Shareholders’ sensitivity to corporations' unethical
behavior and the different impact of
environmental and white-collar crime
Pedram Parsian University of Groningen Faculty of Economics and Business
Supervisor: Drs. Boris van Oostveen January 2016
Abstract
Using an event study methodology, this thesis investigates the sensitivity of shareholders’ reaction to an unethical behavior of companies. Firstly, environmental crime and white-collar crime are analysed separately and afterwards compared with each other in order to find out which type of crime shareholders are punishing more. Secondly, the thesis investigates which industries are more affected by unethical crime. Finally, the paper analyses if peer companies in the same industry are also affected by the unethical behavior. The results show that there is a general trend that shareholders punish unethical behaviour of companies and that indeed shareholders react more negatively to white-collar crime than to environmental crime.
Keywords: Environmental crime, white-collar crime, ethics, shareholders
I. Introduction
In the recent past, there was an increase in public awareness of environmental topics. The main question is whether companies are punished for harming the environment or not. For example Michael Porter and Claas van der Linde (1995) and Klaasen and Mclaughlin (1996) claim that environmental insensitivity lowers a firm's sales and increases its costs. As a consequence companies try to jump on the environmental bandwagon in order not to be penalized by shareholders or by the financial market. However, other studies such as Lanoie and Laplante (1994) and Jones and Rubin (2001) claim that the losses in the stock market through environmental crime are not significant.
In addition, white-collar crime is dominating the headlines worldwide. Edelhertz (1970) defined the crime as “an illegal act or series of illegal acts committed by non-physical means and by concealment or guile, to obtain money or property, to avoid the payment or loss of money or property, or to obtain business or personal advantage”. The recent financial crisis in 2008 showed that white-collar crimes such as fraud, bribery or other criminal activities are still very common in the corporate world and often cause significantly higher damage than any other crime (Lynch and Michalowski, 2006). As a consequence of these events, business leaders and financial practitioners worldwide are reconsidering the fundamentals of the traditional business and try to achieve a more sustainable and ethical capitalism. Karpoff et al. (2008), Armour et al. (2010) and Palmrose et al. (2004) prove in their studies that the initial disclosure of corporate fraud leads to a significant negative abnormal return. In this paper, white-collar crime will mainly be defined by bribery, fraud, accounting fraud and tax fraud and include examples of such crimes.
shareholders and thus the financial market punish white-collar crime more or less than environmental crime. The second part of the research analyzes if certain industries are more affected by unethical behavior. In addition, the industry spillover effect is calculated in order to see if peer companies of the same industry are also punished by the unethical behavior.
The industry and the spillover effect are analyzed by performing an event study methodology.
The contribution of this thesis to the literature is twofold. Firstly, this study adds an empirical contribution to the fields of finance and business ethics by comparing environmental and white-collar crime. There are already papers analyzing the effect of companies’ crimes to the stock prices, but none regarding the difference between the crime types. The aim of this paper is to examine the different impact of environmental and white-collar crime on companies’ stock performance. Furthermore, this is the first study that calculates the consequences on different industries in combination with white collar and environmental crime. In addition the impact of the misbehavior on peers within the industry known as the industry spillover effect is calculated. This further analysis brings the empirical part of this thesis to the next level as it adds valuable information for further research to give more significant statements on the impact of unethical behavior on the different industries. Secondly, this study relies on two unique hand collected datasets that only include companies from the S&P 500 in the period of 2001-2011. The fact that a broad market index is used, can be very useful as it explains the market as a whole and it is not influenced by the analyzed event. This gives the results more significance and statistical power.
The research problem of this thesis is defined as follows:
H1: Environmental crime related to a specific announcement of a company induces a significantly negative reaction in stock returns in the short-term
H3: White-collar crime related to a specific announcement of a company induces a significantly higher negative reaction in stock returns in the short term than environmental crime.
The next section reviews first, the theoretical background of environmental and white-collar crime and second, the industry and spillover effect. The third section analyzes a variety of papers in this field in order to show the empirical outcome. The fourth section describes the methodology that is used in this thesis and afterwards the unique dataset is presented. The sixth section provides the results and the seventh section analyses briefly the hypotheses. The last sections give the conclusion and discuss the limitations of this thesis and suggestions for further research.
II. Literature review
In this section, the main literature is presented. First, I will give a theoretical background regarding the terms white-collar crime and environmental crime. Furthermore, a literature review concerning the characteristics of certain industries and the spillover effect will be provided. Lastly, I will present the empirical results from a wide range of studies in this field.
Theoretical background - White-collar crime
between the various types of white-collar crimes (Geis, 2007; Meier, 2001). Defining the term in a simple and easy way is rather difficult as there are contrary views. Some acts that could be defined as white-collar crime, for example, are codified in the criminal law, while other acts are seen as administrative violations. Furthermore, problems come up when deciding if an individual or a corporate entity is responsible for committing a white-collar crime.
Due to problems in defining white-collar crime precisely, a meeting was organized by criminologists in order to come up with a clear and exact definition that would contain all the elements of white-collar crime:
“White collar crimes are illegal or unethical acts that violate fiduciary responsibility of public trust committed by an individual or organization, usually during the course of legitimate occupational activity, by persons of high or respectable social status for personal or organizational gain.” (Helmkamp, Ball, & Townsend, 1996, p. 351).
In order to understand the term “white-collar”, Friedrich (2009) suggests two potential approaches in defining white-collar crime - typological, and operational. The main goal of the typological approach is to classify white-collar crime into organized categories that include: corporate crime, occupational crime, governmental crime, state-corporate crime, crimes of globalization and finance crime. The aim of this classification is to establish clarity for a better understanding while doing research.
The aim of the operational approach is to provide a starting point for empirical studies as well as to quantitative compare studies. Wheeler et al. (1988) classified white-collar crimes into eight federal crime categories: bribery, tax offenses, credit and lending institution fraud, postal and wire fraud, antitrust violations, securities fraud, bank embezzlement, and false claims and statements. The purpose of this classification was to systematically compare “common” criminals and white-collar criminals.
typological approach is that this approach is based on existing federal crime categories in order to provide researchers with specific types of white-collar crimes that can be found easily in different types of data and be furthermore used for an empirical investigation. In this thesis the operational approach will be used and the focus is on bribery, fraud, accounting fraud and tax fraud.
