Corporate Crime Announcement Effect on Stock Price and Its

Determinants in Malaysia

NURUL IZZA ABD. MALEK*, ROSSAZANA AB-RAHIM, MICHELLE CHANG TING TING,

NUR ZAIMAH UBAIDILLAH

Faculty of Economics and Business,

Universiti Malaysia Sarawak (UNIMAS),

94300 Kota Samarahan, Sarawak,

MALAYSIA

*Corresponding Author

Abstract: - Nowadays, the increasing cases of crimes committed by corporations have posed challenges to

enforcement agencies, especially in Malaysia. It may result in serious damage to financial institutions and

economic performance, as well as generate social disorganisation and lower the level of confidence between

investors and consumers. This study aims to examine the reactions of corporate crime announcements on stock

prices and identify relationships among determinants of stock prices such as firm size, price to book value,

earnings per share, and dividends per share in the context of firms involving crime. The sample consists of 11

announcements by 9 publicly listed companies charged by the Securities Commission for committing a

corporate crime from 2003 to 2020, with a total observation of 162. The market model event study and fixed

effect regression analysis are employed to analyze the data obtained from Yahoo Finance and Bursa Malaysia.

The finding indicates that the AARs on the announcement date are not significant at the 5% level. However, the

CAARs on the announcement date were negative abnormal returns and statistically significant. This reveals

that the stock market is not reacting efficiently to the announcement of corporate crime because the stock price

was not fully reflected in all publicly available information. Furthermore, the results of the fixed effect model

revealed that firm size and dividend per share have a significant effect on stock price, whereas price-to-book

value and earnings per share have insignificant relationships with stock price in the context of firms involved in

corporate crime. This study intends to provide a better understanding of the causes of corporate crime and

prevent corporate crime from becoming widespread in the country, thereby reducing the number of

corporations that participate in crime.

Key-Words: - Corporate crime, Announcement, Stock price, Malaysia, Event study, Fixed effect.

Received: May 21, 2023. Revised: August 7, 2023. Accepted: August 28, 2023. Published: September 8, 2023.

1 Introduction

In Malaysia, corporate crime is not new, and the rate

is on an upward trend based on the cases reported

each year. Based on the KPMG Malaysia Fraud

Survey 2005, their organizations have experienced

an increase of 33% of respondents suffering fraud

relative to the survey in 2002, [1]. In Malaysia,

white-collar crime has caused losses of more than

RM3.93 billion from the year 1999 until 2002, with

approximately 6,000 cases being reported yearly,

[2]. Besides, Malaysia reported RM579 million

engaged in 11,714 white-collar crime cases in 2003,

[3]. There were fewer cases (9,899 cases) recorded

in 2004, however, the total loss increased to

RM836.29 million. In addition, Global Financial

Integrity reported that around $946.7 billion was

recorded in developing countries’ illegal financial

flows in 2011, compared to $832.4 billion in 2010,

[4]. With illicit outflows of $370.38 billion,

Malaysia ranked fourth among all developing

countries in terms of the highest cumulative illicit

financial outflows over the period 2002-2011. In

addition, anecdotal evidence shows a whopping

RM1.775 billion was lost in 2013 in Malaysia by

scams, embezzlement, illegal breach of confidence,

and other white-collar crimes, [5].

Malaysian companies like Sime Darby Berhad,

Alliance Financial Group Berhad, and Kenmark

Industrial Co (M) Berhad experienced a decline in

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stock prices due to the announcement of suspected

fraud and abuse of authority, [6]. For instance,

Kenmark lost about RM100 million in market value

in just a week, whereas Sime Darby lost almost

RM2 billion in its energy and utility segment in

2010. Apart from that, the Corruption Perceptions

Index (CPI) of Malaysia in 2017 was ranked 62

(score of 47) among 180 countries. Corporate fraud

has demonstrated a strong correlation with poor

corporate governance among fraud, corruption, and

bribery.

This is due to those unresolved cases like

1MDB, Sabah Water Development, Felda Global

Ventures Holdings Bhd scandal, and PKR vice

president Rafizi Ramli’s conviction for

whistleblowing, which had a huge influence on the

CPI ranking in 2017, [7]. Corrupt behaviour creates

an unfavourable market climate that encourages

anti-competitive behaviour, reduces income, and

allows organized crime to thrive. It violates the rule

of law, weakens trust in democratic institutions, and

threatens democracy’s values. Thus, corporate crime

is indicated as the most important challenge to the

economic development of a nation, [8].

There have been some important developments

concerning governance-enhancing initiatives in

Malaysia, [9]. The Malaysia Institute of

Accountants (MIA) set up a Practice Review

Committee in 2002 to enhance the performance of

the audit committee's practice. In 2010, the

Securities Commission created the Audit Oversight

Board, which supervises public interest

organizations’ auditors and protects investors’

interests by encouraging confidence in audited

financial statements’ accuracy and reliability.

However, Malaysia does not seem to be able to

eliminate cases of fraud and facilitate

whistleblowing despite all such efforts. Many cases

have been investigated by the Securities

Commission, ranging from the submission of false

and misleading documents, the use of defrauding

schemes, and the participation in defrauding and

short-selling actions, [9].

Furthermore, the Global Crime Report 2009

also found that economic crime has risen despite the

recent economic crisis due to incentives or stresses

(68%), opportunity (18%), and attitude (14%).

Besides, asset misappropriation, accounting fraud,

bribery, and corruption were the three most

prevalent forms of economic offenses encountered

during the economic crisis, [10]. This indicates that

corporate crime cases in Malaysia have shown a

growing trend. However, information about

corporate crime activities significantly affects the

performance of the stock market, especially the

stock price. When the corporate crime

announcement is published to the public, the

company will lose the confidence of its investors,

which will lead to a decline in firm performance.

2 Problem Formulation

2.1 Theoretical Framework

Past studies provide a better understanding of the

effects of corporate crime announcements on stock

price reactions and the determinants of stock prices.

Consequently, it guided the ideas and results that

were developed in prior studies. The main

underlying theories in past studies are the Efficiency

Market Hypothesis (EMH), Behavioral Finance, and

Fama French Model. These theories consider the

efficiency of information transfers in the market and

the associated stock price movements.

The Efficient Market Hypothesis (EMH) is a

hypothesis that states that all information reflects

the share price at which it is possible to generate

consistent Alpha, [11]. According to the EMH

theory, stocks are still trading at their fair value on

exchanges, making it difficult for investors to

purchase undervalued equities or sell overvalued

stocks. As a result, selecting stocks or market timing

to outperform the entire market is unlikely, and the

only way an investor may earn a greater return is to

acquire riskier investments.

The EMH establishes that markets are efficient

when analyzing data and achieving equilibrium

security rates, [12].

Most studies find that stock prices reflect

information about the actual value of the underlying

asset that is publicly accessible. If managers are

truly owners’ agents, increasing shareholder wealth

is a good measure to analyze managerial behavior

(shareholders). Meanwhile, negative returns from

the stock market should discourage managers from

engaging in unethical behaviour.

Behavioral finance is the study of psychology’s

effects on investors' or financial analysts’ actions. It

covers cognitive psychology, which refers to how

people think and most of the literature on this broad

psychology has been documented. This theory also

assumes that the markets can make unbiased

predictions but not predict the future, whereas the

financial markets with the consideration of some

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situations are assumed to be informationally

inefficient in behavioural finance, [13]. Besides,

some authors in, [14], found that investors with

particular personality traits are the marginal price

setters for securities with particular traits. The

availability heuristic, the disposition effect,

overreaction, and overconfidence are the main

phenomena utilized in behavioural finance to

explain the financial markets. The personality and

psychological factors of investors will lead them to

make different investment decisions and react in

their own distinct and preferred manner when

dealing with bad news announcements regarding the

firm they have invested their money in.

