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ISSN: 2472-114X
International Journal of Accounting Research
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The Effect of Internal Control Material Weaknesses on Future Stock Price Crash Risk: Evidence from Tehran Stock Exchange (TSE)

Seyed Mohammad Reza Razavi Araghi1* and Zahra Lashgari2

1Bank Supervision Department, Central Tehran Branch, Master’s in Accounting in Islamic Azad University, Iran

2Assistant Professor and Faculty Member, Islamic Azad University, Central Tehran Branch, Iran

*Corresponding Author:
Seyed Mohammad Reza Razavi Araghi
Master’s in Accounting in Islamic Azad University
Central Tehran Branch, Bank Examiner
Bank Supervision Department
Central Bank of the Islamic Republic of Iran
Tehran, Iran
Tel: +982122 565149
E-mail: [email protected]

Received Date: February 02, 2017; Accepted Date: April 10, 2017; Published Date: April 14, 2017

Citation: Araghi SMRR, Lashgari Z (2017) The Effect of Internal Control Material Weaknesses on Future Stock Price Crash Risk: Evidence from Tehran Stock Exchange (TSE). Int J Account Res 5: 150. doi:10.4172/2472-114X.1000150

Copyright: © 2017 Araghi SMRR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Internal controls are methods put in place by a company to ensure the integrity of financial and accounting information, meet operational and profitability targets, and transmit management policies throughout the organization. Since 2012, attention to internal control in Iran became more serious; indicating its importance in entity’s activities. Given that investors tend to be concerned about returns and stock price crashes, the results of this study can be helpful for users of financial statements. Regarding the objective, this is an applied research using a correlational method. This study investigates the effect of internal control deficiencies on future stock price crash risk. A sample of 133 companies listed on Tehran Stock Exchange in the period 2011-2015 was selected and analyzed using a systematic elimination method. Logistic regression model was used for hypothesis testing. The research findings suggested that material weaknesses in internal control have positive and significant impacts on future stock price crash risk.

Keywords

Internal controls; Future stock price crash risk; Sarbanes- Oxley Act

Introduction

According to the internal control guidelines adopted by Tehran Stock Exchange (TSE) in 2012, in order to achieve corporate objectives, the board of directors is required to ensure that internal controls are established and implemented appropriately and effectively, and at least annually, disclose the results in a report entitled “Reports on internal controls”. That is why the topic was the focus of professional associations and experts in the field of accounting. According to the above mentioned definitions, empirical research on the issue is more essential than ever. Weaknesses in internal control can either directly or indirectly affect an entity’s activities. Therefore, this study examines whether internal control weaknesses can affect stock price crash risk.

Internal control is a dynamic system covering all kinds of risks and deviations from policies and procedures Chorafas [1]. Chenhall [2] believes that internal controls are adopted to assist management to achieve business goals and desired results. Ogneva et al. [3] believe that internal control is a fundamental and dynamic process that continuously conforms to the changes that their organizations are faced with Karagiorgos et al. [4] believe that internal control is not just a one-sided tool for controlling the order and rightness of certain situations, but it is a method of detecting the value added up to a company, achieving the index of effectiveness and profitability of the company. Internal control systems reduce risks and help management in ensuring the reliability of financial statements and compliance with laws and regulations Spira and Page [5]. A weak control environment has the potential to allow error in estimation and procedures, earning’s management by opportunistic managers and their tendency to withhold bad news due to various incentives Kothari et al. [6].

Corporate managers tend to exaggerate their financial performance by withholding bad news and accelerating the release of good news, hoping that poor current performance will be camouflaged by strong future performance. If managers withhold and camouflage bad news for an extended period, negative information is likely to be stockpiled within a company. Once the amount of accumulated bad news reaches a certain threshold and concealing them for a longer period is more costly or impossible, it will be released all at once, leading to negative stock returns following to shareholders’ concern and thus stock price crashes Kim and Zhang [7]. The main question is whether the lack of effective internal control or a material weakness in internal control over financial reporting affects the major sources of information that is financial statements provided by corporations for capital market participants so that it leads to future stock price crash risk.

