Earnings management and ipos: differences between industries
Profit management as methods of adjusting financial information to increase the attractiveness of the company to investors. General characteristics of the basic principles of accounting. Acquaintance with key features and problems of profit management.
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Earnings management and ipos: differences between industries
Introduction
Earnings management is the techniques of financial information adjustments to enhance attractiveness of a company for investors. Mostly, these methods are complied either with Generally Accepted Accounting Principles or with International Financial Reporting Standards. It is the problem for today's accounting science because it influences investors, companies and stakeholders. Moreover, this concept is exceptionally complicated to measure. Due to the fact that financial reports reflect the most important financial performance, almost each investor would evaluate the company according to it. Therefore, they could be misled by the companies. The first part of the research is devoted to the problem of measuring such concept, the second is the try to compare industry-based differences of usage such techniques within an IPO process. The fact is that investors have different revenue growth expectations of companies which go public, they differ from industry to industry. According to these expectations, there were chosen two industries for comparison: with high expectation rate and with moderate one. The purpose of the study is to compare companies in two industries which went public with those which did not according to their usage of earnings management.
The topic of this research is “Earnings management and IPOs: Differences between industries”. The main idea of the research is to analyze the usage of earnings management within companies which go public and compare the industries differences within these companies. It means that some companies use financial data adjustment techniques, which could artificially increase the value of the company.
The key notion and the main concept of this research is earnings management. It is crucial to understand this concept. Due to its ambiguous nature, the research is considered as a complicated one. One of the most popular definition is given by Healy and Wahlen, who define it as the situations when accounting managers use different kinds of judgments in organizations of transactions in order to benefit from this, either from investor's misleading or from direct contracts [Healy & Wahlen, 1999]. Significant amount of literatures have documented the existence of managing earnings around initial public offering (IPO) process [Aharony et al., 1993; Liu et al., 2014]. There was described that the differences that are mentioned in opportunist behavior are the result of managers stimulations and decisions to manage revenues and their ability to do so without detection. It is important element, when company goes public and managers want to sell shares on higher bid price. Without a doubt, investors estimate the company's value, expected growth in different indicators such as revenue or net income etc., and other indicators. The main assumption of this paper includes the dependence of revenue growth expectation of companies with the industry. For instance, some industries have higher rate of development and, consequently, growth in revenues. These industries are mainly connected with intangible assets, e.g. technological industries. Hence, the industries for comparison in this study were chosen according to the compound revenue growth expectation level. The industry with the highest rate comparing to the industry with the moderate one. Taking into account everything mentioned above, one of the relevance side of this problem is justified by complexity of revealing such adjustments and how they affect investors, stock exchanges or industries.
Therefore, the clear gap of industry differences appeared, following from the conducted literature review. The IPO process and earnings management are popular topics found in literature. However, there is insufficient number of researches on measures and analysis within several industries and their comparison. Thus, I can claim that the problem is relevant, and the analysis will provide clear explanation in the existing gap of statements could be adjusted within a specific industry, which, consequently, will give the notion of industry differences and how earnings management depends on the revenue growth expectations. More precisely, the research problem of the paper is that companies which go to IPOs and want to list on stock exchanges can manage their financial information and adjust it to sell the shares to the investors on a higher price, it is so called earnings management techniques. After that investors decide whether buy or not and on what price, their analysis also based on the IPO financial statements of the company.
By IPO companies I mean companies which are on the stage of starting to list on stock exchange, their financial statements could be received from IPO prospectuses, which is an open data. For comparison, after that there was gathered the data of the essentially same companies, but for the next years after IPO. These companies in the research are called non-IPO. Then, the four samples were created in order to calculate the usage of earnings management by the means of modified Jones model, which is widely accepted and considered as the best among quantitative methods of calculating the concept. The research aim of the paper is to analyze how these techniques could vary from industry and examine whether the differences in earnings management between IPO firms and non-IPO firms is greater in one industry than in another. That is why the study more likely will reveal the causal relationship between variables and could prove or reject stated hypotheses. The list of objectives includes the following steps:
- Identify industries which are appropriate for the analysis according to expected growth rate in revenue;
- Identify a set of firms that performed an IPO in both industries, separately. Choose companies according to the available information;
- Analyze all the possible models of calculating the earnings management. Choose the most appropriate one;
- Estimate the usage of earnings management in these firms;
- Identify a set of control firms that did an IPO in both industries;
- Gather observations on firms in both industries;
- Estimate the usage of earnings management in IPO and non-IPO firms in both industries;
- Compare earnings management of IPO firms and non-IPO firms in the industries.
