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Essays in Corporate Finance and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays in Corporate Finance and Machine Learning.
Author:	Jha, Manish.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2021
Description:	197 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-10, Section: A.
Notes:	Advisor: Gormley, Todd.
Contained By:	Dissertations Abstracts International82-10A.
Subject:	Finance.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412595
ISBN:	9798597087986

Essays in Corporate Finance and Machine Learning.
Jha, Manish.

Essays in Corporate Finance and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 197 p.

Source: Dissertations Abstracts International, Volume: 82-10, Section: A.

Thesis (Ph.D.)--Washington University in St. Louis, 2021.

This item must not be sold to any third party vendors.

My dissertation focuses on two broad questions. First, why do shareholder’s preferences vary, and how various agents persuade them? And second, how public perceptions about the financial sector and regulations affect economic outcomes? While my research plan contributes to the two distinct fields of literature, a unifying theme of my research is the use of innovative machine learning techniques to overcome the empirical challenges that would typically prevent measuring these sentiments objectively.In my Chapter 1, I use a supervised machine learning model on mutual fund family’s proxy voting choices to estimate their preferences. I find that hedge fund activists tailor their campaigns to appeal to the fund families that own a larger share in the targeted firm. Well-tailored campaigns solicit higher engagement and support from the fund families and are more likely to succeed. My findings suggest that activism helps push shareholders’ implicit agendas. As per my knowledge, the paper is the first to employ a machine learning model to extract mutual fund preferences, opening up possibilities to analyze other issues where decisions of these institutes matter.In Chapter 2 with Professor Gormley, we find that mutual fund families conduct more governance research and are less likely to follow proxy advisor recommendations when a firm’s bonds represent a larger proportion of their overall portfolio. Our findings suggest that the bond holdings contribute to institutions’ incentive to be engaged monitors. In Chapter 3 with Professor Manela and Hongyi Liu, we measure popular sentiment towards finance using a computational linguistics approach applied to millions of books published in eight countries over hundreds of years. We find that the finance sentiment declines after epidemics and earthquakes, but rises following droughts, floods, and landslides. These heterogeneous effects of natural disasters suggest finance sentiment responds differently to the realization of insured versus uninsured risks.

ISBN: 9798597087986Subjects--Topical Terms:

183252
Finance.
Subjects--Index Terms:

Corporate finance

Essays in Corporate Finance and Machine Learning.
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300 $a 197 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-10, Section: A.
500 $a Advisor: Gormley, Todd.
502 $a Thesis (Ph.D.)--Washington University in St. Louis, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a My dissertation focuses on two broad questions. First, why do shareholder’s preferences vary, and how various agents persuade them? And second, how public perceptions about the financial sector and regulations affect economic outcomes? While my research plan contributes to the two distinct fields of literature, a unifying theme of my research is the use of innovative machine learning techniques to overcome the empirical challenges that would typically prevent measuring these sentiments objectively.In my Chapter 1, I use a supervised machine learning model on mutual fund family’s proxy voting choices to estimate their preferences. I find that hedge fund activists tailor their campaigns to appeal to the fund families that own a larger share in the targeted firm. Well-tailored campaigns solicit higher engagement and support from the fund families and are more likely to succeed. My findings suggest that activism helps push shareholders’ implicit agendas. As per my knowledge, the paper is the first to employ a machine learning model to extract mutual fund preferences, opening up possibilities to analyze other issues where decisions of these institutes matter.In Chapter 2 with Professor Gormley, we find that mutual fund families conduct more governance research and are less likely to follow proxy advisor recommendations when a firm’s bonds represent a larger proportion of their overall portfolio. Our findings suggest that the bond holdings contribute to institutions’ incentive to be engaged monitors. In Chapter 3 with Professor Manela and Hongyi Liu, we measure popular sentiment towards finance using a computational linguistics approach applied to millions of books published in eight countries over hundreds of years. We find that the finance sentiment declines after epidemics and earthquakes, but rises following droughts, floods, and landslides. These heterogeneous effects of natural disasters suggest finance sentiment responds differently to the realization of insured versus uninsured risks.
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653 $a Financial technology
653 $a Hedge fund activism
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653 $a Mutual funds
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793 $a English
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