APPROVED:

Linda L. Marshall, Major Professor Michael M. Beyerlein, Committee Member Paul L. Lambert, Committee Member Clifton E. Watkins, Committee Member Vicki Campbell, Chair of the Department of

Psychology Michael Monticino, Dean of the Robert B.

Toulouse School of Graduate Studies

RELATIONSHIP BETWEEN FORTUNE 500 COMPANIES WITH REGULATORY

VIOLATIONS AND/OR CRIMINAL OFFENSES AND RESULTING STOCK VALUES

Tanya A. Bhagwat, M.F.A, M.S

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

December 2009

Bhagwat, Tanya A. Relationship between Fortune 500 companies with

regulatory violations and/or criminal offenses and resulting stock values

The purpose of this study was to determine whether publicly disclosed violations

by U.S corporations, resulting in convictions or settlements, erode shareholder

investment in the offending organizations. This study was designed to assess whether

or not the shareholders’ reactions to corporations’ violations were related to a decline in

organizations’ stock valuations across sectors. In addition, this study attempted to

assess whether or not shareholder support, expressed by stock prices, declined more

after a corporation was prosecuted or reached a settlement for violations, as compared

to corporations that disclosed earnings disappointments. Also, this study investigated

the stock prices of violating corporations compared to the non-offending corporations

from within the same business sector, as well as considered the percentage decline for

repeat offenders for violation two compared to violation one. Opposite to hypothesis,

results showed that stock prices for the violating companies were significantly greater

12 months after the violation compared to the other months and no significant

differences in percent decline between the eight sectors on any of the five decline

measures. There were also no differences between violating companies and their

matched companies. Companies with a violation had significantly greater stock prices

overall than those without a violation.

. Doctor of

Philosophy (Industrial/Organizational Psychology), December 2009, 162 pp., 11 tables,

6 illustrations, references, 45 titles.

ii

Copyright 2009

by

Tanya A. Bhagwat

����

TABLE OF CONTENTS

Page

LIST OF TABLES ............................................................................................................ v

LIST OF FIGURES .......................................................................................................... vi

Chapter

1. INTRODUCTION AND LITERATURE REVIEW ..................................................... 1

Stocks Violations and Crimes in Corporate America Professional Ethics Developments affecting Corporate America Critical Event Research Review of Event Study Findings Long-term Event Studies Concerns Research Questions

� � Purpose of the Study Hypotheses

2. METHODOLOGY ................................................................................................. 28

Sample Data Sources Design and Procedure Procedure for Data Collection

Analysis

3. RESULTS ............................................................................................................ 38

� � Descriptive Statistics Hypothesis Testing Individual Companies Summary

4. DISCUSSION....................................................................................................... 63

Primary Analysis Limitations Future Research Conclusions and Implications

���

Appendices .............................................................................................................. 78

A. CORPORATE FINES AND SETTLEMENTS

B. DATA FOR COMPANIES WITH VIOLATIONS QUALIFYING FOR

STUDY AND MATCHING COMPARISION COMPANIES

C. FIGURES FOR COMPANIES WITH VIOLATIONS

D. FIGURES FOR COMPARISON COMPANIES

REFERENCES ...................................................................................................... 159

��

LIST OF TABLES

Tables Page

1. Frequencies and Percentages for Sector and Repeat Violations ....................... 40

2. Average Monthy Stock Prices for Violating Companies ..................................... 42

3. Average Select Monthly Stock Prices for Violating Companies ......................... 43

4. Means and Standard Deviations for Percent Decline Measures ........................ 46

5. Percent Decline Measures by Sector ................................................................. 47

6. Average Selected Monthly Stock Prices by Sector ............................................ 49

7. Average Selected Monthly Stock Prices for Violating

and Comparison Companies ............................................................................. 51

8. Means and Standard Deviations for Selected Monthly Stock Prices

for Healthcare Sector for Violating and Comparison Companies ....................... 53

9. Means and Standard Deviations for Selected Monthly Stock Prices

for Financial Sector for Violating and Comparison Companies .......................... 54

10. Means and Standard Deviations for Selected Monthly Stock Prices

for Technology Sector for Violating and Comparison Companies ...................... 55

11. Average Quarterly Stock Prices for Companies with Two Repeat Offenses ...... 57

�

�

�

���

LIST OF FIGURES

Figure Page

1. Percentages of Sectors ........................................................................................ 39

2. Monthly Average Stock Price ............................................................................... 41

3. Select Monthly Average Stock Prices .................................................................. 43

4. Select Monthly Average Stock Price by Sector .................................................... 50

5. Select Monthly Average Stock Price by Violators Versus Comparisons .............. 52

6. Select Montly Average Stock Price by Sector and Violators

Versus Comparision ............................................................................................. 56

1

CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

Illegal conduct in corporations hurts companies and stakeholders. The field of

industrial/organizational (I/O) psychology may contribute toward more ethical corporate

behavior, but the field has not paid attention to the problem in research or practice. The

current I/O research draws attention to this need by showing the connection between

illegal or offensive corporate conduct and the resulting effects on the value of stocks,

thereby providing an example of research focused on responsible corporate behavior

that enhances practitioner understanding.

U.S. corporations operate within the society of the United States, and they also

establish their own societies with shared values and standards. These business

standards and values are referred to as business ethics. It has been suggested that

U.S. corporations must be predicated on ethical principles, or they would fail to exist

(Beu, Buckley, & Harvey, 2003). The moral standards and principles applied to

organizations in the United States have often resulted in legal developments. Corporate

acts that contradict set rules or behavior deemed as wrong by the greater society can

be considered violations, and in some cases, even federal crimes. Lately, there has

been more attention given to illegal conduct and violations by corporations and by the

media. More business focus on responsible behavior may be a result of organizations

realizing there could be a link between behavior and organizational performance. “Both

research and examples from the business world demonstrate that building an ethical

reputation among employees, customers, and the general public pays off” (Ferrell,

Fraedrich, & Ferrell, 2005, p. 14). Corporations may also be paying more attention to

2

violations that result in crimes because of the costs, such as fines and/or incarceration,

for management.

Understanding the implications of poor corporate practices and behaviors cannot

only impact how shareholders see an organization, but also how businesses evolve. It is

the hope that the greater the substantiation for businesses to do the right thing and to

act responsibly and legally, the more effort will be made to increase programs about

practice standards, codes of conduct, and interactions within the corporation.

Additionally, it may also illuminate such behavior with individuals or entities outside the

organization. Furthermore, consultants who are interacting, designing and implementing

tools and procedures for an organization may build ethical constructs into their designs

and better sell their services.

This study was an event study which considered if a violation by a large public

corporation resulted in a noticeable change in shareholder reactions. The study was

designed to assess the difference in stock values for offending corporations versus

other corporations in the same sector for the observed time period, as well as analyzing

stock price declines for first violations versus second offenses. Also, the study

compared stock values for violating corporations versus corporations announcing

earnings disappointments for the time periods studied. The results yielded information

about the impact of corporate violations on stock prices, thus providing I/O

psychologists with research about market reaction to corporate responsibility and legal

compliance.

3

Stocks

One way for I/O researchers to evaluate shareholder reactions to a corporation’s

violating behavior is to consider the company’s stock performance. Investigating stock

prices before and after a violation could reveal a decrease, an increase, or no change in

stock value, thereby providing information about shareholder reactions to irresponsible

corporate behavior.

In the United States, the term stock refers to ownership of an organization. A

share of stock represents not only capital raised by the organization for the share, but

also each share represents a unit amount of ownership. Individuals who buy and own

shares of stock, or stocks, are referred to as shareholders or investors. Stocks may also

be called equities or securities. Because the stock market is based on the economic

theory of supply and demand, another way to consider stocks is as a supply (Sincere,

2004).

There are several classes of stock. Common stock, also referred to as capital

stock, is generally what individuals think of when they think of owning stock. Common

stocks usually entitle owners to one vote per share in shareholder voting matters and

board of director elections. Common stock can be classified, or broken down, into

different classes of common stock, usually resulting in Class A and Class B stock.

Classified stocks can have differences in voting power, dividend allocation, and

liquidation privileges. “Classified stock is less prevalent today than in the 1920s, when it

was used as a means of preserving minority control” (Downes & Goodman, 2007, p.

281).

4

Another type of capital stock is preferred stock. Preferred stock does not typically

entitle its owner to voting rights but does have a specified dividend rate, priority in the

payment of dividends, and in liquidation privileges. Dividends are a portion of the

organization’s net profits and are given to shareholders with a certain classes of stock.

The amount of the dividend per share is a fixed value, voted on by the board of directors

prior to payment, and is usually paid in cash but also can be property, scrip, or more

stock (Scott, 2003). Therefore, if one is a preferred stock holder and the company has

limited funds to pay out dividends, the preferred holder’s shares are paid first and then,

if any profit remains, it would get disbursed to the other stock classes.

Stocks are bought and sold in a stock market, also known as a securities

exchange. Stock market is a general term that refers to one of the established

exchanges for organized trading, such as the New York Stock Exchange (NYSE). Other

major U.S. securities exchanges include the American Stock Exchange and the largest

electronic exchange, the National Association of Securities Dealers Automated

Quotation System (NASDAQ). The stock market is simply a place to buy and sell shares

of stock.

In the United States, the term Wall Street has come to represent the financial

institutions and securities exchanges throughout the country. In the late 1700s, stock

buyers and sellers did business on a street corner of Wall Street in New York City. As

the number of shares of stock sold increased, along with the number of buyers and

sellers, 24 brokers and merchants decided to buy and sell stocks for corporations for a

fixed commission (Sincere, 2004). The agreement, signed in 1792, was called the

Buttonwood Agreement and signaled the beginning of the New York Stock Exchange.

5

In order to assess how stocks or the market are doing on any given day, Wall

Street and other investors can refer to newspapers, finance- and trading-based

television shows, radio, and the Internet. One of the primary sources to consider when

determining daily market performance is the Dow Jones Industrial Average (DJIA). The

DJIA was created in 1884 by Charles Dow, a reporter who wanted to develop a way to

measure the daily performance of the stock market. The DJIA was originally the

average of the 12 largest and most popular industrial stocks of the day. Today, the DJIA

is composed of 30 representative stocks averaged to indicate how the overall daily

market is performing.

In addition to the DJIA, hundreds of other indices now exist to measure almost

every industry. After the Dow, two of the most popular indices are the NASDAQ

Composite Index and the Standard and Poor’s (S&P) 500. The NASDAQ Composite is

a list of 5,000 stocks that are listed on the NASDAQ whereas the S&P 500 comprises

the indices of 500 stocks which Standard and Poor’s has decided to use as the

measure of the market.

In addition to looking at the type of stock class, one can consider stocks by

sectors. A sector is the group of industries which share common characteristics.

Businesses in a sector produce the same products or provide the same services. By

considering a corporation’s individual stock, an investor can compare and contrast

specific stocks, not only to the overall market, but more pointedly, to other company

stocks within the same sector. Sectors include business areas such as consumer

staples, consumer discretionary, utilities, materials, industrials, technology, telecom,

financials, health care, and energy. These sectors can be compared across U.S. indices

6

as well as global indices. In addition, sectors can be further defined and researched

based on the products and services the corporation provides. For example, expanded

sector descriptions could include groupings such as airlines, automobiles, oil, retail, and

pharmaceuticals.

Other ways of describing stocks include outstanding shares, market

capitalization, and the float. Corporations issue shares of stock, known as outstanding

shares, that are made available to investors, company officers, and to employees.

Outstanding shares are used in the calculation of earnings per share and book value of

a stock (Scott, 2003). The float is the total number of shares owned by shareholders

and available to be traded. Market capitalization of a corporation is calculated by

multiplying the total number of outstanding shares by the value of a share of stock. In

general, market capitalization indicates the capital size of a company by giving the total

value for all outstanding shares of the corporation. Some investors consider one or all of

the aforesaid classifications when making their trading decisions.

Many variables can affect the price of a stock. Market sentiment is the

investment community’s feeling about the expected movement of the stock market

(Scott, 2003). A company’s stock price can be affected by the general market direction

during a day of trading and major world events. For example, the market fluctuation in

response to the September 11, 2001, disaster saw sentiment dramatically fall, resulting

in perceived value of stocks to fall well below their actual market prices (Lawrence,

McCabe, & Prakash, 2007). A corporate extreme event can also have an effect on a

stock price. For example, after the Saturday May 11, 1996, crash of the Valujet DC-9

7

crash in the Florida Everglades, the company stock experienced a one-day decline of

23% on the following Monday (Loughran & Marietta-Westberg, 2005).

Event market reactions have been shown to take into account not only the

uncovered costs of an extreme event, such as fines, increase in insurance premiums,

and legal expenses, but also the impact on humans and the environment. Blancard and

Laguna (2008) found that the stock market reaction to the Buncefield oil depot explosion

on December 11, 2005, was negative and immediate. Compared to other petrochemical

accidents, the loss to the shareholders of the companies involved in the Buncefield

explosion was relatively weak, possibly because of no obvious human or environmental

damage, unlike other accidents studied (Blancard & Laguna).

Another factor affecting stock price can be the performance of a company’s

sector, including the stock price of companies in the same industry, as general market

conditions will affect all companies in a sector similarly. A company being acquired by

another company can expect a change in its stock price as well as the acquiring

organization (Veale, 2001). When a company becomes part of another company, both

stock prices are considered in the evaluation of the new entity.

Whether or not a corporation is part of an index, such as the NASDAQ 5000, can

also impact stock price, especially when a company’s stock is added to, or omitted from,

the index. Inclusion in the Dow Jones Industrial Average between 1976 and 1996 made

actual stock prices increase 3.5% (Wurgler & Zhuravaskaya, 2002). “Most studies in this

area show that the inclusion or exclusion of shares from indices has significant price

and volume effects on the shares in question” (Ranald & Haberle, 2007, p. 56). Other

potential factors affecting a stock’s price include the company’s introduction of new

8

products or expansion into new markets, as well as new contracts or government

orders.

Furthermore, a company’s stock can be affected by earning results and earning

guidance. The main objective of a company is to make a profit. As a result, a

corporation’s reported earnings, and whether they met, exceeded, or failed to meet

expectations, can have an impact on the stock price. “The price-earnings effect has

been thoroughly documented and is the subject of numerous academic studies”

(Anderson & Brooks, 2006, p. 1063). The price-earnings (P/E) ratio is the current price

of a stock relative to a corporation’s earnings. Earnings announcements have also been

researched and believed to impact a firm’s stock price. In a Loughran and Marietta-

Westberg (2005) study, it was found that earnings disappointments were the most

frequent negative earnings announcement.

Stock splits, share buy-backs from the company, and paid dividends can each be

a factor in the price of a stock. Sometimes the relationship between some of the

aforementioned is interdependent. Fama, Fisher, Jensen, and Roll (1969) studied the

abnormal stock behavior surrounding the time of splits and suggested that because of

the often associated substantial dividend increase associated with splits, the market’s

reactions to a split announcement is due to anticipated dividend increase. In addition to

splits, paid dividends, and share buy-backs, insiders within the corporation, as well as

investment leaders such as Warren Buffett, can alter perceived stock values and signal

insider faith in a company by buying or selling shares of a company’s stock.