However, as each research, this has its limitations, which should be considered beforehand. The main problem of the study is the measuring of earnings management. Such concept is significantly complicated to measure. There exist several basic approaches of measuring the concept, which will be considered further. These approaches are based either on benchmark or on quantitative measures of proxies of earnings management, which is more reliable, but also demand correct interpretations. In addition, one of the limitations is data, which is hard to gather for one researcher. It is going to be manual data gathering as well as data bases with financial information, hence time limits make it much more complicated.
Besides, the research could be regarded as practically significant for the specific field of study. It shows the differences of earnings management usage among different industries; hence it could indicate the expected usage of this techniques in industries with similar expectations, which is important information for investors to take into account. Moreover, discussion is expected, hence further studies are able to analyze and to compare other industries.
1. Literature Review
profit financial accounting
The literature review of this research consists of three parts. First, there will be presented overview of basic definitions of the concept, which is important in order to get the comprehended notion of the concept. There will be considered basic papers, which are the basis for such researches, from these papers the studies on the topic begin. The next thing to consider is measurements of the concept, this is one of the most difficult, because of the nature of the concept. Hence, several points of view and approaches will be considered. The third part is devoted to the earnings management around IPO process, which is the main idea of my research. This will give the understanding of why and how companies adjust earnings and how these two concepts are connected. The major purpose of firms making IPOs is to raise additional capital. Therefore, firms have strong incentives to manage earnings in order to earn more.
Thus, the purpose of theoretical foundation is to give solid understanding of the background of the topic, which, consequently, will allow to identify a gap and use prior researches in this research.
1.1 Definition
The most popular and extensive definition in the literature remains the one given by Healy and Wahlen: 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” [Healy and Wahlen, 1999, p. 368]. Basically, this definition implies that all the manipulations including earnings prediction, conditions of disclosure and evaluation of accruals are the accounting management's choice and desire to deliberately change the results of earnings. Besides the comprehensive analysis of the earnings management in the frames of misled investors and earnings management definition the study also considers the possible ways of smoothing techniques. Specifically, they concentrate on four main questions. First is the motivation of companies to engage earnings management, second question concerns the accrual-based method of investigation and what types of accruals more likely to be managed. The third is about periodicity of earnings management and the fourth question deals with economic impact of such techniques. The most valuable part of these four is the motivation of companies engaging manipulations, because for the rest questions there exist other comprehensive articles and books, which will be considered further in the paper. The examined motivation in the paper includes three main motives to engage earnings management. These are capital market expectations and valuation; contracts of accounting numbers; and anti-trust or other government regulation [Healy and Wahlen, 1999]. The first reason exactly evidences the presented paper. So that the companies engage earnings manipulation according to market expectations in order to report higher earnings. They also indicate a sub-reason which is that management want to meet analysts' forecast [Burgstahler and Eames, 1998]. Thus, considered paper gives the general overview of earnings management, which is crucial for the theoretical foundation.
Mulford and Comiskey wrote the whole book about different accounting practices and their detection. In the book they list possible earnings management techniques such as changing in depreciation methods, useful lives, deciding on the accruals and etcetera [Mulford and Comiskey, 2005]. They give the foundation of accounting manager's principles and techniques of how to make flexible statements and how possibly one could break accounting rules to adjust earnings. They claim that earnings - one of the most important line and it affected by manipulations mostly and that two main conditions might underlie earnings management. Basically, they name the same motives of market expectations and desire to avoid the decrease in market value of the company. Moreover, the study presents American evidence of earnings management and how Securities and Exchange Commission in the United Stated initiated a big campaign against abusive earnings management in 1998. The authors also argue that if earnings management is not detected, it eventually could benefit for the company doing public. But only in case of secrecy. Hence, the study reveals some methods of earnings management and gives useful insights of how to adjust earnings.
