The transformation of accounting numbers to fulfill the predetermined managerial motives by taking the advantage of existing rules and regulations can be explained as earnings management. It is an art of manipulating books of accounts in a creative way by managers either to maintain steady earnings growth or to avoid reporting red flags that may indicate presence of something fishy. The manager’s discretion/ ability/ incentives to manipulate the earnings undermine the quality and integrity of financial reporting system.
To meet the expectations of the market participants regarding profit projections for the coming years and to reduce the volatility in earnings in financial statements may be managed either northward or southward. To exploit an opportunity or maximize shareholders wealth, the managers go for various earnings manipulation methods like aggressive revenue recognition, aggressive capitalization, accounting choice method, misrepresenting cash flows, big bath accounting and cookie jars reserves.
Fraud has become a universal phenomenon and affects business organisations irrespective of their size, profitability and industry (Mangala and Kumari, 2015). In the last few decades, a series of accounting frauds have raised a question mark on the authenticity of financial reporting system. Enron, WorldCom, Satyam scam and the on-going fraud case of Kingfishers airlines is the real life example which caused huge financial loss, but depleted the investors’ confidence on financial system. Small misstatements of earnings subsequently develop into a full-fledged fraud. Therefore, Fraud starts from where the earnings management ends. To prevent fraud, the prime need is to limit the edges of earnings management.
There are mainly four types of incentives namely, capital market incentives, management compensation contract incentives, external contracts incentives and regulatory and political costs incentives which encourage the managers to participate in earnings management. There is a need of strict enforcement of law related to investors’ protection and financial reporting. Auditors role should also be made more responsible for quality reporting and simultaneously corporate houses must be encourage to adopt good corporate governance practices. Dechow et al., (1996) found the likelihood of opportunistic earnings management is more if the corporate governance is low. United States Sarbanes-Oxley Act (2002) was a landmark initiative to curb fraudulent accounting activities and protect the investors’ interest. The implication of act considerably reduced to the extent of earnings management (Fang, 2009). In India, SEBI guidelines provide a clause 49 of the listing agreement on corporate governance which has been applied on all listed companies and improves the corporate governance practices.
The present study uses a sample of 618 firm year observations representing BSE 500 index companies included over a period of three years and investigate the presence and magnitude of earnings management practices. It also discovers earnings management practices with respect to industries, sectorial classification and sizes of the firms.
REVIEW OF LITERATURE:
Presence of earnings management has been extensively debated and vigorously researched across the world. Healy and Wahlen (1999) define “Earnings management occurs when managers use judgment in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company or to influence contractual outcomes that depend on reported accounting numbers”.
Dechow and Skinner (2000) categorized earnings management into three parts like conservative accounting, neutral earnings and aggressive accounting. Earnings manipulation within limits of GAAP is called earnings management and when earnings manipulation violates limits it becomes fraud (Yaping, 2005). As against the general view, Yaping (2006) argued that earnings manipulation are of three types in which earnings fraud is harmful, creative accounting might be harmful, but earnings management is not harmful.
Sun and Rath, (2008) confers that earnings management has two perspectives: opportunistic and informational perspective. Opportunistic perspective indicates that managers use information asymmetry between insiders and outsiders to influence contractual outcomes and the informational perspective states that managers reveal the private information about the firm’s future cash flow to ultimate users which enhance their ability to estimate firm’s future performance (Hamdi and Zarai, 2013).
There are limited number of studies have been done in India in the area of earnings management as compared to US empirical literature. Empirical results illustrated that accrual accounting plays an important role in earnings management. As compared to real accounting, accrual accounting bids a lot of opportunities to managers to determine earnings according to their discretion (Gakhar, 2013). SEBI DRG study by Ajit et al. (2013) found that average discretionary accrual is 2.9 per cent of total assets in relation to around 1 to 5 per cent of total assets in Europe, UK and elsewhere in the world. Roy and Debnath (2015) found that service sector industries are involved in income decreasing earnings management and small sized firms have more chances to manage earnings. Mishra (2016) found average discretionary accruals is valued at 5.6 per cent of total assets and small cap companies are more indulged in earnings management as compared to large and medium companies. Rudra and Bhattacharjee (2012) found that IFRS adoption does not ensure control of earnings management in context of India. Shen and Chih (2005) found the less profitable banks are more inclined to manage their earnings. Kumari and Pattanayak (2015) highlight incidence of earnings management practices in Indian banking sector. Goel (2012) ascertains the manifestation of accruals management in India and provide classification on sectorial basis. Rajpal (2012) argued that hectic schedule of independent directors adversely affects their competency to check earnings management and propose that when independent directors hold multiple directorships, they become more able to constrain earnings management with their diverse experience. Sarkar et al. (2008) found high quality governance mechanism reduces the level of earnings management. Kumari and Pattanayak (2014) found a significant positive relationship between CEO duality and earnings management. Asija, Marisetty and Rangan (2014) found that pledging reduces the likelihood of accrual based earnings management. Singh and Singla (2016) correlates the clustered resignation of independent director to lower earnings management in following year. Most of the studies have extensively used modified jones model to illustrate their conclusion.
OBJECTIVES:
The main objectives of the study are
· To examine the presence and extent of earnings management in BSE 500 companies in India.
· To investigate earnings management practices across different industries, sectors and sizes of the business.
DATA AND METHODOLOGY:
The present study covers the constituent companies of BSE 500 index as on 20 October 2016 over a three years period ranging from 2013 to 2015. Secondary data has been collected from Prowess database maintained by CMIE i.e. Centre for Monitoring of Indian Economy. Due to different reporting practices, financial companies have been excluded from the sample. Following criteria has been used to select the companies for present study:
· The companies are in private sector (non- financial and non-government companies).
· Their accounting and market data are available for the study.