Market analyst evaluation of a stock and any patent approval can have a bearing

on a stock’s value. A stock price can also be understood to reflect the perceived value

9

of a company. Another aspect of the stock market is investor perception. Investor

perceptions can be based on fear, greed, supply and demand, fashion or fad, politics, or

economics, and can make the difference between a stock’s price and its fundamental

value (Kahn, 2006). Investors can follow and increase trends in the buying and selling of

a stock. Investors often want a stock that they perceive others are buying even without

any other stock analysis. Herd mentality is a part of technical analysis of the market,

and assumes that humans have a need for acceptance and will follow others for their

psychological safety, making the stock market the sum of all participants’ perceptions

and actions (Kahn).

Investors sometimes make investments in corporations or mutual funds they

perceive to be ethical or socially responsible. “Ethical or socially responsible investment

(SRI) has emerged in the last decade or so as a reasonably legitimate focus of

discussion about investor choice” (Beal, Goyen, & Phillps, 2005, p. 66). The increased

investor interest in ethical investing has contributed to the increased establishment of

ethical mutual funds and research into trying to define and identify socially conscious

firms. Investors’ choices can be made based on their perceived morality of an

investment. Principles have been created to try to determine moral rightness or moral

wrongness of an investment, as well as trying to define and identify “evil” companies

(Irvine, 1987). The ethical implications of investing have been a growing area of concern

leading to research considering issues including identifying ethical investors, historical

returns for SRI mutual funds, effect of SRI on corporate behavior, etc. (Beal et al.,

2005). While the aforesaid is not an exhaustive list, it does outline many of the main

factors that can affect stock price.

10

Violations and Crimes in Corporate America

Violations by corporations and/or their officers and/or directors can occur as

misrepresentations to the public, inaccurate financial statements, reckless homicide,

bribery, damaging the environment or hiding damages to the environment, as well as a

large number of other actions. Corporate violations can be: (1) actions that violate state

or federal agency regulations, in which case, after negotiations between the agency

enforcement personnel and the corporation, the corporation pays fines; or (2) actions

that result in criminal charges being brought against the corporation and/or its

executives. In the case of criminal charges, corporations can either plea bargain with

the prosecutor or, if they go to trial and are found guilty of the charges, they can be

sentenced to pay fines, serve probation, make restitution, issue public notices of

conviction, or make forfeiture (Federal Sentencing Guidelines for Organizations, 1992).

Statutes or regulations can define crimes but only the judicial system can decide if a

person’s or corporation’s action constitutes a crime.

In the United States crimes can fall into one or more of several categories

including, but not limited to: antitrust, environmental, fraud, campaign finance, bribery,

obstruction of justice, public corruption, tax evasion, food and drug, false statements,

financial crimes, illegal boycott, illegal exports, and worker death (Mokhiber, 2007), and

can occur as violations of federal or state statutes. Corporations can commit crimes in

one or many categories of crime by committing one single act, or by repeating the act

many times over a long period of time. Many times organizations commit one type of

crime and then proceed to commit others in attempts to cover up for the initial misdeed.

11

In the United States, violations that are also criminal acts can end with some sort

of negotiated settlement, a fine, imprisonment, or a combination of two or all three types

of punishments.

Professional Ethics

Underlying the idea of what is responsible corporate behavior and/or what

constitutes a corporate crime is ethics. There are many different areas of study within

the field of applied ethics. Most professional fields have developed evolving codes of

conduct as well as shared ethical principles and standards. The legal profession defines

ethics as “the study of what constitutes right or wrong behavior” (Jentz, Clarkson, &

Cross, 2006, p. 243). The American Psychological Association (APA) outlined in 1992

ethical principles for psychologists involving: competency, integrity, professional and

scientific responsibility, social responsibility, respect for the rights and dignity of

individuals, and concern for the welfare for others (Fisher, 2003). In 2002, the APA

revised its Ethics for Psychologists and Codes of Conduct to include more

industrial/organizational (I/O) situations or applications, but the aforesaid principles are

still embraced and outlined.

The term business ethics is generally used to refer to the behavioral morals and

standards adhered to and supported by an organization and its agents. The majority of

business ethics definitions contain the notion that there are rules, standards, and moral

principles, either implicitly or explicitly, set and/or modeled by an organization and the

individuals working for the organization. Business ethics include the principles and

standards that guide behavior in the business world (Ferrell et al., 2005). Business

ethics seem to be arrived at not only through study and practice, but also through

12

legislation. For example, in response to the Enron scandal, the 1992 Federal

Sentencing Guidelines for Organizations created incentives for organizations to develop

ethical practices, and the Sarbanes-Oxley Act of 2002 defined stricter accounting

standards and guidelines. An organization’s ethical conduct can potentially impact the

organization in several ways including employee commitment, investor loyalty, customer

satisfaction, and profits (Ferrell et al.).

Developments affecting Corporate America

There have been many developments that have had an impact on corporations in

the Unites States leading to the evolution of notions of corporate responsibility, business

violations, and standards of conduct. These developments include acts, laws, and

research initiatives that have led to what is currently considered corporate violations.

It was during the 1970s that a few universities began offering courses related to

business corporate responsibility and business codes of conduct, centers dealing with

aforementioned issues formed, and professional societies were established. Field-

related textbooks and professional journals began including ideas about appropriate

corporate behavior. Conferences allowed academics from a variety of fields to interact

with businesspeople and to exchange ideas about business standards of behavior

issues. After increased public and media scrutiny, some corporations tried to directly

address conduct and responsibility issues by attempting to create corporate codes,

increase awareness about corrupt behavior, and develop individual ethics programs

(DeGeorge, 2005). At the federal government level, President Jimmy Carter’s

administration saw the passing of the Foreign Corrupt Practices Act which made it

13

illegal for a U.S. corporation or a U.S. citizen to bribe foreign government officials for

unfair advantage to gain or maintain business.

The 1980s witnessed the consolidation of ideas around corporate responsibility

and behavior. One of the steps was the creation of the Defense Industry on Business

Ethics and Conduct (DII). The DII consists of six principles which set forth the

expectation that organizations will establish support for codes of conduct, calls for the

distribution of clear and informative codes of conduct, and calls for the development and

maintenance of ethics training plus continuous support between training sessions.

Additional principles state that member organizations should understand and adopt the

concept of public accountability, uphold the integrity of the defense industry, carry out

internal audits and regular internal reporting, and practice financial disclosure. The final

principle of the DII requires participating organizations to develop an open atmosphere

where employees feel free to report issues without the threat or belief of recrimination.

By the end of the 1980s, many Fortune 500 corporations had made efforts to

establish codes of conduct, develop employee outlets for reporting concerns, put into

place organization-wide responsibility and awareness training programs, and created

ethics committees at the board of directors level (DeGeorge, 2006).

Congress passed the Federal Sentencing Guidelines for Organizations (FSGO)

in 1991. The FSGO attempted to deter federal crimes by imposing penalties for crimes

committed by organizations and any of their agents acting in official capacity. The

guidelines created financial incentives for corporations to establish or improve and

solidify their compliance programs, and the FSGO also put forth standards for judges

when sentencing organization representatives convicted of federal crimes.

14

In addition to setting penalties and establishing financial incentives, the FSGO

also encouraged companies to establish internal legal compliance programs in order to

prevent poor corporate behavior. Such a program includes establishing standards and

procedures to be followed by employees, assigning a high-level official to oversee the

program, taking steps to communicate its standards, taking steps to monitor and audit

compliance, enforcing the standards through disciplinary mechanisms, and taking steps

to modify its program after an offense is detected (DeGeorge, 2006).

The guidelines offered a reduction of penalties for an organization’s misconduct if

the company had made efforts to deter illegal behavior. In order for an organization to

be seen as having made efforts, U.S. companies needed to take action to detect and

prevent legal violations and to create environments with high legal standards.

Additionally, companies needed to create and support strong corporate values and not

tolerate criminal and/or inappropriate behavior. Empirical evidence suggests that the

implementation of these programs, resulting from the enactment of the FSGO, has

enhanced and increased the level of legal behavior in organizations (Izraeli & Schwartz,

1998).

The 21st century ushered in a stage of intensified media and public attention on

business misconduct/illegal behavior because of the revelation of significant corporate

scandals early in the century. Tyco, WorldCom, Enron, Arthur Anderson, Sunbeam,

Quest, and Halliburton were just some of the large corporations that faced allegations of

illegal behavior for individual and organizational gain. Several executive members of

corporations were indicted, fined, and sentenced. The stock value of companies with

previously respectable reputations tumbled, taking employees’ livelihoods, retirements,

15

and insurance with the collapse. Investors lost billions of dollars. The American public

became increasingly aware of questionable accounting practices, fraudulent financial

reports, misappropriation of funds, obstruction of justice, and tainted stock sales (Ferrell

et al., 2005). It became apparent that the business culture of having little or no concern

with laws and responsibility greatly benefited, often temporarily, some organizations and

their leaders. Vintage yachts, extravagant parties, and priceless paintings were just

some of the personal luxuries executives reaped.

The public was outraged and pushed for improved business and legal standards.

“A survey by BAC News and the Washington Post found that 75% of the public has only

limited confidence in large corporations, and 63% believe that corporate regulation is

necessary to protect the public” (Ferrell et al., 2005, p. 13). The Sarbanes-Oxley Act of

2002 was in direct reaction to the fraudulent accounting practices by Arthur Anderson

for WorldCom and Enron. The act set forth tougher standards and guidelines for

accounting practices and made fraudulent practices a criminal offense. The Sarbanes-

Oxley Act called for greater transparency, establishment of an accounting oversight

board, and development of standards for financial practices. Furthermore, the act

required executives to sign their organizations’ financial disclosures, forced executives

to immediately disclose executive stock sales, outlined stiff penalties for financial

misrepresentations, and disallowed loans for executive management.

One casualty of the act was that some small organizations went out of business

when they were unable to handle the administrative load and costs. In addition, there

have been some international consequences, but in general the consensus seems to be

that overall there has been a positive effect. Sarbanes-Oxley opponents claimed that

16

the act created an overly complex regulatory environment and reduced U. S.

international competitiveness against foreign financial service providers.

The 21st century has also experienced the further globalization of business. The

increasing expansion of U.S. businesses into international markets, and increased

collaborations with international businesses has led to the need to develop codes of

conduct, standards of behavior, and universally acceptable business practices. Some of

the efforts to establish global codes of conduct and business standards include the

North American Trade Agreement (NAFTA), the European Union (EU), the Common

Market of Southern Cone (MERCOSUR), the Ethical Trading Initiative, the World Trade

Organization (WTO), and the Council on Economic Priorities Social Accountability 8000

(SA 8000). In addition, the development of global codes of conduct by groups consisting

of businesses, political figures, and interest groups, such as the Caux Round Table,

have outlined some common concerns for global firms in an effort to ensure globally

responsible behavior (Ferrell et al., 2005).

It remains to be seen how, in the remainder of the 21st century, corporate

responsibility and the definition(s) of acceptable business standards will evolve.

Examples of dispersion of inferior and harmful products among countries continue. The

enormous financial crisis that gained worldwide exposure in the early fall of 2008 may

reveal many major corporate violations, as could the dispersion in funds from

government bailout attempts. Failed and/or failing Fortune 500 corporations could be

found to be guilty of illegal behavior. Key decision makers in a number of affected

corporations have been exposed for, among other things, questionable practices. Other

exhibitions of questionable judgment include executives taking separate corporate

17

planes to ask for taxpayer bailout money or spending their bailout payoffs on items that

would be considered luxuries by Middle America.

Because of such a significant worldwide market event, the early part of the

century may need to be studied differently and could even cause some to revisit

previous conclusions about the latter part of the 20th century. In addition, the significant

2008 market adjustment may re-contextualize previous market growth, business

directions, and warrant investigation into how the business culture contributed not only

to the growth of the market, but also to aforesaid market adjustment.

Critical Event Research

A study which considers the affect of corporate violations upon a corporation’s

stock prices, in financial and economic literature, would fall under the category of an

event study. Event research considers whether specific corporate, market, world or

news events can cause short and/or long term changes in stock returns. “Events are

typically defined as extreme daily price changes, and three-day event windows are

typically employed” (Larson & Madura, 2003, p. 114). Informed events are events that

have corresponding news/media attention, which means that they have been explained

or discussed in major publications. Uninformed events are events that do not have

corresponding news coverage. Events studied can include: annual earnings predictions,

company restructuring, director share dealing, initial price offerings (IPOs), legal issues,

legislative actions, election results, military actions, natural disasters, seasoned equity

offerings (SEOs), profit warnings, and new contracts.

In general, event studies tend to look for short term stock price fluctuations.

“Much of the literature on extreme events has focused on stock returns one day

18

following the event” (Loughran & Mariett-Westberg, 2005, p. 580). Some studies try to

determine if there is a pre-announcement reaction, while other studies look for post-

announcement changes in stock prices. Additional studies also include the effects of

monetary activity and money supply on stock prices, stock price response to fraud and

litigation, stock price reaction to dividend initiations, macroeconomic news and resulting

stock prices, effects of write-offs on stock prices, returns and trading volume resulting

from firm specific news, political event effects on stock prices, and stock price response

to major news announcements.

Some event studies try to support or discredit efficient market hypothesis,

momentum or contrarian investment strategies, or behavioral investor models. Efficient

market hypothesis (EMH) is the idea that new information on a corporation is

immediately incorporated into the company’s stock price. Behavioral models of irrational

investor behavior include under-reaction and over-reaction. These two models,

however, are contradictory. The premise of overreaction is that investors have a

tendency to overreact, have a reaction beyond what the situation calls for, to both good

and bad information about a corporation, thus leading investors to price stocks below

their intrinsic value after the release of negative information or above their intrinsic value

following the release of positive information. The premise of under-reaction is that

investors have the tendency to respond enough to good or bad information, thus leading

investors to price stocks above their intrinsic value after the announcement of negative

information and below their intrinsic value after the release of positive information.

19

Review of Event Study Findings

In order for I/O researchers to gain a better understanding of their event study

results, a brief understanding of prior results from economic and financial studies should

be considered. Reviewing event studies from other disciplines can potentially shed light

on the value and degree of I/O event focused on responsible corporate behavior. There

is a considerable amount of research by economists on causes of stock price

fluctuations, and some of their work may provide useful models for I/O psychologists.

However, no research specifically addresses the impact of announcements of ethical

violations. “Although it is obvious that stock prices respond to events, it is not easy to

match particular events to particular changes in stock prices” (Fair, 2002, p. 713).

Niederhofer (1971) found in his study of 432 events from 1950 through 1966 that

the market does react to the events and differentiates between good and bad events.

Niederhofer studied the percentage changes in the DJIA before and after all types of

events including disasters, political shifts, and legislative actions, and determined that

the market overreacts to bad events then readjusts in a positive direction. In a study of

economic and political events, Reilly and Drzycimski (1973) observed percentage

changes in several market indices for seven widely known events and also observed

that the adjustment preceded the announcement date, suggesting that the economic or

political events might have been anticipated.