Schrand and Zechman conducted the research of companies and it showed that approximately one quarter of the firms misstated the earnings not intentionally and was in the frames of legal standards [Schrand and Zechman, 2012]. On the other hand, they researched the pattern of executives' behavior when the first misreporting step is made on the basis of overconfident position toward future earnings. Once executives made such decision, they followed their optimistic pattern in following situations. That is why they called such process “slippery slope”. Moreover, in the paper the authors considered firms' attitude toward such decisions and if there are any regulation methods, which allow company to control manager's decisions. The analysis showed that remaining three quarters of companies are more likely be in position when they intentionally need to misstate the earnings and, in these companies, occurs the effect of optimist executives. In addition, the authors relied on the effect of variable part of executives' compensation, which could influence the decisions-making process. In other words, the less share of salary depending on the performance of the company, the more executives engaged in misreporting and earnings management [Shuto, 2007]. This fact helped in terms of ongoing misreporting even with weaker incentives from the point of view of executive to do so. The paper mostly concerned with behavioral part of managers' manipulation and misreporting of earnings. However, it also gives informative overview of reasons to manipulate and executives' attitude to it. Furthermore, the study suggests significant theory of “slippery slope” and how it influences subsequent misreporting in bigger volumes. Hence, I consider this study as relatively helpful and complied with my topic because it also suggests that there is a share of companies that managing earnings unintentionally but due to other reasons, such as legal requirements.
Another important issue concerning the concept of earnings management is accounting rules and standards. The problem of earnings manipulation even in the frames of fully standardized accounting principles and rules makes a lot of significant and controversial conclusions and the researches concerned as well, hence there are a lot of studies on this topic.
Overall, the overview of the studies concerned with the definition of earnings management gives the understanding of the concept. Moreover, these studies are used in the further paper in order to rely on them.
1.2 Measurements
The following part will consist of explanation of general methods in measuring earnings management. There exist several basic models and methods to detect earnings management and its traces. The main task for this exact part is to critically evaluate previous studies and how they dealt the measuring aspect of earnings management. It is widespread knowledge that earnings management is not a simple concept to measure. This happens due to ambiguous character of the concept; it is complicated to reveal it and it is even more complicated to create a way of measuring such unclear notion. In the following part I will try to consider the main findings and popular models for measuring earnings management techniques. First thing to mention here is that there are several methods of measuring the concept. Generally, it might be said that there are five basic approaches of detecting earnings management [Sun & Rath, 2010].
The first approach is a basic accounting choice. Accounting choice is a flexible tool which could be chosen by management. Several previous fundamental studies examined the ways of managers choose accounting policies. Basically, there is a direct relation of accounting policy choice and manager's benefits from this [Watts and Zimmerman, 1978]. These benefits might result positively either for companies, such as tax decreasing, political costs reduction etc., or for managers themselves. Accounting policy choice is one of the approaches to measure earnings management, but it does not give any quantitative estimation or direct indication on the earnings management usage. Moreover, one company could use multi-dimensional accounting policy and change it due to different reasons. However accounting policy could have indirect indication on earnings character, in addition to this, accounting policies are chosen by management, therefore it considered as controllable.
Another way of manipulating earnings is real transactions. This approach allows management to move earnings by real transactions, e.g. sale activities. The first researcher who actually described real transactions as one of the manipulation techniques was Schipper, 1989. “A minor extension of this (earnings management) definition would encompass `real' earnings management, accomplished by timing investment or financing decisions to alter reported earnings or some subset of it” [Schipper, 1989, p. 92]. However, there is a very complicated process of detecting earnings management through real transactions because it cannot provide any market average or unified benchmark. Nevertheless, the research of Roychowdhury actually identifies the common ways of managers' manipulation and what they are trying to achieve. "Specifically, I find evidence suggesting price discounts to temporarily increase sales, overproduction to report lower cost of goods sold, and reduction of discretionary expenditures to improve reported margins" [Roychowdhury, 2006]. This is one of the most important studies in the field of real transaction approach, because it reveals the actual activities of manipulating and its reasons.
