Detecting Earnings Management via Statistical and ...

Fmis FnneeJu120-21. MIS. 17171Pm VIl

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Detecting Earnings Management via Statistical and Neural Networks Techniques Mohammad Namazi. Mohammad Sadeghzadeh Maharluie

AktmbRCdicthg m ~ a gm r g m u n t is vital fm rhe capital mvlrcl psrticip8at.s. fftanc~ala ~ l j %and manager+ The mrn of rhi rsrpanh is aurmpting to rrapond to this query. Is them a aignifium dIEb*wren dm re$rcsMn mMkl and neural mow models in predicting w i n g s menagmt. and which onc leads to s superior pndmlon of it? In appmeching this qucrtim. a Linear Regramon (LR) rmdtl was compared with two neural netuwb

inelMns Nulti-ky~~Pereepbm (MLP), snd G m a a l i d Rcgrmiim Neural W a k (GRNN).The population of this pudy ineluda 94 listed cempaslla in Tehran Stool. Exchanse W E ) market fmrn 2003 to 201 1. A A a the nsulo of all models werr scquttd, ANOVA war e x d ta ren (he hypotheses. In gcncral. the utmmary of ruristial d t s rhawd that the pmi&m of GRNN did mu exhibit a sipificat diffanre iu cMnparism with MLP. In addition. the rman square amr of fhe MLP and GRWN showrrl .a significant d i k c e wich the mvlti variable LR modal. These findins n r p p ~(he~ norion of d i n - behavior of the w i n g s mmganmt. T h ~ it is ~ m , m appropri1w fm capital rnukd parricipents m analyze earnings mayemen$ bapcd upon d ncrwds mhniques. and not to adopt I~narrc@cm~on moQlr. gcyntorQg--E.minllsmuragcment.garcnlind~onneural

mark. Itnear rrgmsaon. muln-laya pmepaon. T e h n slodr cxcl~mge.

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I, ~CIRODUC~ON ARNINGS management is a popular and appealing concept in contempomy management aCC0IInhIIg mearch. and is considered as on of the most significant current topic in accounting [I]-[2]+3]. It encovarious concepts such as: signaling private infomtion by management, oppomni$tic choices within fhe GAAP &&on. and co&Cting mi&kes and 6n.sUds. Undcrsmang d i h t methods of earnings management and the m m tehind its oxmion can help standard setting boards in promulgating or changing related standards. In addition. tammgs management has m t l y bGcn investigated as one of the key subjects in inlrwtigating the success of deploying international accounting standards in the world (41. Alsn. behind the m n e o f recent scandals st~chas Xnox and WorldCom. the role -f earnings msnagemcnt cannot be igsaured. Given rhcse ro?sans accobntins researchers have widely s t a n d to investipdte this phenomenon from the middle of 1980s 141-151. Afta changing accounting mndards in ihe

Namn I t Full Rorrmr or Accountmng. S.4. 32 Ummll). I n n author Tel -98917118.4375 cau~t. mnarmzl@ms h i m w ir) M. Sade&a~ChM&.l&e is a PH D. C nd~dakeC Accwnlinp. Shim L'n~vtrsil).Inn l e ma1 rnohammpnl, m&h~-aO(k#yahm.can I. M (co-ng

