Earnings management and investment efficiency - RBGN - Fecap

RBGN

REVISTA BRASILEIRA DE GESTÃO DE NEGÓCIOS Review of Business Management

ISSN 1806-4892 e-ISSN 1983-0807

© FECAP

295

Earnings management and investment efficiency

Received on 02/23/2017 Approved on 01/17/2018

Flavio Sergio Linhares

Responsible editor: Prof. Dr. Ivam Ricardo Peleias

Federal University of Mato Grosso, Rondonópolis, Brazil

Fábio Moraes da Costa

Evaluation process: Double Blind Review

Fucape Business School, Vitória, Brazil

Aziz Xavier Beiruth

Fucape Business School, Vitória, Brazil

Abstract Purpose – To verify the relationship between the quality of the accounting information, measured by earnings management, and the efficiency of investments made by Brazilian companies listed on the BM&FBovespa. Design/methodology/approach – Firstly, the investment efficiency benchmark was established, analyzing the level of investment and the growth in sales as shown in equation (1). After this classification, the results of the extreme quartiles, which were classified as over/ under investment, were used as dependent variables to analyze the relationship between income and investment management above or below predictable levels. Finally, to test this relationship, a multinomial logistic regression was used to evaluate the probability of over and under investments in companies that practice earnings management, compared to the benchmark. The sample that served as the basis for this study is composed of all the publicly-held companies that had or still have shares listed on the BM&FBOVESPA, covering the period from 1996 to 2012. Findings – The data analysis revealed empirical evidence that earnings management is positively related to levels investment and this can interfere in the probability of a company being classified as under or over investing. Therefore, based on the results found, it is confirmed that “the higher the level of earnings management, the greater the probability of the company deviating from the ideal level of investment.” Originality/value – The results were consistent with the idea that the quality of accounting information plays a relevant role for managers in order to analyze the efficiency of investments. Keywords – Investments, Efficiency, Earnings quality, Earnings management.

Review of Business Management DOI: 10.7819/rbgn.v20i2.3180

295 Rev. Bras. Gest. Neg. São Paulo v.20 n.2 apr-jun. 2018 p.295-310

Flavio Sergio Linhares / Fábio Moraes da Costa / Aziz Xavier Beiruth

1 Introduction In this paper we study the relationship between investment efficiency and the quality of accounting information, measured by earnings management practices (discretionary accruals). Previous studies (Healy & Palepu, 2001; Bushman & Smith, 2001; Lambert, Leuz, & Verrechia, 2000) report that raising quality in financial reporting leads to relevant economic implications for firms, including in the efficiency of investments made. In order to verify the relationship between the level of investment and earnings management in the Brazilian market, this paper analyzes the probability of companies that practice earnings management deviating from the predicted level of investment, classified as a benchmark. Biddle, Hilary, and Verdi (2009) point out that higher quality financial reporting improves the efficiency of investments, as it reduces information asymmetry, this reducing the cost of fundraising and the cost of monitoring managers. Biddle et al. (2015) show that the adoption of international financial reporting standards (IFRS) improves the efficiency of capital investment in companies, especially in countries with weaker investor protections, as in the case of Brazil. Previous studies carried out in Brazil have focused on the determinants of accounting information quality (Lopes & Martins, 2005; Paulo & Martins, 2007; Martinez, 2001; Lopes, 2002; Paulo 2007). Thus, there is a gap in assessing the consequences of higher or lower quality information. In line with what has already been investigated in the North American market (Biddle, Hilary, & Verdi, 2009), it is expected that this paper will contribute to understanding the relationship between levels of earnings management and investments in the Brazilian market. Biddle, Hilar y, and Verdi (2009) emphasize that financial reports with better quality information could reduce adverse selection in the issuance of shares by companies, since they tend to demonstrate companies’ projects in a clearer and more efficient way for the investor.

They also emphasize that investment efficiency means projects with a positive net present value in a scenario of conflict of interest in the market, such as adverse selection and agency costs. In the literature, when a company rejects an investment opportunity that would have a positive net present value, it is defined as underinvestment. Conversely, overinvestment consists of investing in projects with a negative net present value (Biddle, Hilary, & Verdi, 2009). According to Martinez (2001), the users of accounting information are the economic agents who seek to perfect their decision-making models. According to the author, as soon as it generates useful information, it also has economic implications for the various agents. Beaver (1981) states that it is possible to identify some relevant economic consequences of accounting information, including how this information can affect the way in which investments are allocated by companies. In the same sense, Biddle, Hilary, and Verdi (2009) report that the association between financial reporting and investment efficiency reduces information asymmetry and that reports with higher quality levels can lead to greater capital attraction and, consequently, to a greater volume of resources to be invested. Firstly, to measure the quality of the accounting information, a quality management measure was used as a proxy for quality, according to the modified Jones model (Dechow, Sloan, & Sweeney, 1995). Then, the ideal level of investment was evaluated via the relationship between sales growth and investments in order to measure the efficiency of these investments, according to the model proposed by Biddle, Hilary, and Verdi (2009). The residuals of this relationship were used in order to classify the levels of investments. The residuals were classified into quartiles and served as the basis for the classification of investments; that is, investments below the ideal level were identified as underinvestment, investments above the optimal level were identified as overinvestment. The classifications in the middle



of the quartiles were defined as references classified as the benchmark. The results indicate that companies with higher quality financial reports are less likely to deviate from the optimal degree of investment. The study also showed that control variables, such as auditing, levels of corporate governance, total assets, and earnings, are related to the efficiency of investments. The results showed that companies that manage their earnings are more likely to deviate from the expected level of investment. Thus, this study is expected to contribute to both earnings management literature and information users, since they are usually the providers of resources.

2 Literature review 2.1 Accounting Information Quality Biddle, Hilary, and Verdi (2009) define the quality of accounting information as the ability of financial reports to convey information about a firm’s operations, especially regarding its expected cash flow.

2.1.1 Earnings Management Dechow et al. (2012) point out that earnings management is an important issue for academics and practitioners in the field of accounting, which makes research that involves examining the causes and consequences of earnings management routine. In the context of information quality and investment efficiency, there is evidence that the quality of accounting information is associated with both low investment and excess investment (BIDDLE et al., 2009). Thus, companies with higher quality accounting reports are less likely to invest resources in amounts significantly above or below the level considered optimal. Therefore, the results presented by the aforementioned authors are consistent with the idea that the quality of accounting information is important in investment decision-making processes.

