Date of Award

5-2020

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Business Administration

First Advisor

Chi Wan

Second Advisor

Mine Etugrul

Third Advisor

Jianqiu Bai

Abstract

This dissertation is composed of three related essays, with the first essay exploring the financial policy implications of documented economic linkages across firms, and the other two essays documenting new text-based linkages across firms and the asset pricing implications of capital market participants overlooking these. The first essay examines the impact of cybersecurity breaches on attacked firms’ cash holding reactions, as well as those related and similar to it. Literature finds that attacked firms are more prone to litigation and credit risk, both factors that can lead to precautionary cash holding behavior. I then examine if other firms, especially those that are related to an attacked firm, take any precautionary measures, after their peer gets attacked. Results of increased cash holdings by attacked firms, and its peers through industry, location and supply chain linkages, indicate that cybersecurity attacks are a motivation for precautionary cash holding. As firms increasingly rely on technology for success, this essay contributes to the links between firms’ operational and financial policies. The second essay contends that overlaps in offshore sales activities of peer firms subject them to identical foreign shocks, and therefore generate a complex economic linkage between them that that is not captured by proximity on other fronts. The literature suggests that investors are slow in pricing geographic dispersion in firms, and the central hypothesis is therefore that focal firms exhibit predictive lag in their returns relative to peers they share offshore sales destinations with. It adds to the literature by indicating that while firms are increasing their geographic foothold internationally, capital markets are slow to price the impact of such actions. The third essay examines the impact of overlaps across narrative-based risk disclosures across firms that are beyond the traditional industry and momentum variables discussed by literature. It uses machine learning techniques to identify latent topics discussed in Item 1A of annual reports issued by firms and then gauges proximity among firms using cosine similarity. The findings also echo the importance of narrative disclosures in analyzing the risk environment of firms and the need for more concise, yet effective, reporting on that front.

Comments

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