1990s and financial and accounting scandals, rcsemhm' iMerrst la earnings nrenagement's topic has also increased [4]. A major quay which erises here is: How earnings management can be d m Ttadiiional approaches for invmigm'ng earnings management, have xllsinly applied the Linesr R e ~ i o n/LR) model [6]-[71. Howrvcr, this technique w i t s several Limitstions, iadudiig linearity and otha ptcd&cmined model and regression assumptions such as -isttnce of cornlation, homomdasticity and nonflexibility of the mgmsion models [S]. In addition. the utisdng ([6]-[9]) unambigmdy demonstrate that firms' earnings can be chmctciized in t a m s of non -linear relations. Thus. Artificial Neural Network (ANN) kchniqus rnbe employed in these sinretions. A subtle question that arises here, bowever, is whether ANN techniques p i t a superior performsnce 1n detecting m h g s m m a g m t than the LR model? The major aim of this m a r c h is -ding to the preceding question .In order to provide mpiriml data infomtian relating to the firms list& in Tetnan Stock Exchange (TSE) market will be pmvided. The investigation of this subject is important and relevant at least for four reasons. First, in the Light of the prewous financial scandals m i n g s numigemem has become a ~ivotal intanational issue in -the dikwions surmundin'g the credibility of the Raancial markets. Second, as [2] indicates, m and implications in the findings of this there is a b r d i literatme. Third, the dcbMes urncaning mmings $Egement detection was and still is an importaat topic efdayau! impact of the accounting standards .Fourth. prediction of earnings management for the financial sratemcnts users in the process of evaluation of neent economic ~ n n a n c c ,predicting Rrmn income and waluatian of the valw of companies. provides a high priority and impmanee [3]. The originality of the paper is d a t e d to the fart that, for the first time. it compares the prediction power of the LR model with ANN models in an emerging stock market- TSE. This paper is organired as folfows: In section 11 litetature review and hypotheses would be provided. The r e ~ a l c h design and various elated variables wi.old be explained in w o n 111. Section IV a d V would describe the r ~ ~ e g r d r models and the results of the study respectively. The discussion and conclusion. and sumzestions would be presented in sections Vr and VI1 rrspec$up.n:>cdIr.;:s.,n$ olgorixhn>b,. ~l,cnn.. l u x

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While these studies have been conducted in other countries. and not in Iran. we would like to test the following hypotheses to see whether the preceding findings would hold true in a developing country (TSE market) or not? The premises of these hypotheses arc also explained further in section IV: HI : The precision of Generalized Regression Neural Networks (GRNN) is greater than Multi-Layer Perceptron Neural Networks (MLP) in predicting earnings management. HZ: The precision of Multi-Layer Perccphon Neural Networks (MLP) is greater than Linear Regression (LR) in predicting earnings management. H3: The precision of Generalized Regression Neural Networks (GRNN) is greater than Linear Regression (LR) in predicting earnings management.

111. RESEARCH METHODOLOGY This study examines detectingeamings management via LR and ANN models in the TSE market empirically. Therefore, it is a quantitative research in the domain of the positive studies on the basis of the historical data. and utilized a one-way quasi-experimental research plan. The data is mainly derived from the firms'audited fmancial statements of the TSE, and Tadbir Pardaz software. Theoretical informalion was gathered from the Pmian and English literatures. A. Poptrlation and Sample offhe Reseorclr The population of this sndy encompassed all related TSE listed companies from 2003 to 201 1. TSE is Iran's largest stock exchange and was opened in February 1967. During its first year of activity. only six companies were listed. Then government bonds and certain state-backed certificates were traded in that market. Today TSE has evolved into an exciting and growing marketplace where individuals and institutional investors trade securities of over 342 companies in 39 industries. In fact. TSE. which is a founding member of the Federation of Euro-Asian Stock Exchanges. has been one of the world's besi performing stock exchanges in the years from 2002 to 201 1.11 is also an emerging stock market [20]. A purposive sampling. however. was exerted and companies which had the following criteria were selected: 1. It must have been listed before the end of 2002 in the TSE. 2. Selected companies must have not been par1 of the investment and financial companies and also in extracting the oil and agriculture aclivities. 1. The financial war of the companies anust !::n.z endc ! to the last day of tllc Inninn calendar. I . Sclected companies ~nlrsthave not ;hanged thc financlJ yeat. during the Ccsignated period. 5. The required information for meawring discretionary accruals for the investigation period must have been available. Aftcr exerting tllcse critcna. the sa~nplcstudy was reduced to 94 companies.

5.Rerearch Voriobles Research variables were divided into three groups: dependent. independent and control variables.