The Fama French Model is an asset pricing

model that applies to the Capital Asset Pricing

Model (CAPM) by adding to the business risk factor

of the CAPM size risk and value risk factors. This

model is the result of a historical stock price

econometric regression. The fact that value and

small-cap inventories outperform stocks regularly

takes this model into account. By adding these two

variables, the model adapts to this outperforming

trend, which is thought to make it a better tool for

assessing the outcomes of managers. Fama and

French emphasized that the additional short-term

uncertainty and occasional undervaluation that may

arise over a short time period must be resolved by

investors. Outperformance is usually clarified in

favour of market efficiency by the excess risk that

value and small-cap stocks face as a result of their

higher capital costs and higher business risk. In

order to help the market inefficient, outperformance

is clarified by the incorrect pricing of the value of

these firms by market participants, which, in the

long run, offers an excess return when the value

changes, [15].

2.2 Corporate Crime Announcement

Effect on Stock Price Reaction

There is a broad and well-known literature on the

effect of information releases on stock market

returns. For that matter, there is consensus that event

study methodology is useful to analyze the effect of

an announcement on returns. In past studies, the

standard event study methodology was applied,

[12]. He examined 16 public and multinational

companies that were involved in bribery, scandals,

white-collar crime, and illegal payments in the US

from 1989 to 1993. The results found that lower

expected market-adjusted returns were the actual

stock performance for those businesses. In addition,

on the day of the white-collar crime announcement,

the stock values of the firms decreased by -5.72%.

In contrast, the author in, [16], found that the

predicted adjusted market returns were lower than

the actual stock performance of the Indian

companies listed. In addition, the company’s stock

price increased by 1.42%, and abnormal returns and

surrounding days on the announcement day of the

right issue by those companies were statistically

significant.

Furthermore, a study was carried out on market

reaction to corporate news on 6,500 U.S. companies

trading publicly on NASDAQ, NYSE, and AMEX

between April 2006 and August 2009, [17]. The

periods before and after the financial crisis are

separately studied to observe that the response to

certain types of news has changed. By using event

study methodology, they found return volatility

typically increases and liquidity decreases in the

month after the announcement. During the crisis

period, news signalling higher and more stable

future cash flows, such as corporate restructuring

announcements, new corporate partners, successful

research completion, FDA approvals, and legal

settlements, contribute to more positive price

reactions. Besides, events that are perceived to

reduce future cash flows and increase their

uncertainty have a more negative impact on stock

prices, such as announcements of legal problems,

FDA rejections, and unsuccessful research attempts.

In South Korea, some authors, [18], examine the

effect of crime type (white-collar crime vs. street

crime, operational vs. financial) on the relationship

between corporate crime announcements and stock

market reaction. A sample of 832 announcements of

South Korean companies from the years 2001 to

2010 is examined. They find that there is a negative

reaction to stock prices around the announcements

of corporate crimes, but that there is no significant

difference in reactions between announcements of

individual and organizational crimes. Besides,

individual white-collar crimes have a greater

negative effect on stock prices than the average for

individual street crimes, while financial crimes have

a significantly greater negative impact than

operational crimes in organizations.

Besides, some authors in, [19], studied the

impact of corporate crime announcements on the

performance of companies in Malaysia. They

concluded that the stock market did not respond

effectively to the announcement of corporate crime.

The outcome is the same as with authors in, [6], in

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which Malaysia’s stock market was found to be

inefficient due to the negative reaction of the stock

price to the white-collar crime announcement effect

on the subsequent 10 trading days after the date of

the announcement. The study in, [20], also found a

negative abnormal return to the announcement

effect of white-collar crime among Malaysian public

listed companies during the period of 1996-2013.

The market is not responding effectively to the

disclosed information concerning the incidence of

white-collar crime in Malaysia.

Apart from that, some authors in, [21],

investigate stock market reactions to the news from

2003-2016 about corporate tax avoidance and

evasion in Germany. They concluded that the tax

evasion news had a negative abnormal return,

although there was no general effect on the tax

avoidance news. When the tax risk of businesses is

low, however, they find positive stock price

reactions to legal tax planning.

In addition, some authors in, [22], examined

changes in the level of information asymmetry and

corporate fraud of companies in the emerging

market of Malaysia from 2000 to 2016. The results

indicate that the asymmetry of information increases

when fraud is discovered by using event analysis,

OLS regression, and simultaneous equation models.

Subsamples classified by the type of regulation and

the type of misconduct were also analyzed in the

study. However, there is no proof of a difference in

the asymmetry of information between these

classes. Overall, the results strengthen the

reputational perception that fraud hurts the

credibility of corporations and increases the stock

market’s volatility.

Moreover, some authors in, [23], investigate the

response of Indian banks’ stock prices to the

announcement of fraud. By using the event study

methodology, the findings indicate that fraud

announcements affect the stock prices of banks

experiencing fraud. The study found significant

abnormal losses in most cases of fraud under

consideration, further confirmed by the results of the

abnormal volume ratio. The highest abnormal loss is

found in the Punjab National Bank stock price

(8.74%) which includes the Nirav Modi fraud. The

confidence of the investors is adversely affected by

increased fraud in the banking sector, which can

further lead to implications for the banks concerned.

2.3 Determinants of Stock Prices

Several studies have been carried out by previous

researchers on the determinants of stock price

reaction. The author in, [24], introduced the share

price determinants for the US market and defined

dividends, net profit, operating earnings, and book

value as significant factors influencing the price of

shares in the US. By using the multiple correlation

method in 1954 and the year 1955, the correlation of

other determinants with stock prices in 1954 was

closer than in 1955, except for net profits. The

coefficient of the dividend rate with the price is

0.9257, which is the highest and the most significant

and reliable single price-setter in that year.

Meanwhile, with a coefficient as low as 0.7624, net

profit is less significant in 1955.

In India, some authors in, [25], used panel data

and evaluated three sectors, namely automobiles,

healthcare, and public sector undertakings over the

period 2000-2009 to deduce the main factors

affecting share prices. They examined the effects of

dividend, profitability, price-earnings ratio, and

leverage on share prices using the panel

cointegration test and fully modified least squares.

The empirical findings showed that the share prices

of all three sectors were positively influenced by the

dividend per share and price-earnings ratio. The

results also showed that the debt-equity ratio is

negative and a major factor influencing share prices.

Meanwhile, profitability was found to affect share

prices only in the automobile sector.

Moreover, some authors in, [26], have identified

the Indian stock market determinants. Panel data is

used for thirty BSE SENSEX companies over the

period 2010 to 2014 by the fully modified OLS

method. The research indicates four determinants of

Indian stock market share prices, which are leverage

variables, price-earnings ratio, profitability, and

DPS. They found that price-earnings ratio, DPS, and

profitability have a positive association with stock

prices, while leverage has an inverse relationship

with stock prices. The inverse relationship between

leverage and share prices indicates that debt-to-

capital raising requires periodic interest payments

on behalf of the company. Besides, the increase of

companies using debt will lead to higher interest

payments and thus lower earnings for equity

shareholders. Thus, investors typically prefer

companies with lower debt.

Furthermore, the factors influencing banks’

stock market prices in Nigeria in 2012 and 2013

were investigated in the study, [27]. Using the

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linear regression model and partial correlation, the

four variables that influence stock market prices are

net asset per share, price-earnings ratio, price-book

value ratio, and dividend per share. The study shows

that in both years, net asset value per share and

price-book value ratio have a substantial effect on

the price of the stock market. However, the dividend

per share and the price-to-earnings ratio are relevant

factors for 2013, but not major factors for 2012. In

addition, the analysis also found that there is a

positive correlation between the stock price, net

asset value per share, the PBV ratio, and DPS, but

that the price-earnings ratio is weakly positive.

Some authors in, [28], use a panel data

collection of 41 companies listed on the Bahrain

stock exchange for the period 2006 to 2010 to

evaluate the factors influencing the share price.