According to Hutton et al. [8] and Kim and Zhang [7] stock price crash implies that a corporate’s stock price has suffered a sharp decline in the year under review. As the severe reductions in stock price can be the result of a general decline in market prices, the overall situation in the market should also be noted and the sharp reduction in stock return compared to the returns of the market should be defined. The stock price crash in the above-mentioned studies was defined as a qualitative variable that equals one for a firm-year that experiences atleast one crash month during the fiscal year period, and zero otherwise.

Literature Review

Lee et al. [9] in a study on the effect of internal control weakness on investment efficiency found that firms with internal accounting control weakness tend to make over and under-investment. They also found that internal accounting control weakness is negatively associated with investment efficiency.

Lashgari et al. [10] studied the relation between internal control weakness and accruals quality using data obtained from 200 companies listed on Tehran stock exchange between 2008 and 2013. Findings of their study showed that accruals quality is very strongly associate with the internal control weakness and indeed, quality of accounting information (accruals) is highly dependent on both providing information environment and information resources.

Zhou et al. [11] conducted a study on the material weaknesses in internal control and future stock price crash risk based on the evidence obtained under Section 404 of Sarbanes-Oxley Act (SOX). They selected a sample of 19397 companies in the period 2004-2011 and found that corporate with material weaknesses in internal control are more crashprone than companies with effective internal control. Findings of their study also suggest that corporate managers with material weaknesses in internal control tend to withhold bad news, and this increases the likelihood of stock price crash risk.

Cheng et al. [12] studied the relationship between earnings opacity, cash flows and stock price crash risk in 255 companies, during the period 2004-2010. Findings of their study showed that earnings opacity and operating cash flows sensitivity result in the lack of revealing bad news, and this increases the risk of stock price crash.

Research methodology

In terms of objective, this is an applied research using a correlational method. The present study is considered a descriptive accounting research. At 95% confidence level, the information needed for evaluating and testing the research hypotheses was obtained from financial statements and reports submitted to Tehran Stock Exchange, Rahavard Novin software, Codal site, and companies financial information CD. Excel (2010) and Eviews7 were used to perform the calculation, data preparation, and analysis.

After performing the steps outlined, descriptive statistics was used to describe the data. The Kolmogorov-Smirnov test was used to determine the parametric or nonparametric statistical methods. Spearman’s rank correlation coefficient and Augmented Dickey-Fuller test (ADF) were used to determine the correlation coefficient between variables and the stationary research variables, respectively. Logistic Regression is used to test the research hypothesis. In other words, logistic regression, also called a logit model, is used when there are dummy variables, particularly when there are some qualitative variables. In addition to solving problems such as heterogeneity of variance and abnormality, the Logit model measures the estimated probability between 0 and 1, and the results are logically satisfying and interesting.

Developing hypothesis

Given the above-mentioned definitions, the research hypothesis is as follows:

Material weaknesses in internal control affect the future stock price crash risk.

Population and sampling method

The research population consists of the companies listed on Tehran Stock Exchange during the period 2011-2015. The study used a systematic elimination sampling method to select samples and eliminate the companies that do not meet the following conditions:

• In order to comply with the comparability of the companies, the fiscal year end is March 31.

• The fiscal year cannot be changed during the research period.

• They should not belong in the categories of banks, insurers, and financial institutions (Investment companies, financial intermediaries, holding and leasing companies).

Ultimately, by applying the above requirements, the population consists of 133 companies listed on Tehran Stock Exchange.

Models and variables

Hypothesis testing was conducted using the following model developed by Ogneva et al. [3]. The control variables of the previous studies of GeWelli and McVay [13] and Ashbaugh-Skaife et al. [14] as the factors affecting material weakness in internal control were documented and entered in the model:

Prob(WEAKi)=β01RESTRUCTUREi,t2SALEGRWi,t3INVEN TORYi,t4LOG_MKTVi,t5LOSSi,t6RZSCOREi,t7LOG_AGEi,tit (1)

It should be noted that before testing the hypothesis, the effect of all variables, in the left side of the model, on the weaknesses of internal controls was tested and proven in an alternative hypothesis.