According to Martinez (2001), earnings management is qualified as discretionary choices by the company manager. Therefore, the manager makes choices because of some specific objective that leads him to report a result that is different from the one derived from the reality of operations. There are several definitions of earnings management. For Schipper (1989), earnings management is a purposeful action in financial statements, with the aim of obtaining some particular benefit; that is, it refers to an action that is primarily based on the intention of executives and not on transparency of information. For Healy and Wahlen (1999), earnings management occurs when managers use their own goals to prepare financial reports with the intention of changing them without drawing the attention of stakeholders to the company’s economic and financial performance. Cosenza and Grateron (2003) define earnings management as a way of modeling the economic and equity reality of a company, using information manipulation practices in order to demonstrate a reality that meets managers’ goals. Martinez (2001) points out that, in relation to the motivations involved, there may be several earnings management modalities, which are described as: a) Target Earnings: is earnings management to change profit by increasing or decreasing it. The goal is to achieve benchmarks that may be above or below the period. b) Income Smoothing: aims to reduce variability and maintain results at a certain level, without oscillations. c) Big Bath Accounting: aims to reduce current profits in favor of future profits. According to Martinez (2001), companies manage their current profits to reflect this act in future results. For Kraemer (2005), the practice of earnings management is a way for managers to use accounting standards in order to meet specific objectives, without breaking the accounting principles.



2.2 Accounting Information Quality and Investment Efficiency Biddle, Hilary, and Verdi (2009) point out that firms invest until the benefit is equal to the marginal cost in order to spend excess cash, which is the excess positive net present value for investors. The authors also mention that there are a priori literatures that recognize the possibility of companies moving away from this expected level and investing above or below predictable levels. These authors also cite as an example the previous research that identifies two reasons, that is, moral hazard and adverse selection, caused by the asymmetry of information between managers and investors, and that can therefore affect the efficiency of investments. Merely for their own interests managers often make investments that are not in the investor’s interest, that is, they invest in projects with negative net present value (BERLE & MEANS, 1932; JENSEN & MECKLING, 1976). Jensen (1986) predicts that managers driven by private incentives and bonuses tend to push their firms beyond their ideal size. On the other hand, investors recognizing this problem can restrict capital, which can lead to fewer investments than necessary. Adverse selection models suggest that executives have more privileged information than investors about the prospects of companies. They will try to issue more expensive bonds and if they are successful, they will be able to invest these excess resources (BAKER, et al, 2003). On the other hand, studies show that when managers act in favor of shareholders and the company needs to raise funds for a project with a positive net present value (NPV), managers can refuse to withdraw funds at a discounted price, even though this implies not investing in projects that would have a positive NPV (MYERS & MAJLUF, 1984). According to Biddle, Hilary, and Verdi (2009), the discussions mentioned above suggest that information asymmetry between managers

and investors can reduce the efficiency of investments, resulting in friction such as moral hazard and adverse selection, and leading to over or under investment. Prior studies report that the quality of financial reporting can improve the efficiency of investments as financial information is used by shareholders to monitor managers and is also an important source of information for investors (Healy & Palepu, 2001; Bushman & Smith, 2001; Lambert, Leuz, & Verrechia, 2000). A study by McNichols and Stubben (2008) indicates significant results in which profit-making firms carry out more investments than would be expected based on investment fundamentals. The authors argue that conservative firms invest more and issue more debt in environments prone to a lack of investment, with greater effects for firms characterized by greater information asymmetries. Lara, Osma, and Penalva (2016) argue that conservatism improves investment efficiency. Therefore, the aforementioned literature suggests a relationship between the quality of accounting information and the efficiency of investments. Biddle et al. (2015) found that the adoption of IFRS is significantly associated with higher capital investment efficiency, measured by the sensitivity and flow of cash investments and higher value risk taking. But other control mechanisms can also be associated with the efficiency of investments, as demonstrated in the following section.

2.3 Accounting Information Quality and Control Mechanism One of the ways for Brazilian companies to raise funds is through the international market (ADR - American Depositary Receipts). Leuz and Wysocki (2008) point out that US securities legislation protects foreign investors more than their own. Archambault and Archambault (2003) also point out that companies tend to be influenced by the disclosure policies in the market in which securities are traded, and are subject to compliance with the laws of the country and their enforcement.



In Brazil, Murcia and Santos (2009) found evidence in their work on determinants of the levels of disclosure that Brazilian companies audited by the “Big Four” demonstrated a higher level of disclosure of accounting information. Analyzing institutional controls, Jensen (1986) states that corporate control can serve as a more rigid and rigorous follow-up mechanism, thus reducing investments beyond those foreseeable. Cohen et al. (2004) state that one of the primary functions of corporate governance is to guarantee quality in the preparation of financial statements, through interaction between agents inside and outside the firm. Hope, Thomas, and Vyas (2016) stress that the cost of providing quality information can outweigh the benefit of this information to stakeholders in the case of some privately held companies. Ahmed and Courtis (1999) highlight the positive relationship between disclosure level and firm size. Corroborating the idea, several papers identified a positive relationship between company size and disclosure level. The results suggested that the largest companies presented a higher level of disclosure (Singhvi & Desai, 1971; Cooke, 1989).

3 Methodology To evaluate the level of earnings management, the Modified Jones model was used (Dechow, Sloan, & Sweeney, 1995). Section 3.3 describes the methodology used to classify under or over investment. After this classification, the test was carried out to evaluate if earnings management is directly associated with the (greater\lower) probability of a company investing below or above the market norm (benchmarking).

3.1 Research Question Development The development of the research question arose from the work of Biddle, Hilary, and Verdi (2009). In this paper, a regression model was estimated to evaluate the relationship between investment and quality of information, evaluated through the quality of accruals, derived from

the work of Dechow and Dichev (2002) and McNichols (2002). The authors also evaluated the quality of the information using the model proposed by Wysochi (2008). In light of the studies presented by Biddle, Hilary, and Verdi (2009) and the international literature that points to a relationship between the quality of accounting information and investment efficiency, the following research question was defined: Do companies that manage their earnings tend to deviate from the ideal investment level? The question is also based on studies by Martinez (2001), in which the author argues that earnings management can cause serious inefficiencies in resource allocation between firms. To answer the research question, the study followed the following steps: firstly, the investment efficiency benchmark was established, analyzing the level of investment and the growth in sales, as shown in equation (1). After this classification, the results of the extreme quartiles, which were classified as over/under investment, were used as dependent variables to analyze the relationship between income and investment management above or below predictable levels. Finally, to test this relationship, a multinomial logistic regression was used to evaluate the probability of over and under investments in companies that practice earnings management, compared to the benchmark. The study sample is composed of all the publicly-held companies that owned or still own shares listed on the BM&FBOVESPA, covering the period from 1996 to 2012. The data for the 2008 and 2009 periods were excluded from the analysis since they refer to the period in which the Brazilian accounting model was in transition towards the international one (IFRS). All the variables were collected from the Economática database.