They identify eight factors, namely return on equity

(ROE), the book value of the share (BVS), earnings

per share (EPS), dividend per share (DPS), price-

earnings (PE), dividend yield (DY), debt to the total

asset (DA) and firm size (LogMCAP). The

empirical results show that ROE, BVS, DPS, PE,

and Log MCAP have a positive and significant

relationship with MPS. However, a negative

dividend yield-MPS relationship. This implies that

to gain various buyers, dividend decisions are made.

Consistencies in measurements have been observed

in both models of estimation. Therefore, any group

that expects short-term and regular returns will

show its effect as a positive relationship with the

share price, while the group that is unaffected by

dividends will reflect a negative relationship with

the stock price.

In addition, some authors in, [29], analyze the

effects of the debt-to-equity ratio (DER), earnings

per share (EPS), price-to-book value (PBV), and

return on equity (ROE) on the stock prices of listed

manufacturing companies on the Indonesian Stock

Exchange in the food and beverage sub-sector of the

consumer goods sector. The financial data for the

period 2012–2018 is included in the research data.

By using panel data regression, the variables that

influence stock prices based on the Random Effect

Model are earnings per share and price to book

value. EPS and PBV can be explained by the

91.19% variability of the stock prices of

manufacturing firms in the food and beverage

consumer goods sector, as shown by the value of R-

squared.

Apart from that, the authors in, [30],

investigated the determinants of the stock price

reaction to allegations of corporate misconduct from

January 1982 to December 1996 in the US. In this

study, they focus on earnings, risks, and firm size

effects. They found that allegations of misconduct

are followed by statistically significant modified

control-firm reductions in reported earnings, rises in

the variability of stock returns, and a decrease in

concordance among the earnings estimates of

analysts. The magnitude of market-imposed

penalties accompanying allegations is systematically

related to the type of misconduct, firm size, and the

rise in uncertainty. However, the statistical link

between the adjustments in earnings around the

allegations and the effects of criminal allegations on

wealth is still uncertain.

2.3.1 Firm Size and Stock Price

The magnitude of a negative impact on shareholder

returns caused by alleged corporate misconduct is

inversely related to firm size. A systematic influence

of firm size on the wealth effects associated with

announced allegations can be described in two

methods, [30]. First, a simple economy of scale

argument: if criminal conduct has fixed costs in

terms of legal fees, fines, and loss of goodwill, the

percentage of wealth will be smaller, and the

company’s capitalization will become higher.

Besides, firm size may have an impact on its

relationship with corporate “reputation” or the

“value of brand-name capital” accumulated by the

company being accused. Firms with more

reputational capital have more to lose from a loss of

reputation, but they are also in a better position to

counteract the reputational damage caused by an

allegation. In addition, some authors in, [31],

analyze the influence of formal corporate

indictments on shareholder returns over the 1980s

decade to assess the extent of market-imposed

consequences of corporate criminality. They find

that the 83 companies they look at have a two-day

average abnormal return of -1.9%. They find that

only company size and a period dummy variable, as

an indicator of changes in market attitudes about

illegal business activity, are weakly significant in

explaining the negative stock price reaction. The

potential fine initially stated by the prosecution does

not appear to be significant in explaining the loss of

shareholder wealth. The statistical link between the

size of the corporation and changes in shareholder

wealth as a result of the announced allegation might

be examined to test these contrasting theories.

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2.3.2 Price to Book Value and Stock Price

The reputational implications of criminal allegations

may be tied to the extent to which corporate value

depends on future growth potential, [30]. Intuitively,

one would expect reputational damage to be greater

(even among large organizations) for firms with a

relatively high percentage of value dependent on

growth potential. This is because a large part of

these companies’ value is reliant on launching new

items or entering new markets where they have not

yet built up a base of goodwill, or “track record”,

among consumers and suppliers, [30]. The lack of

goodwill will exacerbate the negative effect of

allegations of misconduct. Price to book value is

significantly linked to future equity value

forecasting, [32]. Besides, the author in, [33],

provides a methodology for forecasting the impact

of price-to-book value in stock price prediction.

According to his findings, price-to-book value

shows a positive relationship with future stock

returns for the companies studied. As mentioned,

increasing the worth of a company is a success if it

is performed with the aspiration of its owners,

because as the firm’s value rises, then the owners’

wealth also increases, [34]. This indicated that a

high corporate value signifies a high level of

shareholder wealth. The greater the PBV value, the

higher the investor’s assessment of the company’s

shares, causing the stock market price to rise and the

capital return to increase. Thus, higher PBV value

firms are expected to suffer greater shareholder

wealth losses in the criminal allegations.

2.3.3 Earnings per Share and Stock Price

Earnings per share are the common indicator of a

company’s performance and provide investors with

information about a company’s value, [35].

Managers have some flexibility in evaluating

earnings while complying with general accounting

rules. For instance, firms can modify reported

earnings by speeding revenue recognition and

postponing expense recognition. This effectively

shifts earnings from a previous period to the present

quarter. Firms can also change earnings by altering

inventory accounting methods, updating projected

amounts such as bad debt expenses, or employing a

range of other tactics. It is feasible that companies

will employ discretionary accounting to manage

earnings statements around particular corporate

events. However, some authors in, [36], find small

evidence of a fall in reported earnings following the

allegations, but no indication of a relationship

between earnings changes and the degree of the

stock price response to the allegations. While

financial theory would suggest that loss of goodwill

or reputational loss is just a reflection of

shareholders’ expectations for future decreases in

earnings or cash flows to the company, no

significant link has been discovered between various

allegations and changes in corporate earnings. On

the other hand, the authors in, [37], discover

evidence of earnings manipulation by companies

that violate debt covenants. Results management

behavior seems especially probable around the time

of new stock offerings, given the well-established

relationship between earnings and stock prices. This

is because a company’s most recent earnings are

likely to have an impact on its capital costs. Thus,

announcing fraud could reveal the manager’s

negative news on the company’s future earnings.

2.3.4 Dividends per Share and Stock Price

From the standpoint of agency theory, declaring a

dividend may be considered a manner of resolving

the agency problem because outsiders prefer current

dividends to held earnings. If the dividend is not

paid out in cash, insiders will have the option of

using the money for personal gain or investing it in

a non-profit initiative for the benefit of the insider,

[38], [39]. Managers with free cash flow may

enhance dividends that would otherwise be spent on

low-return initiatives or wasted, regarding Free

Cash Flow Hypothesis, [40]. Dividend payments to

shareholders diminish the amount of money under

the manager’s control and hence reduce the

manager’s power. The declaration of dividends

notifies shareholders that the managers are acting in

their best interests. Alternatively, the existence of

taxable dividends could encourage additional

institutional shareholders, who may be directly or

indirectly involved in the firm’s corporate

governance framework, allowing it to operate

effectively, [41]. However, there is criticism that by

issuing a stock dividend, the board of directors

would manipulate the stock price, [42]. Thus, the

declaration of dividends will influence the stock

returns and stock prices.

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3 Problem Solution

3.1 Data and Method

The objective of this study is to examine the

corporate crime announcement effect on stock price

reaction and its determinants in Malaysia. The event

study methodology is employed to analyze the

effects of corporate crime announcements on the

stock price reactions of Malaysian publicly listed

companies over the period 2003 to 2020. There are

two main variables for the estimation model of an

event study, namely the announcement date of the

corporate crime announcement and abnormal return.

The announcement date is compiled from Securities

Commission Malaysia, which shows that public

companies listed in Bursa Malaysia have committed

crimes from 2003 to 2020. The first appearance of

an announcement of corporate crime by the sample

company is referred to as the announcement date

and denoted as t=0. Meanwhile, the abnormal return

data was taken from the Bursa Malaysia website

from the stock price of a committed corporate crime

company and the event window is designed to be 90

days before and after the event (-90 to +90). This

research follows prior studies by using daily stock

return data to allow more precise measurement of

abnormal returns and more informative studies of

the announcement effect, [20]. Abnormal returns

can be determined by finding the difference between

actual returns and the expected return of the stock.