The next approach concerns two-part nature of earnings. This approach is called accrual-based and it refers to the notion that earnings consist of cash flow from operations and another part which is called accruals. Accruals in its turn could be divided into discretionary and non-discretionary parts. The discretionary part is the one that could be manipulated depending on the management choice. That is why it is one of the best proxy for earnings management, moreover it could be calculated and the following models are describing the ways of doing so. The main problem here is to calculate it correctly, because there are many other factors that could influence discretionary accruals part. The paper of Dechow et al. compared five models of the process generating non-discretionary accruals measurements [Dechow et al., 1995]. They needed to put all the models into one general framework, so the conditions are the same for comparability. The models are: DeAngelo model, Healey model, the Jones model, Industry model, the modified Jones model and the. The Jones model assumes that non-discretionary accruals depend on the change in revenues and the level of property, plant and equipment. The reasoning is that firm's working capital requirements depend on sales, while its depreciation depends on the level of property. Once the model is estimated, the researcher uses forecasted values to evaluate non-discretionary accrual. With this model any accrual not treated as non-discretionary is included as discretionary accruals. Most of the critics to Jones model are based in the misclassification problem, which reduces the power of the test. In worst case, it could cause the misinterpretation and proving the existence of the earnings management when there is no any in fact. In the modified Jones model, non-discretionary accruals are estimated during the event period and the only adjustment relative to the original Jones Model is that the change in revenues is adjusted for the change in receivables in the event period. Dechow et al. indicated that although all the five models are aimed to divide the discretionary part of accruals, it is the common problem of all models that they have low descriptive power of tests to detect earnings management. This lack of power happens because manipulated earnings, discretionary part of accruals should be relatively significant comparatively to overall earnings.
The next approach is based on identifying specific accruals [Mchichols and Wilson, 1988]. Basically, the approach is concentrated on specific industries where each accrual needs to be actually or physically justified. This allows to apply it to specific industries and certain types of companies. However, this method could be applied in limited number of firms due to industry's specific features [McNichols, 2000].
Finally, the fifth approach concerns industry average. Degeroge et al. developed the model for measuring earnings manipulations. The basic idea in that model is benchmarking [Degeroge et al. 1999]. They identified behaviors of overall earnings around one industry. Thus, one just need to compare the deviation with the benchmark. The obvious advantage of this model is that there is no need in evaluating the discretionary accruals and subsequent regression model, which could be incorrect due to the dummy variables and other factors influencing the model in some cases.
1.3 IPO and Earnings Management
A firm which goes to the initial public offering is unknown and is a dark horse, no one knows anything about the firm. Therefore, the financial reports in the company's prospectuses are crucial element to estimate its performance and rationale to invest. It makes financial reports one of the most important sources of information for investors. Therefore, this part will be devoted to the exact problem of firms' engagement in earnings management before initial public offerings and how they could manage it. The prior studies have considered the problem before; thus, I will try to consider these studies and evaluate them from the point of usefulness. This needs to be done because the presented paper is directly devoted to the topic of firms before initial public offerings across different industries.
The general premise which literature supports is that companies which go public are more likely to be engaged in manipulating earnings to sell the stocks on higher price [Aharony et al., 1993]. This paper evidence the claim that IPO companies want to contribute more to the earnings due to the fact that they want to meet the investor expectations. These expectations needed to be met because of the bid price on stocks. In other words, companies adjust their earnings before initial public offerings in order to increase the price on stocks. Another important aspect here is that investors, who buy or want to buy the stocks are unable to identify manipulations in short-term perspective. The complicated problem here is the comparison, so that private companies often do not disclose their financial information and consequently, it is hard for investors to check whether earnings management was engaged or not. Another work of Aharony et al. provides an essential analysis of Chinese government owned companies that went public. Generally, the study gives not only evidence of underperformance after engaging the manipulations, but also provides the analysis of related parties to the IPO firms. Specifically, the Chinese market was analyzed, and the related parties of the sample firms opportunistically used adjustments.
Another aspect to be mentioned here is that earnings management before IPO provides only short-term performance on stock exchanges [Teoh et al., 1998a]. Basically, in this research the authors used a sample of around a thousand of U.S. listing companies and their stock performance three years after the initial public offering. In long-term perspective those firms underperform because they were overestimated in the beginning. The quantitative interpretation was that such aggressive companies underperform in stocks during the first three years almost on 20 %. It is crucial paper for the presented research because it gives an important detail of the following performance, which could be estimated along with engagement in earnings management, also it could warn companies to adjust their earnings. Thus, the terms of IPO and earnings management are closely connected with each other and form general assumption about firms going public. However, there was an opposite research which argued the previous one. Ball & Shivakumar argue with the checked hypotheses stated by Teoh et al., they claim that previous study used inconsistent sample and the results are biased [Ball & Shivakumar, 2008]. Also, the authors claim that their results are complied with the general assumption that IPO companies adjust their earnings in some extent, but the evidence of their sample is that most of UK companies used in the sample have conventional behavior and do not manipulate the earnings in excessive extent. These results do not rule out the influence of other factors in the concept. I would connect such discrepancy to the samples and probable mistakes in calculations. Due to the fact that the modified Jones model is not perfect and has its own flaws, the contradiction could occur.