3.2 Accounting Information Quality Metrics Development The original model proposed by Biddle, Hilary, and Verdi (2009) is based on the Dechow and Dichev (2002) proposal, where quality level



is measured by the standard deviation for the

Therefore, the earnings management

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accruals with an error when d exercised. According to the modified According toDechow, Dechow, Sloan,and and Sweeney (1995), the the modified model According to Sloan, modified Jones is is (1995), the modified model formulated to Sweeney theis(1995), modified model, non-discretionary accruals + accruals with anJones error whenisdiscretionary revenue exercised. According toJones the model modified According to Dechow, Sloan, and Sweeney (1995), the modified Jones model is de eliminar tendências previstas do modelo deis Jones para mensurar os accruals accruals with anan error when discretionary revenue exercised. According tomodel, the modified accruals with an error when discretionary revenue exercised. According to the accruals with error when discretionary revenue exercised. According to the modified non-discretionary According to Dechow, Sloan, and Sweeney (1995), modified Jones model is i in accru eliminate expected trends from the Jones model are estimated as: : corresponds to the total assets oftrends theto company iis inis period t-1the ;to :measure error term ofmodified company formulated to eliminate expected from the Jones model to discretionary formulated to eliminate expected trends from the Jones model measure discretionary model, non-discretionary accruals are estimated as: According to Dechow, Sloan, and Sweeney (1995), the modified Jones model is formulated to eliminate expected trends from the Jones toé exercida. measure De discretionary measure discretionary accruals with an error when discricionários com erro, aare discricionariedade as model receitas acordo model, non-discretionary accruals estimated as: model, non-discretionary accruals are estimated as:sobre model, non-discretionary accruals are estimated as: Equation (1) quando formulated toan eliminate expected trends from the Jones model to measure discretionary accruals with error when discretionary revenue is exercised. According to the modified period t. accruals with an error when discretionary revenue is exercised. According to the modified formulated eliminate expected trends from isthe Jones model to measure discretionary accruals with antoerror when discretionary revenue exercised. According the modified com oaccruals modelo modificado, oswhen accruals não discricionários são estimados como: to to with an error discretionary revenue is exercised. According the modified model, non-discretionary accruals are estimated as: model, non-discretionary accruals are estimated as: with an error when discretionary revenue is exercised. According to the modified s of firm i in period t;accruals For the accruals calculation, for the years prior to as: 2007, the data used model, non-discretionary accruals are estimated model, non-discretionary accruals are estimated as: (1995), the modified Jones model is to Dechow, Sloan, and Sweeney m the Balance According Sheet.model, For thisnon-discretionary calculation, the following econometric variables accruals are estimated as: were used:

urrent formulated Liabilities, fincp,to debcp. For the calculation as of trends 2010,Equation the from difference between eliminate expected Jonesprofit model to measure (2) the In which: discretionary Equation 2 Equation (2) Equation (2) Equation (2) In which: change in net flow was used, which used the following variables: profit and fcxoper. In which: which: Innet which: In which: are the the non-discretionary accruals with an error wheni discretionary revenue is exercised. According: to modified acc t; change in revenue of company In the t; non-discretionary accruals of company in(2) period Equação 2 i(2) Equation Equation ny i between periods t : - are 1 and change in accounts receivable for t; company ichange in net revenue of company i non-discretionary of of company i ini (2) t; t; t; in net revenue of of company it - i1 and : are the non-discretionary accruals of company company iperiod change in net revenue company :the are the non-discretionary accruals of company i period inperiod period t; change change in net revenue ofofcompany i t; : are the non-discretionary accruals inin change in net revenue company Em que: between periods vable for company i :between periods t-1 and t; accruals Equation model, non-discretionary accruals are estimated as: In which: In which: Equation (2) i between periods t 1 and t; change in accounts receivable for company i between periods t-1 and : são os accruals não discricionários da empresa i no período t; é a variação da receita líquida na between periods t 1 and change accounts receivable for company i between periods t-1 and t; 1 and t; :Incorresponds to property, plant, and equipment ofEquation company i in period t; which: (2) periods -períodos change in accounts receivable forfor company i between periods t-1 and t;and between periods t1-and 1t;to and change in accounts receivable for i between periods t-1 t; between periods t1-and t;the change invariação accounts receivable company i between periods t-1 and t; toto :ofcorresponds corresponds éand acompany contas da empresa i entre os períodos empresa ostthe t-1 et; t; accruals corresponds to property, plant, equipment of t;company icompany in period y i in period t; t;between corresponds In iwhich: : ::are non-discretionary accruals in period t;a receber change net revenue of company : entre are the non-discretionary of of company i ini do period change in in nett; revenue company i i property, pla : corresponds to property, plant, and equipment of company i in period t; : corresponds to the In which: corresponde imobilizado no t; :period corresponde ao ativo da i t-1total etotal t; assets of company ii in period t-1; :: error of company company t. revenue sponds to thethe the companyao in period t-1 da ;of empresa errori iterm term of i iinin :assets areof:the non-discretionary accruals company inperíodo period in net of total company : corresponds to property, plant, and equipment of of company i t;ini t; period t;change :net corresponds tocompany the : corresponds to property, plant, and of company i period in period t; : corresponds to thei i in period t-1 : corresponds to property, plant, and equipment company in t;change : revenue corresponds to the total assets of company are the accruals ofequipment company i inreceivable period in oft-1 i in period t. empresa no período between periods t1-and 1 and change accounts receivable company i between periods t-1 between periods tt-1. -non-discretionary t; t; change in in accounts forfor company i between periods andand t; t; : are the non-discretionary accruals of company i in period t; change in net revenue of company i total assets of company i in period t-1; change : errorinterm of company i in period t. between periods t 1 and t; accounts receivable for company i between periods t-1 and t; total assets of of company i-in t-1;t-1;t-1;: error of company ireceivable ini period t. total assets of company int;period : term error term of company in period t. total assets company i period ini period : error term of company ini period t.company between periods t 1 and change in accounts for i between periods t-1 and t; : corresponds property, plant, equipment company period : corresponds : corresponds to to property, plant, andand equipment of of company i ini in period t; t; : corresponds to to thethe between periodstot -property, 1 and t; plant, and equipment change inofaccounts receivable fort;company i between periodstot-1 t; : corresponds company i in period : corresponds theand It should be noted that some adaptations The last step is to calculate discretionary Deve-se destacar que foram realizadas algumas adaptações em função de It should be noted tha re made due to Brazilian accounting : corresponds to property, plant, and equipment of company i in period t; : corresponds to the total assets of company i in period t-1; : error term of company i in period t. total assets company i innoted period t-1; some : error term of company imodel in made periodist.due to Brazilian accounting g to Dechow, Sloan, and Sweeney (1995), the modified Jones It of that adaptations were made due tobe Brazilian accounting accruals, based onto theBrazilian that these are to the :should corresponds toperiod property, plant, and equipment of were company i in period : corresponds the total assets of company ibe in t-1; : error term of company imade inmade period t. It Itshould be that some adaptations were due accounting It should noted that some adaptations were made due toBrazilian accounting should benoted noted that some adaptations were due tot;reasoning accounting peculiaridades da contabilidade brasileira. A variável receita representa aBrazilian receita líquida peculiarities. The REV variabl g income and the PA variable consists Equation (2) total assets of company i in period t-1; : error term of company i in period t. peculiarities. TheThe REV variable represents net results of the difference total accruals eliminate expected trends from the model measure peculiarities. REV variable represents net operating income andt.thebetween PA variable consistsand total assets company iJones in period t-1; to : error term of discretionary company i in and period In which: peculiarities. Theof REV variable represents net operating income the PA variable consists