The equation to obtain an abnormal return can be

expressed as in Equation (1):

    (1)

where,

the return of stock on the firm in period 

 risk-free rate or the intercept term.

  the riskiness of the stock to the market rate

of return.

market index returns in period ; and

  residual error from firm-specific events.

The abnormal performance stock can be

measured by taking the residual, . In other words,

abnormal returns  are the residuals from the

regression during the estimation period. The

residual error,  can be computed for each period as

in Equation (2):

 󰇛 󰇜 (2)

Hence, if  or  is less than zero, then the

stock’s actual return,  is less than the expected

return . Since the anticipated expected return  is

equal to  , Equation (2) can be simplified

as . Therefore, it means that an

abnormal return is given for all stocks in period  as

in Equation (3):

 (3)

In addition, the Average Abnormal Returns

() can be defined over the sample of a firm’s

stocks,  at each day which can be specified to

minimize idiosyncrasies in measuring such

particular stocks. The estimator of the average

abnormal return () for each day  can be

computed as follows in Equation (4):

 



 (4)

where,

average abnormal return in period 

 the estimator of the abnormal return for

stock 

 number of stocks in the sample

As mentioned, the unrelated details will be

removed from the analysis because the effect of the

event must be reflected on average and the abnormal

returns are all based on the event, [43]. Furthermore,

the authors in, [44], used the residual within the

event period to estimate the abnormal return from

the market model. They revealed that the summary

of the  that has been estimated over months to

assess the average cumulative effects on the sample

of specific stock information of the company

reaching the market from the beginning of the

research period to any event date  In addition, the

computation on  is not adequate due to the

uncertainty of the event date’s probability, [45].

Hence, the of any date before or during the

event window will be accumulated to avoid bias

from uncertainty, [6]. This is the sum of the

Cumulative Average Abnormal Returns () as

shown in Equation (5):

 

 (5)

Some authors in, [46], have mentioned that the

statistical significance of  and  is

determined to test the null hypothesis of no effect of

a merger announcement on the share price by using

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a simple t-test. Besides, the authors in, [47],

suggested that the average stock returns of bad news

generally result in a negative return rather than

positive returns (good news). Therefore, this study

observes the significance of the negative abnormal

return of the event announcement and can reflect the

impact of the announcement on stock prices. The

null hypothesis is defined as follows in Equation

(6):

   (6)

The t-test has been used for a given sample to

examine the level of significance for abnormal

returns and estimate the standard error of the returns

to ensure their reliability and stability from the time

series of  for the estimation period. The t-test

formula for is computed as follows in

Equation (7):

  

󰇛󰇜 (7)

The t-test formula for testing is

computed as follows in Equation (8):

  

󰇛󰇜 (8)

Many researchers applied the estimation of

standard deviation for CAAR in their studies to

analyze the pattern and speed of the price

adjustments towards the event, [6], [12], [19]. Aside

from that, this study also aims to investigate the

determinants of the stock price in the context of

firms involved in corporate crime. In this study, we

are focused on four determinants of stock price,

namely firm size (LogMCAP), price to book value

(PBV), earnings per share (EPS), and dividends per

share (DPS). The secondary data for these variables

will be obtained from the annual financial

statements of a publicly listed company in Bursa

Malaysia that reported a corporate crime to the

Securities Commission Malaysia during the period

2003 to 2020. Meanwhile, the yearly closing stock

price is obtained from Bursa Malaysia with the

companies that committed crimes from 2003-2020.

3.2 Description of Variables

3.2.1 Stock Price

Stock price (Y) is the dependent variable in this

study. The stock price is defined as the cost of

buying a security on a stock exchange, [28]. The

share price of the stock depends on several

variables, such as earnings per share, dividends per

share, business size, dividend yield, and others.

Investors are always careful when buying stock

because the stock price is known to fluctuate

dramatically in this particular market. By following

the authors in [28], the stock price is measured by:

Y = Closing stock price as of 31st December for the

year studied

3.2.2 Firm Size

Firm size (LogMCAP) is one of the determinants of

the stock price in the context of firms involved in

corporate crime, [30]. They defined LogMCAP as

the natural log of market capitalization at the fiscal

year-end before the announcement. In their research,

 was the calculation of shareholder wealth,

which is proxies for stock price reactions, whereas

market capitalization is proxies of the market size of

the firm as the independent variable to study the

reputation of a firm. As mentioned, the firm size is

suggested to use log form to mitigate the high

skewness of firm size data and standardize the data

based on the rule of thumb in corporate finance,

[48]. By following the authors in, [30], the

LogMCAP is measured by:

LogMCAP = Current share price x Number of

shares outstanding

3.2.3 Price to Book Value

Price to book value (PBV) calculates the relative

value of a company compared with its market value.

This ratio indicates how much equity investors pay

for every dollar of net assets. It is important to

prospective investors and analysts because it shows

whether the company is undervalued or overvalued.

A high PBV ratio implies an overvalued stock price,

where the stock market price is greater than the

book value of the balance sheet equity. This will

impact investors because they will not be able to

buy a particular company’s shares at an overvalued

price. By following the authors in, [49], PBV is

calculated by:

PBV = Price per share / Book value of equity per

share

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3.2.4 Earnings per Share

Earnings per share (EPS) is the efficiency of both

management and business results, [28]. Company

EPS information illustrates the scale of the net profit

of the company that is ready to be distributed to the

owners of the company. This ratio indicates how

much benefit (return) per share the owner receives

from investors. A higher EPS means that the

business would give investors a great income

opportunity. By following the authors in, [28], the

EPS is calculated by:

EPS = Net income / Number of shares outstanding

3.2.5 Dividend per Share

Dividend per share (DPS) is the return earned per

share. The ratio of DPS ignores income held in the

company. The net profit after taxes belongs to

shareholders, but the amount of profits distributed

and charged as a cash dividend is the money

shareholders receive. It is a reward for the

investment risk taken by the investor. It is a share of

the company’s profit that is distributed among its

shareholders. DPS is a strategic payout to a class of

shareholders of a part of the company’s taxable

earnings managed by a board of executives. By

following the authors in, [28], the DPS is calculated

by:

DPS = Dividends paid / Number of shares

outstanding

3.3 Statistical Analysis

There are several statistical analyses employed in

this study to obtain empirical results. A descriptive

statistic is one of the analyses that summarize the

data of the variables by including central tendency

and variability measurements. Central tendency tests

include mean, median, and mode, whereas

variability measurements include standard

deviation, maximum and minimum value. Besides,

Pearson correlation analysis is a measure of the

linear association between two variables.

Furthermore, panel regression analysis is used

to investigate the determinants of the stock price in

the context of firms involved in corporate crime

from 2003-2020. The panel regression model is a

statistical method to examine two-dimensional data

with a combination of cross-section data and time

series, where the same unit cross-section is

measured at different times, [50]. This study is used

to examine relationships between dependent (stock

price) and independent variables, which are built

from firm size (LogMCAP), price to book value

(PBV), earnings per share (EPS), and dividend per

share (DPS). In the regression model, the  and 

represent the y-intercept and slope. The  to 

represents the correlation coefficient between the

dependent and independent variables. If the

estimated  to  is statistically significant, it

shows a significant effect of independent variables

on the dependent variable. The panel regression

model can be expressed as:



      

 

Generally, three estimation models are

employed in this study, namely the Pooled Ordinary

Least Squares (OLS) model, the Fixed Effect (FE)

model, and the Random Effect (RE) model. The

OLS model is unique in that it does not measure the

impact of its variables as separate entities. Instead, it

just measures the independent variables' overall

effects on the dependent variable. The pooled OLS

regression model can be expressed as:



      

 

By using this model, the coefficients and

intercept are assumed to be homogeneous. Besides,

the error term in this model should have a zero mean

and be uncorrelated with the independent variables,

ensuring that the OLS result is unbiased and

consistent. As a result, if the error term is associated

with the independent variables, the assumptions are

invalidated, and the OLS regression model becomes

biased and inconsistent. Therefore, the FE and RE

models are two alternative models.