Theoretical Background - Environmental Crime
The important question that arises is if environmental crime hurts the reputation of companies’ and therefore lowers their financial performance. A company has to evaluate many decisions that will of course effect the total environmental treatment by that company. It has to make choices about product choice, level of pollution cleaning, waste output and many other aspects of environmental performance. It is obvious that companies will not just consider the impact on the environment when making these decisions because this certainly would not be in the best interest of the stockholders. Indeed, firms have to consider different costs and benefits of each possible option. The main goal of the corporation is to maximize profits and therefore take these actions, which add the most value to the company (Anderson-Weir & Charles H., 2010).
this theory, positive environmental choices will have a negative effect on the value of the company.
In the early literature, based on Friedman’s view, the “social responsibility of business is to increase its profits,” (Friedman 1962, p.122). Social and ethical topics were seen as unnecessary costs that would decrease profits and as a consequence violate against the contractual relationship with the shareholders. The introduction of a new recycling program, for example, would incur different types of costs such as the installation of new physical capital and the training of employees. There is also some evidence that environmental regulation may affect productivity because it forces companies to commit resources to non-productive uses such as environmental auditing, waste treatment and litigation (Gray & Shadbegian, 1995).
However, Freeman’s stakeholder theory of 1984 proposed, that corporations should consider the interests of a broader group of stakeholders. Porter (1991), in particular, argued, that it is possible to decrease pollution and in the same time to be profitable. According to Porter, efforts to reduce pollution through for example improved processes might not only reduce a corporation’s environmental footprint but also strengthen its competitiveness. The authors Porter (1991) and Porter and van der Linde (1995a, 1995b) claim that stricter designed environmental regulations were able to encourage innovation and enhance competitiveness in various ways. Regulation, for example, tells companies about likely resource inefficiencies and potential technological improvements. In addition, regulation that is focused on collecting information can induce major benefits by raising corporate awareness. This is known as the “Porter Hypothesis (PH),” which was a great success but also caused a large debate in the literature on environmental regulations.
or their production process.
However, the Porter Hypothesis is also criticized because it is not compatible with the assumption of profit-maximizing companies and often companies ignore profitable opportunities (Palmer et al., 1995). There are many reasons, why companies do not take the optimal choice such as imperfect information or market failures.
In conclusion, Porter argues that environmental regulations are able to help companies to identify inefficient uses of costly resources and thus help to overcome organizational inertia. Not only Porter but also Dowell et al. (2000) argument in favor of environmental regulation with higher profits in the long run and lower costs.
Theoretical background - Industry Spillover effect
The majority of papers concerning unethical behavior are concentrating on the consequences of the misconduct for the accused company. Yet the impact on the different industries and furthermore on the peer companies are frequently neglected.
Goldman et al. (2012) analyzed the spillover effect and made the assumption that a public announcement of an investigation into ethical violations will have two possible effects on the stock prices of peer companies:
The first possible effect, the industry competition hypothesis, suggests that a peer company from a particular industry will benefit from such an investigation through reduced competition from the company under investigation. This implies a positive abnormal stock return following the announcement.
The second possible effect, the information spillover hypothesis, suggests that a peer company suffers from the investigation because it will be assumed that the information that is provided is valid for the whole industry and not just for the accused company. This implies a negative abnormal stock return following the announcement.
In conclusion, there are three possible outcomes.
within the industry because it is thought that the information provided by the companies is not trustworthy anymore.
II. The second option would be that the peer companies in the industry that did not acted unethically benefit through a higher abnormal return. This happens because there is a customers outflow from the accused company and in further consequence a decrease in competition.
III. The last possible outcome would be that there is no effect. This could be possible if the company neither derives a benefit nor will be punished because of the misbehavior.
III. Empirical evidence
There is a large number of academic studies in this field, but so far no clear consensus has been reached on whether shareholders punish companies’ unethical behavior. In theory, it is possible to justify a positive, a negative, or no relationship between companies social and financial performance (Brammer et al., 2006). In this section the empirical evidence will be reviewed.
White-collar crime evidence
The majority of papers that are dealing with white-collar crime, especially with fraud, are mainly about the financial consequences of this misconduct for the accused company. Most of the papers come to the conclusion that the initial announcement of corporate fraud, bribery etc. induces - on average - a negative and in most cases significant abnormal returns for the accused company (Karpoff et al. (2008); Armour et al. (2010))
Bauer and Brown (2010) also come to the conclusion that various types of allegations such as fraud and bribery can cause negative short- as well as long-term performance effects. The two authors conclude that the financial and economic effects can be substantial and that the stocks will gradually decline.
a sample of 115 observations - only weak evidence of a decrease in earnings growth in a five-year period after the allegation. No evidence of analysts' anticipation of bad news is found in 15 observations. Finally, they conclude that most of the economic and statistically significant loss in shareholder wealth is due to a loss in reputation. The authors define reputational loss as the costs that accrue for the accused company in addition to the costs for the legal action.
The authors Armour et al. (2011) prove that it is not just the legal sanctions that explain the losses of the company. In addition, the company that is accused for its unethical behavior suffers a loss of reputation. Legal sanctions are simply fines, fees or penalties that the company is obliged to pay. Reputation can be defined as “expectations of partners of the benefits of trading with it in the future”.
In addition, Armour et al. (2011) examine that stock prices of companies found guilty experience abnormal losses of around nine times the penalties paid.
In conclusion, the literature regarding white-collar crime finds evidence that if companies do not behave ethically correct, the stock market will punish them whether in form of reputational losses or negative returns.
Environmental crime evidence
There are various studies that have investigated the effects of environmental crime on the stock prices. Margolis and Walsh (2001) reviewed 13 papers regarding environmental event studies. They conclude that the results are very mixed and confusing, six studies found a positive relation, three a negative relation, one with a positive as well as negative relation and three studies document no relation at all.
Marciukaityte, Szewczyk, Uzun, and Varma (2006), which showed for environmental crime no significant stock price reaction.
Further studies by Hamilton (1995) and Klassen and McLaughlin (1996) use the event study approach to show that news of high level of toxic emissions result in significantly negative abnormal returns. They conclude that firms with strong environmental management practices produce higher stock returns than companies with poor practices following an environmental crime. In addition, Dowell et al. (2000) found out that a good environmental performance has a positive reputational effect on the company and therefore increases the value of the company. Finally, they conclude that the positive reputational effect may include not just investors’ impression of a firm’s environmental performance but also investors’ impression of a firm’s management abilities.