Overall, there are a lot of comprehensive literature on the topic and conducted analyses on this problem. It is obvious, because the phenomena of earnings management appeared about thirty years ago and was analyzed in detail by different researches and analyses. However, I may state that after performed literature overview there is still uncovered problem of industry differences. Actually, I could not find any articles or papers specifically concentrated on industry analysis and which could give a comprehensive analysis of any industries and its companies that want to go public. Therefore, I can clearly state that there is a gap in previous studies, which have not identified differences between any two industries. The gap is formulated on the basis of theoretical foundation review analysis. To summarize, there were a lot of literature foundations on the topic of earnings management and its connection with initial public offerings. I divided the theoretical foundation part into three main parts, which present definition, measurements and earnings management and IPO connection. This structure seems to be optimal and gives the clearest understanding of the previous foundations on the topic. The part about measurements give the full notion about models which could be used for measuring earnings management, it also gives necessary proxies for measurements. In other words, which indicators are used for detecting the manipulations.
2. Statement of the research question
In this part of the thesis I will elaborate more on the research question, stated hypotheses and justification of the proposed methods of analysis. As it was mentioned before, the theoretical foundation of the presented research gave the understanding of the prior fundamental and specific studies which analyzed the connected issues. The clear picture of the topic was formed. After the conducted analysis of the prior literature the gap was stated. There are plenty of researches that analyze initial public offerings and engagement of companies into the process within an IPO, the studies mostly concentrated on different regions, they use or extent the methodology part or used models to estimate earnings management. There are not so many, but still there are papers on industries' analysis and how the earnings management usage could fluctuate in one industry. However, during the literature analysis I was not able to find studies which somehow compare two or more industries with each other. Moreover, there are no such comparative studies within an initial public offering process. Therefore, the gap of the study was correctly and logically stated. There is a gap of industry difference of earnings management engagement within an IPO process.
The main research problem of the research is that companies which want to list on stock exchanges tend to adjust their earnings on the paper. In other words, financial statements of companies could be modified according to legal techniques. The main complexity is to recognize and detect companies' usage of such manipulations. There are special models for investigation of these manipulations, they were specifically discussed and considered earlier in the measurement part of the theoretical foundation. Moreover, there exist proved industry-based differences in earnings management usage. Following from the problem, the presented paper is aimed to consider and compare IPO companies and non-IPO companies in one industry and in another. Therefore, there will be better understanding of the companies' differences across two industries. Hence, the research question of the paper is the following: “How companies from different industries engage in earnings management techniques within an IPO process?” In other words, I will consider IPO companies from the earnings manipulation point of view, then I will consider non-IPO companies' adjustments of earnings in both industries. This will allow me to conclude, for example, that in the first industry, IPO companies use earnings management more. As it was mentioned in the theoretical foundation part, there are several basic approaches in measuring the concept of earnings management. This study is focused on the research format; hence it implies any sort of analysis. The analysis will be conducted using the modified Jones model, a regression which eventually allows to estimate the earnings management engagement of firms. I consider this type of analysis as quantitative and the most appropriate due to the fact that it gives a detailed analysis of the current business.
The reasonable question following after this is what industries to compare. The general assumption of this is that companies in industries with higher investors' expectation of earnings tend to engage more earnings management than in other. Therefore, the industries are picked according to the value of compound annual growth rate of revenue in these industries. More precisely I will elaborate on industry choosing in methodology part. The fact is that I choose telecommunication services and automobile & trucks industry to compare. The first is one of the most fast-growing industries.
The next point I want to mention is the hypotheses statement. According to the literature review and studied papers, there is a proved claim that companies which go public are more likely to be engaged in manipulating earnings. Besides, I assume that the manipulation directly connected with investors' expectations, hence companies in fast-growing and relatively significant revenue growth expectations could engage more earnings management. It was mentioned before; however, I still need to check the assumption that IPO companies engage more in earnings management. It is useful to check such assumption because the second hypothesis will be based on this one. Therefore, the first hypothesis could be formulated as following: IPO companies engage in more earnings management than non-IPO companies. Also, the general hypothesis I want to consider, and check is H2: the difference in EM engagement among IPO companies and non-IPO companies is stronger in the telecommunications industry than in the automobile industry.