The variables in the model are described as follows:

Prob (Weak): Index that is equal to one if the company has material weakness in internal control, otherwise it is equal to zero. Since 2013 companies were required to report their internal controls. Therefore, the study used items mentioned in companies audit reports on material weaknesses in internal controls.

Restructure: Index that is equal to one if the company has restructured the organization in that period, otherwise it is equal to zero.

Salegrw: Index that is equal to one if the sale is below the average industry sales, otherwise it is equal to zero.

Inventory: Total inventories over total assets.

Log_MKTV: Logarithm of the market value of equity. (Price per share multiplied by the number of shares issued at the end of the period).

Loss: Index that is equal to one if earnings before items of emergency show loss, otherwise it is equal to zero.

Rzscore: Altman (1986) bankruptcy index in which a higher level of index indicates weakness and a higher probability of failure.

Log_age: Logarithm of the number of years that the Company has been listed on the Stock Exchange (It is assumed that older firms have better internal controls).

The dependent variable in this hypothesis was stock price crash risk, which was measured by Hutton et al. (2009) criterion:

Rj,θj1j rm,θ-22j rm,θ-13j rm,θ4jrm,θ+1+β5jrm,θ+2+ε (2)

In this relation:

r: Monthly stock returns during the period θ.

Rm,θ: Monthly market returns for month θ. To calculate monthly market return, subtract the starting price index from the ending price index; then, divide the index’s change by the starting price.

Following Hutton et al. [8] to calculate stock price crash risk, first the model should be tested for each year-company and then the residual or the firm-specific monthly stock return (Wi,θ) should be calculated using the following formula. We include “crash months” as those in which a firm experiences firm-specific monthly returns 3.2 standard deviations below the mean firm-specific monthly returns over the entire fiscal year. Thus, when the obtained amount of (W) in a fiscal year is 3.2 standard deviations below, it means that stock price crash has taken place in that period. In this case, the CRASH variable is equal to one; otherwise it is equal to zero.

Wi,θ=LN(1+ε) (3)

The effect of internal control weakness on stock price crash risk based on the proposed model of Zhou et al. [11] is tested as follows:

Crash Riskt=α+Crash Risk Determinantst–1+ICW Determinantst–1t–1 (4)

In this relation:

Crash Riskt: Stock price crash risk in t period as a dependent variable.

Crash Risk Determinantst–1: Determinants of (Variables) future stock price crash risk in the previous year.

ICW Determinantst–1: Determinants of (Variables) internal control weaknesses in the previous year.

Finally, following the models proposed by Ogneva et al. [3] and Zhou Jie et al. [11], the impact of factors affecting the internal control material weaknesses and other control variables on future stock price crash risk are estimated in maximum likelihood regression-binary logistic model:

Crash Riskit = β01CHASHRISKi,t-1+ β2WEAKi,t-13RESTRUCTUREi,t4SALEGRWi,t-15INVENTORYi,t-16LOG_MKTVi,t-17LOSSi,t-18RZSCOREi,t- 19LOG_AGEi,t-110RETi,t-111SIZEi,t-112LEVi,t-113ROA i,t-114OPAQUE i,t-1i t-1 (5)

In this relation:

The control variables that influence future stock price crash risk and were used in the above model, are as follows:

RET: Average monthly return which is calculated as bellow:

equation (6)

SIZE: Natural log of total net sales at the end of fiscal year.

LEV: Long-termdebt to total asset ratio

ROA: The ratio of net income to the average total asset at the end of company’s fiscal year.

OPAQUE: Opacity of financial statements (Discretionary accruals obtained from modified-Jones model).