Industry spillover effect evidence
Goldman et al. (2012) analyzed the paper of Karpoff et al. (2008). In total the sample contained 132 cases and the authors conclude that on average both, the companies that are directly connected to the fraud as well as the peer companies suffer a significant decrease in value in the short-term event window. Among the peer companies that are operating in less competitive industries, a higher Cumulative Abnormal Return (CAR) is observed. Furthermore, the authors found out that if the peer company belongs to a less competitive industry and has high sales, CAR is even higher although it is still negative on average. There is also evidence for an information spillover effect. The more negative the CAR of the company that acted unethically is, the lower is the CAR of the peer company. This rule holds except for competitive industries where the industry competition effect dominates. For companies with higher uncertainty, the market will consider more recent negative information and the CARs will be on average lower. Goldman et al. (2012) conclude that companies suffer the most through the industry competition effect.
period of fraud. This could indicate that investors perceive fraud as a risk for the whole industry and as a result restrict the availability of financing sources. Furthermore, the results revealed that the stocks of the peer companies suffer negative returns around the initial announcement.
Finally, the authors Beatty et al. (2013) analyzed the impact of corporate fraud on the investments of peer companies. They examined cases during the financial scandals and found out that capital expenditures of peer companies are significantly higher during fraud periods. Furthermore, the authors found out that peers’ investments are higher in those industries, in which cost of capital is lower and the private benefits of managers higher. However, there was no significant difference in those effects when comparing high and low growth industries or competitive industries.
IV. Methodology
The fourth section describes the event study methodology.
Event study methodology
The event study methodology is the main and most reliable method of measuring the effect of an event from financial market data. The event study has many applications and has been widely used in finance research. It considers and measures the market value effects of both firm specific and economy wide events (MacKinlay, 1997). Through this method it is possible to investigate the stock price reaction to the announcement that a company committed white-collar or environmental crime.
The basic idea behind an event study is to test whether any abnormal return can be earned by shareholders in a particular event (Peterson, 1989). In this thesis, the event study methodology will be roughly performed in accordance with the method described in MacKinlay (1997).
date the event actually happened. It could well be that the event had happened the previous day, before the closing of the stock exchange. This problem is called “event uncertainty” in the finance literature. The main and usual method to handle this problem is to expand the event window.
The event window, which is represented as t1 to t2 refers to the period over
which the behavior of the stock returns are analyzed. In this thesis, three trading days [-1,1] will be used in order to analyze any short-term changes in abnormal returns as well as to observe whether a news release was anticipated. Furthermore to cross check the results, a 5-day event window will be used. Then, an estimation window represented as t0 to t1 is used to determine the normal behavior of a stocks return with respect to a market index, which is in this thesis the S&P 500. Attention should be paid to the fact that the event window itself is not included in the estimation window. According to MacKinlay (1997) approach, the estimation window should contain at least 120 days directly prior to the event window. The estimation window selected is supposed to be a period that was free of any problems, that reflects the normal price movements of a stock.
Of course many things may happen during such a long window but the assumption is that these events constitute at most “noise” and are not suitable being studied (Benninga, 2014). The post event window will not be used in this thesis as it would not be the best solution to show the impact of an event study.
Figure 6
Time line of an event study
The most common method is to use the market model, which is a regression of the company’s stock returns and the returns of the market index.
The market model for a stock i can be expressed as:
!!"= !!+ !! !!" (1)
In the formula above, rit and rmt represent the stock and the market return on
day t. The coefficients αi and βi are estimated by running an ordinary
least-square regression over the estimation window. It is common to use a broad based stock index like the S&P 500, which is easily available at Yahoo Finance. Due to the equation (1) in the estimation window, it is now possible to calculate the impact of an event on the stock’s return in the event window. For a particular day t in the event window, we define the stock’s abnormal return as the difference between its actual return and its predicted return: !"!"= !!"− (!!+ !! !!") (2)
Where rit is the actual stock return in an event window day t and the whole
term in the bracket is the return predicted by stock’s α, β, and market return. We interpret the abnormal return during the event window as a measure of the impact the event had on the market value of the security.
The event window in this thesis includes a 3 and a 5-day event window. For that reason the abnormal return for each day in the event window must be aggregated over time to observe the effect of the announcement of the event. The aggregation of the total abnormal returns during the event window is called Cumulative Abnormal Return (CAR). The CAR is the sum of all the abnormal returns from the beginning of the event window T1 until a particular day T2 in the window (Benninga, 2014):
!"# !1, !2 = !!!!!!!"!! (3)
The CAR is nothing more than the wealth change the investor faces by
included in the defined event window.
The Average Abnormal Return and the Cumulative Average Abnormal Return:
In order to come to a conclusion about the general significance of the event, company specific reactions and non-event related effects need to be eliminated. This can be achieved by averaging the abnormal returns of all the companies in the sample. This is called the Average Abnormal Return and is calculated as:
!!"!= !
! !"!" !
!!! (4)
The abnormal return observations must be aggregated in order to make conclusions for the event of interest (MacKinlay, 1997).
Abnormal returns have to be aggregated through time in order to capture the impact of an event on stock returns over the entire event period.
Cumulative average abnormal return (CAAR) follows the equation: !""# !1, !2 = !!!!!!"(!1, !2) (5)
The CAAR is a useful statistical analysis in addition to the AAR because it shows the aggregate effect of the abnormal returns. Particularly if the influence of the event during the event window is not exclusively on the event date itself, the CAAR can prove very useful (Benninga, 2014).
Methodology Industry effect:
Figure 7
Distribution of the industries according to the SIC code
The industries 01-09, 15-17, 50-51 and 91-99 are not included in the sample and thus are not included in calculating the industry effect. Next the Average Abnormal Return (AAR) and the Cumulative Average Abnormal Return (CAAR) for each industry are calculated in order to see which industries are more affected by the unethical behavior.
Methodology industry spillover effect:
The spillover effect is calculated for every industry of the sample. The average of the CAAR of all events within one industry is compared with the average of the companies that are in the same industry and that did not commit any crime.
The original sample of companies that acted unethically contains 65 events in total. The companies are divided in different industries according to the methods applied above.
If we take the Services Industry, for example, our original sample looks like this with the following firms:
Source Company Ticker Date Action SIC code SiC division
WSJ Tenet healthcare THC 29.03.07 fraud 8062 Services
WSJ Xerox XRX 02.04.02 accounting fraud 7379 Services
WSJ Microsoft MSFT 03.06.02 accounting
fraud 7372 Services
The peer companies of the Services peer industry are the following:
Company- Services Ticker Date of rival SIC code Stock market
Adobe Systems Incorporated ADBE 03.06.02 7372 NASDAQ
Autodesk ADSK 02.04.02 7372 NASDAQ
DaVita Healthcare Partners DVA 29.03.07 8092 NYSE
As an example, I have three companies within the Service industry that acted unethically in my original sample. These three events have to be taken into account to calculate the spillover effect. The average outcome of these three events is compared to the average of the peer companies of this industry that did not commit any type of crime.
