Electronic Theses and Dissertations

Date

2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Business Administration

Committee Chair

Pankaj Jain

Committee Member

Members: Thomas H McInish

Committee Member

Jeff R Black

Committee Member

Andrew J Hussey

Abstract

First, I examine how the forces of automation, competition, and demutualization are rapidly changing the industrial organization, ownership, and capital structure of the financial exchange industry. I propose the conditions under which demutualization becomes optimal from the perspective of mutually owned exchange owners. I then proceed to build an empirical dataset characterizing the evolution of the leading stock and derivative exchanges around the World along these dimensions. I empirically find that technology driven growth opportunities, product driven growth opportunities and increases in market concentration are the main stimulants for demutualization. In this paper I examine market reaction at the firm-level to sudden changes in investor mood. I use the results of professional sports championships in North America as our primary mood variable. To test the effect of investor mood, we analyze the return patterns of publicly traded firms headquartered geographically near those teams. This paper examines the effects of instructors’ attractiveness on student evaluations of their teaching. I build on previous studies by holding both observed and unobserved characteristics of the instructor and classes constant. Our identification strategy exploits the fact that many instructors, in addition to traditional teaching in the classroom, also teach in the online environment, where attractiveness is either unknown or less salient. I utilize multiple attractiveness measures, including facial symmetry software, subjective evaluations, and a novel, proxy methodology that resembles a “Keynesian Beauty Contest.” Also, In the aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machine learning techniques has been shown to be effective in recognizing these documents for further information extraction.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to ProQuest.

Notes

Open access

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