Research Findings

Descriptive statistics

Descriptive statistics for each variable for the selected 665 firm-year is as follows (Table 1):

Variable Symbol Mean Median Maximum Value Minimum Value Standard Deviation Skewness Coefficient of Expansion
INVENTORY 0/236 0/220 0/730 0/000 0/136 0/798 3/788
LOG_MKTV 11/618 11/480 15/570 9/930 0/796 1/328 452/6
LOG_AGE 1/088 1/110 1/650 0/600 0/236 0/135 2/711
RET 34/855 14/590 820/164 -78/463 82/866 4/297 3/189
SIZE 13/068 12/979 18/492 7/101 1/428 0/394 4/503
LEV 0/085 0/051 0/971 0/000 0/106 5/010 3/167
ROA 0/109 0/098 0/631 -0/339 0/132 0/337 4/678
WEAK Firms with 51.3% weaknesses in internal control, firms with no weakness in internal control
RESTRUCTURE Firms have restructured 14%, firms have not restructured 86%
SALEGRW Firms with below the average industry sales 57.4%, firms with average industry sales 42.6%
LOSS Firms with financial loss 12.3%, firms without financial loss 87.7%
RZSCORE Firms going bankrupt 48.3%, firms not going bankrupt 51.7%
OPAQUE Firms with financial statements opacity 53.7%, firms with transparent financial statements 46.3%
CrashRisk Firms with future stock price crash risk in t period 50.7%, firms with no crash risk in t period49.3%
CrashRisk t-1 Firms with future stock price crash risk in the previous year 53.5%, firms with no crash risk in the previous year 46.5%

Table 1: Descriptive Statistics for the Research Variables in Sample Companies.

Data normality test

The first step of the hypothesis testing process is the assessment of the normality of data. Kolmogorov-Smirnov test was used to test the hypothesis, the results of which are as follows:

The results of Kolmogorov-Smirnov test indicated that the firm all variables follow a lognormal distribution, while the rest of the variables follow a skewed distribution. Given that the dependent variable (Future stock price crash risk) is not normally distributed (Because it is qualitative, and the qualitative variables 0 and 1 are not normal); therefore, nonparametric methods are used for these variables (Table 2).

Variable Symbol k Statistic Asymp (sig)
Total Inventory to total assets INVENTORY 0.053 0.000
Market value of equity LOG_MKTV 0.065 0.000
Logarithm of firm age LOG_AGE 0.061 0.000
Average monthly return RET 0.177 0.000
Firm size SIZE 0.063 0.000
Leverage LEV 0.223 0.000
Return on assets ROA 0.084 0.000

Table 2: Kolmogorov-Smirnov Test Results.

Correlation between variables

Spearman’s rank correlation coefficient was used to determine the correlation between quantitative variables, the results of which are shown in Table 3.

Variable Total Inventory over Total Assets Market Value of Equity Logarithm of Firm Age Average Monthly Return Firm Size Leverage Return on Assets
INVENTORY 1            
LOG_MKTV **-0/238 1          
Sig 0/000            
LOG_AGE **0/213 *0/077 1        
sig 0/000 0/046          
RET -0/019 **0/178 0/012 1      
sig 0/617 0/000 0/762        
SIZE **-0/102 **0/687 0/217 *0/077 1    
sig 0/009 0/000 0/000 0/048      
LEV -0/018 **-0/199 0/006 -/008 *-0/087 1  
sig 0/646 0/000 878/0 846/0 0/025    
ROA **-0/105 **0/429 -0/056 **0/329 **0/173 **-0/222 1
sig 0/006 0/000 0/146 0/000 0/000 0/000  

Table 3: Spearman’s Correlation Matrix for the Quantitative Variables.

Unit root test (stationarity) variables

An augmented Dickey-Fuller test (ADF) was used to test for stationarity, the results of which are shown in Table 4. The results of Table 4 indicated that all research variables with a 99% confidence level are stationary.

Variable Symbol t Statistic Significance Level
Total inventory to total assets INVENTORY -17/289 0/0000
Market value of equity LOG_MKTV -23/508 0/0000
Firm age logarithm LOG_AGE -9/993 0/0000
Average monthly return RET -23/112 0/0000
Firm size SIZE -10/292 0/0000
Leverage LEV -16/016 0/0000
Return on assets ROA -15/288 0/0000

Table 4: Unit Root Test by Augmented Dickey-Fuller Test.