Based upon the result, the Average Abnormal Return (AAR) and the Cumulative Average Abnormal Return (CAAR) are calculated for the peer industries and then compared to the original industries. So for example, the CAAR of the Services industry in the original sample on the day -1 will be compared to the Services industry of the peer sample on day -1. This is done for day 0 as well as for +1.
The objective of this method is to calculate the industry spillover effect for each industry.
Determining the statistical significance of the abnormal return:
The next step in the event study is to analyze whether the estimated abnormal returns due to the event are statistically significant in order to support the validity of the research hypothesis. Some basic assumptions underlie this test: the abnormal returns need to be independent and identically distributed, and the abnormal returns need to be normally distributed.
H0: The event has no impact on the stock returns (AAR = 0) H1: The event has an impact on the stock returns (AAR ≠ 0) !0: !!"!= 0; !1: !!"! ≠ 0
! !!" = (!!"#)/(! !!"#! ) (6)
H0 is rejected at a significance level of 95% when t-value is ± 1.96, and H0 is rejected at a significance level of 99% when t-value is ± 2.57.
For the CAAR we have following equations:
!0: !""# !1, !2 = 0, !1: !""# !1, !2 ≠ 0 (7) ! !""# = (!""#$)/(!(!""#$)/√!)
V. Data
In this section, the type of data and how the data collection was conducted will be explained.
Data description
The data concerning environmental and white-collar crime news were collected case by case from the LexisNexis and Factiva database. It should be noted that the moment the incident was reported and not the actual incident date was used for the thesis. The majority of the companies are listed on the NYSE and a few also on the NASDAQ.
stock exchange but only 65 are taken into consideration. The reason is that only companies listed on the S&P 500 are considered due to the availability of data. All the articles collected and relating to white-collar and environmental crime were published in the Financial Times (FT) and in the Wall Street Journal (WSJ) during the period from January 1st 2001 to December 31st 2011.
The reason for taking the FT and the WSJ was that these two papers are well- known and widely distributed economic newspapers. They are published in English and therefore reach a lot of investors in Europe and in the US.
Environmental news were the most difficult and challenging to find. It is hard to find out what proportion of the number of environmental crimes ever get reported. These for example often do not appear in the news. The lack of press releases about environmental violations for example in the WSJ or FT hint to the fact that many corporations manage to cover up their unethical behavior and hide it from their investors.
In order to classify companies in different industries, the Standard Industrial Classification (SIC) code, a four-digit code, is used.
Each and every firm has a primary SIC code. This number indicates a company’s primary line of business. The main driver of the company’s primary SIC code is what generates the highest revenue for that company at a specific location in the past year. The first two digits of the code identify the major industry group, the third digit identifies the industry group and the fourth digit identifies the industry.
In this thesis certain criteria for the news selection have been used. Firstly, all the articles were published in the Financial Times and the Wall Street Journal during the time period of January 1st 2001 – December 31st 2011. Secondly,
Environmental crime:
§ Toxic/hazardous/radioactive waste/spill/leak/emission § Water/air pollution
§ Contamination
White-collar crime:
§ Allegation, accusation, claim, charge § Crime, neglect, fraud
§ Tax fraud, accounting fraud, embezzlement
Figure 1 illustrates the firms by stock exchange of listing. As we can see below, the majority of the companies in this sample are listed on the NYSE and just six percent are on the NASDAQ. One possible reason why most of the companies are from the NYSE could be the fact that it is the world largest stock market. Many companies listed on the NYSE are powerful and play an important role in today’s economy. This is the reason why they are mentioned in the FT and WSJ because they are relevant to many investors.
Figure 1
Firms by stock exchange of listing
Figure 2 illustrates the different types of unethical behavior that occur within white-collar crime. The sample in this thesis contains mainly fraud, accounting fraud and bribery.
94% 6%
NYSE
Figure 2
Types of white-collar crime
Figure 3 illustrates the different types of unethical behavior within the category of environmental crime. The main environmental crime violations are “water pollution”, “contamination”, “oil spill”, “pollution” and “emission”.
Figure 3
Types of environmental crime
Figure 4 shows the different industries of environmental crimes sorted and classified by the Standard Industrial Classification (SIC) code. The major industries involved are “Manufacturing”, “Mining”, “Finance, Insurance, Real Estate”, “Retail trade” and “Transportation & Public utilities”.
Figure 4
Types of industries - Environmental crime
Figure 5 illustrates the different industries of white-collar crimes sorted and classified according to the Standard Industrial Classification code. The major industries derive from “Manufacturing”, “Finance, Insurance, Real estate”, “Mining”, “Services”, “Wholesale trading” and “Retail trade”
Figure 5
Types of industries - White-collar crimes 52% 18% 12% 9% 9% Manufacturing Mining Finance, Insurance, Real estate Retail trade Transportation & Public utilities 38% 35% 9% 9% 3% 3% 3% Manufacturing Finance, Insurance, Real estate Mining Services Wholesale trading Retail Trade
VI. Empirical results
In this chapter, the empirical outcome of the event study is presented. First, the results for the 3-day and the 5-day event window of environmental and white-collar crime are presented to validate hypothesis 1 and 2. Secondly, the results of these two are compared in order to validate hypothesis 3. After that, the results of the industry effect for both event windows are presented and lastly, the outcome of the industry spillover is analyzed.
Results of environmental crime
The event study analysis starts with testing hypothesis 1, concerning whether stockholders react negatively to environmental crime. Appendix 1 illustrates the AAR and the CAAR over the 3-day and 5-day event window, estimated by the OLS regression using the market model. For the analysis below, we will take into consideration only the CAAR as illustrated in table 1. This helps us to obtain a sense of the aggregate effect of the abnormal returns. The CAAR can prove to be particularly useful, if the influence of the event during the event window is not exclusively on the event date itself.
Table 1
CAAR 3-day environmental crime
CAAR environmental crime
Event day CAAR significance level
-1 0,00069
0 0,00292 ***
1 -0,00027 ***
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Estimation window of 252 days was used to estimate the market model.
leakage before the official announcement of the crime and in addition that at this early stage of the event window little is known about the impact of the crime on the stock market. However, the day after the announcement +1, the shareholders reacted negatively to the environmental crime and punished the companies with the CAAR being -0,00027 and statistically significant at the 1% level.