The Random Effect (RE) model is a statistical

model in which the parameters are varied randomly.

The RE model is frequently used in panel data

analysis to estimate the variance of the groups and

error term and it assumes that the intercept and

slope are constant. The demeaning factor () has

been added to the RE model. The value of λ ranges

between zero and one and is based on the estimation

of the variance components. However, if the

standard error of the model is discovered to be high,

the RE model will not apply because the dummy

variable is included in the error term. Hence, the RE

model in this study can be expressed as follows:

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

      

  

On the other hand, the Fixed Effect (FE) model

is a statistical model in which the parameters of its

components are fixed rather than random. When

there are differing intercepts among groups, it is

commonly used as a measure. Ordinary Least

Squares (OLS) regressions with dummies can be

used to test this model. In contrast to the RE model,

where the dummy is part of the error term, the

dummy in the FE model is part of the model’s

intercept. As a result, the dummy variable must be

included in the intercept. Hence, the FE model can

be expressed as follows:



  󰇛  󰇜   

  

Furthermore, some diagnostic tests were

conducted in this study to further determine the

nature of the data employed. For instance, the

Breusch-Pagan LM test is used to choose between

the pooled model and the random effect model,

whereas the Hausman test is used to check whether

the random effect model or fixed effect model is

more appropriate for the study. In addition, the

multicollinearity test is also used to test the

correlation between explanatory variables in a

regression model. The diagnostic test also includes a

normality test, autocorrelation test, and

heteroscedasticity test.

3.4 Result

3.4.1 Event Study

Table 1 (Appendices) shows the list of companies,

the nature of the offence, and the announcement

date of the corporate crime event. The official

website of the Securities Commission Malaysia will

be used to compile a list of publicly traded firms in

Bursa Malaysia that have committed corporate

crimes. Based on trade activity and data availability,

the target company is chosen. After the filtering

process, 11 announcements involve 9 companies in

committing corporate crimes over the period 2003

to 2020. Based on Table 1 (Appendices), the nature

of offences in Malaysia is dominated by insider

trading and the furnishing of false statements.

Besides, two companies have been found to commit

corporate crimes on two occasions, which are Inix

Technologies Holding Berhad and Three-A

Resources Berhad.

Once the final sample of companies has been

found, the stock’s abnormal return (AR) will be

calculated individually. The AR will be measured

daily to manage the factors that have an impact on

stock returns within the event window of 90 days

before the announcement and for the following 90

days. The computation of AARs can remove

irrelevant details from the analysis due to the

influence of the event must be reflected on average

and the abnormal returns are all based on the event,

[43].

Figure 1 shows the plot of AARs for the target

company. The y-axis represented the AARs in

percentage and the x-axis referred to the trading day

in the event window of [-90, 90]. The announcement

date of an event is denoted as 0 on the x-axis.

Besides, the AARs imply that the extra profits

earned by shareholders for the holding period of

issued shares are released after the announcements.

The abnormal returns that are related to the

corporate crime announcement exist when the

AARs are less than 0. Meanwhile, if the

announcement were considered something bad, it

would likely cause the stock price to react

negatively. This can be proved by prior studies that

show that stock prices will react negatively more

than estimated, [6], [12], [19].

Table 2 (Appendices) shows the daily AARs

and CAARs for event days -90 to +90 with the t-

value. The first column refers to the trading day of

the event window [-90, 90]. The second column

represents the AARs in percentage, while the third

column is the t-value for the AARs. Besides, the

fourth column refers to the CAARs in percentage

and the last column is the t-value for CAARs.

Referring to Table 2 (Appendices), the findings

found that the AARs on the announcement date

(t=0) are -0.6787% and it is insignificant at the 5%

level. Hence, the null hypothesis in terms of AAR

cannot be rejected and it is concluded that the

announcement day does not show a negative

abnormal return. However, the results were against

the author in, [12], who reported a significant

negative AAR on the day of the announcement. In

addition, the AARs on three days prior to

announcement day is 0.7189% decreasing to

0.3188% on the day before announcement day.

While on the first day after the announcement of

corporate crime (t=1), the AARs increased to -

0.1378% and subsequently increased to 0.0616% on

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the second day. However, these values are not

significant at the 5% level. The empirical findings

indicated that the information leakage or rumors of

the event had reached the market before the

corporate crime was announced, [51]. In short, there

are no significant abnormal returns to shareholders

in this sample of the target company that is

associated with corporate crime across the holding

period. The t-test showed that the AARs for the ten-

day interval before and after the date of the

announcements are not significant at the 5% level.

This indicates that the announcement of corporate

crime would not affect the company’s stock price.

This reflects that the investors of the charged

company do not behave immediately in terms of

selling out their stocks even though the

announcement is released. Therefore, the result is

aligned with the authors in, [6], which stated that the

AARs are not significant surrounding the day of

information releases.

Figure 2 illustrates the plot of CAARs based on

daily returns within the event window for 90 days

before and after the announcement date. The x-axis

represented the stock trading days in relation to the

announcement date, whereas the y-axis represented

the CAARs value. CAARs are important to capture

the announcement effect of an event on a company’s

stock return because some of the stock market

reaction to the event may exist on the surrounding

days or the actual announcement day within the

event window, [51]. Referring to Table 2

(Appendices), the CAARs show a drastic decrease

from 7.9075% (three days before the announcement

date) to -6.1023% (two days prior to the

announcement date). This situation demonstrates

that rumors about corporate crime have leaked to the

public. It happens frequently in Malaysia because

Bursa Malaysia Securities Berhad will arrest those

who inquire before a company is charged under the

Securities Commission, [19]. Furthermore, the

findings also indicate statistically significant and

negative CAARs on the announcement day. Hence,

it can be concluded that there is a significant

negative abnormal return on share price relative to

the announcement effect of corporate crime in

Malaysia. By following the arguments of the author

in, [12], if the market is efficient, the market can't

have significant negative abnormal returns on event

day and subsequent days due to the spontaneous

reaction of stock price towards the announcement.

From the findings, a significant negative abnormal

return exists on the announcement date, day 3, and

day 5 after the announcement reflecting that the

market is not efficient. This finding is aligned with

the study of the authors in, [19], [20], which

concluded that the stock market in Malaysia is not

reacting efficiently to the announcement of

corporate crime.

3.4.2 Descriptive Statistics Analysis

Table 3 (Appendices) tabulates the results obtained

from the descriptive analysis. This analysis

illustrates data from all variables used in this study,

namely stock price, firm size, price-to-book value,

earnings per share, and dividends per share. It

provides basic information about variables, which

include the mean, standard deviation, minimum and

maximum value of the variables.

Based on Table 3 (Appendices), the mean of Y

is 0.8864 with a minimum of 0.045 and a maximum

of 4.446. The standard deviation is 0.9753,

indicating that the data are centred on the mean.

This indicates that the data for Y was stable and

there was less fluctuation. Besides, the mean of

LogMCAP is 7.9140, with a minimum value of

6.5213 and a maximum value of 10.3806. The

standard deviation of the LogMCAP is 0.9362,

which indicates that the LogMCAP data has high

skewness and is stable.

In addition, PBV has a mean of 0.8229 with a

standard deviation of 1.0425. This indicates that the

investor will infer a PBV of less than one to indicate

that a stock is undervalued, on average. Besides, a

maximum value of PBV (4.43) implies an

overvalued stock price when the market price is

greater than the book value of shareholders' equity.

The minimum value of PBV (-8.09) indicates that

the firm that is involved in crime has sustained

negative shareholder equity during the period of

study.