In their study centered on measuring the effect of political events on the U.S.

defense industry, McDonald and Kendall (1994) found that stock prices for the 16

sampled defense firms tended to rise in response to military action. They included 17

events that would have had a high likelihood of military action by the United States or

20

the Soviet Union, considered a 181-day window of time centered on each event, and

derived cumulative Z-scores and cumulative t-scores looking for significance at the .01

and .05 levels. McDonald and Kendall did not find statistically significant support for

general market anticipation of the events because the significant stock price increases

occurred on or the after the dates of the events, not prior to the events. However, for

events that could have led to U.S military action, there was a negative adjustment to

U.S. defense industry stock prices after the events significant at the .05 level, possibly

indicating market anticipation even though no significant stock price changes occurred

prior to the events.

Using daily data, Cutler, Poterba, and Summers (1989) attempted to link the 50

largest daily changes in the Standard and Poor’s 500 Stock Index (S&P 500) to events

between 1946 through 1987, finding only a few instances where, with any confidence, a

change could be linked to an event. Following up on Cutler, Poterba, and Summers’

research, Fair (2002) considered 1- to 5-minute large price changes in the S&P 500 for

the years 1982 through 1999. Fair outlined large price changes as 1- to 5-minute price

changes equal to or greater than .75% in absolute value, with the standard deviation of

the 1- and 5-minute price changes in the study respectively being .48% and .112%. Out

of the 220 1- and 5-minute price changes, only 69 had corresponding events. Of the 69

events, most were nonmonetary macroeconomic announcements and direct monetary

announcements, and included only three events that were concerned with particular

companies.

Ewing (2002) specifically investigated the macroeconomic variables: inflation,

monetary policy, economic growth, default or market risk, and the effect of these events

21

on financial sector organizations listed on the NASDAQ Financial 100 index from 1988

through 2002. Ewing found that monetary policy shocks resulted in reduced financial

sector returns for approximately two months. It was also discovered that inflation

announcements were associated with a negative impact on returns lasting for about one

month and that an increase in risk resulted in an immediate negative response with the

effect not lasting past the initial month. Finally, it was discovered that unexpected

economic growth resulted in a positive initial financial sector return response, but the

effect did not persist. Significance was determined by the use of confidence intervals

that were plus or minus two standard deviations.

Firm write-offs and write-downs have been another focus of event research.

Fried, Schiff, and Sondhi (1989) considered firms that were reported in the Dow Jones

News Service to be taking write-downs or write-offs of long-lived assets from 1980

through 1986. They analyzed the monthly returns of 117 corporations and found that the

sample’s mean return was below market returns for the six months prior to and six

months following the event with the greatest decline of the unadjusted returns occurring

on the event date. Strong and Meyer (1987) reported negative, but insignificant, short-

term write-off events for their sample of 78 organizations analyzed from 1981 through

1985.

Bartov, Lindahl, and Ricks (1998) conducted research considering organizational

write-offs and/or write-downs from 1984 through 1985, with the sample including 373

announcements from 298 different organizations, in which 229 companies made single

announcements and 69 companies made multiple announcements. The study

considered short and long term abnormal returns. Using a two-tailed t-test for the

22

complete sample of 373 write-off and/or write-down events, Bartov, Lindahl, and Ricks

found short-term (four days surrounding the event) market reaction to write-off

announcements to be small (mean = -0.75%, median = -0.49%) with marginal

significance. As for long-term abnormal returns for the entire sample, they found for the

two years following announcements a substantial negative drift in returns that was

statistically significant for the first year after the event, but for the second year there is a

difference for one-time offenders versus multiple-time offenders. The companies with

only one announcement did not show a significant decline in the second year following

the event. However, firms that had multiple write-off and/or write-down events showed a

-21.5% drift in the first year and a -21.5% drift in the second year.

Several studies have investigated the relationship between money supply events

and stock prices, often concluding that monetary expansion or contractions lead the

stock market (Sorenson, 1982). Sprinkel (1964) studied the S&P 425 Industrial Index

between 1918 through 1963 and found that contractions in money growth led declines

in stock prices by nine months, whereas monetary expansions led the increases in

stock prices by two months. Rozoff (1974) found that the stock market anticipated the

changes in monetary growth, suggesting that the stock market acted as a predictor of

money supply growth with a one- to three- month lead.

Sorenson (1982) tested the hypothesis that unanticipated changes in money

growth have more significant effects on the stock market than do anticipated changes in

money growth. Using quarterly data to explain the year-over-year money supply and

quarterly stock results as related to money growth testing at 95% confidence level,

Sorenson found that the stock market does not react abnormally to anticipated

23

monetary activity, but unanticipated monetary activity does result in large changes in

stock price returns.

Several researchers have investigated the impact of litigation events, specifically

securities fraud litigation, on stock prices. “The dominant focus of scholarly and

regulatory attention has been on the stock price decline associated with a corrective

disclosure that alerts the market to an alleged fraud” (Griffin, Grundfest, & Perino, 2004,

p. 23). Bhagat, Brickley, and Coles (1994), in looking at class action filing (CAF) date,

found a median and significant announcement effect of -0.58% for the day of

announcement and the day following the announcement. Ferris and Pritchard (2001)

found a significant negative response to the CAF date with the average three-day

excess return of -3.47% for their sample of 89 issuers, but did not find support for the

idea that the market anticipates the outcome of a litigation.

Griffin et al. (2004) focused on the CAF date for 2,194 federal securities class

actions from 1990 through 2002. The study utilized a three-day window, days -1 to 1,

and for the two-tailed t-test used a significance level of p < .001. Their results indicated

a significant and immediate negative response on the CAF date with the investor

response at CAF date: mean (median) -4.1% (-1.7%) with t = -11.10. Griffin and

colleagues also found some support for the idea that the market might at least partially

anticipate litigation outcomes.

Long-term Event Studies Concerns

Fama (1997) stated that long-term event studies, especially those utilizing the

buy-and-hold abnormal returns method, can cause problems because the expected

return assumption versus the information content causes the abnormal returns. The

24

buy-and-hold method uses raw annual returns from both the sample portfolio of stocks

and the control portfolio. Seiler and Chakornpipat (1997) posited three theoretical

biases in return computations in long-term event studies: the bid-ask spread, the

performance measurement, and nonsynchronous trading. The bid-ask spread is the

difference between the closing price of returns and the true price. The last documented

price of a stock can be a based on bid or an ask price, which means the closing price of

a stock can be an ask price or a bid price (Seiler & Chakornpipat, 1997). The bid-ask

error is thought to be more substantial for small company returns. Stoll and Whaley

(1983) indicated that the magnitude for the bid-ask bias for large corporations is

approximately .001%. In addition, the bid-ask bias might be less for monthly returns

than for daily returns (Seiler & Chakornpipat).

Blume and Stambaugh (1983) suggested that because many long-term event

studies compute portfolio returns by using arithmetic or rebalancing portfolios of stock

returns, both of which are biased by the bid-ask bias for each individual return, therefore

these methods of performance measurement themselves are biased. Seiler &

Chakornpipat (1997) recommended utilizing a buy-and-hold strategy, which suggests

that the diversification effect will reduce the bid-ask bias through the investment of equal

amounts in each n in the initial period.

Abnormal returns in event studies are the differences between observed returns

and the market model returns. The inclusion of frequently traded stocks and infrequently

traded stocks in research can lead to non-synchronous trading bias. Because the

inclusion of infrequently traded securities would have an underestimated beta estimate,

and the inclusion of frequently traded shares would also have an overestimated beta

25

estimate, then the effect should continue on to the cumulative abnormal returns (Seiler

& Chakornpipat, 1997).

In addition to non-synchronous trading bias, performance measure bias, and bid-

ask bias in long-term event research, portfolio construction can be vulnerable to cross-

sectional variance (Harrington & Shrider, 2007). Mitchell and Stafford (2000) and

Harrington and Shrider suggested utilizing the calendar-time portfolios approach to

avoid cross-sectional dependence. Fama (1998) strongly advocated the calendar-time

approach for measuring long-term abnormal returns because monthly returns are less

susceptible to the bad model problem. Additionally, by using the calendar-time

portfolios, all cross correlations of event-firm abnormal returns are accounted for in the

portfolio variance. The traditional calendar-time approach includes calculating an

average return for the cross-section of companies and measures the risk-adjusted

performance by estimating a multifactor time-series regression model. The calendar-

time approach does have critics, and added or different approaches continue to be

tested in finance research.

Research Question

This study focused on whether or not publicly announcing corporations’

convictions or settlements of violation(s) was related to shareholder support, identified

by monthly stock prices, declined after the announcement, and whether or not the

degree of decline in stock prices after conviction or settlement was the same across

sectors. In addition, this study examined whether or not the decline of a stock value

after a conviction or settlement was greater than the decline of corporate stocks within

the same sector, and how declines in shareholder support of corporations with earnings

26

disappointments compared with shareholder support of corporations with announced

violations. This study also researched the quarterly decline in stock prices for repeat

offending corporations.

Purpose of the Study

The purpose of this study was to determine whether or not the violations of

corporations resulted in the withdrawal of shareholder support as identified by stock

price declines. In addition, this study compared the declines in shareholder support after

announced convictions or settlements to shareholder support after earnings

disappointments, as well as compared percentage of decline in stock prices after each

offense by repeat offending corporations.

Hypotheses

Hypothesis 1A. The monthly mean stock price of corporations will decline

significantly related to public announcement of prosecution or settlement for regulatory

violations and/or criminal offenses.

Hypothesis 1B. The degree of decline of the monthly mean stock prices of

corporations will be the same across industry sectors related to public announcements

of prosecutions or settlements for regulatory violations and criminal offenses.

Hypothesis 1C. The monthly mean stock price of corporations will decline

significantly compared to that of other companies in the same sector in relation to public

announcement of prosecutions or settlements for regulatory violations and criminal

offenses.

27

Hypothesis 1D. The percentage of decline in quarterly mean stock prices will

increase in relation to each announcement of prosecution or settlement for regulatory

violations and/or criminal offenses of repeat-offender corporations.

Hypothesis 2. The monthly mean stock price for corporations that publicly

announced prosecutions or settlements for violations will decline more than

corporations reporting significant earnings disappointments in the same quarter after

public announcement of prosecution or settlement for regulatory violations and/or

criminal offenses.

28

CHAPTER 2

METHODOLOGY

Sample

In order to study the effects on corporation’s stock prices before and after

corporations have been convicted or made settlements for violations, the current study

evaluated stock prices from U.S. based Fortune 500 corporations. The study collected

data for 37 months of each company’s stock prices: 24 months prior to the company’s

violation, the stock price for the month of the violation, and 12 months after the date of

the violation. The study compared the difference in stock prices between companies

that experienced announced violations and companies that announced earnings

disappointments by comparing stock prices of similar companies, upon similar timed

individual announcements.

Corporations studied were selected from several different business sectors. The

violating organizations had committed regulatory violation(s) and/or criminal offense(s).

A history of large corporate crimes and settlements, covering the years 1994 through

2004, is outlined in Appendix A and Appendix B. The tables were compiled from

multiple resources investigating and/or reporting corporate violations. Some of these

investigative resources included: (a) individual corporation Website histories; (b)

Websites such as http://CNNMONEY.com; (c) the location for current and archival

Fortune 500 information; (d) articles by individuals like Russell Mokhiber (2007), whose

writings are included in the Corporate Crime Reporter; and (e), an active list compiled

by George Draffin (2009). In addition, announcement dates for violations were

corroborated by reviewing Lexis/Nexis.

29

The table in Appendix A identifies the organization, the violation, and the

settlement or fine. In addition, the three final columns in the table identify whether or not

the corporation in question was a Fortune 500 company for the years considered,

whether or not the general name and history of the company was the same for the study

period, and whether or not five years of company stock records existed for the studied

timeframe for each corporation. The table in Appendix B identifies the organization, the

sector, the industry, the incident year, the amount of the fine(s), and the number of

incidents for each corporation for the relevant timeframe. The second table eliminates

the organizations that did not meet the requirements for the study, stock history,

Fortune 500 listing for studied time period, and consistent corporate identity, and

includes the groupings of corporations by the number of violations incurred for the

relevant timeframe.

Once the companies with violations were identified in the 10-year span of the

Fortune 500 lists, I then identified companies that had earnings disappointments in the

same year and potentially the same quarter as the dates of violations for the companies

that had been identified as violators. When comparing violations and earnings

disappointments, companies were matched according to their business industry, their

organization size, and trading volume. The first step in matching each violating

company was to identify comparison companies in the same industry. Violators

represented 24 different industries; agricultural, aircraft manufacturing, apparel,

automotive, banking, chemical, computing, defense contractor, electronics, energy,

food, healthcare, insurance, media, petroleum, pharmaceutical, power, railway, retailing,

software, technology, telecom, utility, and wood products. For each violating company,

30

the second step was to identify comparison companies in the same industry with an

organization size (measured by total revenue) within $2500 and trading volume within

250,000 of the violating company. Of these potential comparison companies, the one

closest to the violating company with earnings disappointments in the same quarter as

the incident date was chosen as the comparison company.

Data Sources

Information on stocks of publicly traded organizations is public knowledge. Media

announcements of ethical convictions, earnings expectations, and actual earnings are

also open for public scrutiny. Many public and private sources exist for gathering the

aforesaid information, including the companies studied. Because stock prices, earnings,

and levied violations are fixed numbers, there should be no variation from one source to

another. Sources for stock prices, sector averages, and trading volume information was

collected from the Yahoo Finance and the Center for Research in Security Prices

(CRSP).

For this study, the media utilized for the timestamp of public violations and

earnings disappointments announcements was the national media sources in print

and/or on the Internet, such as, The Wall Street Journal, New York Times,

http://www.Bloomberg.com, Edgar online, www.SEC.gov, and

http://www.CNNMoney.com. The Wall Street Journal (WSJ) is a daily newspaper

published by Dow Jones & Company in New York City, with Asian and European

editions. It has a worldwide daily circulation of approximately 2,069,463 as of 2006, with

931,000 paying online subscribers. The WSJ had the largest circulation of any

newspaper in the United States until November 2003. The Wall Street Journal focuses

31

primarily on national and international business as well as finance issues and news. It

was founded July 8, 1889, by Charles Dow, Edward Jones, and Charles Bergstresser.

The newspaper has won the Pulitzer Prize 33 times.

The New York Times is the largest U.S. metropolitan newspaper published in

New York and distributed daily. The New York Times was founded in 1851, has won 98

Pulitzer Prizes, and also prints other newspapers including the Boston Globe. In

addition to these large national newspapers, data retrieved from Internet Websites such

as http://www.Bloomberg.com, one of the five most popular financial Websites, and

http://www.CNNMoney.com, which provides Fortune 500 current and archival data. In

addition, news events was further verified through Lexis/Nexis and web-based internet

sites, such as http://www.pro-edgar.com, http://www.secfilings.com, and

http://www.finance.yahoo.com.