peculiarities. The REV variable represents net operating income and PA variable consists peculiarities. REV variable represents net operating income and thethe PAas variable consists operacional, e should aThe variável Imob consiste noof valor contábil do ativo imobilizado (Lopes & of of the book value 007). operating income and the PA variable consists non-discretionary accruals, shown inaccounting equation 3. fixed asse be noted that some adaptations were made due Brazilian It Itshould be noted that some adaptations were made due to toBrazilian accounting n error when of discretionary revenue is exercised. According to the modified the book value of fixed assets (Lopes & Tukamoto, 2007). It should be noted that some adaptations were made due to Brazilian accounting the book value fixed assets (Lopes & Tukamoto, the book value of of fixed assets (Lopes && Tukamoto, 2007). of the book value of fixed assets (Lopes & Tukamoto, 2007). of the book fixed assets (Lopes Tukamoto, 2007). Tukamoto, 2007). :ofare the non-discretionary accruals ofrepresents company i operating in operating period t;income change inBrazilian net revenue of company i Itofvalue should be noted that some adaptations were madeand due toPA accounting peculiarities. The REV variable net income and PA variable consists peculiarities. The REV variable represents net thethe variable consists retionary accruals are estimated as: It should be noted that some adaptations were made due to Brazilian accounting 2007). peculiarities. The REV variable represents net operating income and the PA variable consists A última etapa consiste emchange calcular accruals discricionários, dovariable raciocínio peculiarities. The REV variable represents net operating income ande parte thei PA consists between of periods tbook - 1value and t;of of in&os accounts receivable for company between periods t-1 and t; of the value fixed assets (Lopes & Tukamoto, 2007). the book fixed assets (Lopes Tukamoto, 2007). peculiarities. The REV variable represents net operating income and the PA variable consists 300 of the book value of fixed assets (Lopes & Tukamoto, 2007). de que eles sãovalue os of resultados da (Lopes diferença entre os 2007). accruals totais e accruals não of the book fixed assets & Tukamoto, Rev. Bras. 018 p.INCIAL-FINAL : corresponds to property, equipment company iRev. inBras. period : corresponds toGest. the Neg. Gest. Neg.t;São Paulo v.20 n.2 apr-jun. 2018 p.295-310 of the book value ofplant, fixed and assets (Lopes &ofTukamoto, 2007). Rev. Bras. Gest. Neg. São Paulo v.20 n.2 abr-jun. 2018 p.INCIAL-FINAL discricionários, conforme demonstrado na Equação 3. Rev. Bras. Gest. Neg. São Paulo v.20 n.2 abr-jun. 2018 p.INCIAL-FINAL Rev. Bras. Gest. Neg. Paulo v.20 abr-jun. 2018 p.INCIAL-FINAL Rev. Bras. Gest. Neg. SãoSão Paulo v.20 n.2n.2 abr-jun. 2018 p.INCIAL-FINAL

are arethe theresults resultsof thedifference differencebetween betweentotal totalaccruals accrualsand andnon-discretionary non-discretionaryaccruals, accruals,as are the results ofofthe the difference between total accruals and non-discretionary accruals, asas shown in equation 3. shown equation shown inin equation 3.3.

Equation (3)


In which: : discretionary accruals of firm i in period t; Equation (3)(3) Equation Equation (3) 3 Equation : total accruals of firm i in period t; In In which: which: Inwhich: which: In : non-discretionary company : discretionary accruals ofaccruals firm i inof period t; t;i in period t. : discretionary accruals firm : discretionary accruals ofof firm i ini iin period t; t; discretionary accruals of firm inperiod period : : total total accruals ofoffirm i in period t; t;t; total accruals firm in period accruals firm period : :total accruals ofof firm i iiniin period t;

3.3 Earnings Management and Investment Efficiency :: non-discretionary non-discretionary accruals of of company i ini iperiod t. t. t. accruals company inperiod period non-discretionary accruals company : :non-discretionary accruals ofof company i inin period t.

In this paper, investment efficiency means that a firm has chosen projects with a 3.3 Earnings Management and Investment Efficiency 3.3 Earnings Management and Investment 3.3 Earnings Management and Investment Efficiency I n t h i s s e c t i o n w e d e s c r i b e t h e 3.3 Earnings Management and Investment Efficiency methodology occurs to identify theaprobability overpositive net present value (NPV). As such, underinvestment when firm losesofthe Efficiency or underinvestment and its relationship with

opportunity to implement a efficiency project with a means positivethat NPV. Conversely, overinvestment occurs In thisthis paper, investment means In investment efficiency aa a paper, investment efficiency meansearnings thataa firm afirm firmhas haschosen chosen projects with management. Theprojects first step with iswith to estimate InInthis this paper, paper, investment efficiency means that has chosen projects thatwhen a firma has chosen projects with a positive net firm implements projects withsuch, a negative NPV (Biddle, Hilary, & Verdi,based 2009). the optimal level of when investment on Equation positive occurs the positivenet netpresent presentvalue value(NPV). (NPV).As such,underinvestment underinvestment occurs whenaa firm afirm firmloses loses the positive net present value AsAssuch, underinvestment occurs when loses the present value (NPV). As such,(NPV). underinvestment 4 (below). We use the residuals of this regression In this section we describe thea methodology toConversely, identify theoverinvestment probability of occurs over- or opportunity implement with NPV. opportunity implement aproject project with apositive NPV. Conversely,overinvestment overinvestmentoccurs occurs occurs whento a to firm loses athe opportunity topositive opportunity to implement a project with a positive NPV. Conversely, as a proxy for deviations from the optimal level underinvestment and its relationship with earnings management. The first step is to estimate implement a project with a positive NPV. when a firm implements projects with a negative NPV (Biddle, Hilary, & Verdi, 2009). whena afirm firmimplements implementsprojects projectswith witha anegative negativeNPV NPV (Biddle, Hilary, &Verdi, Verdi, 2009). (Biddle, Hilary, and&Verdi, 2009): when (Biddle, Hilary, 2009). Conversely, overinvestment occurs when a firm the optimal level of investment based on Equation 4 (below). We use the residuals of this In wewedescribe the to the thissection section describe themethodology methodology identify theprobability probabilityof over-or InInthis this section describe the methodology totoidentify identify the probability ofofoveroveroror implements projects with awe negative NPV (Biddle, regression as 2009). aand proxy for deviationswith fromearnings the optimal level (Biddle, Hilary, andisVerdi, 2009): underinvestment its relationship management. The first step to estimate Hilary, & Verdi, underinvestment and relationship withearnings earnings management. The first stepisis estimate underinvestment and itsits relationship with management. The first step toto estimate the optimal level of investment based on Equation 44 (below). We use the residuals of this the optimal level investment based Equation 4(below). (below). We use the residuals this the optimal level ofof investment based onon Equation We use the residuals ofof this regression as aaproxy for deviations from the optimal level (Biddle, Hilary, and Verdi, 2009): regression aproxy proxy for deviations from the optimal level (Biddle, Hilary, and Verdi, 2009): regression asas for deviations from the optimal level (Biddle, Hilary, and Verdi, 2009): Equation4(4) Equation