Moreover, there is a wide variation in the

minimum and maximum values of EPS, which are -

75.32 and 70.12, respectively. The mean of EPS is

7.718 and the standard deviation of EPS is 21.8189,

which means that the EPS in the sample is widely

dispersed. Furthermore, the results of DPS show a

mean value of 4.6467 with a minimum value of zero

and a maximum value of 38.2. There is a

respectable difference in the minimum and

maximum values because there are companies that

are not paying dividends at all in the sample. The

standard deviation of DPS is 8.5075, which implies

that the value of DPS on the date is farther away

from the mean, on average. The standard deviation

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of DPS is 8.5075, which implies that the values of

the DPS in the data set are farther away from the

mean, on average.

3.4.3 Pearson Correlation Analysis

Table 4 (Appendices) shows the analysis of Pearson

correlation among the variables studied, namely

stock price (Y), firm size (LogMCAP), price to

book value (PBV), earnings per share (EPS), and

dividends per share (DPS).

From Table 4 (Appendices), the stock price has

a strong positive correlation with firm size and

dividends per share, which are 0.8588 and 0.8370,

respectively. This is in line with the study of [28],

who found that there is a positive correlation

between stock price and firm size. Dividends per

share are positively correlated with the stock price,

with an increase in dividend per share ratios leading

to an increase in stock price, [52]. Furthermore,

earnings per share and stock price have a positive

correlation of 0.6381 and a statistical significance of

1%, which is consistent with the author in, [52].

Besides, the price to book value has a positive

correlation of 0.1696 with the stock price, which is

aligned with the authors in, [53].

In addition, the correlation between firm size

and dividends per share indicates a strong positive

correlation, which is 0.7787 and it is statistically at a

1% significance level. Moreover, a positive

correlation between firm size and price to book

value is statistically at a 5% level. Companies with

large total assets have reached the maturity stage

and are considered to have good prospects, [54].

When a company has a large total asset base, the

ease with which the company can be controlled will

increase the company’s value. Besides, a positive

and significant correlation is found between firm

size and earnings per share. The greater the

company size, the more likely it is that profitability

will increase and the value of the company's

earnings per share will increase, [55]. This is

because the larger the company, the more assets it

has that can be used to generate profits, increasing

the earnings-per-share ratio.

Furthermore, earnings per share and dividend

per share have a positive correlation of 0.7534, and

this correlation is significant at the 1% level. This is

consistent with the findings of the authors in, [56],

who found that the firm’s dividend payout is

majorly influenced by its performance for the period

and that real earnings are preferred by investors over

capital gains. Meanwhile, the correlation between

earnings per share and dividends per share with the

price-to-book value is positive, however, no

evidence has been found to support the significance

of the positive relationship. Besides, it is against the

authors in, [57], who found a significant and

positive correlation between earnings per share and

price to book value.

3.4.4 Panel Regression Model

Table 5 (Appendices) shows the results obtained

from three different panel regression analyses.

Based on Table 5 (Appendices), LogMCAP and

DPS are found to be significant at a 1% significance

level, while EPS is insignificant in all three models.

Besides, PBV was found to be significant at a 10%

significance level in the pooled OLS model. The

study has continued with the Breusch-Pagan LM

test and Hausman test to determine which of these

models would best fit and represent the data

employed in this study.

Breusch-Pagan Lagrange Multiplier (LM) test is

conducted to choose between the pooled OLS model

and the random effect model. It tests the null

hypothesis of whether the total OLS estimate is

sufficient to satisfy the selection of the random

effect model. The null and alternative hypotheses

are as follows:

H0: POLS is appropriate.

Ha: RE model is appropriate.

From the results in Table 5 (Appendices), the p-

value for the Breusch-Pagan LM Test is less than

0.05 and it is significant at a 1% significance level.

Hence, this indicates that the null hypothesis is

rejected and it concludes that the random effect

model is more appropriate compared to the pooled

OLS regression model.

Furthermore, the Hausman test is carried out to

determine whether the random effect model or fixed

effect model is better or appropriate for the study.

The null and alternative hypotheses are as follows:

H0: RE model is appropriate.

Ha: FE model is appropriate.

Based on the results from Table 5 (Appendices),

the p-value for the Hausman test is 0.0000, which is

less than 0.05. Hence, the null hypothesis is rejected

and it is concluded that the fixed effects model is

more suitable for this model. In short, both the null

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hypothesis from the Breusch-Pagan LM test and the

Hausman test has been rejected, which means that

the FE model has been found to be the most

appropriate model in this study.

3.4.5 Diagnostic Test

Table 6 (Appendices) shows the results of the

diagnostic tests that were carried out to test the error

term structure in the fixed effect model in this study.

The diagnostic test includes a normality test,

multicollinearity test, autocorrelation test, and

heteroscedasticity test.

Normality tests will be performed with the

purpose to check whether the interference is

normally a distribution. To carry out an effective

hypothesis test, the normality of the residual is

necessary because it can ensure that the t-statistics

and p-value of the F test are reliable. In this study,

the normality assumption will be tested by the

Jarque-Bera Test. Based on Table 6 (Appendices),

the p-value of the Normality test is 0.000, which is

less than 0.05. Hence, the null hypothesis is rejected

and it is concluded that the error term is not

normally distributed. This is because the data is

affected by the announcement effect of corporate

crime and becomes not normally distributed.

A multicollinearity test is required to detect

whether a multicollinearity problem exists in the

model. In this study, Variance Inflation Factor (VIF)

will be conducted to see whether the

multicollinearity problem exists in this model. If the

VIF exceeds 10, there might exist a multicollinearity

problem. Based on Table 6 (Appendices), the mean

VIF is 2.51. Hence, the null hypothesis will not be

rejected and there is no perfect multicollinearity

between the explanatory variables.

An autocorrelation test was employed to detect

the serial correlation problem in the model. It is

employed to decide whether the values of the error

term are correlated. In this study, Breusch-Godfrey

(BG) tests are performed to detect the existence of

autocorrelation. Based on Table 6 (Appendices), the

p-value of the BG test is 0.2147, which is more than

a 5% level of significance. Hence, the null

hypothesis will not be rejected, and it is not of

statistical significance. It can be concluded that no

autocorrelation problem exists in the model.

The heteroscedasticity test is used to detect the

error term must be homoscedasticity, which means

that the variance of the error terms must be constant.

Inversely, heteroscedasticity occurs when different

observations have different error variances. In this

study, the Modified Wald test was employed to test

the heteroscedasticity problem in the fixed effect

model. Based on the result in Table 6 (Appendices),

the p-value of the test is 0.000, which is less than a

5% significance level. Hence, the null hypothesis

will be rejected and it can be concluded that there is

the presence of a heteroscedasticity problem in the

model.

As summarized in Table 6 (Appendices), there

is a heteroscedasticity problem that occurred in the

fixed effect model. Hence, the robust standard error

is conducted to eliminate the heteroscedasticity

problem. The robust standard error is a technique

for obtaining unbiased standard errors of the fixed

effect model under heteroscedasticity.

3.4.6 Fixed Effect Model with Robustness

Table 7 (Appendices) shows the result of fixed

effect regression after using a robust standard error

method to arrange the heteroscedasticity problem.

The fixed effects regression can be expressed as:



    

   

From Table 7 (Appendices), there is a positive

relationship between the firm size and the stock

price. When the firm size increases by 1%, the stock

price will rise by 1.0057%.

The p-value of the firm size is 0.0003, which is

less than the 0.05 significance level. Thus, the null

hypothesis is rejected and it is statistically

significant at 1%, 5%, and 10% levels of

significance. This indicates that firm size has a

significant effect on the stock price. This finding is

consistent with the authors in, [30], who found that

significant positive coefficients of firm size have

shown that large market-size firms would

experience minor losses of the shareholder’s wealth

related to announcements of allegations of corporate

misconduct. If a particular criminal act imposes a

significant component of fixed costs in terms of

legal expenses, fines, and loss of goodwill, the

percentage wealth decline will be smaller, as the

firm's capitalization increases. Firms whose value is

based on growth opportunities appear to suffer

greater wealth losses as a result of the criminal

allegations, and the findings suggest that firm size

and reputation are important determinants that

should be taken into account when evaluating cross-

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sectional differences in wealth losses associated

with corporate crime.