Design and Procedure

This study evaluated cumulative shareholder reactions by viewing stock prices

before and after violation announcements. The study examined whether or not stock

valuations for corporations that have paid fines or arranged settlements for violations

and regulatory offenses declined at the same rate across violating corporation sectors,

and whether mean stock prices declined for violating corporations compared to other

corporations within their business sector. In addition to the violations and the reactions

to corrective measures, the study compared shareholder reactions to violations

compared to earnings disappointments.

32

Procedure for Data Collection

Companies with Violations

As a first step in determining the companies for the final violator list, I confirmed

that each of the violating companies were a Fortune 500 company during the study

period (1994 to 2004). For example, if Wisconsin Energy had a single violation in 2003,

and they were a Fortune 500 company in 2003, they were selected in the first round of

data collection.

From this initial list of 215 different violations from 135 different companies (see

Appendix A), the violator’s average trading volume was collected. The average trading

volume for each company was determined by locating the monthly trading volume on

stock prices websites that provided historical data and then calculating the average

based on the data available. Specifically, if 12 months of trading volume was available,

then the 12 months were averaged to determine the average trading volume. In the

event that only a few months of trading volume were available, the data from those few

months were averaged and served as the average trading volume. Each violating

company’s monthly adjusted stock prices were then collected. These adjusted stock

prices were gathered based upon the company’s specific date of violation. That is, stock

prices were collected for each of 24 months prior to the violation date, the month of

violation, and each of the 12 months after the violation, for a total of 37 months of stock

prices.

Strict criteria were established in order to collect the most accurate adjusted

stock prices available. Stock prices were not always readily available because several

of the violation companies were either dissolved (e.g., Enron) or were bought by

33

another company (e.g., Warner-Lambert was purchased by Pfizer in 2000). In other

instances, companies simply did not file their stock prices in their annual report to the

SEC. Thus, a multi-step strategy was employed in gathering the data on the adjusted

stock prices for the companies of interest. The first step involved searching for the stock

prices during the specific months of interest (i.e., 24 months prior to violation, violation

month, 12 months after violation) on http//www.finance.yahoo.com, or a similar website.

Any data available for the 37 month period was recorded. In the event that no data or

only partial data was available on the websites, the second level of criteria for pulling

the data was employed. This second step involved using the average quarterly stock

price for companies with incomplete data for any month. The average quarterly stock

prices are often included in company annual reports, which are readily available online.

Finally, if company stock prices did not meet the criteria as described above, then that

particular violating company was excluded from the list and subsequent analysis.

Seventy-nine violations from 51 companies met these criteria.

Comparison Companies

After the initial list of violating companies with trading volumes and adjusted

stock prices were recorded, the list comparison companies was compiled. Similar to the

selection of the violating companies, a multi-step procedure was also utilized to select

the comparison companies. The comparable companies were first selected by matching

their industry and/or company sector with the violating company’s industry and company

sector. This strategy ensured that no comparison company would be matched with a

violating company that was not in the same industry or sector. The potential comparison

companies were then selected based on their gross revenue and trading volume. That

34

is, a potential non-violating company was matched such that the revenue was

comparable in size to that of the violating company. Using these criteria prevented large

revenue violating companies from being matched with small revenue comparison

companies. Finally, each potential comparison company was researched using

Lexis/Nexus, Edgar Online, and online SEC filing websites (e.g.,

http://www.secfilings.gov) to find any quarters with earnings disappointments.

Specifically, if a violating company had a violation in a particular year and they were in

the Fortune 500 list for that year, I only searched for potential comparison companies

with earnings disappointments in that same quarter. In the event that a violating

company did not have a matched comparison company based on revenue, industry,

sector, or earnings disappointment in the same quarter or year of the violation, the

violating company was excluded from the final list (see Appendix B for final violating

company with matched comparison company list). Seventy-two violations from 46

companies with comparison companies were included for analysis.

The adjusted stock prices for the comparison companies were collected using

the same criteria as had been established for the violating companies. The monthly

adjusted stock prices for the comparison companies were obtained for each of the 37

months, matching the dates of the violating company. For example, Reliant Energy had

a violation in October 2003 and was matched with the comparison company of OG & E.

Thus, the adjusted stock prices for OG &E during each of the 24 months prior to

October 2003, the average price in October 2003, and the prices for each of the 12

months after October 2003 were obtained for analysis. Finally, in the event that the

35

monthly stock prices were not readily available, the average quarterly stock prices were

used.

Analysis

The purpose of the analysis for Hypotheses 1A, 1B, 1C, and 2 was to determine

whether or not announcement of settlements or fines for violations affect stock values,

to determine if there was a difference reflected in the stock price for corporate violators

compared to corporations announcing earnings disappointments, and to determine

whether stock prices declined the same across and within business sectors for

offending corporations. Each stock price change was determined by reviewing the

various stock prices for each calendar month, the comparison of prices at the calendar

month between and across sectors, and the examination of stock prices over a 36-

month period.

The study included each corporation’s monthly mean stock price for each of the

37 months considered for Hypotheses 1A, 1B, 1C, and 2. The study utilized monthly

mean stock prices because daily stock prices were too vulnerable to confounding

variables such as the day of the week, national or international events, or government

announcements made on any given day.

The data for each hypothesis was analyzed using repeated measures analysis of

variance (ANOVA). One important assumption of the repeated measures ANOVA is

sphericity, that is, equality of the variances for the difference scores. In the event that

sphericity is violated, the degrees of freedom for the F-test are adjusted to compensate

for the violation. The Huynh-Feldt is a moderately conservative adjustment and will be

reported in the current results. In addition to repeated measures ANOVAs, non-

36

parametric tests including Kruskal-Wallis and Wilcoxon’s tests were utilized as

alternatives to ANOVA and repeated measures ANOVAs. These tests were used to

compare the percent decline of stock prices from various time points. Due to the large

variability in the rate of decline as well as the small number of records in various

sectors, non-parametric tests were deemed more suitable than the parametric

equivalents.

The analyses for Hypothesis 1A, Hypothesis 1B, Hypothesis 1C, and Hypothesis

2 included the dependent variable as stock price, the independent variable as time, and

the level of analysis at the corporation level. The violating and non-violating companies

were matched based on event months and also by business sector. Hypothesis 1B

utilized across-time averages for each represented sector for the level of analysis of

across- sector changes. The level of analysis for Hypothesis 1C was within business

sector. The analysis for Hypothesis 2 included corporate violators and matched non-

violators with announced earnings disappointments, matched by sector and

announcement time.

The purpose of the analysis for Hypothesis 1D was to determine if there was an

increase in the percentage decline of the stock price for each repeat corporate violator.

Quarterly stock prices were considered to determine if there was a related percentage

decline in a corporation’s stock price after critical event 1 and critical event 2. Quarterly

stock prices were utilized because monthly means may be too susceptible to monthly

effects of critical events that might be months or years apart, and may be too sensitive

to reveal the full percentage effects for repeated offenses. Because of sample size

limitations, the corporations studied as a part of Hypothesis 1D were those that have

37

had two violating events for the study period (1994-2004). The analysis for Hypothesis

1D utilized a one-way repeated measure of analysis of variance (ANOVA) with the

dependant variable of stock price and the independent variable of critical events.

Because of the limited sample size for repeat violating corporations, companies were

combined across business sectors, but with each quarterly stock price adjusted by

respective weighted sector average.

38

CHAPTER 3

RESULTS

This study sought to determine if shareholder support for corporations that have

announced convictions or settlements for violations, as determined by stock values,

decreases after the event. Specifically, it was hypothesized that there would be a

significant decline in the monthly mean stock values for a corporations announcing

convictions or settlements for regulatory violation and/or criminal offenses and that the

degree of decline in stock prices would be the same across sectors. Furthermore, it was

hypothesized that there would be a significant decline in the monthly mean stock price

for violating corporations compared to other non-offending firms. The study also sought

to determine if shareholder support decreased in relation to each violation

announcement for companies with more than one event in the time period observed. An

increase in the percentage of decline for each violating event was hypothesized. Finally,

it was hypothesized that the monthly mean stock price for corporations that announced

convictions or settlements for regulatory violation and/or criminal offenses would

experience a greater decline than corporations announcing earnings disappointments.

Descriptive Statistics

The final sample for analysis included 75 records for 50 different violating

companies and their matched company for comparison. However, three companies

were removed due to lack of data or comparatively high stock prices, resulting in a total

sample of 72 records reflecting 47 unique companies. As shown in Figure 1, 40.3% of

the companies were not repeat violators and nearly 40% of the companies had only two

violations (38.9%). Smaller proportions had more than two violations: 4.2% of the

39

companies had three violations, 6.9% had 5 violations, and 9.7% had seven violations.

The data included stock price records from eight different sectors (see Figure 1). One-

quarter of the records were from companies in the financial sector (25.0%), 16.7% were

from the technology sector, 13.9% were from healthcare, and 12.5% were from

consumer goods. The remaining records were utilities (8.3%), basic materials (8.3%),

industrial goods (8.3%), and services (6.9%). Interestingly, companies with seven

repeat violations were from the technology sector and those with five repeat violations

were from the financial sector (see Table 1).

______________________________________________________________________

Figure 1. Percentages of sectors. ______________________________________________________________________

40

______________________________________________________________________

Table 1

Frequencies and Percentages for Sector and Repeat Violations ______________________________________________________________________

Frequency %

Repeat Violations

0 Violations 29 40.3

2 Violations 28 38.9

3 Violations 3 4.2

5 Violations 5 6.9

7 Violations 7 9.7 ______________________________________________________________________

The data file included monthly mean stock prices for 37 months for each

company. The 24 monthly mean stock prices before the incident month (i.e., when the

violation occurred), the mean stock prices for the incident month, and the 12 monthly

mean stock prices after the incident month were included for analysis. In other words,

the data file included stock prices for each month during the two years before the

violation as well as stock prices for each month during the one year after the violation.

For some of the analysis, the three years were grouped into quarters and averaged,

resulting in 12 mean stock prices.

Hypothesis Testing

Hypothesis 1A

Hypothesis 1A sought to examine the monthly mean stock prices in relation to

the month of the public announcement of the violation. Thus, the average monthly stock

41

prices were examined for companies that had regulatory violations. A repeated

measures ANOVA was conducted using the 37 monthly mean stock prices as the within

subjects effect. The results failed to reveal significant differences between the average

monthly prices, F (36, 2340) = 2.49, p = .077 (see Figure 2 and Table 2). Thus,

Hypothesis 1A was not supported.

______________________________________________________________________

Figure 2. Monthly average stock price. ______________________________________________________________________

Mon

thly

Avg

. Sto

ck P

rice

in U

SD

Month Before or After Incident Month

42

______________________________________________________________________ Table 2

Average Monthly Stock Prices for Violating Companies (N = 66) ______________________________________________________________________

95% Confidence Interval Mean SE Lower Upper

24 months Prior Incident 29.92 2.41 25.10 34.73 23 months Prior Incident 29.24 2.31 24.64 33.85 22 months Prior Incident 28.92 2.22 24.49 33.35 21 months Prior Incident 28.62 2.17 24.29 32.95 20 months Prior Incident 29.43 2.35 24.73 34.12 19 months Prior Incident 29.04 2.21 24.64 33.45 18 months Prior Incident 28.04 2.12 23.81 32.28 17 months Prior Incident 27.47 2.12 23.23 31.71 16 months Prior Incident 27.50 2.10 23.31 31.70 15 months Prior Incident 27.38 1.98 23.42 31.33 14 months Prior Incident 27.41 1.93 23.56 31.25 13 months Prior Incident 27.45 2.02 23.43 31.48 12 months Prior Incident 27.70 2.11 23.49 31.91 11 months Prior Incident 27.23 1.92 23.40 31.05 10 months Prior Incident 27.01 1.87 23.28 30.74 9 months Prior Incident 28.23 2.12 24.00 32.46 8 months Prior Incident 28.73 2.18 24.38 33.09 7 months Prior Incident 28.69 2.15 24.40 32.98 6 months Prior Incident 28.84 2.13 24.60 33.09 5 months Prior Incident 28.62 2.07 24.49 32.75 4 months Prior Incident 28.32 2.00 24.32 32.32 3 months Prior Incident 28.56 2.11 24.36 32.77 2 months Prior Incident 28.62 2.19 24.25 33.00 1month Prior Incident 28.81 2.19 24.44 33.18 Month of Incident 29.04 2.17 24.71 33.37 1 month Post Incident 29.17 2.17 24.83 33.50 2 months Post Incident 29.35 2.16 25.05 33.66 3 months Post Incident 29.81 2.32 25.18 34.43 4 months Post Incident 30.18 2.43 25.32 35.05 5 months Post Incident 30.46 2.53 25.40 35.52 6 months Post Incident 30.98 2.48 26.02 35.94 7 months Post Incident 31.59 2.52 26.56 36.63 8 months Post Incident 31.53 2.41 26.71 36.34 9 months Post Incident 31.04 2.28 26.49 35.60 10 months Post Incident 31.06 2.34 26.39 35.72 11 months Post Incident 31.57 2.38 26.82 36.31 12 months Post Incident 31.21 2.29 26.64 35.79

43

For further comparison, the average monthly stock prices for one year prior to the

violation (i.e., 12 months), one month prior to the violation, the incident month, one

month after the violation, and 12 months after the violation were compared to each

other. More specifically, a repeated measures ANOVA was conducted using these five

monthly mean stock prices as the within subjects effect. The results revealed a

significant effect for month, F (4, 272) = 4.46, p < .05 (see Table 3 and Figure 3).

Pairwise comparisons indicated that the stock prices for the violating companies were

significantly greater 12 months after the violation (M = 31.62, SE = 2.38) compared to

the other months, including 12 months prior (M = 27.72, SE = 2.09, p < .01), one month

prior (M = 28.91, SE = 2.18, p < .01), the incident month (M = 29.16, SE = 2.16, p <

.05), and one month post (M = 29.33, SE = 2.17, p < .01).

______________________________________________________________________

Table 3

Average Select Monthly Stock Prices for Violating Companies (N = 69) ______________________________________________________________________

95% Confidence Interval Mean SE Lower Upper

12 months Prior Incident 27.72 * 2.09 23.54 31.89

1month Prior Incident 28.91

2.18 24.56 33.26

Month of Incident 29.16

2.16 24.85 33.46

1 month Post Incident 29.33

2.17 25.01 33.66

12 months Post Incident 31.62

2.38 26.88 36.36 ______________________________________________________________________

Note: * Mean differed significantly from other four means in the analysis (p < .05).

44

______________________________________________________________________

Figure 3. Select monthly average stock price. Note: 12 months post was significantly different from all other means (p < .05). ______________________________________________________________________

Hypothesis 1B

Hypothesis 1B involved examining the degree of decline of the monthly mean

stock prices in relation to industry sector. In order to evaluate the data for differences in

the degree of decline, five different measures of percent decline were calculated. The

strategy for calculating the percent decline was as follows: [(value at the end – value at

the beginning) / value at the beginning]*100.

