Date of Award

May 2019

Degree Type


Degree Name

Doctor of Philosophy


Management Science

First Advisor

Valeriy Sibilkov

Second Advisor

Lilian Ng

Committee Members

Donghyun Kim, John R. Huck, Richard D. Marcus


This dissertation investigates two important topics: idiosyncratic shock aggregation in customer-supplier network and impacts of trade secret litigations on stock performance. The first essay studies the underlying factors for stock returns comovement between a customer and supplier firm. The investigation further explores the idiosyncratic shocks propagation and aggregation in the network. The second essay documents the stock market reactions to trade secret lawsuit outcomes and its economic meanings to the industry.

The first essay, Idiosyncratic Shocks Aggregation in Customer-Supplier Network, is inspired by Acemoglu, et al. (2012)’s theoretical work and Cohen and Frazzini (2008)’s empirical study. Traditional theory regarding idiosyncratic shocks suggests diversification effect averages out microeconomic shocks within each sector of an interconnected network. However, more recent studies show that idiosyncratic shocks may translate into aggregate shocks if the interconnected system is asymmetric. Empirical research in customer-supplier network shows that the stock returns of a customer and a supplier firms comove strongly. Idiosyncratic information and earnings news are the key drivers of the stock return comovement induced by the establishment of the customer-supplier relationship. By studying the return connections between customer and supplier firms, I find idiosyncratic shocks propagate and aggregate in this network. A new risk factor formed by aggregating idiosyncratic returns of customer firms is evidently priced in suppliers’ returns. This study builds on existing customer-supplier network research and contributes to the literature by pinpointing the information channels and contents that drive stock return comovement and document a new risk factor in the customer-supplier network.

The second essay, Economic Outcomes of Corporate Espionage, uses a unique hand-collected trade secret lawsuit dataset, and documents strong stock market reactions to trade secret lawsuit outcomes. Trade secret lawsuit data including file date, plaintiffs and defendants, and court rulings are manually collected from the Lexis-Nexis database and carefully screened to determine the directions of court rulings. The empirical results indicate the stock market reacts to court outcomes not only at the firm level but also at the industry level. Further regression and difference-in-differences analysis suggest strong intellectual properties protection system encourages firms’ R&D investment and future growth opportunities.