Event Studies for Financial Research

A Comprehensive Guide

D. Kliger, G. Gurevich

• Publisher: Palgrave Macmillan
• Year: 2014
• ISBN:
  9781137368799
• Topic: Business
• Subtopic: Managerial Accounting
• Index: Business

Event Studies are overwhelmingly widespread in financial research, providing tools for shedding light on market efficiency, as well as measuring the impact of various occurrences on public firms' security prices. Mastering the Event Study approach is essential for researchers and practitioners alike.Event Studies for Financial Research aims to help readers obtain valuable hands-on experience with Event Study tools and gain technical skills for conducting their own studies. Kliger and Gurevich provide a detailed application of their approach, which consists of: a description of the method; references; guided applications; and elaborated framework for implementing the applications.

Chapter 1

Introduction

Event studies are overwhelmingly widespread in financial research. They encompass tools that are well suited for assessing the impact of numerous types of finance-related episodes on security prices and trading activity of publicly traded firms. Furthermore, they are capable of capturing the flow of information into security prices, shedding light, by doing so, on the efficiency of capital markets.

The event study approach (ESA) is considered one of the major instruments of modern corporate finance research. Its importance and wide usage are evident from the vast and still growing research literature. The scope of activities amenable to this analysis is very broad. It includes, naming just a few, such events as earnings announcements; releases of quarterly and annual financial reports; announcements of dividends, stock splits, and mergers; as well as initial public offerings.

Mastering the ESA is essential to researchers and practitioners. The goal of this book is to serve as a guide to event studies and help readers to become familiar with the capacities and facets of ESA. The book enables the readers to acquire hands-on experience with conducting event studies and gain the required technical skills for devising their own comprehensive event studies. It is accompanied by an online library of exercises and solutions, in the shape of spreadsheet templates, facilitating down-to-practice experimentation of the introduced empirical tools. The links for accessing chapter-specific data sets for practice are contained throughout this volume.

The sophistication of state-of-the-art statistical methods often renders the research literature that is dealing with event studies virtually inaccessible for many potential users. Our aim is to help the readers to overcome this obstacle by providing a concise summary and detailed application of the approach. We hope our target audience of researchers (including graduates and senior undergraduates) and practitioners (including financial analysts, fund managers, and institutional and private investors) would benefit from gaining access to this multipurpose tool through this focused guide.

The chapters of this book are organized in a way that permits gradual acquaintance with the subject, starting from theoretical background and introduction of ESA down to practical applications accompanied by spreadsheet templates, walkthrough instructions, and complete solutions.

Chapter 2 provides the background of ESA, focusing on the celebrated efficient market hypothesis (EMH). Specifically, the chapter outlines the theoretical foundations of EMH and its three (weak, semistrong, and strong) versions and presents some well-known empirical studies of EMH and the diversity of their results. In particular, the chapter delves into the aspects of EMH that are most pertinent for ESA and discusses their implications for conducting event studies.

Chapter 3 is the core theoretical chapter. It introduces the basic logic behind ESA, proceeds with technical specifications that are essential for the implementation of the approach, and concludes by presenting the nature of the results, their interpretation, and possible implications for market efficiency and information content of the analyzed events.

Chapter 4 presents a simplified ESA example, which is aimed at facilitating the more technical discussion presented in chapters 5 and 6 and the hands-on exercises in chapters 7 and 8. Specifically, we investigate the impact of air crashes on stock prices of involved airline companies. We construct the example on a very small data specimen to make the analysis as transparent as possible and concentrate on the core features of data processing, leaving various potentially complicating caveats for the sequel.

Chapter 5 introduces a basic, yet complete, ESA design. Specifically, it describes the main stages of data manipulation, hypothesis testing, construction of test statistics, and corresponding statistical analysis. Toward the end of the chapter, we devise a couple of hypothetical ESA cases and illustrate how they may be used to (1) quantify share price reactions to the studied events and (2) shed light on the question of market efficiency.

