國立虎尾科技大學 |

Event Study Analysis - Methods for Estimating the Effects of Events on Financial Securities.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Event Study Analysis - Methods for Estimating the Effects of Events on Financial Securities./
Author:	Ippolito, T. E.
Description:	1 online resource (93 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798381176773

Event Study Analysis - Methods for Estimating the Effects of Events on Financial Securities.
Ippolito, T. E.

Event Study Analysis - Methods for Estimating the Effects of Events on Financial Securities. - 1 online resource (93 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Northwestern University, 2023.

Includes bibliographical references

This dissertation is a collection of three papers that concern methods for estimating the effects of events on financial securities, which is colloquially referred to as event study analysis.The first chapter is a literature review concerning the mathematics of event study analysis. In this paper, we present the mathematical development of event study analysis methods from 1933 to present. The primary purpose of this paper is to present the seminal event study analysis methodological research and respective context. Event study methods have largely been developed by financial economists with the goal of using the methods to study asset pricing and corporate finance theories. The event study analysis methodological developments were merely means to study asset pricing and corporate finance theories. In this paper, we primarily focus on the mathematical development of the event study analysis methods and only provide contexts with the goal of providing readers with information regarding how researchers used their proposed event study analysis methods.The second chapter concerns the mathematics of measuring and testing the effect of a single event on a single financial security and outlier detection methods DFFIT(S), Cook's Distance, and DFBETA(S). In this paper, we present the single-index model and single-index model assumptions, and single-index model parameter estimation. We present the residual excess return measure, residual excess return variance, residual excess return sampling distribution and residual excess return test statistic. We also present outlier detection methods DFFIT(S), Cook's Distance, and DFBETA(S) and respective thresholds in an event study analysis framework. We apply the single-index single-event methodology to the United States Supreme Court case Halliburton Company. v. Erica P John Fund, Inc. and illustrate how DFFIT(S), Cooks Distance, and DFBETA(S) can be used to measure the effect of an event on a financial security. We also illustrate that DFFIT(S), Cooks Distance, and DFBETA(S) can be used to identify and measure estimation window events which may have implications for measuring and testing the effect of an event via single-index single-event methodology.The third chapter concerns the mathematics of measuring and testing the effect of a particular event typology with respect to multiple financial securities, measuring and testing the cumulative effect of an event on a single financial security, and measuring and testing the cumulative effect of a particular event typology with respect to multiple financial securities. In this paper, we present the single-index model and single-index model assumptions, and single-index model parameter estimation. We present the average residual excess return measure, average residual excess return variance, average residual excess return sampling distribution, and the average residual excess return test statistic. We also present the cumulative residual excess return measure, cumulative residual excess return variance, cumulative residual excess return sampling distribution, and cumulative residual excess return test statistic and the cumulative-average residual excess return measure, cumulative-average residual excess return variance, cumulative-average residual excess return sampling distribution, and cumulative-average residual excess return test statistic. We present a methodology to approximate the average residual excess return variance and conclude the paper with an event-driven investment company application.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381176773Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Event study analysisIndex Terms--Genre/Form:

554714
Electronic books.

Event Study Analysis - Methods for Estimating the Effects of Events on Financial Securities.
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504 $a Includes bibliographical references
520 $a This dissertation is a collection of three papers that concern methods for estimating the effects of events on financial securities, which is colloquially referred to as event study analysis.The first chapter is a literature review concerning the mathematics of event study analysis. In this paper, we present the mathematical development of event study analysis methods from 1933 to present. The primary purpose of this paper is to present the seminal event study analysis methodological research and respective context. Event study methods have largely been developed by financial economists with the goal of using the methods to study asset pricing and corporate finance theories. The event study analysis methodological developments were merely means to study asset pricing and corporate finance theories. In this paper, we primarily focus on the mathematical development of the event study analysis methods and only provide contexts with the goal of providing readers with information regarding how researchers used their proposed event study analysis methods.The second chapter concerns the mathematics of measuring and testing the effect of a single event on a single financial security and outlier detection methods DFFIT(S), Cook's Distance, and DFBETA(S). In this paper, we present the single-index model and single-index model assumptions, and single-index model parameter estimation. We present the residual excess return measure, residual excess return variance, residual excess return sampling distribution and residual excess return test statistic. We also present outlier detection methods DFFIT(S), Cook's Distance, and DFBETA(S) and respective thresholds in an event study analysis framework. We apply the single-index single-event methodology to the United States Supreme Court case Halliburton Company. v. Erica P John Fund, Inc. and illustrate how DFFIT(S), Cooks Distance, and DFBETA(S) can be used to measure the effect of an event on a financial security. We also illustrate that DFFIT(S), Cooks Distance, and DFBETA(S) can be used to identify and measure estimation window events which may have implications for measuring and testing the effect of an event via single-index single-event methodology.The third chapter concerns the mathematics of measuring and testing the effect of a particular event typology with respect to multiple financial securities, measuring and testing the cumulative effect of an event on a single financial security, and measuring and testing the cumulative effect of a particular event typology with respect to multiple financial securities. In this paper, we present the single-index model and single-index model assumptions, and single-index model parameter estimation. We present the average residual excess return measure, average residual excess return variance, average residual excess return sampling distribution, and the average residual excess return test statistic. We also present the cumulative residual excess return measure, cumulative residual excess return variance, cumulative residual excess return sampling distribution, and cumulative residual excess return test statistic and the cumulative-average residual excess return measure, cumulative-average residual excess return variance, cumulative-average residual excess return sampling distribution, and cumulative-average residual excess return test statistic. We present a methodology to approximate the average residual excess return variance and conclude the paper with an event-driven investment company application.
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538 $a Mode of access: World Wide Web
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653 $a Financial securities
653 $a Mathematical development
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