The first measure of decline used the difference from the incident month to one

month after the incident, i.e., [(post month one – incident month) / incident month]*100.

A positive value indicated that the stock prices increased from the incident month to one

month after the incident. A negative value, on the other hand, indicated that the stock

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prices decreased from the incident month to one month after the incident. The second

used the difference from one month prior to the incident to the month of the incident. A

positive value indicated an increase in stock prices from the month prior to the incident

month and a negative value indicated a decrease in stock prices from the month prior to

the incident month.

The third measure included the incident month to one year post (12 months). A

positive value revealed an increase in stock prices from the month of the incident to one

year post. A negative value revealed a decrease in stock prices from the incident month

to one year post. The fourth calculation used one year prior to the incident (12 months)

to the incident month. A positive value indicated that prices increased from one year

prior to the incident month and a negative value indicated that prices decreased from

one year prior to the incident month.

The fifth and final calculation involved the difference between the average of the

quarter including the incident month (incident month, one month prior, two months prior)

to the average of the quarter immediately following the incident month (one month, two

months, three months post incident). A positive value revealed that the price decreased

from the quarter prior to the incident to the quarter after the incident, whereas, a

negative value revealed that the price increased from the prior quarter to the post

quarter. The descriptive statistics for the percent decline measures are displayed in

Table 4.

46

______________________________________________________________________

Table 4

Means and Standard Deviations for Percent Decline Measures (N = 69) ______________________________________________________________________

N Mean SD Minimum Maximum

Percent Decline from Incident Month to Post1 Month 72 1.43 8.21 -20.69 32.53 Percent Decline from Prior1 Month to Incident Month 72 1.59 7.23 -14.07 20.27 Percent Decline from Prior Qtr1 to Post Qtr1 72 -2.21 13.36 -32.24 68.27 Percent Decline from Incident Month to Post12 Month 72 12.51 27.87 -41.61 107.68 Percent Decline from Prior12 Month to Incident Month 69 13.37 38.26 -71.66 163.30

______________________________________________________________________

Due to the small number of records within each sector, including utilities (n = 6),

basic materials (n = 6), industrial goods (n = 6), and services (n = 5), as well as the

variability in the percent decline measures (see Table 4), the Kruskal-Wallis test was

used to examine the data for differences in decline by sector. The Kruskal-Wallis test

represents the non-parametric equivalent to the ANOVA and is used to compare three

or more groups when the assumptions of the ANOVA are not met. Non-parametric tests

are often referred to as “distribution-free” tests because they do not require or make any

assumptions about the distribution of the data. In addition, these tests can be used

when small sample sizes are present in the data or subgroups for comparison. Thus five

separate Kruskal-Wallis tests were conducted on the five different percent decline

47

measures to examine the data for differences between sectors. As shown in Table 5,

the results failed to reveal significant differences between sectors on any of the five

decline measures (all p > .05). Thus, Hypothesis 1B was not supported.

______________________________________________________________________

Table 5

Percent Decline Measures by Sector ______________________________________________________________________

N Mean Rank �2 p

Percent Decline from Incident Month to Post1 Month 12.26 0.092 Utilities 6 50.50 Basic Materials 6 27.83 Industrial Goods 6 45.83 Healthcare 10 23.15 Consumer Goods 9 41.11 Services 5 23.80 Financial 18 36.22 Technology 12 42.54

Percent Decline from Prior1 Month to Incident Month 2.62 0.918 Utilities 6 37.75 Basic Materials 6 40.17 Industrial Goods 6 36.33 Healthcare 10 36.05 Consumer Goods 9 32.78 Services 5 32.20 Financial 18 33.31 Technology 12 43.88

Percent Decline from Prior Qtr1 to Post Qtr1 12.84 0.076 Utilities 6 18.33 Basic Materials 6 45.83 Industrial Goods 6 21.67 Healthcare 10 44.70 Consumer Goods 9 34.89 Services 5 47.60 Financial 18 40.39 Technology 12 32.25

48

______________________________________________________________________

Table 5 (continued). ______________________________________________________________________

N Mean Rank �2 p

Percent Decline from Incident Month to Post12 Month 6.55 0.477 Utilities 6 49.83 Basic Materials 6 36.33 Industrial Goods 6 47.67 Healthcare 10 32.40 Consumer Goods 9 40.00 Services 5 27.40 Financial 18 34.83 Technology 12 31.42

Percent Decline from Prior12 Month to Incident Month 8.45 0.295 Utilities 6 41.50 Basic Materials 6 24.17 Industrial Goods 6 22.00 Healthcare 9 31.67 Consumer Goods 9 43.11 Services 5 26.60 Financial 16 38.38 Technology 12 39.08

______________________________________________________________________

Hypothesis 1C

In order to address Hypothesis 1C, which sought to examine the monthly

mean stock prices by sector, a repeated measures ANOVA was conducted on the

monthly mean stock prices using sector as the between subjects effect. The healthcare,

financial, and technology sectors were included in the analysis; the utilities, basic

materials, industrial goods, consumer goods, and services sectors were excluded from

the analysis due to comparatively small record counts. The within effect included five of

the monthly mean stock prices as dependent measures (12 months prior, one month

49

prior, incident month, one month post, 12 months post). The results failed to reveal a

significant effect for month, F (4, 136) = 1.53, p = .223 (see Table 6). Similarly, the

interaction effect for month x sector was not significant, F (8, 136) = 1.07, p =.381.

There was, however, a significant effect for sector, F (2, 34) = 5.33, p < .05. Overall,

stock prices in the financial sector were significantly greater (M = 40.96, SE = 4.02) than

stock prices in the technology sector (M = 21.19, SE = 4.64; see Figure 4). Thus,

Hypothesis 1C was not supported.

______________________________________________________________________

Table 6

Average Selected Monthly Stock Prices by Sector ______________________________________________________________________

Healthcare Financial Technology Overall (n = 9) (n = 16) (n = 12) (N = 37)

Mean SE Mean SE Mean SE Mean SE

12 months Prior 31.19 5.84 37.56 4.38 19.22 5.06 29.32 2.96

1month Prior 27.97 5.26 40.59 3.95 20.94 4.56 29.83 2.67

Incident Month 28.41 5.14 40.51 3.85 21.34 4.45 30.09 2.61

1 month Post 27.91 5.44 40.83 4.08 22.17 4.71 30.30 2.76

12 months Post 30.11 6.48 45.30 4.86 22.30 5.61 32.57 3.28

Overall 29.12 5.36 40.96 4.02 21.19 4.64 ______________________________________________________________________

50

______________________________________________________________________

Figure 4. Select monthly average stock price by sector. ______________________________________________________________________

A second analysis was conducted to further address Hypothesis 1C by

determining whether or not the violating company stock prices were different from the

comparison company prices. Thus, a repeated measures ANOVA was conducted using

the matched comparison company and the five monthly stock prices (12 months prior,

one month prior, incident month, one month post, 12 months post) as within subjects

effects. The results revealed a significant effect for month, F (4, 232) = 4.36, p < .05

(see Table 7). Pairwise comparisons indicated that stock prices 12 months after the

violation were significantly greater (M = 28.38, SE = 1.66) than the prices in the other

four months, including 12 months prior to violation (M = 25.38, SE = 1.45, p < .05), one

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month prior to violation (M = 25.62, SE = 1.45, p < .01), the incident month (M = 25.78,

SE = 1.42, p < .01), and one month after the violation (M = 25.82, SE = 1.45, p < .01) .

In addition, there was also a significant effect for company, F (1, 58) = 5.17, p < .05.

Violating companies had greater stock prices overall (M = 29.55, SE = 2.18) than their

matched non-violating companies (M = 22.84, SE = 1.87, p < .05). However, the results

failed to reveal a significant interaction effect for month x company, F (4, 232) = .33, p =

.737, indicating that there were no differences between monthly stock prices based on

whether or not the company had a violation (see Figure 5).

______________________________________________________________________

Table 7

Average Selected Monthly Stock Prices for Violating and Comparison Companies ______________________________________________________________________

Violating Company

Matched Comparison

Company Overall (n = 59) (n = 59) (N = 59)

Mean SE Mean SE Mean SE

12 months Prior 28.34 2.27 22.43 2.03 25.38a 1.44

1month Prior 28.94 2.19 22.29 2.04 25.61a 1.45

Incident Month 29.10 2.16 22.47 2.01 25.78a 1.42

1 month Post 29.30 2.19 22.33 2.00 25.82a 1.45

12 months Post 32.09 2.52 24.66 1.93 28.38b 1.66

Overall 29.55 2.18 22.84 1.87

Note: Means with different superscripts differed significantly, p < .05.

52

______________________________________________________________________

Figure 5. Select monthly average stock price by violators versus comparisons. ______________________________________________________________________

Analysis was also conducted to examine the data within each sector for which

there were enough records (healthcare, financial, technology). More specifically,

separate repeated measures ANOVAs were conducted using the records from the

healthcare, financial, and technology sectors. The analyses used the matched

comparison company and the five monthly stock prices (12 months prior, one month

prior, incident month, one month post, 12 months post) as within subject’s effects. The

analysis on the healthcare records failed to reveal a significant effect for month, F (4,

32) = .33, p = .708; company, F (1, 8) = .62, p = .454; or month x company, F (4, 32) =

.41, p = .697 (see Table 8). Although the violating companies had lower mean prices (M

= 29.12, SE = 2.84) than the matched companies (M = 34.03, SE = 5.22), the results

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failed to reveal significant differences between violating and matched companies. The

matched companies were more variable with higher standard errors, which likely

influenced the lack of significance.

______________________________________________________________________

Table 8

Means and Standard Deviations for Selected Monthly Stock Prices for Healthcare

Sector for Violating and Comparison Companies ______________________________________________________________________

Violating Company

Matched Comparison

Company Overall (n = 9) (n = 9) (N = 9)

Mean SE Mean SE Mean SE

12 months Prior 31.19 5.59 32.45 5.55 31.82 2.45

1month Prior 27.97 3.02 34.50 5.86 31.23 3.71

Incident Month 28.41 3.25 33.57 5.47 30.99 3.54

1 month Post 27.91 3.30 32.73 5.20 30.32 3.51

12 months Post 30.11 4.50 36.91 5.68 33.51 3.56

Overall 29.12 2.84 34.03 5.22 ______________________________________________________________________

The results for the financial sector revealed a significant effect for month, F (4,

48) = 7.55, p < .01 (see Table 9). Stock prices 12 months after the violation were

significantly greater (M = 36.53, SE = 4.23) than the other months for the financial

sector, including 12 months prior (M = 28.97, SE = 3.16, p < .01), one month prior (M =

32.28, SE = 1.58, p < .05), the incident month (M = 4.32, SE = 1.87, p < .05), and one

54

month post (M = 32.07, SE = 3.03, p < .05). In addition, stock prices one year prior to

the incident (12 months) were significantly less (M = 28.97, SE = 3.16) than stock prices

one month prior to the violation (M = 32.28, SE = 1.58, p < .05), the incident month (M =

4.32, SE = 1.87, p < .05), and one year after the violation (12 months; M = 36.53, SE =

4.23, p < .01). There was also a significant effect for company, F (1, 12) = 18.27, p <

.01, indicating that financial companies with a violation had significantly greater stock

prices overall (M = 42.93, SE = 4.98) than those without a violation (M = 21.89, SE =

2.80). The interaction effect for company x month, however, was not significant, F (4,

48) = .63, p = .548 (p < .001).

______________________________________________________________________

Table 9

Means and Standard Deviations for Selected Monthly Stock Prices for Financial

Sector for Violating and Comparison Companies ______________________________________________________________________

Violating Company

Matched Comparison

Company Overall (n = 13) (n = 13) (N = 13)

Mean SE Mean SE Mean SE

12 months Prior 39.06 5.41 18.88 2.54 28.97a 3.16

1month Prior 42.48 4.80 22.08 2.89 32.28c 3.11

Incident Month 42.37 4.42 22.05 2.80 32.21a 2.90

1 month Post 42.53 4.65 21.61 2.81 32.07ac 3.03

12 months Post 48.21 6.41 24.84 3.39 36.53b 4.23

Overall 42.93 4.98 21.89 2.80 ______________________________________________________________________

Note: Means with different superscripts differed significantly, p < .05.

55

The results for the technology sector revealed a significant effect for month, F (4,

40) = 4.99, p < .05 (see Table 10). Stock prices in the technology sector one year prior

to the incident were significantly less (M = 15.65, SE = 2.87) than stock prices one

month prior to the violation (M = 18.15, SE = 3.24, p < .01), the incident month (M =

18.09, SE = 3.29, p < .05), one month after the violation (M = 18.55, SE = 3.54, p < .05),

and one year after the violation (12 months; M = 20.60, SE = 3.88, p < .05). The results

failed to reveal a significant effect for company, F (1, 10) = 1.50, p = .249. Finally, the

interaction effect for month x company was not significant, F (4, 40) = 1.12, p = .345

(see Figure 6).

______________________________________________________________________

Table 10

Means and Standard Deviations for Selected Monthly Stock Prices for Technology

Sector for Violating and Comparison Companies ______________________________________________________________________

Violating Company

Matched Comparison

Company Overall (n = 11) (n = 11) (N = 11)

Mean SE Mean SE Mean SE

12 months Prior 20.11 5.46 11.18 1.74 15.65a 2.87

1month Prior 21.48 5.90 14.82 2.31 18.15a 3.24

Incident Month 21.81 5.97 14.36 2.22 18.09a 3.28

1 month Post 22.57 6.45 14.52 2.20 18.55a 3.54

12 months Post 23.20 6.17 18.01 2.68 20.60b 3.87

Overall 21.83 5.99 14.58 1.87 ______________________________________________________________________

Note: Means with different superscripts differed significantly, p < .05.

56

_____________________________________________________________________

Figure 6. Select monthly average stock price by sector and violators versus comparisons. ______________________________________________________________________

Hypothesis 1D

Hypothesis 1D sought to determine whether there were differences in monthly

stock prices for companies with repeat violations. As previously presented (see Table

1), 38.9% of the records had two violations, 4.2% had three violations, 6.9% had five

violations, and 9.7% had seven violations. The only group with enough records for

analysis was the companies with two violations. Thus, the analysis for Hypothesis 1D

selected only those records that were repeat offenders with two violations. In addition,

the average quarterly stock prices were used in the analysis in order to determine

whether there were differences between quarterly averages for repeat offenders.

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A repeated measures ANOVA was conducted using the 12 quarterly prices as

the within-subjects effect. The results revealed a significant effect for quarter, F (11,

275) = 3.03, p < .05 (see Table 11), indicating that there were significant differences

between quarterly stock prices for the repeat offenders. Pairwise comparisons were

examined to determine which quarters were significantly different from each other.

Stocks in the third (M = 30.06, SE = 4.46) and fourth quarter (M = 29.61, SE = 4.44)

after the violation were significantly greater than stocks in the third (M = 25.84, SE =

3.61, p < .05), fourth (M = 24.49, SE = 3.40, p < .05), fifth (M = 25.03, SE = 3.56, p <

.05), and sixth (M = 26.08, SE = 3.68, p < .05) quarters prior to the violation.