Table 2
CAAR 5-day environmental crime CAAR environmental crime
Event day CAAR significance level
-2 -0,00197 ***
-1 -0,00154 ***
0 0,00256 ***
1 -0,00067 **
2 -0,00002
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Estimation window of 252 days was used to estimate the market model.
the event date itself for the 3 and 5-day event window.
In conclusion, the overall results of the environmental crime sample for the 3 and 5-day event window imply that the stock market punishes unethical behavior of companies after the event happened for both event windows. These results support to some extent hypothesis 1 where shareholders react negatively to environmental crime at least after the actual event happened. However, as the results are mixed before the event date in the 3-day window and we have positive results additionally for the event date in both event windows, therefore we reject the hypothesis 1. This result is also in line with Margolis and Walsh (2001) where the authors analyzed several studies and also found mixed results.
Results of white-collar crime
In this section, we start with analyzing hypothesis 2, regarding whether shareholders react negatively to white-collar crime. Appendix 2 shows the AAR and the CAAR over the 3-day and 5-day event window, estimated by the OLS regression using the market model. For the analysis below, we take into consideration the CAAR again, as it helps us get a sense of the aggregate effect of the abnormal returns.
Table 3
CAAR 3-day white-collar crime CAAR white-collar crime
Event day CAAR significance level
-1 -0,00828 ***
0 -0,02842 ***
1 -0,02611 ***
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Estimation window of 252 days was used to estimate the market model.
are for the -1,0 and for day +1 statistically significant on the 1% level. Also we can see that there might be some information leakage as well as insider trading because of the negative effect prior the announcement. This effect turned worse on the day of the disclosure and recovered slightly although it stayed significantly negative.
Table 4
CAAR 5-day white-collar crime
CAAR white-collar crime
Event day CAAR significance level
-2 0,00023
-1 -0,00803 ***
0 -0,00793 ***
1 -0,00475 ***
2 -0,00681 ***
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Estimation window of 252 days was used to estimate the market model.
Now we want to analyze the 5-day event window in order to use them as a cross check for the 3-days event window.
Table 4 illustrates that on day -2 the CAAR was positive with 0,00023 although not statistically significant. After that the CAAR was negative for the days -1, 0, +1 and +2 implying that the stock market reacted negatively to white-collar crime and shareholders punished the companies by driving down the share price. All the results from these days are also statistically significant on the 1% level. These statistical results provide evidence in favor of Hypothesis 2 that white-collar crime incurs a negative Cumulative Abnormal Average Return and that the announcement has a lasting short-term effect on the stock price. These results are in line with Karpoff et al. (2008) and Armour et al. (2010) who also found significant negative returns after the misconduct.
Comparison of white-collar and environmental crime
company specific announcement induces a significantly higher negative reaction in stock returns in the short term than environmental crime.
Figure 8
Comparison 3-day event window environmental and white-collar crime
Figure 8 illustrates the 3-day event window CAAR for both environmental and white-collar crime. As already expected from the separate analysis in section 4.1 and 4.2, white-collar crime induces a considerably significant higher negative effect on the stock market than environmental crime. If we look at the outcome in detail, on the day before the announcement -1 and on the event day 0 the CAAR of white-collar crime already had a negative effect while environmental crime was still positive. The day after the release of the news, the CAAR of an environmental crime finally became negative although it is considerably lower than the CAAR of white-collar crime. This result implies that although the shareholders are punishing both types of crime after the event date, white-collar crime is considered to be worse.
There are several reasons for this result. First, if we exclude major oil spills like the BP disaster, the penalties imposed by the financial market might be on average lower than on crimes like fraud, bribery or insider trading. The reason for that could be that environmental crime often does not affect directly different stakeholders of the company. It is often the case that environmental crime imposes costs on other parties and not only on those with whom the
polluting firm does business. If a company dumps toxic chemicals into the canalization for example, the fishermen downstream are damaged. These fishermen however, have no business relation to the firm, and the firm’s customers and shareholders have no direct incentive to lower their demands for the firm’s products if the quality of those products is not affected. As a result, the polluting firm could experience no reputational costs (Karpoff et al., 2005). Another argument for this result could be that shareholders do not punish companies for their unethical practice because the financial gains are often higher than the penalty to be paid for the misbehavior.
A further evidence for this hypothesis could be that it is often very difficult to find the party at fault while looking at white-collar crime it is often clear who was responsible for the misconduct as it is easier to monitor and to trace back the culprit. In 1979 for example, it was discovered that an entire housing project in the town of Lekkerkerk in the Netherlands had been built on all sorts of chemical waste that was illegally dumped by an unknown company some years before. In the case of this project, the reliable company was finally found but still it was not possible to convict the company because of the legislation at that time. Consequently, the government had to pay several millions of Euros to clean up and the company responsible for that environmental disaster could not be held liable for that crime.
Figure 9
Comparison 5-day event window environmental and white-collar crime
Figure 9 illustrates the 5-day event window CAAR for both environmental and white-collar crime. The results are similar to the 3-day event window. The negative impact on the CAAR of the white-collar crime is significantly higher than the CAAR of the environmental crime. In addition, this outcome is also in line with theory and supports the main idea behind hypothesis 3.
Results of Industry effect
Figure 10
Industry effect 3-day event window
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Figure 10 illustrates the CAAR of the different industries for the 3-day event window. The results are to some extent surprising. If we look at the day prior the announcement, we can see that the Finance, Insurance, Real estate industry were punished the most for unethical behavior. The CAAR is -0,01949 and statistically significant on the 1% level. As especially this particular industry had a bad reputation and was responsible for a lot of significant frailties in the last ten years, this result is not surprising. If we look further at the day -1, also the Industries Transportation; Services and Mining have a negative CAAR although statistically not significant. A possible explanation why the results are not significant could be that there is rarely information about the consequences of this misconduct available in this early phase of the event window. Stakeholders and the general public are not provided with information which they can use to form for example an accurate expectation of costs. The industries Manufacturing and Retail Trade show positive CAAR with the former being statistically significant on the 1% level and the latter not. This implies that in these two industries there is no information leakage before the official event date.