Besides, price-to-book value has a positive

relationship with the stock price. When 1% increase

in price to book value, the stock price increases by

0.0196%. The p-value of the price to book value is

0.646, which is insignificant at 1%, 5%, and 10%

significance levels. This indicates PBV is

insignificant to stock price. Meanwhile, the finding

is against the result of the authors in, [29], [53], who

found that PBV is positively significant to explain

the variability of stock price. Increasing the worth of

a company is a success if it is performed with the

aspiration of its owners, because as the firm’s value

rises, then the owners’ wealth also increases, [34].

This indicated that a high corporate value signifies a

high level of shareholder wealth. The greater the

PBV value, the higher the investor’s assessment of

the company’s shares, causing the stock market

price to rise and the capital return to increase. Thus,

higher PBV value firms are expected to suffer

greater shareholder wealth losses in the criminal

allegations, although the PBV coefficient is not

statistically significant.

Furthermore, earnings per share have a positive

relationship with the stock price, with a coefficient

of 0.0008. This indicates that the stock price will

increase by 0.0008% when the earnings per share

increase by 1%. The p-value of the EPS is 0.657,

which is more than the significance level. Hence,

the null hypothesis will not be rejected and it is not

significant at 1%, 5%, and 10% levels. This

concluded that there is no significant relationship

between EPS and stock price. Some authors in, [36],

explored weak evidence of a reduction in reported

earnings following the allegations of fraud

allegations. However, there is no evidence of a link

between various allegations and changes in

corporate earnings. The findings on the link between

shareholder wealth losses and analysts’ anticipation

of bad news were challenging to interpret.

However, this is against the author in, [58], who

found that earnings per share did have a significant

effect on organizations that had fraud present and

organizations that did not have fraud present while

controlling for a stock buyback.

Moreover, the coefficient between dividends per

share and stock price is 0.0390, which shows a

positive relationship. When there is a 1% increase in

dividends per share, the stock price will increase by

0.0390%. The p-value is 0.086 and it is significant

at a 10% significance level. Hence, the null

hypothesis will be rejected and it can be concluded

that there is a significant relationship between DPS

and stock price. This is consistent with the authors

in, [59], findings, which demonstrated a positive

correlation between dividends and stock prices.

Besides, shareholder wealth is maximized when the

company pays regular dividends to shareholders and

when the stock price appreciates on the stock

market, resulting in financial gains for the investor,

[60].

In short, it has been concluded by testing the

hypothesis, and the following results were obtained

that there is a significant effect of firm size and

dividends per share on stock price in the context of

firms involved in corporate crime. Besides, R-

squared is a measure of the explanatory power of

the model between dependent and independent

variables. This study has disclosed that a 79.8%

variation in stock price is explained by variables

LogMCAP, PBV, EPS, and DPS. While the

remaining 20.2% was explained by other factors. In

addition, rho is used to determine the similarity

correlation in this model and the results show that

the fixed effect model will cause around 63.82% of

the fraction of variance. The value of F-statistics is

29.82 and it is significant at 0.0001%, which

implied the model is a good fit.

4 Conclusion

Investigating the effect of corporate crime

announcements is a very interesting and intriguing

matter. Facts indicate that corporate crime has been

continuously increasing and such corporate crime,

particularly financial statement fraud and asset

misappropriation, would result in significant

financial losses to the company. Instead, of huge

financial repercussions, corporate crime may have a

deterrent effect on society. In short, it may incur

some non-financial losses such as lowering social

morale and creating social disorganization, as well

as damage to the country’s reputation, customer

relationships, and the firm’s equity value. However,

studies that investigate the effects of corporate

crime, especially the announcement effects on the

stock market are limited. Most studies are conducted

in developed markets but rarely found in developing

market contexts like Malaysia. So far, there are only

the authors in [6], [19], [20], investigating the effect

of, [20], investigating the effect of corporate crime

in corporate crime in Malaysia.

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In this study, the efficiency of the stock market

towards the corporate crime announcement and the

determinants of stock prices are analyzed. The event

study methodology was applied to examine the

reaction of corporate crime announcements on the

stock price. Following the authors in, [12], [19],

[20], the finding of this research shows that the

AARs on the day of the announcement were

negative and not significant at the 5% level. This

means that the information or rumors about

corporate crime had been leaked to the public before

the actual date of announcements. Meanwhile, the

CAARs on the announcement day were reported

negative and statistically significant at the 5% level.

This implies that there is a negative abnormal return

to the announcement effect of corporate crime

among Malaysian public listed companies during

the period 2003-2020. The results indicate that the

market is not reacting efficiently to the information

released regarding the incidence of corporate crime

because the stock price was not fully reflected in all

publicly available information.

Aside from that, the finding from the fixed

effect model indicates that firm size and DPS are

positively significant with stock price in the context

of firms involving corporate crime. This finding is

aligned with the authors in, [30], who found that

large market-size firms would experience minor

losses of the shareholder’s wealth related to

announcements of allegations of corporate

misconduct. Besides, shareholder wealth is

maximized when the company pays out regular

dividends to shareholders and when the stock price

appreciates on the stock market, resulting in

financial gains for the investor, [60]. However, the

findings show that the PBV and EPS had an

insignificant effect on stock price in the context of

firms involved in corporate crime. This indicated

that there is no evidence to be found on the PBV

impact on the reaction of stock prices. This is

against the findings from the authors in, [29], who

found that price to book value is positive and

statistically significant to explain the variability of

stock price. Additionally, there is no significant

relationship between EPS and stock price. This is

consistent with the study by, [36], who found no

significant relationship between various allegations

and changes in corporate earnings.

In addition, the findings of corporate crime and

market efficiency may lead to better implications for

investors. This research intends to boost investor

awareness of corporate governance issues,

particularly in publicly traded corporations. The

growing relevance of reliable corporate reporting

allows the organization to improve its image while

also increasing public confidence. According to

previous research, ineffective corporate governance

in Asian countries has been linked to the dominant

power of controlling shareholders.

Furthermore, by developing new programs, this

study provides stakeholders with a better

understanding of the causes of corporate crime.

However, it is essential to improve stakeholder

knowledge and assist them in reducing the

opportunities for corporate crime within the firm. As

a result of this knowledge, stakeholders are

becoming more informed and reducing corporate

crime cases in Malaysia.

Further, it is difficult to determine the true

announcement effect of corporate crimes on the

company’s stock return due to the challenge of

obtaining all of the data for all companies that have

committed corporate crimes. This is because the

companies that are facing bankruptcy as a result of

illegal activity would be deleted by Bursa Malaysia.

It makes it difficult for researchers to acquire

historical data for those companies. Due to the

exclusion of those companies that have become

history in the sample of the study, the results will be

imperfect. Furthermore, as the sample size of the

study is limited, it will result in a small degree of

freedom for the t-test in hypothesis testing. As a

result, determining relevant facts to illustrate the

arguments may be challenging. Therefore, the

evidence regarding the significant effect of

corporate crime announcements on stock prices is

weak due to a lack of significant findings to support

the research. Due to the lack of research conducted

in this area, researchers are encouraged to do more

research about the corporate crime announcement

effect and its determinants in the future as this study

has become more prominent to the public.

Acknowledgment:

This work is supported by Universiti Malaysia

Sarawak and the Malaysian Ministry of Higher

Education.

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Appendices

Table 1. Target Companies and Announcement Date of Corporate Crime

No.

Company name

Nature of Offence

Announcement Date

1.

Multi-code Electronics Industries

(M) Berhad

Fraud in connection with

the purchase of securities

13 March 2009

2.