· The industry must have at least ten firms.
The final sample consisted of 206 companies over three years leading to 618 firm years observations. The companies have been classified into thirteen industries as per NIC (National Industrial Classification) Code at two digit level. Further, telecom and computer software industry have been included in service sector whereas the remaining eleven industries fall in non-service sector. Companies have also been segregated on the basis of their size, as measure by their total assets, into three categories, namely, large, medium and small. Here quartiles basis of average assets has been taken as criteria for determining the firm size as large, medium and small. If firms having total assets are less than Q1, considered as small in size and if total assets is more than Q3, these firms assumed to be large sized and firm with total assets lies between Q1 and Q3 are measured as medium sized firms.
Earnings management cannot be detected directly from financial statements. Several models have been used by researchers for detecting earnings management. Modified Jones Model has been widely used in the research of earnings management (Dechow et al., 1995; Defond and Park, 1997). Consistent with previous studies, the present study has deployed Cross Sectional Modified Jones Model to estimate discretionary accruals which reflect the magnitude of earnings management. Total accruals are the sum of both discretionary and non-discretionary segment of accruals. Discretionary segment is centred on manager’s judgement while non-discretionary segment is based on business conditions. Total accruals can be computed by using either cash flow approach (Hariber and Collins, 2002) or balance sheet approach. In cash flow approach, total accruals are measured by subtracting cash flow from operations from net income as shown by equation (1). This approach estimates accruals directly from cash flow statement:
...……. (1)
Where
TAit =Total accruals for firm i in year t
NIit=Net income for firm i in year t
CFOit= Cash flow from operations for firm i in year t
Total accruals can also be computed by using balance sheet approach:
…. (2)
Where,
= Change in current assets between t and t-1 for firm i
= Change in current liabilities between t and t-1 for firm i
= Change in cash and cash equivalents between t and t-1 for firm i
= Change in debt included in current liabilities between t and t-1 for firm i
= depreciation and amortisation expenses for firm i
Ai,t-1 = Lagged total assets for firm i
Cash flow approach is mostly used for the computation of total accruals as compared to balance sheet approach. Therefore, in present study cash flow approach has been used as suggested by Hariber and Collins, 2002.
Equation (3) is used to estimate the Industry specific coefficients for each year and industry basis by using cross sectional data of all companies listed in BSE. In this study, only those industries have been taken which have at least 10 firms to ensure that representative parameter estimates.
..(3)
∆Revit = Change in revenue from period t-1 to t for firm i
∆Recit = Change in receivables from period t-1 to t for firm i
PPEit = Property, Plant and Equipment for firm i
At-1 = Lagged total assets
,
, 
= are the regression coefficients
The estimated coefficients ( for each industry and year are used in equation (4) to estimate the non-discretionary accruals.
...…….. (4)
After computing total accruals and nondiscretionary accruals, discretionary accruals are calculated by equation (5) as follows:
......(5)
RESULTS AND DISCUSSION:
The descriptive statistics of discretionary accruals (DA) for the sample of BSE 500 companies in India during the period of 2013-15 is given in table 1. The average discretionary accruals are valued at 1.789 per cent of the average total assets of about Rs. 94451.84 million for the period of 2013-2015. The results are consistent with the finding of Roy and Debnath (2015) and Ajit et al. (2013) who also found discretionary accruals at a level of 1.93 per cent and 2.9 per cent of total assets respectively. Though, Mishra and Malhotra (2016) found a higher level of discretionary accruals at 5.6 per cent of total assets. Though all these studies pinpoint towards single digit positive earnings management still the observed variations in results of these studies might be due to varied sample size and different discretionary accruals estimation model used by the researcher. The range of discretionary accruals apparent from the results is very broad ranging from -79.537(representing high downward manipulation of earnings) to 199.405 (representing high upward manipulation of earnings).
Table 1: Descriptive Statistics of Discretionary Accruals
|
Statistics |
Discretionary Accruals (%) |
Total Assets (Rs. in Millions) |
|
Mean |
1.789 |
94451.84 |
|
Standard Deviation |
19.706 |
197756.99 |
|
Kurtosis |
3891.071 |
19.19 |
|
Skewness |
461.472 |
4.00 |
|
Minimum |
-79.537 |
349.2 |
|
Maximum |
199.405 |
1819049 |
|
Observations |
618 |
618 |
Table 2 shows the industry and year wise picture of discretionary accruals in the sample companies during the study period of three years (2013-2015). The DA for each industry have been given for the individual years and for all the three years taken together. Presence of discretionary accruals values are measured in both negative and positive direction and indicate the alternation in accounts. Manufacturing of Food Products Industry have highest magnitude of discretionary accruals (31.46 per cent) on an average basis. The Machinery and Equipment Industry, Electrical Equipment’s Industry and Computer Software Industry have experienced positive discretionary accruals in all the years of the study period. Such value of positive discretionary accruals signifies that earnings have been managed in upward direction depicting aggressive accounting practices adopted by such industries. While Rubber and Plastic Products Industry and Non-Metallic and Mineral Products Industry have recorded the negative discretionary accruals throughout the study period. The negative value implies their conservative accounting nature as earnings have been managed downward. Transport Equipment Industry and Civil Engineering Industry reported negative discretionary accrual at the first year of study period but convoluted in positive discretionary accruals in forward years. Some industries have shown income increasing discretionary accruals whereas others are found to be engaged in income decreasing discretionary accruals as verified by their positive and negative averages respectively. Industry wise variations in earnings management practices may be discerned during the study period. The results indicate the alteration in accounts as evident from presence of discretionary accruals, both positive and negative, across the industries. The extent of DA of all the industries taken together is 1.789 per cent (Table 1) which is the weighted average mean of discretionary accruals of all the industries where weights are the number of companies in each industry.