____________________________________________________________________

Table 11

Average Quarterly Stock Prices for Companies with Two Repeat Offenses (N = 26) ______________________________________________________________________

95% Confidence Interval Mean SE Lower Upper

Prior Qtr 8 26.55 3.60 19.13 33.96

Prior Qtr 7 26.32 3.45 19.22 33.42

Prior Qtr 6 26.08 3.68 18.50 33.66

Prior Qtr 5 25.03 3.56 17.70 32.37

Prior Qtr 4 24.49 3.39 17.50 31.48

Prior Qtr 3 25.84 3.61 18.40 33.28

Prior Qtr 2 27.19 3.79 19.38 34.99

Prior Qtr 1 28.91 4.11 20.45 37.36

Post Qtr 1 27.57 3.75 19.84 35.29

Post Qtr 2 28.79 4.27 19.98 37.59

Post Qtr 3 30.06 4.46 20.88 39.25

Post Qtr 4 29.61 4.44 20.46 38.76

58

In addition, stocks in the third quarter after the violation were significantly greater

(M = 30.06, SE = 4.46) than stocks in the eighth quarter prior to the violation (M = 26.55,

SE = 3.60, p < .05), as well as stocks in the quarter immediately following the violation

(M = 27.57, SE = 3.75, p < .05) and the second quarter after the violation (M = 28.79,

SE = 4.27, p < .05). In addition, the quarterly stock prices in the quarter immediately

after the violation were significantly greater (M = 27.57, SE = 3.75) than prices in the

fourth quarter prior to violation (M = 24.49, SE = 3.40, p < .05) and prices in the third

quarter after the violation (M = 30.06, SE = 4.46, p < .05).

Quarterly stock prices in the quarter immediately before the violation were

significantly greater (M = 28.91, SE = 4.11) than those in the third (M = 25.84, SE =

3.61, p < .05), fourth (M = 24.49, SE = 3.40, p < .05), and fifth quarters (M = 25.03, SE =

3.56, p < .05) prior to the violation. The stock prices in the fourth quarter before the

violation were significantly less (M = 24.49, SE = 3.40) than prices in all of the later

quarters (second and third quarter prior to violation, quarter immediately prior to

violation, and all quarters after the violation; means ranged 25.84 to 30.06). Finally,

stock prices in the third quarter before the violation were significantly less (M = 25.84,

SE = 3.61) than prices in the two quarters before the violation (M = 27.19, SE = 3.79; M

= 28.91, SE = 4.11, p < .05).

An additional repeated measures analysis was conducted to determine whether

there were differences in quarterly stock prices based on first or second violation. Thus,

the analysis used quarterly stock prices as the within subjects effect and violation (first

vs. second) as the between subjects effect. The results failed to reveal a significant

effect for violation, F (11, 264) = .005, p = .945. There were also no differences in

59

quarterly stock prices based on the first or second violation; interaction effect for quarter

x violation, F (11, 264) = 1.09, p = .366. Thus, hypothesis 1D was not supported.

In order to examine the data for potential differences in the percentage of

quarterly decline based on first or second violation, an additional analysis was

conducted. More specifically, due to the variability in the percent of quarterly decline

measure, the Kruskal-Wallis test was utilized to test for differences based on violation

(first vs. second). The results failed to reveal significant differences in percent decline

for first and second violations, �2 (1) = 1.07, p = .301.

Hypothesis 2

The second hypothesis involved examination of the quarterly stock prices for

violating companies and their matched comparison company. A repeated measures

ANOVA was conducted using the average quarterly stock price (before and after the

violation) and company as the within-subjects effects. The results failed to reveal a

significant effect for quarter, F (1, 63) = 2.83, p = .098, indicating that there were no

overall differences between prices the quarter before and the quarter after the violation.

There was, however, a significant effect for company, F (1, 63) = 4.33, p < .05. Violating

companies had greater overall quarterly stock prices (M = 28.56, SE = 2.02) compared

to their matched comparison companies (M = 22.64, SE = 1.92). The interaction effect

for quarter x company was not significant, F (1, 63) = .30, p = .586.

The percent decline from the quarter prior to and including the incident month to

the quarter after the incident month was also compared between violating companies

and non-violating companies. Due to the variability in the percent decline measure, the

Wilcoxon signed-rank test was used. The Wilcoxon signed-rank test is a non-parametric

60

test used for two related or repeated measures on a sample. It is the non-parametric

alternative to the paired t-test or repeated measures ANOVA and does not make any

assumptions about the distribution of the measures. The results failed to reveal a

significant difference in the quarterly decline measures for violating and non-violating

companies, z = -.02, p = .984.

Individual Companies

While across all the violating companies, there was an increase in stock prices

after the incident month, there were individual companies whose monthly average stock

prices showed the expected pattern of a decrease after the violation incident month.

Companies with this expected decrease included Archer Daniels Midland (2nd

violation), Bank of America (1st violation), Coca Cola, Costco, Eastman Chemical,

Enron, Exxon Mobile (1st violation), Fleet Boston Financial, Ford, Haliburton (2nd

violation), HCA, Health South, Morgan Stanley (1st violation) Pfizer (1st and 2nd violation,

Rockwell Automation (1st violation), Tenet Healthcare, and Unocol Corporation.

However, these decreases in stock prices were not as large or as frequent as the

companies that had increases in stock prices after the incident. Appendix C and D

contain the average monthly stock prices for each individual violating and comparison

company. Examination of these violators with decreases showed that they were equally

as likely to have a major violation (violation amount equal to or greater than $50 million).

Slightly more than half were not companies who had repeat violations (55.6%).

Approximately a quarter of these companies were from the Basic Materials (22.2%) and

Healthcare (22.2%) sectors, followed by consumer goods (16.7%) and Financial

(16.7%).

61

Summary

The stock prices for the violating companies were significantly greater 12 months

after the violation compared to the other months (12 months prior, one month prior,

incident month, one month post). There were no significant differences in percent

decline between the eight sectors on any of the five decline measures. There were no

differences between violating companies and their matched companies. In addition,

there were no differences in monthly stock prices. However, the results did indicate that

financial stocks were, on average, significantly greater in price than technology stocks.

The results revealed that stock prices 12 months after the violation were significantly

greater than the prices in the other four months (12 months prior, one month prior,

incident month, one month post). In addition, violating companies had greater stock

prices overall than their matched non-violating companies. There were no differences

between monthly stock prices based on whether or not the company had a violation.

There were no significant effects for the healthcare sector. There were significant

differences between months and between violating companies and non-violating

companies in the financial sector. Stock prices 12 months after the violation were

significantly greater than the other months for the financial sector (12 months prior, one

month prior, incident month, one month post). In addition, stock prices one year prior to

the incident (12 months) were significantly less than stock prices one month prior to the

violation, the incident month, and one year after the violation (12 months). Finally,

companies with a violation had significantly greater stock prices overall than those

without a violation (collapsing across the five months). There were also significant

differences between months for companies in the technology sector. Stock prices in the

62

technology sector one year prior to the incident were significantly less than stock prices

one month prior to the violation, the incident month, one month after the violation, and

one year after the violation (12 months).

There were significant differences between quarterly stock prices for the repeat

offenders. Stocks in the third and fourth quarter after the violation were significantly

greater than stocks in the third, fourth, fifth, and sixth quarters prior to the violation. In

addition, stocks in the third quarter after the violation were significantly greater than

stocks in the eighth quarter prior to the violation as well as stocks in the quarter

immediately following the violation and the second quarter after the violation. In

addition, the quarterly stock prices in the quarter immediately after the violation were

significantly greater than prices in the fourth quarter prior to violation and prices in the

third quarter after the violation.

Quarterly stock prices in the quarter immediately before the violation were

significantly greater than those in the third, fourth, and fifth quarters prior to the violation.

The stock prices in the fourth quarter before the violation were significantly less than

prices in all of the later quarters (second and third quarter prior to violation, quarter

immediately prior to violation, and all quarters after the violation). Finally, stock prices in

the third quarter before the violation were significantly less than prices in the two

quarters before the violation. There were no differences between quarterly stock prices

for violating companies and those reporting earnings disappointments.

63

CHAPTER 4

DISCUSSION

The main purpose of the current study was to explore the impact of a

corporation’s announcement of convictions or settlements for violations on shareholder

support as identified by a corporation’s stock price. The sample for the study consisted

of U.S.-based Fortune 500 corporations. The time period studied was 1994-2004 and

repeated measure ANOVA was utilized for the analysis of each research question. For

this study, monthly average stock prices were collected, however, not all data was

found for all violating organizations within the time period, therefore only those where all

data was available were utilized.

Primary Analyses

Based on the findings of this study, the opposite of the predicted shareholder

reaction occurred. The hypothesis anticipated a decrease in shareholder support in the

wake of corporate violation announcements when in fact an increase in shareholder

support appeared to occur. Possible reasons for not finding a decline in stock prices

following an announcement could be: shareholder reactions are shorter lived and a daily

or even hourly investigation into stock prices pre and post event may show an initial

decline, prior to the announcement stock prices may have absorbed the upcoming

event and had already adjusted for market reaction due to leaks, and public information

of charges of violations, and perhaps shareholders simply do not care about violations if

they perceive those actions to be incidental to the corporation’s financial performance.

64

Violations, Announcements, and Stock Prices

Contrary to the first hypothesis which stated that the monthly mean stock price

would decline significantly related to public announcement of prosecution or settlement

for regulatory violations and /or criminal offenses, the announcement of prosecution or

settlement for regulatory violations and/or criminal offenses failed to negatively impact

the stock prices of corporations. The average monthly stock prices were examined for

companies that had regulatory violations and/or offenses. No significant differences

were found for the monthly stock prices one year prior to the violation month, the

incident month, one month after the violation, or one year after the violation. Additionally

the study revealed significantly greater stock prices for the violating companies one year

after the violation.

Unlike the findings from the present study, Fried, Schiff, and Sondhi (1989), in

their analysis of monthly stock prices for corporations that had announced either taking

write-downs or write-offs, found decline of unadjusted market returns for six months

prior and six months after the event, with the greatest decline occurring on the actual

announcement date. However, Strong and Meyer (1987) reported insignificant, but

negative, short-term declines following write-off events.

Event research focused on the impact of litigation, specifically fraud litigation, on

stock prices has reported short-term negative announcement effects. Bhagat, Brickley,

and Coles (1994) found a significant negative event effect the day of and the day

following the litigation announcement. Also, Ferris and Pritchard (2001) reported a

significant negative reaction for the three-day period following a litigation

announcement. Whereas Griffin, Grundfest, and Perino (2004) also utilized a three-day

65

window for their study of the impact of federal class actions on stock prices, they

reported an immediate negative response on the class action filing date.

Based on the aforesaid research, perhaps the analysis in the current study did

not reveal a significant negative impact on stock prices after violation announcements

because the study focused on monthly averages, which may be too long of a time

period for this type of event research. Perhaps announcements of convictions or

settlements did not yet have a significant impact on shareholder support. In fact,

shareholder support, as indicated by stock prices, revealed an increase in support post

announcement. The actual announcement may be too late of an indicator of withdrawal

of shareholder support. Perhaps the news of possible violations versus the actual

settlement and/or judgment is the better event to reveal shareholder perceptions. It may

that by the time the actual settlement or fine is announced, the news of potential

judgments has already been integrated into the stock price of a corporation so once the

determining announcement is made, the market has long since adjusted and so stock

prices have already dropped and post announcement the stock price is perceived as

good value to shareholders. Another possibility is that not enough companies were

represented in the present study because of the inability to access data for all offending

corporations within the time period under study.

A further consideration about the non-significant results is that perhaps the study

period (1994-2004) was not a long enough time period to review. However, it is

interesting that the stock prices for the studied corporations experienced no significant

decline post announcement, yet increased 12 months after the event. Perhaps the

market and shareholders did not have a significant reaction to the event, but once they

66

felt the issue had been properly disclosed and addressed by the corporation, they had

more trust that the corporation would act more responsibly in the future and no more

bad news about the corporation’s behavior would surface. Another consideration is that

shareholders will forgive corporations pushing or stepping over boundaries if they

believe the perceived pay off to be greater than the penalty and/or risk. .Maybe bending

the rules is tolerated or even supported by shareholders if perceived to be an effort to

increase revenue, market share, diminish costs, and enhance the overall success of a

corporation.

Also, it is possible that the year is some sort of market perceived appropriate

correction period, or waiting period, to determine if new bad information is revealed as a

result of the announcement. It is important to note that both the non-significant finding

and the increase in stock prices 12 months after the violating event add information not

previously included in I/O event research. The results possibly provide information for

future study designs, focus, and offer results to build upon in future research.�Maybe

looking at shorter time segments surrounding announcements may reveal more

significant information. It may be that shareholder reactions are more immediate and

better revealed by considering stock price changes within a day versus a month. Also,

looking at the actual announcement of allegations versus the announcements of

settlements and/or fines may show a timelier shareholder reaction. Perhaps the

shareholder perceptions are most volatile at the first indication of potential troubles

instead of after more concrete legal judgments and settlements.

Across Sectors and Within Sector. The results did provide support for hypothesis

1C, stating that there would be no differences in stock price declines across sectors for

67

corporations announcing prosecutions or settlements for regulatory violations and

criminal offenses. The analysis revealed no significant differences in percent decline

between the eight sectors on any of the five decline measures. The five decline

measures included: incident month to one month after incident, one month prior to

incident to the incident month, incident month to one year after incident, the average of

the quarter including the incident month to the average of the quarter immediately

following the incident month. It is possible that because there were no significant

differences in corporations’ stock prices after a violating event, there were also no

differences in decline across sectors. Future research should consider if a corporation’s

sector is not a factor impacting shareholder support following event announcements.

As for hypothesis 2 that predicted that the monthly mean stock price of

corporations that announced prosecutions or settlements would decrease for regulatory

violations and/or criminal offenses and would decline significantly compared to other

companies within the same industry sector, no support was found for violating

corporations compared to matched non-violating corporations within the same sector.

No significant differences were found in the average monthly stock prices for violating

corporations one year prior to the violation, one month prior to the violation, the incident

month, one month after the violation, or one year after the violation. While there were no

differences in monthly stock prices, financial stocks were, on average, significantly

greater in price than technology stocks. Also, the comparisons between violating and

non-violating corporations, matched by sector, for the same five months revealed that

stock prices one year after the violation were significantly greater than the prices in the

68

other four months and there were no differences between monthly stock prices between

violators and non-violators.

As for the further analysis of within sector data, there were no significant effects

for the healthcare sector. In the financial sector, there were significant differences

between months and between violating companies and non-violating companies, and

one year after the incident stock prices were significantly greater. Also there were

significant differences between months for companies in the technology sector with

stock prices one year prior to the incident being significantly less than stock prices one

month prior to the violation, the incident month, one month after the violation, and one

year after the violation. Differences in sector results may be due to factors such as

specific economic conditions influencing shareholder perceptions that impact sectors

differently or factors that are exclusive to each sector studied.