United U-Li Corporation Berhad

Furnishing false statement

28 April 2009

3.

LFE Corporation Berhad

Criminal breach of trust

24 June 2010

4.

Inix Technologies Holding Bhd

Furnishing of false

statements

23 September 2010

5.

Inix Technologies Holding Bhd

Furnishing of false

statements

29 September 2011

6.

Sime Darby Berhad

Insider trading

20 July 2012

7.

Lii Hen Industries Berhad

Market manipulation

4 September 2012

8.

Malaysia Pacific Corporation

Berhad

Insider trading

10 January 2014

9.

Transocean Holdings Berhad

Insider trading

8 December 2015

10.

Three-A Resources Berhad

Insider trading

25 October 2016

11.

Three-A Resources Berhad

Insider trading

13 February 2018

Sources: Securities Commission Malaysia (2020).

Fig. 1: Plot of AARs for Event Days -90 to 90

-1,5000%

-1,0000%

-0,5000%

0,0000%

0,5000%

1,0000%

1,5000%

-90

-45

0

45

90

AAR

Trading Days

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DOI: 10.37394/23207.2023.20.165

Nurul Izza Abd. Malek, Rossazana Ab-Rahim,

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E-ISSN: 2224-2899

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Table 2. Daily AARs and CAARs for Event Day -90 to +90 with the t-value

Trading Days

AAR

AAR t-stat

CAAR

CAAR t-stat

-90

0.6642%

1.7644

7.3065%

5.8518*

-89

0.1988%

0.5281

2.1868%

1.7514

-88

-0.3397%

-0.9023

-3.7365%

-2.9925*

-87

0.1033%

0.2745

1.1365%

0.9103

-86

0.3123%

0.8295

3.4350%

2.7511*

-85

0.0413%

0.1097

0.4542%

0.3637

-84

0.0613%

0.1628

0.6743%

0.5401

-83

0.0975%

0.2591

1.0730%

0.8593

-82

-0.3004%

-0.7979

-3.3044%

-2.6465*

-81

0.0330%

0.0877

0.3633%

0.2910

…

-10

0.3773%

1.0023

4.1505%

3.3242*

-9

0.5579%

1.4820

6.1369%

4.9151*

-8

0.3970%

1.0544

4.3665%

3.4971*

-7

-0.1880%

-0.4995

-2.0685%

-1.6567

-6

-0.2234%

-0.5935

-2.4577%

-1.9684*

-5

-0.3563%

-0.9465

-3.9198%

-3.1393*

-4

0.7121%

1.8916

7.8333%

6.2737*

-3

0.7189%

1.9095

7.9075%

6.3331*

-2

-0.5548%

-1.4736

-6.1023%

-4.8873*

-1

0.3188%

0.8468

3.5066%

2.8085*

0

-0.6787%

-1.8028

-7.4657%

-5.9793*

1

-0.1378%

-0.3661

-1.5159%

-1.2141

2

0.0616%

0.1637

0.6781%

0.5431

3

-0.3099%

-0.8232

-3.4089%

-2.7302*

4

0.0180%

0.0478

0.1979%

0.1585

5

-0.5061%

-1.3443

-5.5668%

-4.4585*

6

0.5250%

1.3946

5.7754%

4.6255*

7

0.4253%

1.1297

4.6783%

3.7469*

8

0.2871%

0.7627

3.1583%

2.5295*

9

0.0177%

0.0470

0.1944%

0.1557

10

0.2882%

0.7655

3.1702%

2.5390*

…

81

-0.1282%

-0.3405

-1.4101%

-1.1293

82

-0.1638%

-0.4352

-1.8022%

-1.4434

83

-0.2769%

-0.7355

-3.0458%

-2.4393*

84

-0.4426%

-1.1758

-4.8690%

-3.8996*

85

0.1996%

0.5302

2.1956%

1.7584

86

0.1340%

0.3559

1.4739%

1.1804

87

0.1132%

0.3008

1.2457%

0.9977

88

-0.1795%

-0.4768

-1.9744%

-1.5813

89

0.2206%

0.5860

2.4265%

1.9434

90

0.2705%

0.7185

2.9754%

2.3830*

Note: * indicates significance at 0.05 level.

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Fig. 2: Plot of CAARs for Event Days -90 to 90

Table 3. Descriptive Statistics Analysis

Variables

Obs

Mean

Std. Dev.

Min.

Max.

Y

162

0.8864

0.9753

0.045

4.446

LogMCAP

162

7.9140

0.9362

6.5213

10.3806

PBV

162

0.8229

1.0425

-8.02

4.43

EPS

162

7.7181

21.8189

-75.32

70.12

DPS

162

4.6467

8.5075

0

38.2

Table 4. Pearson Correlation Analysis

Y

LogMCAP

PBV

EPS

DPS

Y

1.0000

LogMCAP

0.8588***

1.0000

PBV

0.1696**

0.1686**

1.0000

EPS

0.6381***

0.5902***

0.1088

1.0000

DPS

0.8370***

0.7787***

0.0397

0.7534***

1.0000

Note: p ˂ 0.01 = ***, p ˂ 0.05 = **, p ˂ 0.1 = *

Table 5. Results obtained from Pooled OLS, Random Effect and Fixed Effect Model

Pooled OLS Model

Random Effect

Model

Fixed Effect Model

LogMCAP

0.5235***

0.7771***

1.0057***

PBV

0.0629*

0.0542

0.0196

EPS

0.0001

0.0006

0.0008

DPS

0.0507***

0.0401***

0.0390***

Constant

-3.5440

-5.4985

-7.2768

R-squared

0.8138

0.9257

0.9186

Observation

162

Breusch-Pagan LM

test

31.69***

(0.0000)

-

Hausman test

-

30.25***

(0.0000)

Note: p ˂ 0.01 = ***, p ˂ 0.05 = **, p ˂ 0.1 = *

-15,0000%

-10,0000%

-5,0000%

0,0000%

5,0000%

10,0000%

15,0000%

-90

-45

0

45

90

CAAR

Trading Days

Cumulative Average Abnormal Return for Event Day -90 to 90

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Table 6. Diagnostic Test

Diagnostic Test

Normality Test

Chi2 = 40.64

P-value = 0.0000

Multicollinearity Test

Mean VIF = 2.51

Autocorrelation Test

F-stat = 1.816

P-value = 0.2147

Heteroscedasticity Test

Chi2 = 17207.90

P-value = 0.0000

Remedies

There is a heteroscedasticity problem that exists in the

fixed effects model. Hence, the robust standard error

method is used to eliminate the problem.

Table 7. Fixed Effect Regression

Y

Coefficient

Robust Std. Err.

P-value

LogMCAP***

1.005719

0.2331624

0.003

PBV

0.0196163

0.0410583

0.646

EPS

0.0008395

0.0018212

0.657

DPS*

0.0390097

0.0199626

0.086

ˍcons

-7.276799

1.810659

0.004

R-squared

0.7980

Observation

162

F (4,8)

29.82

Prob ˃ F

0.0001

Corr (uˍi, Xb)

-0.8020

sigmaˍu

0.47722729

sigmaˍe

0.3592822

rho

0.63824833

Note: p ˂ 0.01 = ***, p ˂ 0.05 = **, p ˂ 0.1 = *

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Nurul Izza Abd. Malek is the main author,

Rossazana Ab Rahim, Michelle Chang Ting Ting, &

Nur Zaimah Ubaidillah are co-authors who

contribute to the consultation, methodology, data

analysis, final solution, and overview of research.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This work is supported by Universiti Malaysia

Sarawak and the Malaysian Ministry of Higher

Education.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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DOI: 10.37394/23207.2023.20.165

Nurul Izza Abd. Malek, Rossazana Ab-Rahim,

Michelle Chang Ting Ting, Nur Zaimah Ubaidillah

E-ISSN: 2224-2899

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Volume 20, 2023