The presented part is devoted to the research design and research question formulation. It is crucial component of the study, it explains why it is important to study the topic, how the topic is connected to the analysis and what are proposed methods of the research.
3. Methodology
profit financial accounting
The methodology of the study is primarily determined by the research design and the research goal. In the previous parts there was mentioned that the primary goal of this research is to compare IPO and non-IPO companies' usage of earnings management in two different industries. This analysis implies the identification of industry engagement in earnings management of non-IPO as well as IPO companies. The main goal of the methodology part is to describe the process of the research analysis. Methodology is divided into three main parts, which structure it best. These parts are: description of the methods used, data that was used for the analysis and the process of analysis itself. In all the parts there will be considered limitations of each. Due to the fact that there is a significant number of limitations and all they are connected with different research stages. As for stages of the analysis there are main parts:
- Define and describe the research design;
- State the hypotheses relying on previous researches;
- Choose the industries for analysis;
- Define the model to calculate earnings management. Define the tools for analysis;
- Choose sources and gather all the required data;
- Build the model and collect the results.
The study will reveal the causal relationship between variables and could prove or reject stated hypotheses. Therefore, the research purpose of the paper is explanatory, or I also could consider it as descripto-explanatory study because I can describe some related phenomena beforehand. Due to the fact that there will be considered different cases of companies which go public and their usage of earnings management with following quantitative analysis, I would consider the research strategy as multiple case study. Moreover, the research requires to have the quantitative analysis of financial indicators to prove hypotheses and accomplish results. The data that was gathered will be considered later in data part. The phenomenon will be perfectly illustrated by real-life historical examples which will give detailed information about such problem. I state historical, because it is impossible to claim and analyze the company, its usage of earnings management and IPO process if it is not finished. We cannot predict and analyze the investor's behavior as well. Therefore, multiple case study analysis with quantitative analysis of earnings management for some causal explanation is the research strategy of this specific paper. Concerning the time horizons of the study, I would consider it as cross-sectional because it is important to have the sample of several cases as of one point of time to increase the validity of the research. In other words, the analysis is conducted as of one point of time, for example, the point in time before company goes public and listed. There is an evidence that cross-sectional analysis is the better alternative to time-series analysis due to several factors: in time series data there could not be enough data within one company and also in used estimation model, which will be described in detail later, there could be result of serially correlated residuals. According to the theoretical foundation analysis, consequent gap formulation and research design, the hypothesis was stated.
The whole study is aimed to test differences between IPO and non-IPO companies across two industries. The very first assumption that was made based on the prior literature review is that companies which are going to have an IPO use the earnings management more actively. Such hypothesis was approved by [Aharony et al., 1993]. However, due to different samples and possible differences in research design it is useful to check whether this fact is a true for my research. Therefore, there is a first hypothesis stated. H1 is that companies which are going to list on stock exchanges are more likely to adjust their earnings and engage in earnings management. Based on this hypothesis I develop another, which is directly connected to the differences between industries. The H2 hypothesis in this section is that the IPO firms in the industry with higher revenue growth expectation engage more earnings management that do companies in industry with less revenue growth expectations. The major reason for such behavior could be in company's desire to achieve better bid price for their stocks. To check the stated hypotheses, I decided to use accrual-based approach, which is the best quantitative method of detecting earnings management. In the following parts I will more precisely describe the required data and the analysis itself.
Concerning the data analysis tools, there will be extensive usage of two tools. The first is Microsoft Excel program. It is used in order to process and clean up the data and also to make insignificant calculations. Moreover, MS Excel is a good tool for data analysis too, but for more comprehensive analysis there was used Stata data-analysis package. It is a widespread analytical tool, which is used in most studies, therefore it is reasonable to use it.