These findings are contrary to Bartov, Lindahl, and Ricks (19987) who found a

substantial negative drift in returns two years after announcements. However, they

found significant effects for the first year only. In addition, Ewing’s (2002) study on the

impact of macroeconomic variables on the financial sector returns found that regardless

if the news was good or bad, the effects did not last beyond two months, possibly

suggesting that the news had already been integrated into the stock prices by that time.

Perhaps the lack of findings in this study was because the cost of the announcement

had already been factored into perceived shareholder value of the stocks. For example,

if there was market anticipation of potential news there may have been before

announcement changes in stock values. It is interesting that offending companies from

the financial sector in particular experienced significantly greater stock prices 12 months

69

after violations. Both technology and financial sector stocks experienced an increase

from 12 months prior to 12 months post incident, which may reflect general growth in

those sectors for that time period.

Some factors that can influence a sector include: their cyclical nature, the fact

that sectors can be artificially inflated if they are in high favor with the market, and that

sectors can be vulnerable to negative sentiment resulting in a few companies within a

sector deflating the group. Any of these influences could have impacted the results in

the present study. Additional sector specific research could enhance the understanding

about sector specific variables as there are very little across or within sector studies

available. The present study was one of the first I/O psychology studies that had

considered sector specific variables within and across sectors and may provide

groundwork for future research into the impact on shareholder perceptions of ethical

practices on stock prices as related to sectors.

Repeat Offenders

As for hypothesis 1D which declared that a significant negative percent decline in

quarterly stock prices would increase in relation to each event for repeat-offender

corporations, no support was found. In fact, the current findings demonstrated an

increase in stock value post-event. There were significant differences between quarterly

stock prices for the repeat offenders, however not as predicted. In general, the stock

prices for corporations with two offenses within the time period studied did not

experience a percentage decline after each announcement, but instead experienced an

upward trend in stock value.

70

The increase in stock prices in the quarters following the event was interesting

because based on the pre-and-post data there appears to have been a general upward

trend of the stock values. It is possible that if more groupings of repeating violating

corporations could have been studied, more insight into shareholder reactions to repeat

offenders would have been discovered. Since there was only enough complete data to

study the group of corporations that had two offenses for the time studied, it may have

limited the cumulative impact that may have been observed in groups with four or more

repeat violations. For example, contrary to the present findings, Bartov, Lindhal, and

Ricks (1998) found that corporations with multiple write-off and/or write-down events

showed a significant negative drift after each event. However, their findings may be

exclusive to write-downs and/or write-off events and that is why there was a lack of

support found for violations and offenses.

Earnings Disappointments versus Violations

Finally, hypothesis two, which states corporations announcing prosecution or

settlement for regulatory violations and/or criminal offenses would experience greater

withdrawal of shareholder support than companies announcing earnings

disappointments was not supported. There were no differences between quarterly stock

prices for violating companies and those reporting earnings disappointments. The

percent decline from the quarter prior to and including the incident month to the quarter

after the incident month was compared between violating companies and non-violating

companies. There were also no differences in the percent decline. When comparing

quarterly stock prices immediately prior to and including the incident month and

immediately after the incident month for violators to their matched non-violating

71

company, there were significant differences between companies. That is, violating

companies had higher stock prices overall compared to non-violating companies.

These results could indicate that earnings disappointments or violations do not

result in significant post-event stock price declines, perhaps because the market

integrates the upcoming event for each case in the months preceding the

announcement. Thus, the market may not allow for significant declines post-incident. It

is also possible that analyzing monthly and quarterly data may be too broad of a view,

and instead the differences between earnings disappointments and violations should be

reviewed through daily or even hourly data. In their attempt to link the 50 largest daily

changes in the S&P 500 Index to significant events, Cutler, Poterba, and Summers

(1989) utilized one to five minute price change observations. Fair (2002) also used a

one to five minute price change approach in his research on linking price changes with

significant events.

Limitations

The findings presented in the current study should be interpreted with the

understanding of several limitations. First, the nature of stocks and stock prices,

specifically the variability between companies should be considered. While repeated

measures and percent decline calculations were utilized to control for some of the

variability, other sources of stock variability could not be controlled for statistically. For

instance, some companies may simply fare better than others even with a violation due

to factors including: market share, name branding, industry, sector, and market

capitalization. Some corporations may have greater stock prices than other

corporations. In the present study, for instance, the financial companies had much

72

greater stock prices than some of the corporations in other sectors. In addition,

seasonal or calendar considerations were not accounted for in the design of the current

study. Similarly, the current research did not examine or control for other factors that

may affect stock prices, such as world and political events or natural disasters.

A second important limitation for the present study was that complete data was

not available for all violating corporations during the study period (1994-2004), thus

reducing the sample size (215 to 72). It is possible that if all violating corporations that

met the criteria were included, the results may have been somewhat different. In

particular, a larger sample size could have allowed for more sector comparisons. Third,

in all cases sample sizes were small and therefore reduced the power to see real

differences. Fourth, the current study considered monthly and quarterly time periods,

which may have been too broad.

While Fama (1998) suggested monthly returns were less susceptible to the bad

model problem, and Seiler and Chakornpipat (1997) thought monthly returns were less

susceptible to the bid-ask bias, a large percentage of the event studies in the past have

utilized daily or hourly stock prices. Perhaps a shorter time period perspective, for

example employing daily stock prices, may have revealed more, or slightly different

results, about violating companies and shareholder support. It is possible that monthly

data may lose some of the information about the fluctuation of stock prices before and

after events.

Additionally, stock price alone may not be an exclusively accurate measure of

shareholder perceptions and resulting reactions. Perhaps PE ratios or trading volume

may be better dependant variables or utilizing a co-variate like EBITDA would yield

73

more significant results. Another limitation of the current study was that it was the only

I/O psychology study found to date that had examined ethical and responsible corporate

business practices as related to shareholder perceptions. In addition, no economic or

financial event studies covering the specific research questions addressed in the

present research were located. Thus, the opportunity for direct comparisons of findings

to prior research and the ability to design a study building off of prior results was not

possible in the current study.

Future Research

The results from this study suggest several directions and considerations for

future research. Due to the longer time period perspective utilized in the present study,

it may yield interesting results to consider the same data from a shorter time

perspective. Another study or follow up to this study could consider the daily and hourly

stock prices of corporations announcing convictions or settlements for violations in order

to assess whether there are more significant short term changes in shareholder support.

In addition, perhaps examining beyond one year after the event may also yield

additional information further illuminating the research area. For instance, Bartov,

Lindahl, and Ricks (1998) found a significant negative drift in returns for the first year

after companies announced a single write-off and/or write-down, but not for the second

year after the event. In addition, it may be interesting to control for some of the other

factors that can affect stock prices including: national and international political events,

seasonal and calendar variables, and legislative development. Also, additional research

into the companies in this study that did show the expected pattern may reveal unique

74

considerations as well as investigating corporations that were not included in this study

due to bankruptcy following the event.

Since this study included only large corporations (Fortune 500 companies), future

research could consider the same questions, but utilize small to mid size corporations.

Perhaps, these large public corporations are better equipped to absorb the impact of

violating and/or offending event announcements. They may be better positioned

because of possessing significant market share, brand name strength, and/or garner

significant consumer support.

Based on the limited amount of I/O psychology research available focusing on

organizational behavior and resulting shareholder reactions, additional research in this

area should be conducted. Further research into ways to contribute to and affect

organizational behavior, as well as market and shareholder reactions may yield data

that supports existing theories, and also may further define the relationship between

corporate practices and a corporate valuation. This research can provide valuable data

that I/O psychologists could present to corporations in order to support proposed

interventions and implementations. In addition, the future research may also contribute

to the methods that I/O psychologists can use to design implementations and

interventions.

Additional research into legal and responsible corporate practices could expand

an I/O psychologist’s understanding about designing and/or coaching human resources

(HR) groups on developing and maintaining responsible behavior in the groups’

ideologies and practices. Assessments can incorporate not only standards of practice,

but also test underlying standards and knowledge in their assessments of current and

75

potential employees. In addition, in the assessment process the organizations’ ethical

standards and expectations can be communicated to all involved in the process.

Research could substantiate the idea that compensation for employees and

management should be linked to responsible standards of behavior by encouraging and

rewarding best practices at all levels of the organization. The aforesaid issues represent

only a few of the areas where increased understanding about the impacts of corporate

behavior on shareholder support would translate into not only business practices, but

also I/O psychology practices, impacting how they work with corporations.

Conclusions and Implications

Despite the limitations of the current study, this is one of the first I/O psychology

studies to examine corporate behavior and the resulting shareholder reaction. The

results of the study indicated no significant decline in stock prices in corporations

announcing regulatory violations and/or criminal offenses compared to non-violating

corporations. In addition, there were no differences in declines for either within sector or

across sector stock prices for violating corporations compared to non-violating

corporations. The present study also did not find that corporations announcing

settlement or prosecution for violations and/or offenses experienced a greater decline in

quarterly stock prices compared to non-violating corporations announcing earnings

disappointments. Finally, the results did not find that corporations with two different

violation announcements during the study period experienced any significant decline for

the first or the second event. In fact, several increases in stock prices were found post

event. In general, no significant support was found to substantiate a link between

corporations announcing violations and/or offenses to the withdrawal of shareholder

76

support as defined by the decline in corporations stock values after events. However,

support was found that that there were no significant differences in decline for violating

corporations across sectors.

The current study represents an initial foray into an otherwise ignored research

area, and therefore, the findings offer a tentative basis for further research. The study

has important implications for I/O researchers, because although it did not show causal

relationships between organizational behavior and shareholder support, it did begin to

consider ways to quantify the possible implications of responsible corporate behavior

and shareholder support.

I/O psychology can benefit from more quantifiable information about corporate

behavior and outcomes, not only for further refining professional practices, but also for

refining I/O theories. Data emerging from additional studies ideally can be utilized by I/O

psychology in several areas, including executive coaching, organizational

design/redesign, training, performance appraisal systems, assessment, recruiting/hiring,

compensation, and human resource design or redesign.

Increased knowledge about responsible corporate behavior and internal

practices can be used in executive coaching and employee training programs. With

regard to training and coaching, more research into ethical corporate behavior can

provide I/O practitioners with an increased basis for coaching the importance of

corporations acting responsibly. Organizational designs or redesigns can be further

refined to support legal procedures, establish standards of behavior and practices,

include administrative processes for the reporting of inappropriate or violating behavior

concerns, and create methods for establishing and supporting a legal and ethically

77

based organizational culture. Performance appraisal systems can include items

assessing and teaching ethical behavior at the individual, group, and organizational

level.

The more quantifiable the data obtained, linking responsible behavior in

organizations to perceived shareholder values and actual support, the more internal and

external I/O psychologists can make a financial case to corporations about designing

and implementing best practices throughout the organization. The stronger the link

between the bottom line and public perception, the more likely ethical behavior in

corporate America will improve. The greater focus on improving business practices can

mean more opportunities for I/O psychology to influence the progress toward more

ethical corporate behavior. Executive recognition of the link between practices and

stock prices may motivate corporations to bring in I/O psychology for appropriate

interventions.

78

APPENDIX A

CORPORATE FINES AND SETTLEMENTS

79

Violation Date Corporation Violation

Fine or Settlement

Amount Industry Fortune

500 Y/N

5 yr. History

Y/N

5 yr. Stock Price Y/N

11/24/1994 Consolidated Edison Environmental 2 million Utility Y Y Y 3/21/1994 Unocal Corporation Environmental 1.5 million Petroleum Y Y Y 2/6/1995 Teledyne Industries Inc. Financial 4 million Defense

contractor Y Y Y

1/16/1995 Ortho Pharm. Corp. Obstruction of Justice

5 million Pharmaceutical Y Y Y

4/3/1995 Ketchikan Pulp Co. Environmental 3 million Wood Products N N N 7/3/1995 Bethship-Sabine Yard Environment 500,000.00 Ship Building Y Y Y

7/31/1995 Consoli dated Rail Corp. Environmental 2.5 million Railway N N N 12/4/1995 Warner-Lambert Co. Food & Drug 10 million Pharmaceutical Y Y Y

10/23/1996 Aqua Leisure Campaign Finance 6 million Leisure N N N 10/15/1996 Archer Daniels Midland Financial Crimes 100 million Agricultural Y Y Y 2/12/1996 John Morrell & Co. Environmental 2 million Food N N N 4/15/1996 Rockwell Int. Corp. Environmental 6.5 million Technology Y Y N 5/6/1996 Blue Shield of California Fraud 1.5 million Insurance Y Y Y

5/20/1996 Summitville Mining Co., Inc.

Environmental 37 million Mining N N N

6/3/1996 Iroquois Pipeline Op. Co. Environmental 15 million Petroleum N N N 6/3/1996 Case Corporation Illegal export 1 million Agricultural Equip. Y Y Y

10/14/1996 Damon Clinical Lab. Inc. Fraud 35.2 million Healthcare N N N 10/14/1996 Arizona Chemical Co.,

Inc. Environmental 2.5 million Chemical N N N

10/21/1996 Archer Daniels Midland Antitrust 100 million Agricultural Y Y Y 11/25/1996 Allied Clinical Lab. Inc. Fraud 5 million Healthcare N N N 1/27/1997 Crop Growers Corp. Campaign Finance 2 million Agricultural Equip. N N N 3/24/1997 ConAgra, Inc. Fraud 4.4 million Food Y Y Y 6/2/1997 Copley Pharm., Inc. Food & Drug 10.65 million Pharmaceutical N N N

9/29/1997 Warner-Lambert, Inc. Environmental 3 million Pharmaceutical Y Y Y 10/13/1997 Empire Sanitary Landfill,

Inc. Campaign Finance 8 million Waste Treatment N N N

11/17/1997 Andrew & Williamson Financial 200,000.00 Pharmaceutical N N N 1/5/1998 Tyson Foods, Inc. 4 million Food Y Y Y

4/13/1998 UCAR International, Inc. Antitrust 110 million Electronics N N N 5/25/1998 Doyon Drilling, Inc. Environmental 1 million Petroleum N N N 10/2/1998 Boeing Illegal Exports 10 million Aircraft

Manufacturing Y Y Y

6/8/1998 Louisiana-Pacific, Co. Environmental 37 million Wood Products N Y Y 6/8/1998 Browning-Ferris, Inc. Environmental 1.5 million Waste Disposal Y Y Y 6/8/1998 Royal Caribbean, Ltd. Environmental 9 million Travel N Y Y

7/20/1998 Blue Cross & Blue Shield Fraud 4 million Insurance Y Y Y 7/27/1998 Odwalla, Inc. 1.5 million Food N N N 8/10/1998 Ryland Mortgage, Co. Financial 4.2 million Mortgage N N N 8/17/1998 Saybolt, Inc. Environmental 3.4 million N Y N 8/17/1998 Sun-Land Products of

Ca. Campaign Finance 400,000.00 N N N

10/5/1998 Eastman Chemical Co. Antitrust 11 million Chemical Y Y Y 10/23/1999 Royal Caribbean, Ltd. Environmental 18 million Cruise N Y Y