Chapter 6 draws a map of a smorgasbord of issues in ESA design that are likely to be encountered in practice and shows how they could be addressed. In particular, we discuss the issues of event clustering, nonparametric ESA design, bidirectional hypotheses, bond and whole-firm reactions, long-run reactions, and return frequency.

Chapter 7 is the core practical chapter. It provides readers with basic hands-on experience in the form of a complete ESA exercise. The chapter is accompanied by a spreadsheet template (one from the online library of templates and solutions), which is predesigned to accommodate the event study solution steps. Guiding instructions are presented, which would take the readers hand-in-hand through the solution process. For solving the exercise, the readers may either choose to follow the guiding instructions and fill the provided template or download a copy of the dataset and work independently. The chapter concludes with a detailed explanation of the exercise results.

Chapter 8 offers further hands-on exercises, based on the data provided in the previous chapter. In particular, it addresses several of the event study-related issues raised in previous chapters. The chapter is enforced by spreadsheet templates, for all of which complete solutions are provided. Specifically, templates and solutions (downloadable from the online library) are included for the following issues: conducting a clustering-adjusted event study procedure, applying nonparametric testing tools, applying the single-factor model of returns as an alternative benchmark of normal returns (the naïve model is applied in the basic exercise in chapter 7; both models are introduced in chapter 3), analyzing the effect of the magnitude of the surprise embedded in the disseminated information, and dissecting the sample to discover differential effects by the companies’ sectors. As in the previous chapter, the readers may either solve the exercises by following the guiding instructions and filling the provided templates or download a copy of the dataset and work independently.

Chapter 9 provides concluding remarks and the book’s summary.

Chapter 2

Infrastructure: The Efficient Market Hypothesis

During the past several decades, the efficient market hypothesis (EMH) has been recognized as one of the basic building blocks of modern financial economics. Due to the profound effect of EMH on financial thought, researchers and practitioners nowadays perceive the rationale behind it as intuitive. In a nutshell, it asserts the following: as investors strive to earn profit from market trading, they exploit every useful piece of data, thereby causing market prices to reflect all of the relevant information at any given moment.

Apparently, the origins of this idea of “wisdom of the crowds” are quite old. As Robert Shiller points out (1992, 438), attempts to provide a formal representation of the concept may be traced as far back as 1889, when George Gibson wrote in his book on major stock exchanges: “When shares become publicly known in an open market, the value which they acquire there may be regarded as the judgment of the best intelligence concerning them.”

Following this logic, news arriving at the market must bring about an immediate and appropriate market reaction, and because news, by definition, relates to the unexpected component of the information, the future conduct of market prices is unpredictable. Consequently, traders’ inability to consistently “beat” the market, that is, generate systematic excess gains by trading, is a sign of market efficiency.¹

Such theoretical considerations led Louis Bachelier, in 1900, to the first formulation of what we know today as random walk theory, an immediate consequence of EMH. Random walk theory states that prices in efficient markets move randomly, thus precluding any possibility of using available information for generating sustained extra trading profits.

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In some instances, the alleged ability of exceptionally talented investors to beat the market turns out to be a plain fraud. One blatant example is the story of Bernard Lawrence Madoff. Started in 1960 as a small over-the-counter trading firm, Madoff’s empire eventually spread from Hollywood to Abu Dhabi, acquiring clients among prominent individual investors and solid institutions.

In retrospect, Madoff’s early actions in the 1970s proved successful as his revolutionary and cash-backed belief in electronic trade has definitely paid off. Somewhat more questionable was his custom of paying fees to get brokerage orders, which attracted clients but also accusations of bribery. Madoff’s reaction was nonapologetic, as he claimed this practice enhanced competition and reduced brokerage costs. That way or another, according to de la Merced (2008), by the end of the 1980s, Madoff was running up to 5 percent of the total New York Stock Exchange trading volume and, at some point in 2000, he was the largest market maker on the NASDAQ electronic market. The returns of Madoff’s hedge fund seemed exceptionally steady and consistently beating the market, while none of the rival funds were able to perform similarly.