calculate Investment based on capex scaled by total assets and as the We We calculate Investment based on capex variables, we SalesGrowth use logistic regressions Equation (4)dependent Equation (4) Equation (4) scaled by totalchange assets and SalesGrowth the to evaluate probability of a firm being percentage in net operating asrevenues between years the t and t-1. Following Biddle, percentage change in net operating revenues classified in the top (bottom) quartile compared Hilary, and Verdi Investment (2009), webased estimate the residuals in total Equation (4)and and then rank them into calculate on capex scaled assets SalesGrowth the We calculate Investment based on capex scaled bytotal total assets and SalesGrowth the betweenWe years t and t-1. Following Biddle, Hilary, to by the benchmark firms. In order as to consider We calculate Investment based on capex scaled by assets and SalesGrowth asas the quartiles (yearly). the bottom quartile are classifications classifiedt-1. as Following underinvesting firms. and Verdi (2009), weinCompanies estimate thefrom residuals in between all three underinvest, percentage change revenues years percentage change netoperating operating revenues between yearstt and tand andt-1. t-1.(overinvest, FollowingBiddle, Biddle, percentage change ininnet net operating revenues between years Following Biddle, Equation (4) and thenthe rank them intoare quartiles we firms. also use a multinomial logistic Companies from topwe quartile classified asbenchmark), overinvesting Companies from the Hilary, and Verdi (2009), estimate the residuals in Equation (4) and then rank them into Hilary, and Verdi (2009), we estimate the residuals in Equation (4) and then rank them into Hilary, and Verdi (2009), we estimate the residuals in Equation (4) and then rank them into (yearly). Companies from the bottom quartile regression. intermediary quartiles are classified as achieving the are optimal level as of underinvesting investment (benchmark quartiles (yearly). Companies from the bottom quartile classified firms. quartiles (yearly). Companies from the bottom quartile are classified underinvesting firms. are classified as underinvesting firms. Companies quartiles (yearly). Companies from the bottom quartile are classified asas underinvesting firms. 3.3.1 Control Variables firms). from the top quartile aretop classified as overinvesting Companies from the quartile are classified as overinvesting firms. Companies from the Companies from thetop top quartile are classified overinvesting firms. Companies from the Companies from the quartile are classified asas overinvesting firms. Companies from the firms. Companies from the intermediary quartiles Prior literature (for example, Dechow et We then create two indicator variables. The first indicator variable (overinvest) takes intermediary quartiles are classified as achieving the optimal level of investment (benchmark intermediary quartiles are classified as achieving the optimal level of investment (benchmark intermediary arethe classified achieving theal., optimal investment (benchmark are classified asquartiles achieving optimalaslevel of 2010) level showsof that earnings quality is related to the value(benchmark of 1 if thefirms). firm is classified as an overinvesting firm, and 0 if it is classified as a firms). investment the size of the audit firm. Accordingly, we use an firms). firms). We then create two indicator variables. The The first indicator variable BigFour, which takes the value We then create indicator variables. indicator variable (overinvest) takes We then create two indicator variables. Thefirst first indicator variable (overinvest) takes Rev. Bras.two Gest. Neg. São Paulo v.20 n.2 abr-jun. 2018 p.INCIAL-FINAL We then create two indicator variables. indicator variable (overinvest) takes first indicator variable (overinvest) takes the value The of 1 if the firm is audited by any of the four largest the 11 if the firm isis classified as an overinvesting firm, and 00 if itit PWC, isisis classified as aa aand the of 1if the firm overinvesting firm, and 0if if it classified of 1 value if value the of firm is if classified asis anclassified overinvesting audit firms (EY, KPMG, or Deloitte), the value of the firm classified asas anan overinvesting firm, and classified asas firm, and 0 if it is classified as a benchmark 0 otherwise. Rev. Bras. Gest. Neg. SãoSão Paulo v.20 n.2n.2 abr-jun. 2018 p.INCIAL-FINAL Rev. Bras. Gest. Neg. Paulo v.20 abr-jun. 2018 p.INCIAL-FINAL Rev. Bras. Gest. Neg. São(underinvest) Paulo v.20 n.2 abr-jun. 2018 p.INCIAL-FINAL firm. The second indicator variable Prior literature (Chiang and Chia, 2005) takes the value of 1 if the firm is classified as an demonstrates that a higher level of transparency underinvesting firm, and 0 if it is classified as a is related to better future predictions. And it benchmark firm. Due to the characteristic of our also shows that better corporate governance 301 Rev. Bras. Gest. Neg. São Paulo v.20 n.2 apr-jun. 2018 p.295-310