1/4/1999 Northern Brands Int. ,Inc. Fraud 5 million Tobacco Y Y Y 2/3/2009 Louisiana-Pacific Co. Environmental 37 million Manufacturing N Y Y

2/15/1999 Sears Bankruptcy Mgt Svc

Fraud 60 Million Retail Y Y Y

3/1/1999 Colonial Pipeline Co. Environmental 7 million Petroleum N N N 3/15/1999 Banker’s Trust Financial 60 million Banking N N N 4/19/1999 Genentech, Inc. Criminal 30 million Biotech Y Y Y

80

5/6/1999 Merrill Lynch Financial 25 million Banking Y Y Y 5/19/1999 Northern Brands Int., Inc. False Statements 5 million Pharmaceutical N Y Y 7/26/1999 Kimberly Home Health

Care, Inc. Fraud 10.08 million Healthcare N Y N

7/26/1999 Lucas Western False Statements 18.5 million Banking N N N 7/26/1999 Pfizer Antitrust 20 million Pharmaceutical Y Y Y 8/9/1999 Tyson Foods, Inc. Criminal 6 million Food Y Y Y

1/13/2000 Koch Industries Environmental 30 million Petroleum N Y N 3/5/2000 Beverly Enterprises Financial 175 million Healthcare N Y N

4/21/2000 Diamond Shamrock Environmental 13 million Petroleum N N Y 5/10/2000 Boeing Illegal export 100 million Aircraft

Manufacturing Y Y Y

7/15/2000 Phillip Morris Fraud Law Passed Tobacco N N Y 7/18/2000 Lockheed Martin Export Violations 13 million Aircraft

Manufacturing Y Y Y

8/25/2000 Arthur Andersen Fraud 7 million Accounting N Y Y 10/5/2000 Argenbright False Statements 15 million Security N Y N 12/5/2000 HCA Criminal 840 million Healthcare N N N

12/19/2000 Wal-Mart Wage Laws 50 million Retail Y Y Y 1/20/2001 Coca Cola Race

Discrimination 192.5 million Food Y Y Y

3/3/2001 Costco Financial 1.7 million Retail Y Y Y 4/0/2001 Boeing Illegal Export Illegal

export Aircraft Manufacturing

Y Y Y

4/3/2001 Smithfield Foods Environmental 25 million Food Y Y Y 4/6/2001 BankOne Fraud 1.8 billion Banking Y Y Y

4/16/2001 TAP Pharmaceuticals Criminal 885 million Pharmaceutical N N N 4/18/2001 JP Morgan Financial 135 million Banking N Y N 5/6/2001 Abbott Financial 875 million Pharmaceutical Y Y Y

5/14/2001 Sara Lee 200,000 Food Y Y Y 5/16/2001 Enron Fraud Energy N N N 7/21/2001 Exxon Environmental Petroleum Y Y Y 1/2/2002 Merrill Lynch Financial 100 million Banking Y Y Y 1/2/2002 JP Morgan Chase Financial 50 million Banking Y Y Y 4/5/2002 Halliburton Illegal Export 2 million Defense

Contractor Y Y Y

5/10/2002 Boise Cascade Financial 4.35 million Manufacturing N Y Y 8/5/2002 Halliburton Financial 3.8 million Defense

Contractor Y Y Y

8/15/2002 Morgan Stanley Financial 50 million Banking Y Y Y 9/12/2002 Bear Stearns Financial 50 million Banking Y Y Y 9/20/2002 Lehman Bros. Financial 50 million Banking Y Y Y 9/20/2002 Citigroup Financial 215 million Banking Y Y Y

10/10/2002 Burlington Northern Illegal Boycott 2.2 million Railroad Y Y Y 12/20/2002 Goldman Sachs Financial 50 million Banking Y Y Y 3/12/2003 Wisconsin Energy Financial 3.2 million Utility Y Y Y 3/17/2003 Merrill Lynch Fraud 80 million Banking Y Y Y 4/8/2003 Hughes Space & Com. Illegal Boycott 32 million Space & Comm. N N N 4/8/2003 Boeing Illegal Boycott 32 million Aircraft

Manufacturing Y Y Y

4/13/2003 Dominion Power Va. Environmental 1.3 billion Energy N Y Y 4/28/2003 Citigroup Fraud 400 million Banking Y Y Y 4/28/2003 Merrill Lynch Fraud 200 million Banking Y Y Y 4/28/2003 Morgan Stanley Fraud 125 million Banking Y Y Y 4/28/2003 Lehman Brothers Hlds. Fraud 80 million Banking Y Y Y 4/28/2003 Bear Stearns Fraud 80 million Banking Y Y Y 4/28/2003 JP Morgan Chase Fraud 80 million Banking Y Y Y 4/28/2003 Goldman Sachs Group Fraud 110 million Banking N Y Y

81

4/28/2003 U.S. Bancorp Piper Jaffray

Fraud 32.5 million Banking N N Y

5/1/2003 Dial Harassment 10 million Consumer. Products

N Y Y

5/1/2003 Master Card Financial 1 billion Credit Cards N Y Y 5/20/2003 WorldCom Fraud 500 million Telecommunicatio

ns N N N

6/27/2003 Astra Zeneca Fraud 355 million Pharmaceutical Y Y Y 7/10/2003 Exxon Mobile Environmental 11.8 billion Petroleum N Y Y 7/21/2003 Microsoft Antitrust 1.1 billion Software Y Y Y 7/25/2003 PayPal (eBay) Criminal 10 million Payment Services N N Y 8/14/2003 Microsoft Patent

Infringement 520 million Software Y Y Y

9/3/2003 Canary Cap. Partners Financial 40 million Hedge Fund Y N N 9/6/2003 Microsoft Antitrust 750 million Software Y Y Y 9/6/2003 Microsoft Antitrust 23.25 million Software Y Y Y

9/11/2003 AIG Fraud 10 million Insurance Y Y Y 9/12/2003 Nike Labor Abuse 1.5 million Apparel N Y Y 9/30/2003 Microsoft Antitrust 10.5 million Software Y Y Y 10/2/2003 Flextronics Antitrust 934 million Electronics Y N Y 10/3/2003 Reliant Resources Fraud 50 million Energy N N Y 10/3/2003 Reliant Resources Fraud 13.8 million Energy N N Y

10/10/2003 Abbott Food & Drug 600,00,000 Pharmaceutical Y Y Y 10/29/2003 Microsoft Antitrust 200 million Software Y Y Y 12/26/2003 Microsoft Antitrust 60 million Software Y Y Y 1/14/2004 Microsoft Antitrust 520 million Software Y Y Y 1/28/2004 Exxon Mobile Environmental 6.75 billion Petroleum Y Y Y 2/12/2004 Raymond James Fraud 2.6 million Financial N Y Y 2/13/2004 United Airlines Gender

Discrimination 36.5 million Airline N Y Y

3/10/2004 Bank of America Fraud 10 million Banking Y Y Y 3/15/2004 Bank of America Fraud 375 million Banking Y Y Y 3/24/2004 Microsoft Antitrust 609 million Software Y Y Y 4/2/2004 Microsoft Antitrust 2 billion Software Y Y Y

4/16/2004 Boeing Gender Discrimination

40.6-72.5 million

Aircraft Manufacturing

Y Y Y

4/26/2004 Medco Health Sol. Fraud 29 million Pharmaceutical N N N 5/1/2004 American Airlines Vio. Fed. Aviation

Rules 2.5 million Airline N Y Y

5/7/2004 Conesco Fraud 15 million Financial Y Y Y 5/8/2004 Inviva Fraud 5 million Insurance N Y N

5/14/2004 Pfizer Fraud 430 million Pharmaceutical Y Y Y 5/17/2004 Lucent Technologies Fraud 25 million Technology N Y Y 5/27/2004 Citigroup Fraud 70 million Banking Y Y Y 6/21/2004 Archer Daniels Midland Fraud 400 million Agriculture Y Y Y 6/24/2004 Gemstar-TV Guide Int. Patent

Infringement 67.5 million TV Technology N N N

6/24/2004 Gemstar-TV Guide Int. Fraud 10 million TV Technology N N N 6/29/2004 Microsoft Antitrust 34 million Software Y Y Y 7/6/2004 MCI Financial 19.5 million Communication N N N 7/7/2004 Knight Trading Fraud 79 million financial N Y Y

7/11/2004 Clear Channel Indecency 1.75 million Radio Y Y Y 7/15/2004 BancOne Fraud 50 million Banking N N N 7/15/2004 Goldman Sachs Financial 2 million Banking Y Y Y 7/19/2004 Enron Antitrust 35 million Energy N N N 7/20/2004 Ernst and Young Duty Failure 1.5 million Accounting N Y N 7/12/2004 Morgan Stanley Gender

Discrimination 54 million Financial Y Y Y

7/15004 Time Warner Fraud 210 million Media Y Y Y

82

7/21/2004 Phillip Morris Lost Case Documents

2.75 million Tobacco N N N

7/29/2004 Morgan Stanley Disclosure 2.2 million Financial Y Y Y 7/29/2004 Coral Energy Resources Fraud 30 million Energy N Y N 7/30/2004 Bristol Myers Squibb Financial 300 million Pharmaceutical Y Y Y 7/30/2004 Schering Plough Kickbacks 345.5 million Pharmaceutical Y Y Y 8/3/2004 Fidelity Brokerage Fraud 2 million Financial N Y N 8/3/2004 Halliburton Fraud 7.5 million Defense

Contractor Y Y Y

8/3/2004 First American Bank Race Discrimination

6 million Banking Y Y Y

8/4/2004 Citigroup Fraud 10.9 million Banking Y Y Y 8/4/2004 Bristol Myers Squibb Fraud 150 million Pharmaceutical Y Y Y 8/4/2004 Franklin Advisers Fraud 50 million Financial N N N 8/6/2004 Microsoft Antitrust 31.5 million Software Y Y Y 8/6/2004 American Airlines Antitrust 3.3 million Airline N Y Y

8/12/2004 United Health Fraud 9.7 million Insurance Y Y Y 8/22/2004 Tyco Environmental 10 million Electronics N Y Y 9/10/2004 Quest Fraud 250 million Telecom Y Y Y 9/13/2004 Enron Fraud 85 million Energy N N N 9/13/2004 Enron Fraud 321 million Energy N N N 9/30/2004 IBM Financial 320 million Technology Y Y Y

11/16/2004 Abercrombie and Fitch Illegal Boycott 40 million Retailing Y y y 11/30/2004 AIG Fraud 126 million Insurance Y Y Y 12/4/2004 HealthSouth Financial 320 million Healthcare N Y Y

12/21/2004 Tenet Healthcare Criminal 395 million Healthcare Y Y Y

83

APPENDIX B

DATA FOR COMPANIES WITH VIOLATIONS

QUALIFYING FOR STUDY AND MATCHING COMPARISION COMPANIES

84 �

Violator Company Industry Violation Date Fine or Settlement

Amount Comparison Company

AbbottLab Pharmaceutical May-01 875m Bristol-Myers Squibb AbbottLab Pharmaceutical Oct-03 600m Amgen Archer Daniels Midland Agricultural Oct-96 100m Nabisco Group Holdings Archer Daniels Midlands Agricultural Jun-04 400m Land O'Lakes BankofAmerica Banking Mar-04 10m U.S. Bancorp BankofAmerica Banking Mar-04 375m Marsh & McLennan BankOne Banking Apr-01 1.8b Wells Fargo Boeing Aircraft Manu. Apr-04 72.5m Raytheon Boeing Aircraft Manu. May-00 100m Raytheon BurlingtonNorthern Railway Oct-02 2.2m Tesoro CocaCola Food Jan-01 92.5m Georgia-Pacific CONAGRA Food Mar-97 4.4m Bestfoods CONESCO Insurance May-04 15m Host Marriott ConsolidatedEdison Railway Nov-94 2m Tosco Cosco Retailer Mar-01 1.7m Rite Aid DominionPower Energy Apr-03 1.3m Xcel Energy EastmanChemical Chemical Oct-98 11m FMC Enron Power May-01 PG&E Corp. EXXON Petroleum Jul-01 Marathon Oil ExxonMobile Petroleum Jan-04 6.75m Williams GoldmanSachs Banking Dec-02 50m Principal Financial GoldmanSachs Banking Jul-03 2m Delphi Haliburton Defense contractor Apr-02 2m Williams Haliburton Defense contractor Aug-02 3.8m Amerada Hess HCA Healthcare Dec-00 840m Tenet Healthcare HealthSouth Healthcare Dec-02 320m Federal-Mogul IBM Computing Aug-04 320m Sprint JPMorganChase Banking Apr-01 135m Fleming LehmanBros Banking Jul-03 80m Safeco LockheedMartin Aircraft Manu. Jul-00 13m Raytheon Louisana Pacific Wood Products Feb-99 37m Scott Paper Louisana Pacific Wood Products Jun-98 37m USG LUCENT TECHNOLOGY Technology May-04 25m Maytag MedcoHealth Healthcare Apr-04 29m Medtronic

84

85 �

MerrillLynch Banking Mar-03 80m Fleming MerrillLynch Banking May-99 25m Loews Microsoft Software Apr-04 2b Apple Computer Microsoft Software Aug-03 520m Sun Microsystems Microsoft Software Aug-04 31.5m Computer Assoc. Intl. Microsoft Software Dec-03 60m Tesoro Microsoft Software Jan-04 520m Electronic Data Systems Microsoft Software Jul-03 1.1b Motorola Microsoft Software Jun-04 34m Sprint MorganStanley Banking Aug-02 50m Sunoco MorganStanley Banking Jul-04 54m Steelcase MorganStanley Banking Jul-04 2.2m Equity Office Properties MorganStanley Banking Apr-03 125m Safeco Nike Apparel Sep-03 1.5m Georgia-Pacific Pfizer Pharmaceutical May-04 430m Wyeth Prizer Pharmaceutical Jul-99 20m Wyeth QwestComm Telecom Sep-04 250m Charter Communications ReliantEnergy Energy Oct-03 50m Entergy ReliantEnergy Energy Oct-03 13.8m OGE Energy Rockwell automation Technology Apr-96 6.5m Entergy SaraLee Food May-01 200,000 Procter & Gamble Smithfield Foods Food Apr-01 25m Dole Food TenetHealthcare Healthcare Dec-04 395m Health Net TimeWarner Media Jul-04 210m Comcast TycoInternational Electronics Aug-04 10m Silicon Graphics TysonFoods Food Aug-99 6m Smithfield Foods TysonFoods Food Jan-98 4m Dole Food United Health Group Healthcare Aug-04 9.7m Baxter International UnocalCorp Petroleum Mar-94 1.5m Amerada Hess WalMart Retailing Dec-00 50m Rite Aid WisconsinElectricAOL Utility Mar-03 3.2m PG&E Corp.

85

86 �

APPENDIX C

FIGURES FOR COMPANIES WITH VIOLATIONS

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APPENDIX D

FIGURES FOR COMPARISION COMPANIES

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