Interestingly, quite a few years before the scandal erupted, the secrecy surrounding Madoff’s trading strategies began raising the suspicion of several professionals such as financial analyst Harry Markopolos, hedge fund industry reporter Michael Ocrant, and financial journalist Erin Arvedlund. However, the public buzz was not followed by any preventive action by the Securities and Exchange Commission, and the fund proceeded with its undisclosed activities (see Arvedlund, 2009, and Markopolos et al., 2010, for more details). During these prosperous years of Madoff’s hedge fund, entrance into this exclusive “money club” was apparently impossible for a layperson, as new investors could hope to join only via informal recommendations and close friends’ circles. The business was going and growing remarkably smoothly as long as a lengthy queue of investors was struggling to get their money in, and it all came tumbling down as investors started asking to opt out, primarily due to the 2008 financial crisis.

In 2008, Madoff confessed that the miraculous performance of his hedge fund was nothing but a giant Ponzi scheme—a financial pyramid based on new investors’ money being paid to cover upcoming dues. Neither beating the market nor any legitimate financial investment at all was involved. The scale of the revealed fraud was enormous, not the least of which was due to the world markets’ globalization. Charity organizations, university endowments, and pension funds, among other clients, totaling more than 4,000 accounts, had fallen prey to this unprecedented hoax. According to Madoff’s own statement, as much as $50 billion of institutional and individual investors’ money was lost in the quest for high returns, which eventually earned its designer 150 years in prison for 11 criminal counts.

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Figure 2.1 Apropos: “Beating the market?—The case of Bernard L. Madoff.”

A comprehensive historical account of EMH may be found in Sewell (2011).² As elaborated there, the theoretical roots of EMH date back to the work of sixteeenth-century Italian mathematician Girolamo Cardano (c. 1564), who analyzed the principles of games of chance and their implications for gamblers. Further developments and contributions derive from the work of nineteenth-century Scottish botanist Robert Brown (1828) and French mathematician Louis Bachelier (1964). Albert Einstein (1905), Benoit Mandelbrot (1966), Milton Friedman (1953), and several other eminent scientists have contributed rigorous mathematical and economic foundations to EMH. Henceforth, we focus on the modern definition of EMH, its implications, and relation to the ESA. The extensive literature dealing with methodical issues pertaining to ESA (e.g., the classical work of Brown and Warner in the 1980s) is addressed by subsequent chapters, wherein we discuss its practical implementation.

Although the concept of random walk may seem rather convincing, its empirical testing might be complicated. For instance, one must clarify the kind of information that investors employ to devise their trading strategies and how extra profits are to be measured. Many world-renowned financial economists, most notably Eugene Fama, have contributed to the precise theoretical definitions of EMH and the appropriate empirical tools for testing it.³ As we elaborate in the sequel, ESA has gained prominence as an empirical approach designed to deal with the question of market efficiency.

The next section elaborates on the exact formulation of EMH and the way ESA is employed for its empirical testing. The section following it reviews some specific event studies, discusses their results, and explains the benefits of further exploring this area of research.

2.1 EMH: Theory

The primary definition of EMH, as endorsed by Fama et al. (1969) and Fama (1970), refers to security price conduct. In particular, it is asserted that in efficient markets security prices are rapidly adjusted upon the arrival of new information. Subsequently, the definition has been somewhat revised, referring to a market as efficient when the security prices reflect all available information at any given moment (Fama 1991).

Although considerably self-explanatory, the contemporaneous definition of EMH invites some clarification. In particular, the notion of “available information” should be elaborated. To that end, three versions, or forms, of EMH have been proposed:

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In 2013, Eugene Francis Fama was awarded, jointly with Lars Peter Hansen and Robert James Shiller, the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, commonly known as the Nobel Prize in Economics. According to the Royal Swedish Academy of Sciences, the 2013 prize laureates “have laid the foundation for the current understanding of asset prices.”

Widely recognized today as “one of the fathers of modern finance” (Chicago Booth Magazine, Fall 2013), Eugene Fama began his research on financial marke...