mechanisms are positively related to earnings We also include an indicator variable quality (Dechow et al., 2010). We use two proxies for losses (Losses) and control for Size, based to capture these dimensions: Corporate Governance on the logarithm of total assets. These controls and ADR. are commonly used in international papers that Corporate Governance is an indicator evaluate their relationship with either earnings variable that takes the value of 1 if the firm is quality or disclosure (Ahmed & Courtis, 1999; listed in any of the three different Corporate Singhvi & Desai, 1971; Cooke, 1989; Wallace Governance Levels (Novo Mercado, Nível 1, or & Naser, 1995). 4. RESULTS 4. RESULTS Nível 2) as B3. This type of indicator variable is Finally, because there was a change in 4. RESULTS 4.1 Earnings Management commonly used in Brazilian academic literature accounting standards in Brazil due to the adoption TS 4.1 Earnings Management 4.1 Earnings Management gement (Alencar, 2005; Terra & Lima, 2006; Antunes & of IFRS, we use a dummy variable to control for gs Management Mendonça, 2008; Sarlo Neto, 2009; Dalmácio et the pre- and post-IFRS periods. Our first step is to estimate earnings Our first step is to estimate ear al., 2013). ADR is an indicator variable that takes Our first step We is toprese esti p is to estimate earnings management using the Modified Jones Model Sloan, & Sweeney, 1995). 4 RESULTS value of 1 if the firm has American Depositary first step the is to estimate earnings management using the Modified Jones(Dechow, Model (Dechow, Sloan, & Sweeney, 1995). We (Dechow, Sloan, & Sweeney, 1 4. RESULTS Receipts traded on the York Sweeney, 1995). We present the New results in Stock TableExchange, 1 (below). 4.1 Earnings Management Sloan, & Sweeney, 1995). We present the results in Table 1 (below). and This variable has been used as a 4.10 otherwise. Earnings Management Table 1: proxy for higher levels of institutional investors 1: to estimate earnings Our first Table step is Table accruals 1: Estimating discretionary and higher levels of enforcement, since the management using the Modified Jonesaccruals Model Estimating discretionary Estimating discretionary accruals Earnings Management accruals American Our institutional environment is deemed to management (Dechow,using Sloan,the & Sweeney, present first step is to estimate earnings Modified1995). Jones We Model Earnings Mana scretionary accruals Ear (Dechow, Sloan, have Management a stronger level of protection for investments the results in Table 1 (below). Earnings –Modified Jones Model (Dechow, & Sweeney, 1995). present the results in Table 1 (below). (Dechow EarningsSloan, Management –Modified Jones We Model (Mendonça et al., (Dechow, Sloan, & 2010). Sweeney, 1995) (Dechow, Sloan, & Sweeney, 1995)

Table 1:

+

Table 1 Estimating discretionary accruals Estimating discretionary accruals

+

Dependent Variable: Total Accruals Dependent Variable: Total Accruals Dependent Variable: Total Accruals Variables Earnings Management –Modified Jones Model otal Accruals Variables ariable: Total Accruals Earnings Management –Modified Jones Model Variables (Dechow, 1995) Coefficients (Dechow,Sloan, Sloan,& & Sweeney, Sweeney, 1995) Variables Coefficients ture by several researchers. Equation 1-2934.142*** details the + -2934.142*** (-8.34) (-8.34) 0.0311 Dependent Variable: Total Accruals 0.0311 Dependent Variable: Total Accruals Variables Coefficients (1.09) (1.09) -2934.142*** Variables Coefficients + β1 -0.1996*** (-8.34) -0.1996*** -2934.142*** 0.0311 _cons (-7.13) β2 uation (1) _cons (-7.13) (1.09) (-8.34) _cons Obs. 0.523 -0.1996*** Obs. _cons 0.523 0.0311 β3 Obs. (-7.13)Prob>F cruals calculation, for the years prior to 2007, the3.469 data used 3.469 Prob>F Obs. (1.09) Prob>F _cons 0.523 0.0000used: lculation, were Prob>F the following econometric variables 0.0000 -0.1996*** Obs. 3.469 Table 1 presents the outputs of the regression based on 0.0312 or the calculation as of 2010, the difference Prob>Fbetween profit0.0312 0.0000 (-7.13) Table 1 presents the outputs of the regression b 1 presents outputs as of our the pr re use theTable residuals of this the regression puts of the regression based on the Modified Jones Model (Dechow, Sloan, & Sweeney, 1995). We0.0312 change in net lowing variables: profit and fcxoper. 1995). We use the residuals of this regression a nts the outputs of the regression_cons based on the Modified Jones Model (Dechow, Sloan, & 0.523 Sweeney, 1995). We use the residuals of this re total accruals of firm as follows: uals of this regression as our the proxy for earnings management. variables are Jones defined Table 1 presents outputs of the regression basedThe on the Modified Model (Dechow, Sloan,: & Sweeney, 1995). Wei in year t; 3.469 e the residualschange of this in regression asreceivable our proxyfor forcompany earnings imanagement. The variables are defined : total accruals of firm i in y as follows: accountsObs. as our proxy for earnings management. The variables are definedas as follows: follows: total of change in receivables :oftotal firmaccruals i between l accruals of use firmthe i inresiduals year t; of this regression change in revenues of firm i between years t and t- 1; Prob>F 0.0000 : total accruals of firm year t; change in revenues of firm i between years t and taccruals i in year t; change in revenues of firm i between years t and t-1; change in receivables change in receivables of firm i b 1; property, plant, and equipment of company i in period t; change in receivables Equipment of of firm i in year ofoffirm totalassets assets firm i in year t year tt ;; 1; : :total n receivables of of firm firm ii between between years yearsttand and t-1; t-1; : Property, Plant, and Equipment 0.0312 of change in receivables of firm i between years t and t-1; : Property, Plant, and Equipment : total assets of firm i i firm i in year t ; company i ini period error term term –ofour company i indiscretionary accruals. The t statistics are presented in year t-1 t-1;; :: error proxy for in parenthesis and the : and totalthe assets i in year t; The t statistics are firm presented in parenthesis sign : total assets significance of firm i 1inpresents year t-1; : error term –regression our proxy for discretionary accruals. 5%, and represented ***, **, *, respectively. outputs of the onbythe Jonesaccruals. ModelThe (Dechow, Sloan, Sweeney, t statistics are & presented in parenthesis and : totalTable assets oflevels firmare i the in1%, year t-1;and :10% error term – based our proxy forModified discretionary t; The t statistics are presented in paren ***, **, *, respectively. nted in parenthesis andWe the use significance levels 5%, andas10% and represented by management. the the residuals of are this1%, regression for earnings The*,variables are defined ***, **, respectively. s are presented 1995). in parenthesis and significance levels are 1%,our 5%,proxy and 10% and represented by ***, **, *, respectively. 302 : total accruals of firm i in year t; change in revenues of firm i between years t and tas follows: spectively.

Sweeney (1995), the modified Jones model is 1;

change in receivables of firm i between years t and t-1;

rom the Jones model to measure discretionary

Rev. Bras. Gest. Neg. São results Paulo v.20 n.2 apr-jun. 2018 p.295-310 The presented in Table

1 are c in Table The results presented i

: Property, Plant, Equipment of Theand results presented

investment and create our two indicator variab investment and create our two in residuals of Equation (4).We present the results residuals of Equation Earnings management and investment efficiency(4).We pres After estimating the discretionary accruals, we then estimate the expected level of investment and create our two variables 4 (underinvest The results presented in indicator Table 1 are . 2 E a r n iand n g overinvest) s M a n a gbased e m e on n t the and consistent with prior research in Brazil (Paulo, Investment Efficiency residuals of Equation (4).We present the results in Table 2, below: 2007; Rey, 2011). We use the residuals as our proxy for discretionary accruals.