3.1 Analysis. Modified Jones model
As it was mentioned in literature review, there are five basic approaches of measuring the earnings management techniques. For the present study I am going to use accrual-based approach. This approach is devoted to the two parts nature of earnings of a company. In financial statements net income is divided into two parts - first is called cash flow from operations or operating cash flow. International Financial Reporting System standard IAS 37 divides cash flow into three main parts - from operating, investing and financing activities [IFRS, 2017]. Thus, cash flow from operations and cash flow from operating activities are essentially the same thing. The standard defines operating activities as: “[operating activities] are the main revenue-producing activities of the entity that are not investing or financing activities, so operating cash flows include cash received from customers and cash paid to suppliers and employees”. This cash flow from operations refers, basically, to cash earnings of the company. While there are non-cash earnings. This second significant part of the earnings is called accruals. In their turn, accruals could be divided into two parts as well, discretionary and non- discretionary accruals. Commonly, non-discretionary accruals are those which are natural and refer to the nature of business, while discretionary could be manipulated and depend on the management choice of the company. This is the crucial thing to understand, because discretionary accruals are the proxy for earnings management, and they give the picture of company's quality of earnings. In further text I will elaborate more on how to calculate the discretionary accruals and, actually, I will calculate it. This portion of company's earnings allows to conclude on the quality of earnings and the quality of company itself. According to the conducted literature review, the modified Jones model is the best model for calculating the discretionary accruals [Dechow et al., 1995]. Therefore, the justification of the choosing is a benchmark from previous researches and prior conclusions. This model is a multiple linear regression, which basically divides total accruals into two parts. The very first step for calculating discretionary accruals is defining a dependent variable. The formula for this is the following: Net Operating Accruals = Net Income - Cash Flow from Operations. It gives the value of net operating accruals as of specific period of time. Another way of calculating accruals is the following formula: Total Net Accruals = Net Income - ДCash - Cash Dividends - Stock Repurchases + Equity Issuance [Dechow et al., 1995]. However, this formula is too complicated in terms of data availability. Also, there is an opinion that cash-flow approach works better due to the ambiguity of other balance sheet items used in the second formula. After the dependent variable is chosen, there is a need to identify the independent variables. According to the model, the formula is the following:
This model is a regression, where: NOA - net operating accruals;
ATA - average total assets;
Sales - change in sales;
Rec - change in accounts receivable;
GPEE - gross PP&E;
- error.
Betas, coefficients or parameters are estimated by means of an ordinary least squares regression. The model basically checks how dependent variables of sales, accounts receivables and PP&E influence on accruals. The formula above is a regression which calculates the coefficients of independent variables and its influence on the net operating accruals. After the calculation of these coefficients, there could be calculated discretionary and non-discretionary part of accruals. According to the definition, TDA = NOA - NDA, which means that total discretionary accruals are the difference between net operating accruals and non-discretionary part of accruals. It could be obviously concluded that residuals of the mentioned regression are the very discretionary accruals we need to calculate. After this, when discretionary accruals are estimated, in order to prove or reject the first hypothesis, there is a need for the second regression, which will be with so-called dummy variables. The dependent variable is discretionary accruals which have been calculated. The independent variables, also dummy variables are: IPO, which equals to 1 and means that company is IPO, and the other which equals to 0, which is non-IPO companies. Also, there will be added control variables, that could affect dependent variable. Therefore, the second formula is:
Where: TDA - total discretionary accruals;
IPO - dummy variable (IPO or non-IPO companies);
TELE - dummy variable (telecommunication or automobile);
IPO x TELE - variable of influence of industry on engaging EM;
- error.
In this case, I can use the coefficient for the indicator variable in IPO to examine whether TDA are different for IPO observations relative to IPO observations. Thus, this equation checks the hypothesis H1, whether companies engage in more earnings management before going public. These could be checked for both industries. At the same time, the very same approach could be used to check whether industries with higher investors' expectations of revenue growth adjust the earnings more. Hence, there need of second regression as well. In order to test the H2, there will be essentially the same regression, but the dummy variables will differ.
The limitation of the usage of this model are quite significant. There is an evidence that the modified Jones model is the best model among all accrual-based models, however, it is not perfect. It could result a lot of mistakes and could be ineffective in estimation point of view. The first problem which occurs is the number of independent variables, there could be not enough of them. Hence, it will yield the wrong coefficients, endogeneity and eventually the inconsistent model. Therefore, the regression model and, basically, the sample needs different analyses for persistence. These are heteroskedasticity, endogeneity tests and autocorrelation. It will help to have clear and right coefficients of the regressions. In order to cope with heteroskedasticity, there will be applied robust standard errors, so it will eventually give the right t-test, p-value and standard errors.
3.2 Data
This part concerns the data which is going to be used for the analysis. First of all, there will be considered the data which was used for industry choosing. After that, there will be consideration of the necessary data items for the analysis, sample size, means and sources.