On the event date 0, the Mining industry suffered the most of its unethical
misconduct with a CAAR of -0,07818 and statistically significant on the 1% level. This result is credible as the Mining industry does not have the best reputation and the companies often operate in Africa or other 3rd world countries where ethics and in particular regulations are very lax. Further industries punished by shareholders are the Services, Finance, Transportation and Retail industry, all significant at the 1% level except Transportation that is statistically significant at the 10% level. It is quite surprising that on the day of the announcement of the misconduct the Services industry is more severely punished by the shareholders than the Finance, Insurance, Real estate industry. One possible explanation for this outcome might be that the Services industry is more vulnerable to unethical behavior because these companies have - by nature - a more direct customer contact than the financial industry. Consider Tenet Healthcare from the Services industry, which operates in the healthcare industry, as example. The company offers various services for hospitals like organ transplant services or radiology and clinical laboratories. It is obvious that any misconduct would lead to severe damage to patients as well as stakeholders and thus the Services industry might suffer a higher CAAR. The only industry that does not suffer a negative CAAR is again the Manufacturing industry.
Figure 11
Industry effect 5-day event window
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Figure 11 shows the different industries in the 5-day event window. What stands out is the fact that the Manufacturing industry is positive on all days during the event window, as it was the case for the 3-day event window. For the day’s -2 and -1, the CAAR for the Finance, Insurance and Real estate is the most negative one, with -0,00495 and -0,02449, both significant at the 1% level. Furthermore for the day’s prior the event date, the industries Transportation, Services and Retail trade are the ones who are also penalized by the shareholders. We might assume there is some evidence of information leakage prior to earnings announcement. Joshipura’s study (1999) showed that companies have to inform the stock exchange in advance on the agenda of the board meeting before the formal announcement of any events of change in capitalization. Consequently, it may induce some speculative activities in the market and even trigger insider activities.
For the event date 0, the industry Finance, Insurance, Real estate once again was punished the most by the financial market with a CAAR of -0,01491 that is statistically significant on the 1% level. This result is not surprising, as the
industry does not have the best reputation for a long time now. However, the results from the 3-day event window differ from the 5-day event window where the Finance industry also had negative CAARs but not as significant as in the 5-day event window. The reason for that could be that investors do not have full information on the magnitude of the event and a longer event window will disclose all frailties. The Mining industry, which was among the highest CAARs during the 3-day event window, has now also a lower CAAR in the 5-day event window.
For the event window +1 and +2 the Transportation industry was penalized the most by the shareholders with a CAAR of -0,02286 and -0,02807, significant at a level of 1%.
In conclusion, we can say that all industries suffer significantly from acting unethically whether it is in the 3 or 5-day event window except the Manufacturing industry, which always had positive CAARs. This means that in general it does not matter in which industry the company operates, shareholders will punish unethical behavior of the company. It is true that certain industries are penalized more than others, for example the Finance, Insurance, Real estate, the Transportation or the Services industries incurred significant damages by committing environmental or white-collar crime. What we can also observe is that the companies in this sample suffered the most on the event date itself where the news was published in the FT and WSJ. The CAARs are on this event date for all companies statistically significant on the 5% level.
Industry spillover effect
There is a growing literature that is analyzing the effects of unethical misconduct on the industry. However, there is little evidence how unethical misconduct actually affects the peer industry.
the average outcome with the outcome of the industry effect in section 4.4, which will be called original industry for simplicity reason.
The objective of this calculation is to find out if the misbehavior of the original industry also affects the peer industry. This will be performed by comparing for example, the average outcome of the Mining industry in the original sample with the average outcome of the Mining peer industry.
A possible scenario could be that the peer industry is also penalized in case of unethical misconduct and scepticism is in general growing in the industry. For this reason, the peer industry, which did not commit any crime, will be punished by the misconduct of the original industry. The results for the AAR and CAAR are shown in Appendix 4.
Figure 12
Industry spillover effect 3-day event window
Notes: The rejection of the H0: CAAR= 0 at 90% significance level is denoted as*, at 95% as ** and at 99% as***.
Figure 12 shows the 3-day event window of the industry spillover. We can see that prior the event date -1, the Mining peer industry suffered the most from the unethical behaviour of the original sample. The CAAR is -0,0179 and statistically significant on the 1% level. Moreover, the Services, Finance, Insurance, Real estate and the Wholesale peer industry are also punished by the unethical misconduct of the original industry. The peer industries
Manufacturing, Retail trade and Transportation were not affected by the unethical misbehaviour of the original sample and have positive CAARs. The outcome of the Manufacturing and the Retail trade industry are consistent with the results in 4.4 where these industries also had a positive CAAR on the day -1. What is actually surprising is the fact that the Transportation industry suffered a significant CAAR in the original sample but in the peer sample the industry has the highest CAAR which means that the peer industry take advantage of the misconduct.
On the event date, the peer industry Mining was again penalized the most with a CAAR of -0,01260, statistically significant on the 1% level. In addition, the Finance, Retail and Transportation industry have negative CAARs, as it was also the case in the original sample. Even though not all the results are significant, the results show that on the event date also the peer industries are punished by various stakeholders because of a general loss of confidence within the industry. The industry Manufacturing has a positive CAAR as was also the case in the original sample, which implies no effect at all. The Services peer industry has also a positive CAAR, which means that compared to the original sample unethical misconduct does not have an impact on the peer industry.
Finally, we will analyze the day after the event date +1. The Mining peer industry suffers here also a significant CAAR with -0,02715, statistically significant at 1% level. All the other industries, except the Services industry, have negative CAARs, implying that unethical misconduct in the original sample also affected the peer industry as the information provided by the companies is no longer considered reliable.
significantly negative.
A very interesting insight in these results is the fact that the Service peer industry has significant positive CAARs on the event date and in the post event date while in the original industry the CAARs are negative. That implies that the peer industry benefits from unethical behavior through a higher stock price and also through an increase in customers and less competition in the market. Furthermore if we look at the 5-day event window of the retail trade peer industry, the CAARs are all positive while the CAAR in the original sample are negative, implying that in the 5-day event window the Retail trade peer industry also benefit from unethical behavior of the original industry through positive stock returns.
Figure 13
Industry spillover effect 5-day event window
VII. Analysis of the Hypotheses
In this section, the empirical results and their implications and explanations with the purpose of answering the Hypotheses are analyzed. This chapter is divided into five sub-sections to examine the three hypotheses of this study separately and how the empirical evidence either supported or objected to them. Furthermore the outcome of the additional results concerning industry effect and industry spillover effect are discussed and explained.
Environmental crime
H1: Environmental crime related to a specific announcement of a company induces a significantly negative reaction in stock returns in the short-term.
The empirical results imply for the 3- and 5-day event window significant negative CAAR for the post event window, however the prior event results and the result on the event date are positive. The main objective of this analysis is to find out if the shareholders are punishing the companies after the announcement has been made and this is here not the case as the results do not verify a clear outcome. For this reason we can reject H1.