DeTie limitatior. applicati, C.Dependenf I'ariohk shoncom In this study. following many researchers in this area (for I) Line. example: [6]-[13]), the dependent variable, for both regressim A n2 and neural networks models. was total accruals. Consequently does cash flow approach was adopted for calculating total accruals of lhc .and then total accnrals were calculated by subtractrng cash the ? flow from operation from the income before e~tra-ordina~ mu' items as follows [6]: 2) Pred. Wb< TACC, = EXBI - CFO have prob' D.Independent Variab1e.s morrIn this research. based upon the studies of [6]. [I)]. [21], 3) Reg. [22]. and (231 among others, three independent variables were The constdercd: the reverse of the 1 s t year total asset. change in vario revenue. and total non-current assets. It was expected that a core change in revenue would explatn current accruals. and non4) Non , current assets would control non-cumnt accruals. Mul! Consequently calculation of the variables were attempted as wher. follow: 5.Neru Change in revenue: the d~fferencein revenue of year t and year 1-1. Neural and s o w AREV, = REV, - REV,-, shown th~ models is. Non-current assets: the total of non-current assets of the this study company i In year t 1. Mull This nt In order to control the effect of other variables affecting methods companies' performance. which were not captured by the mbstitutic independent variables, cash flow from operation was used as a widely us control vanable. the right . Also. based upon the slud~esof [6]. [13]. 1211. [22]. and nonlinear [23], all variables In the equation were d~videdto total assets MLP inch of last year (1-1) to control for the firms size output la) Input lay, IV. RESEARCHMODEL independe neuron aIn this pa% regression model was compared wlth neural deteminir networks models in order to examine their prediction effects in diRcuh [( earnings managements. Tran~fe, by the !no pureline [. Thc regrcsaion model. which wac used in this research. was 5~11netlnn adopted from Jones (19911. This form of Jones' mdcl first employed suggested by [21] and rllen assitnilated by the rerc;~rchcnsuch predictiun as 161. 1131. [23]. and [24]. among others. Consequently the Logist~rti following equatiorl was derived: the exit la! In [his TACC 1 AREVt PPE numbcr 01 TA,-, T*t 1 one ne!::ol Ihe bidder (3)

--

PO=+PIT~,.,+PZ--'

+

were divided into three groups: t and control variables. 'ing many researchers in this area (for dependent variable. for both repssion 3dels. was total accruals. Consequently s adopted for calculating total accruals Is were calculated by subtracting cash .om the income before extra-ordinary 7C, = EXBI

- CFO

*bles d upon the stumes of [6]. [13]. [21]. h m . three independent variables were of the last year total asset. change in : u m t assets. It was expected that a Id explain current accruals. and nond control non-current accruals. n of the variables were attempted as he difference in revenue of year t and :Vt = REV,

- REVt-,

(2)

he total of non-currmt assets of the

e effect of other variables affecting . which were not captured by the sh flow from operation was used as a studies of (61. 1131. [21]. [22]. and quation were divided to total assets 1 for the firms size. . HEARCH

MODEL

model was compared with neural to examlne their prediction effects in

I

vhlch was used In this research. was I ) Th15 form of Jones' mod~ltint I s s ~ ~ n ~ l ahy t e the d rcw.3rche1- suclr I] amon2 otllers Con>equcntly the -I L cd

DeTieme et al. (2003: 237) point out the following limitations of the linear regression models, and defend the applicatim of the neural networks to eliminate these shortcomings [El: 1) Linear nature of the regression A major disadvantage of the regression analysis is that it does not posit any index to show whcthn the linear style of the data are the best instance. Considering the nature of the social science, the linear models of the statistic is unsuitable. 2) Prcdnermined models When regression models are used, the basic model must have been predetermined 181. This leads to solving the problem easily, but in addition them is a need so guess more. 3) Regression assumptions The performance of rbe regression models, depend on the various assumptions such as non-existence of the correlation and normal distribution of the residuals. 4) Non flexibility Multi variable regressions do not maintain flexibility when one cannot guess the elements of the model. 6.Neural Networks Models