The analysis implies two industries and the hypotheses were stated according to the expected growth rate in revenue in next five years. This data was received from Aswath Damodoran web site [Damodoran, 2019]. In this data, there are gathered and analyzed firms from different industries, and then the compound annual growth rate and expected growth rate in revenues were calculated. More precisely, the first industry I have chosen is telecommunication services, according to Aswath Damodoran, web site statistics on expected revenue growth rate, telecommunication services industry is the leading industry on this indicator, having the value of the next five years revenue growth rate of almost 300%. Therefore, it is one of the best candidates for analysis. The second industry I decided to evaluate is automobile industry, which has the industry expected growth rate of 27% in next five years. Therefore, the comparison of the fastest industry expected growth in revenue and the average will yield the sufficient analysis for the hypotheses checking.
The next thing to mention is compulsory financial data which needed to be gathered. Concluding from the formulas, there is a need of such financial indicators as: net income, cash flow form operating activities, accounts receivables, gross property, plant and equipment and total assets. All this information could be found in three primary financial statements: income statement, balance sheet and cash flow statement. These three give all the necessary information for analysis.
Concerning the data samples, there are at least two limitations to consider. The first one is that for the sample I need financial information of companies, hence there is a need of balance sheets, income statements and cash flow statements. For the IPO sample I will use special prospectuses, which are issued for investors by companies which are going public. These prospectuses actually are open information, which could be found in the Internet. Moreover, the further information is for the following years of IPO, how companies performed during next years. Therefore, basically the sample consists of firms from two industries: telecommunication and automobile, and different observations in time. Thus, there will be observations in the time of IPO (IPO companies) and the observations of the very same companies, but after a few years of IPO (non-IPO companies). In such sample, there is time-based performance, so the companies which are a few years after an IPO are not considered as around IPO time, and thus, in theory, should not engage earnings management.
However, the limitation is in time, there is a manual work of gathering. The sample size is 54 companies in telecommunication industry and 40 companies in automobile industry. The sample, therefore, consists of 188 observations. Thus, there is going to be four samples: IPO companies in telecommunication industry, IPO companies in automobile industry, non-IPO companies in telecommunication industry and non-IPO companies in automobile industry. For the IPO companies there was used Thomson Reuters Eikon data base, which gives all the financial information including company's performance statistics. I should mention again that the process of gathering correct and consistent data is the most complicated part of the presented research. Due to the fact that there is a need for IPO companies and their financial information, I decided to use IPO prospectuses as a source of the main information for such analysis of IPO companies. As it is an information only for IPO companies, I also need the financial results for the year after the IPO and this is my IPO information. Thus, the analysis will be the calculation of earnings management for the company in the year of IPO and for the years after IPO. What exact year after IPO will be considered does not really matter, because it is assumed that there is no need for time adjustments.
After calculating the earnings management by modified Jones model, there is a need to compare IPO and non-IPO firms in both industries. The main issue here is the interpretation of the received results. For the right interpretation the second regression with dummy variables was introduced, it gives clear interpretation of the results. If the coefficients are positive, the earnings management is more. Therefore, by the second regression I avoid the problem of misinterpretation of the results and only one way is possible. Moreover, this regression model allows to check both hypotheses simultaneously.
Thus, as a result of this part, there were described and considered methods used in the research. The primary industry choosing is based on the expected growth rate in revenues for companies. The first industry with the highest value is picked, then the second is with moderate expected growth rate in revenues in the next five years. The firs industry is telecommunication and the second is automobile. After this, the primary analysis was considered, which is accrual-based approach with the help of modified Jones model. It is a regression model which checks the value of discretionary accruals, which, in turn, is the proxy for earnings management. The next thing which was considered is data, more precisely, sample, the size of the samples and sources of information. Thus, the methodology of this research is based on the previous studies and model, which is the best numeric estimation for earnings management.
4. Results
The presented part will be devoted to the results of the research. The sample described previously is used to conduct the analysis of earnings management for checking the hypothesis. The main idea of the analysis is to use modified Jones model for estimation of discretionary part of accruals and then based on this estimation to build another multiple regression which will estimate the engagement of the IPO companies into earnings management process and also will check the second hypothesis whether IPO companies in telecommunication industry adjust earnings more than firms in automobile industry. For estimation there is a need of dummy variables, so IPO companies will be marked as 1 and non-IPO as 0. The same is for industry variable, telecommunication companies are marked as 1 and automobile companies are marked as 0. The very first thing I want to consider is the descriptive statistics of the variables and their distribution. It is important in order to understand the variables, their means, minimum and maximum values and standard deviation. Table [1] shows the summarized descriptive statistics:
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