After estimating the discretionary accruals, we then estimate the expected level of investment and create our two indicator variables (underinvest Tablebased 2: and overinvest) on the residuals of Equation Table 2: (4).We present the results in Table Estimating the expected level2,ofbelow: investment

Estimating the expected level of investm Results based on Equation (4) (B Results based o

Table Table 2 2: Estimating the the expected level of investment Estimating expected level of investment

Dependent Variable: Investiment Dependent Variable: Investiment Variables Results based on Equation (4) (Biddle, Hilary, and Verdi, 2009) Results based on Equation (4) (Biddle, Hilary, and Verdi, 2009) Variables

Dependent Variable: Investiment

Dependent Variable: Investiment

Variables Variables

Coefficients Coefficients _cons

_cons 0.029*** 0.029*** (8.25) Obs.: 4498 (8.25) 0.073***Obs.: 4498 _cons Prob>F: 0.0000 0.073*** (39.41) Prob>F: 0.0000 _cons Obs.: 4498 : 0.0147 (39.41) : 0.0147 Prob>F: 0.0000 Table 2 presents the results from regressing Investm Obs.: 4498 Table 2 presents the results from re R2: 0.0147 follows: = total capital expenditures ( Prob>F: 0.0000 = total capit Table 2 presents the results from regressing Investment and SalesGrowth. We define the variables asfollows: follows: = total :capital expenditures (capex) minus sales of fixed assets scaled by lagged total assets, of firm i in year assets, of firm i in year t; = percentage 0.0147 assets, firm i in year t; = percentage change in revenues, of firm i between years t and t-2. The t statistics are presented in of parenthesis and the The t statistics are presented in parenthesis and the signi Table 2 levels presents the 5%, results regressing Investment and SalesGrowth. We define the variables as significance are 1%, and from 10% and represented by ***, **, *, respectively. The t statistics are presented in parenthe ***,of**, *, respectively. follows: = total capital expenditures (capex) minus sales fixed assets***, scaled lagged total **, by *, respectively. SalesGrowth

assets,We of firm year t; = percentage change in revenues, offirms, firm i between t and t-2. fall ranki in the residuals of the regression (underinvesting) and 0 ifyears the residuals

We the ofbythe presented in Table into quartiles and create twosignificance into the middle quartiles (benchmark firms). We regression The t statistics are 2presented in parenthesis and the levels are 1%, 5%, andrank 10% and residuals represented We rank the residuals of indicator that take the value of 1 for the present the descriptive statistics in Table 3. ***, **, variables *, respectively. two indicator variables that take the value o two indicator variables that tak top (bottom) quartile to identify overinvesting

overinvesting (underinvesting) firms, and 0 i overinvesting (underinvesting) f We rank the residuals of the regression presented in (benchmark Table 2 intofirms). quartiles create Weand present the descriptive s (benchmark firms). We present th two indicator variables that take the value of 1 for the top (bottom) quartile to identify overinvesting (underinvesting) firms, and 0 if the residuals fall 3:into the middle quartiles Tabela Tabela 3:

Tabela 3: vest

erin

Und

1-

up

Gro

Descriptive statistics Descriptive Statistics – main variables

vest

erin

Und

up vest 1-

Und Gro erin

1-

up

Gro

Descriptive statistics (benchmark firms). We present the descriptive statistics in Table 3. Descriptive statistics

Descriptive S

Rev. Bras. Gest. Neg. São Paulo v.20 n.2 a Rev. Bras. Gest. Neg. São

Rev. Bras. Gest. Neg. São Paulo v.20 n.2 abr-jun. 2018 p.INCIAL-FINAL



Tabela 3 Descriptive statistics

Group 3 - Overinvest

Group 2 - Benchmark

Group 1 - Underinvest

Descriptive Statistics – main variables Variables

Obs.

Mean

Min.

Max.

Discretionary accruals

701

.2421

.0000685

3.9903

Discretionary accruals x IFRS

701

.0304

0

3.9094

IFRS

991

.2219

0

1

BigFour

920

.5293

0

1

ADR

984

.2032

0

1

Corporate Governance

700

.2971

0

1

LogTotalAssets

991

12.9639

7.5989

19.7127

Loss

991

.4661

0

1

SalesGrowth

991

.4771

-.9773

50.029

Capex

987

.04915

-.7326

13.1009

Variables

Obs.

Mean

Min.

Max.


1408

.1818

.0001861

4.0373


1408

.0173

0

1.01217

IFRS

1981

.2195

0

1

BigFour

1781

.7097

0

1

ADR

1967

.0889

0

1


1344

.4315

0

1

LogTotalAssets

1981

13.8315

7.6685

18.9641

Loss

1981

.2948

0

1

SalesGrowth

1981

5.0670

-.9998

9736.656

Capex

1917

.0748

-.7706

3.2969

Variables

Obs.

Mean

Min.

Max.


686

.1956

.0000325

3.9241


686

.0191

0

2.2519

IFRS

975

.2184

0

1

BigFour

871

.7990

0

1

ADR

958

.1450

0

1


690

.5217

0

1

LogTotalAssets

975

14.0950

7.4960

20.3342

Loss

975

.1887

0

1

SalesGrowth

975

.2959

-.9857

22.1515

Capex

947

.1682

-.4423

4.6454

Variables

Obs.

Mean

Min.

Max.


2795

.2003

.0000325

4.0373


2795

.0210

0

3.9094

Group 1 represents firms classified as underinvesting firms (bottom quartile); Group 2: represents firms classified as benchmark firms (middle quartiles); Group 3 represents firms classified as overinvesting firms (top quartile). The variables are defined as follows: Discretionary accruals: residuals of the regression based on Equation 3 - Modified Jones (Dechow, Sloan, & Sweeney, 1995); Discretionary accruals x IFRS = interaction between discretionary accruals and the indicator variable for the pre- and post-IFRS periods; IFRS = indicator variable that takes the value of 1 for periods beginning in 2010, and 0 for periods until 2007. BigFour = indicator variable that takes the value of 1 for firms audited by BigFour firms (KPMG, EY, PWC, or Deloitte), and 0 otherwise. ADR = indicator variable that takes the value of 1 for firms with American Depositary Receipts traded on the New York Stock Exchange, and 0 otherwise. Corporate Governance


Earnings management and investment efficiency = indicator variable that takes the value of 1 if the firm is listed in any of the three different levels (Novo Mercado, Nível 1, or Nível 2) as B3, and 0 otherwise. LogTotalAssets = logarithm of total assets. Loss = indicator variable that takes the value of 1 for loss-making firms, and 0 otherwise. SalesGrowth = percentage change in revenues between years t and t-1.

In o rd e r t o e v a l u a t e i f e a r n i n g s management practices are related to the probability of a firm under or overinvesting, we run a multinomial logistic regression comparing

Group 1 (underinvest) and Group 3 (overinvest) with Group 2 (benchmark firms). The results are presented in Table 4, below.