The results of hypothesis testing

The research hypothesis suggests that material weaknesses in internal controls have positive effects on future stock price crash risk. The results of the hypothesis testing based on the regression model for research samples are shown in Table 5.

Variable Symbol Coefficient ScoreZ Prob.
Future stock price crash risk of the previous year CHASHRISK t-1 0/0001 0/001 0/999
Internal Control Weakness in the previous year WEAKt-1 0/755 4/098 0/000
Restructuring in the Previous year RESTRUCTUREt-1 0/552 2/111 0/034
Below average industry sales in the previous year SALEGRWt-1 -0/611 -3/027 0/002
Total inventory to total assets in the previous year INVENTORYt-1 0/218 0/315 0/752
Market value of equity in the previous year LOG­_MKTVt-1 0/418 2/303 0/021
Losses in the previous years LOSSt-1 1/558 3/579 0/000
Altman Bankruptcy Index in the previous year RZSCOREt-1 0/619 2/690 0/007
Firm age logarithm in the previous year LOG_AGEt-1 0/213 0/549 0/582
Average monthly return in the previous year RETt-1 -0/0009 -0/825 0/409
Firm size in the previous year SIZEt-1 -0/234 -2/457 0/014
Leverage in the previous year LEVt-1 -1/844 -2/079 0/037
Return on assets in the previous year ROAt-1 -2/538 -2/318 0/020
Financial statements opacity in the previous year OPAQUEt-1 0/692 3/931 0/000
Stationarity C -2/400 -1/387 0/165
Maximum Likelihood Test (LR Test)
(Prob) Significance Level
156/ 301
(0/000)
HL Statistics
(Prob)Significance level
18 / 268
(0/028)
(Andrews Statistic)
(Prob) Significance level
19/610
(0/021)
McFadden R-SQ 1/0 69

Table 5: Hypothesis Test Results.

The results of hypothesis testing in Table 5 indicated that the overall regression model is significant and is a good fit. The low (Prob.) probability level of Z-score, at an acceptable error level for β2 suggests that with a 99% confidence level, material weakness in internal control has a significant positive effect on future stock price crash risk. Therefore, with a 95% confidence level, the model is confirmed. McFadden coefficient of determination indicated that the variables entered into regression were able to interpret 16% of variations in dependent variables.

Conclusions

The study investigated the material weaknesses in internal controls and their effect on future stock price crash risk in companies listed on Tehran Stock Exchange, for five years, during 2011-2016. Findings indicated that the above hypothesis is confirmed. The results also indicated that firms with higher material weaknesses in internal controls are more prone to stock price crash risk compared with those with effective internal controls. In fact, material weaknesses in internal controls contribute to hiding bad news by managers, increase the risk of error and distortion, influence the quality of information and financial reporting, prevent the firms from achieving their goals, and these all lead to future stock price crash risk.

Therefore, the need for effective internal controls in business units is evident. Internal controls, if properly designed and implemented, can contribute to the accuracy and efficiency of information systems and improvement of financial reporting quality. The findings of this study are consistent with the findings of Zhou [11] of the same topic.

Following the Results of the Study

It is recommended that managers take measures to reduce the future stock price crash risk by designing and implementing effective internal controls, and continuous control monitoring.

It is recommended that Tehran Stock Exchange assist managers in the implementation and enforcement of internal control guidelines (Approved in May 2012) by organizing curriculums and seminars on the growth and development of our capital markets. According to the Article 17 of the internal control guidelines, independent auditors are responsible for making comments on the implementation and enforcement of an effective internal control by companies in their audit reports to the General Assembly of Shareholders. Therefore, the implementation and enforcement of internal controls require compliance with relevant standard guidelines. Auditing Organization and Society of SPAs are expected to take the necessary measures to create enforceable standards.

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