White-collar crime
H2: White-collar crime related to a specific announcement of a company induces a significantly negative reaction in stock returns in the short term.
Comparing Environmental and White-collar crime
H3: White-collar crime related to a specific announcement of a company induces a significantly higher negative reaction in stock returns in the short term than environmental crime.
The empirical results concerning a clear answer for the hypothesis are unambiguous. The outcome shows that shareholders indeed punish companies significantly more in terms of white-collar than concerning environmental crime. If we look for example at the 3-day event window on the event date 0, the CAAR of the environmental crime is positive with 0,00292, significant at the level of 1%. On the other hand, the CAAR on the event date for white-collar crime is -0,02842, significant at the 1% level. The same result is observed for the 5-day event window. The reason is that environmental crime often does not affect shareholders directly and the penalties paid for committing environmental crime are often less than the financial gains that are achieved. Therefore we can accept H3.
Industry effect
The results for the 3- and 5-day event window show that certain industries are more punished by shareholders than others. The outcome shows that the industries like Finance, Insurance, Real estate, Mining, Transport, Services and Retail trade show significant negative CAAR whereas the Manufacturing industry was not penalized by financial market.
Industry spillover effect
The results of the industry spillover provide some interesting insights.
The outcome shows that the peer Mining industry suffered significantly from unethical behavior of the original industry. The reason could be that the Mining industry is a very exclusive segment where there are not many companies operating and that an unethical misconduct may lead to a general loss of trust in the industry.
significantly negative. The results could indicate that the industries’ reputation is damaged especially since the financial crisis in 2008 and since then the events in the financial industry are monitored very closely.
A very interesting insight in these results is the fact that the Service peer industry has on the event date and in the post event date significant positive CAAR while in the original industry the CAARs are negative. That implies that the peer industry benefits from the unethical behavior through a higher stock price and also through an increase in customers and less competition in the market.
VIII. Conclusion
The first part of the research confirms the main hypothesis that white-collar crime is indeed punished more than environmental crime by the financial markets. The results show significant results in the 3- and 5-day event window revealing that white-collar crimes such as fraud or bribery play an essential role in the financial world and that shareholders react very sensitive in the event of an infringement. The results are in line with Karpoff et al. (2008) and Armour et al. (2010) who also found negative and significant abnormal returns for the accused companies.
Even though environmental crime has a marginal significant negative effect on the stock market in the post event, it is evident that crimes related to environmental topics are not seen as grave infringement. The reason could be that the penalties paid for the misconduct are often lower than the gains and in addition it is often not obvious who was responsible for the crime, which makes the legal position very difficult. This result contradicts the findings of Ramiah et al. (2013) and Flammer (2013) who found out that companies who commit an environmental crime experience in all event windows a negative and significant abnormal return.
estate, Mining, Services and Transportation are punished the most. In the 5-day event window the industries Finance, Insurance, Real estate, Transportation and Retail trade have a significant negative CAAR.
Lastly, the thesis analyses the impact of the industry spillover effect. The sample contains of 91 companies divided into industries in order to compare them with the original sample. The outcome shows that the peer Mining industry suffered significantly from unethical behavior of the original sample. The Finance, Insurance, Real estate peer industry are also hit by the crime committed by companies of the same industry while especially the post event CAAR are significantly negative.
Finally, the Service peer industry has a significant positive CAAR on the event date, while in the original industry the CAARs are negative. That implies that the peer industry benefits from the unethical behavior. The results from the industry spillover effect are in line with the paper of Goldman et al. (2012) who detected that the unethical behavior of companies also affects others in the same industry.
In conclusion, the results show a general trend that shareholders and financial markets indeed punish the unethical misconduct of companies and that the initial disclosure of unethical behavior such as environmental or white-collar crime will lead to significant and negative abnormal returns for the accused companies.
IX. Limitations and further research
size is legitimate for an event study methodology. In order to mitigate selection bias while collecting the data for the event sample, specific criteria have been used for the selection process.
References
Armour, J., Mayer, C., & Polo, A. (2011). Regulatory sanctions and reputational damage in financial markets. University of Oxford: Available at: http://ssrn.com/abstract=1678028.
Beatty, A., Liao, S., & Yu, J. J. (2013). The spillover effect of fraudulent financial reporting on peer firms’ investments. Journal of Accounting and Economics 55(2-3), 183-205.
Bonini, S., & Boraschi, D. (2010). Corporate scandals and capital structure. Journal of business ethics, 95(2), 241-269.
Dowell, G., Hart, S., Yeung, B., 2000. Do corporate global environmental standards create or destroy value? Management Science 46 (8), 1059–1074. Edelhertz, H. (1970). The Nature, Impact and Prosecution of White-Collar Crime. U.S. Department of Justice.
Geis, G. (2007). White-collar and corporate crime. Upper Saddle River, NJ: Pearson.
Gunthorpe, D. L. (1997). Business Ethics: A Quantitative Analysis of the Impact of Unethical Behavior by Publicly Traded Corporations. In: Journal of Business Ethics, Vol 16, p. 537-543.
Gunningham, N. (2009). Corporate Environmental Responsibility. Farnham: Ashgate.
Gande, A., & Lewis, C. (2009). Shareholder-initiated class action lawsuits: shareholder wealth effects and industry spillovers. Journal of Financial and Quantitative Analysis 44, 823–850.
Gray, W.B., Shadbegain, R.J., 1995. Pollution Abatement Costs, Regulation, and Plant-Level Productivity. NBER Working Papers, No 4994.
Goldman, E., Peyer, U., & Stefanescu, I. (2012). Financial Misrepresentation and its impact on rivals. Financial Management, 41(4), 915-945.
Guerard, J.B., Jr., 1997a, “Is There a Cost to Being Socially Responsible?” Journal of Investing 6, 11-18.
Hamilton J. (1995). Pollution as News: Media and Stock Market Reactions to the Toxics Release Inventory Data. Journal of Environmental Economic and Management, Vol. 28, pp. 98–113.
Helmkamp, J., Ball, J., & Townsend, K. (eds.) (1996). Definitional dilemma: Can and should there be a universal definition of white collar crime? Morgantown, WV: National White Collar Crime Center.
committing criminal fraud. Journal of Law & Economics, vol. XXXVI.