Table 4 Earnings management and investment efficiency

Underinvest versus Benchmark Firms

Overinvest versus Benchmark Firms

Underinvest

Overinvest

1.5710***

0.9974**

(4.21)

(2.34)

Dependent Variable Discretionary accruals

Discretionary Accruals x IFRS

IFRS

BigFour

ADR


LogTotalAssets

Loss

[4.81]

[2.71]

-0.1281

0.9235

-0.13

(0.91)

[1.13]

[2.51]

0.2770*

-0.3257**

(1.70)

(-2.02)

[1.31]

[1.38]

-0.2073

0.4445***

(-1.47)

(2.75)

[1.23]

[1.55]

-1.0519***

0.3632*

(-2.81)

(1.76)

[2.86]

[1.43]

-0.1915

0.2651*

(-1.32)

(1.95)

[1.21]

[1.30]

-0.0958**

-0.0182

(-2.34)

(-0.44)

[1.10]

[1.01]

0.4002***

-0.6115***

(3.21)

(-4.08)

[1.49]

[1.84]

_Cons

0.4399

-0.8312

(0.85)

(-1.55)

1855

1855

215.42

215.42

Prob>chi2

0.000

0.0000

Pseudo R2

0.0556

0.0556

Obs. LR Chi2


Flavio Sergio Linhares / Fábio Moraes da Costa / Aziz Xavier Beiruth Table 4 presents the results for our multinomial logistic regression. Two categories are considered against the benchmark firms: overinvest and underinvest. Underinvest = firms from the bottom quartile, based on the residuals of estimating Equation 4. Overinvest = firms from the top quartile, based on the residuals of estimating Equation 4. Discretionary accruals: residuals of the regression based on Equation 3 - Modified Jones (Dechow, Sloan, & Sweeney, 1995); Discretionary accruals x IFRS = interaction between discretionary accruals and the indicator variable for the pre- and post-IFRS periods; IFRS = indicator variable that takes the value of 1 for periods beginning in 2010, and 0 for periods until 2007. BigFour = indicator variable that takes the value of 1 for firms audited by BigFour firms (KPMG, EY, PWC, or Deloitte), and 0 otherwise. ADR = indicator variable that takes the value of 1 for firms with American Depositary Receipts traded on the New York Stock Exchange, and 0 otherwise. Corporate Governance = indicator variable that takes the value of 1 if the firm is listed in any of the three different levels (Novo Mercado, Nível 1, or Nível 2) as B3, and 0 otherwise. LogTotalAssets = logarithm of total assets. Loss = indicator variable that takes the value of 1 for loss-making firms, and 0 otherwise. SalesGrowth = percentage change in revenues between years t and t-1. Z-statistics are presented in parenthesis; the odds ratio is presented in brackets and significance levels of 1%, 5%, and 10% are represented by ***, **, *, respectively.

The results presented in Table 4 indicate a positive relationship between earnings management and investment efficiency, both for underinvesting or overinvesting firms (coefficients are positive and significant at the one percent level). These results are in line with those presented by Biddle, Hilary, and Verdy (2009). Companies with lower levels of earnings quality are more likely to deviate from the expected level of investment. We highlight some of the results related to our control variables. IFRS adoption seems to alter the probability of underinvestment or overinvestment in opposite directions. Firms audited by Big-Four audit firms (BigFour) are more likely to overinvest, but there is no difference between the benchmark and underinvesting firms. This result may be related to a higher level of conservatism by Big-Four firms that could lead to overinvestment decisions (for an analytical description of auditor conservatism and investments, see Lu and Sapra, 2009). We find that cross-listed firms (ADR) are less likely to underinvest (negative coefficient and significant at the one percent level). Conversely, we find weak evidence that these firms are more likely to overinvest (positive coefficient and significant at the ten percent level). We fail to find any difference between a listing in one of the three different levels of corporate governance as B3 (Corporate Governance) for underinvesting firms. We find only weak evidence of a higher probability of overinvesting (positive coefficient and significant at the ten percent level).

We show that larger firms (Size) are less likely to underinvest (negative coefficient and significant at the five percent level) and there is no difference between benchmark and overinvesting firms. The results also indicate that controlling for losses (Loss) is relevant: as expected, loss-making firms are more likely to underinvest and less likely to overinvest, compared to benchmark firms (positive and negative coefficients and significant at the one percent level, respectively). Taken together, the results based on Brazilian listed firms are consistent with those presented by Biddle, Hilary, and Verdi (2009) in the U.S. environment: there is a relationship between earnings quality and investment efficiency. Thus, they add to prior literature that shows that increasing the quality of financial statements may have positive economic implications for firms, such as investment efficiency (Healy & Palepu, 2001; Bushman & Smith, 2001; Lambert, Leuz, & Verrechia, 2000).

5 Conclusion We extend the prior literature by presenting empirical evidence that higher levels of earnings management in Brazilian listed firms lead to a higher probability that firms will deviate from the expected level of investment. We therefore show that earnings quality and investment efficiency are also linked in a different institutional environment, like the one in Brazil. We demonstrate that earnings quality has implications for investment decisions,



after controlling for IFRS-adoption, corporate governance mechanisms, cross-listing, loss, size, and Big-Four audit firm. Our results are in line with those linking, in a broader sense, the quality of reported accounting information and corporate decisions (Biddle, Hilary, & Verdi, 2009; Healy & Palepu, 2001; Bushman & Smith, 2001; Lambert, Leuz, & Verrechia, 2000). We hope that the findings will encourage new research related to the theme, especially testing different earnings quality dimensions, such as conservatism, mapping of accruals, and persistence. We also believe that the results may be important for investors and firms by considering how actions toward better financial reporting can lead to better economic outcomes in future periods.

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Flavio Sergio Linhares / Fábio Moraes da Costa / Aziz Xavier Beiruth About the Authors: 1. Flavio Sergio Linhares, Master in Accounting Sciences, Fucape Business School, Vitória, Brazil. E-mail: [email protected] ORCID 0000-0002-2405-0956 2. Fábio Moraes da Costa, PhD in Controlling and Accounting, Fucape Business School, Vitória, Brazil. E-mail: [email protected] ORCID 0000-0002-2098-4006 3. Aziz Xavier Beiruth, PhD in Controlling and Accounting, Fucape Business School, Vitória, Brazil. E-mail: [email protected] ORCID 0000-0001-6962-3044 Contribution of each autor: Flavio Sergio Linhares

Fábio Moraes da Costa

Aziz Xavier Beiruth

1. Definition of research problem

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2. Development of hypotheses or research questions (empirical studies)

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3. Development of theoretical propositions ( theoretical Work )

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4. Theoretical foundation/ Literature review

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5. Definition of methodological procedures

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6. Data collection

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7. Statistical analysis

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8. Analysis and interpretation of data

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9. Critical revision of the manuscript

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10. Manuscript Writing

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