Karpoff, J. M./Lott J. R./Wehrly, E. W. (2005). The Reputational Penalties for Environmental Violations: Empirical Evidence. In: The Journal of Law and Economics, Vol. 48, No. 2, p. 653-675
Klassen, R., McLaughlin, C., 1996. The impact of environmental management on firm performance. Management Science 42, 1199–1214.
Lanoie, Paul, and Laplante, Benoit. “The Market Response to Environmental Incidents in Canada: A Theoretical and Empirical Analysis.” Southern
Economic Journal 60 (1994): 657–72.
Margolis, J. D./ Walsh, J. P. (2001). People and profits: the search for a link between a company's social and financial performance, 1st April 2001. Psychology Press.
Marciukaityte, D., Szewczyk, S.H., Uzun, H., Varma, R. (2006). Governance and performance changes after accusations of corporate fraud. Financial Analysts Journal, Vol. 62, No. 3, pp. 32-41.
Meier, R.F. (2001). Geis, Sutherland, and white-collar crime. In Contemporary issues in crime and criminal justice. Editors H.N. Pontell & D. Shichor. Upper Saddle River, NJ: Prentice Hall.
Mitchell, C. (2009). A short course in international business ethics: combining ethics and profits in global business. World Trade Press, Petaluma, CA.
Palmer, K., W.E. Oates, and P.R. Portney (1995), Tightening Environmental Standards: The Benefit-Cost or the No-Cost Paradigm? Journal of Economic Perspectives 9(4), 119–132.
Payne, Brain K. (2009). White-Collar Crime: The Essentials. Sage Publications
Payne, Brain K. (2009). White-Collar Crime: A Text/Reader. Sage Publications
Porter, M., and C. van der Linde (1995), Toward a New Conception of the Environment-Competitiveness Relationship, Journal of Economic Perspective 9(4), 97–118.
Porter, M. (1991), America’s Green Strategy, Scientific American 264(4), 168. Ramiah V., Martin B., Moosa I. (2013). How does the stock market react to the announcement of green policies?. Journal of Banking & Finance 37 (2013) 1747–1758
Reichert, Alan K., Michael Lockett, and Ramesh P. Rao. “The Impact of Illegal Business Practice on Shareholder Returns.” The Financial Review, 1 (1996), 67–85.
Skantz, Terrance R., Dale O. Cloninger, and Thomas H. Strickland. “Price– Fixing and Shareholder Returns: An Empirical Study.” The Financial Review, 1 (1990), 153–163.
Statman, M., 2000, “Socially Responsible Mutual Fund,” Financial Analysts Journal May, 30-39
Sutherland, E. H. (1983). White collar crime: the uncut version. Yale University Press, New Haven.
Salinger, L. M. (2013). Encyclopedia of white-collar & corporate crime. Thousand Oaks, California.
Smith, M. J./ Blackmore, C./Reynolds, M. (2009). The Environmental Responsibility Reader. Open University.
Stewart, R., 1993. Environmental regulation and international competitiveness. Yale Law Journal 102, 2039–2106.
Porter, M. E./van der Linde, C. (1995). Toward a New Conception of the Environment - Competitiveness Relationship. J. Econ. Persp.
Wheeler, S., Weisburd, D., Waring, E., & Bode, N. (1988). White collar crime and criminals. American Criminal Law Review, 25: 331-357.
Appendices
Appendix 1:
List of companies – original sample
Source Company Ticker Date Action SiC division FT Apple AAPL 19.01.01 pollution Manufacturing WSJ Wal-mart WMT 08.06.01 water pollution Retail trade
FT General electric GE 02.08.01 water pollution Finance, Insurance, Real Estate WSJ Exxon mobil XOM 14.12.01 hazardous
waste
Mining FT Monsanto MON 23.02.02 water pollution Manufacturing WSJ Xerox XRX 02.04.02 accounting
fraud
Services
WSJ Carnival Corp. CCL 22.04.02 water pollution Finance, Insurance, Real Estate WSJ PPG industries PPG 15.05.02 contamination Manufacturing
WSJ Halliburton HAL 29.05.02 accounting fraud
Mining WSJ Microsoft MSFT 03.06.02 accounting
fraud
Services WSJ Tyco international TYC 10.06.02 accounting
fraud
Finance, Insurance, Real Estate FT bristol myers BMY 11.07.02 fraud Manufacturing
WSJ CHEVRONTEXACO CVX 09.01.03 water pollution Manufacturing FT Pepsi PEP 12.08.03 contamination Manufacturing WSJ Monsanto MON 21.08.03 contamination Manufacturing FT CHEVRONTEXACO CVX 29.10.03 toxic spill Manufacturing
WSJ Morgan Stanley MS 17.11.03 fraud Finance, Insurance, Real Estate WSJ Exxon mobil XOM 19.12.03 oil spill Mining
WSJ Ford Motor F 02.03.04 hazardous
waste Manufacturing FT Pfizer PFE 14.05.04 fraud Manufacturing WSJ bristol myers BMY 04.08.04 accounting
fraud
Manufacturing WSJ Chevron corporation CVX 20.08.04 oil spill Manufacturing FT Newmont NEM 24.09.04 water pollution Mining
WSJ AIG AIG 26.11.04 fraud Finance, Insurance, Real Estate WSJ Morgan Stanley MS 13.01.05 fraud Finance, Insurance, Real Estate WSJ Conocophillips COP 28.01.05 emission Manufacturing
WSJ Du pont DD 01.03.05 water pollution Manufacturing
WSJ Firstenergy FE 21.03.05 pollution Transportation&Public Utilities WSJ time warner TWX 22.03.05 accounting
fraud
Transportation&Public Utilities FT Exxon mobil XOM 28.03.05 global
warming
Mining WSJ Newmont NEM 04.04.05 pollution Mining WSJ Exxon mobil XOM 01.06.05 contamination Mining WSJ Wal-Mart WMT 16.08.05 pollution Retail Trade WSJ Du pont DD 15.12.05 toxic spill Manufacturing WSJ Wal-mart WMT 21.12.05 hazardous
waste
Retail Trade WSJ Cummins Inc. CMI 08.02.06 accounting
fraud Manufacturing FT Tyco international TYC 18.04.06 accounting
fraud Finance, Insurance, Real Estate WSJ Raytheon RTN 29.06.06 accounting
fraud Manufacturing
WSJ Dow Chemicals DOW 17.07.06 emission Finance, Insurance, Real Estate FT Coca cola KO 11.08.06 contamination Manufacturing
WSJ Duke Energy DUK 02.11.06 emission Transportation&Public Utilities WSJ Merck MRK 15.02.07 tax fraud Manufacturing