Neural networks models eliminate the preceding obstacles. and some empirical smdies (for example. 161, and [9]) have sbown the advantages of neural networks techniques over LR models in examining camings management issues. Hence, in this sntdy, the following neural networks inodels were applied: I. Multi-Layer Perceptron (MLP) This neural networks model is in a supervised Learning methods classification. and therefore is suitable for subnitution with regression models [6]. MLP is among the widely used neural networks models .In applying it. choosing the right amount of the layers and inputs can approximate a nonlinear mapping with a suitable precision. The structure of MLP includes an input layer, one or more hidden layer and an output layer. Each layer comprises of one or more neumns. Input layer has the same neuron as the number of the independent variables. Also, the output layer has the same neuron as the number of the dependent variables. But determining the neumns and structure of the hidden layer is difficult [6]-(251. Tnrnsfemng function, which has been used for prediction by the most researchers, are: sigmoid, tangent hyperbolic and punline [26]-1271. The mearchen ltsually llave applied the same 1; lnsfer hrnctio~~in a layer, and ~r~c.asiotiallyhave cnlploy~d the same t:~nsfer function fcr all layers. In predictinn problems, reseanrhers usually have 2dopted Logirl~ufunction in the hidden layer and the linear function in the exit layer (sec [XI-(281). In this study. one hidden layer was employed and the nunlber of neurons were determined by trial and error fro111 one neuron lo six neurons. The transfer function for neurons in the hidden layer was hyperbolic tangent. And the ~ransfe:

functlon for the output layer was pureline. The structure whlch duermrnes the least mean square error for the tram sample was used as the f i l model. Nondiscret~onaryaccruals were calculated by rendering the ~ndependent variables from a vector of the data to the Input layer of the multl-layer perceptron. Then the d~screi~onary accruals were calculated by dlshacting non-d~screnonaryaccmls from total accruals. The number of neurons in the h~ddenlayer was changed for the accuracy of the model. The classlficatron of the data lnto three groups of hainmg. validation. and test were 70% 15%, and 15% respectively Table 111 shows the result for evely year By considering the precedtng appmach. 54 models were designed and executed The schematic view of the MLP model in this research is shown In fig. 1 2 Generalized Regmslon Neural Networks (GRNN) S m l a r to the Multi-Layer P e r n p t m (MLP). GRNN adopted a supervised leanung algonthm .It consisted of one lnput layer, one hidden layer, one summat~onlayer and one outpur layer. The number of neumns In input layer and exlt layer were the same as the numbers of the independent and dependent variables. The tram process of the GRNN were performrd m a sweep When some new data were entered In a GRNN, the distance between the input and welght vectors was calculated. Then thts dlsrance got through a rad~alfunction though a lower distance which made a greater exrt value [6]. As Dai et al. (2010. 217) mentioned, the GRNN approach does not need any assumptions about the fonns of the data The m c t u r e of the GRNN, 1s shown m fig. 2. Thn fig. was adopted from [29]). In applylng neural nenvorks techniques, however. the following factors should be cons~dered, I) When using neural networks. there 1s no clear rule for definlng the structure and parameters of the networks, and the common way of defin~ngthe structure of the neural networks n based upon a trial and error [30]. 2) Normally the tram process of the neural networks a more time consumng than the regression models, and 3) Interpretatton of the neural networks is d~fferent from regressbon models. C Models' Conrparrso~~ One problem in evaluntlng earnings management models. whlch are based on the accruals. IS the difliculty of measuring the perfonnance of the models The reason beh~ndthis 1s that the m e amount of earnings management is unknown Hence. in 41s soldv. the pcrfonnance of the ;l~odelswere e,alunted ! d on thc following crmna: 1 Ire dis~rerionar).accrual wa, ~alomtedfor tbr. ,d~~.?le. 1115 ehpccted that the !.lean and t h ~mned~eliof 11ie dl$cr:ilonary accruals for a lnrge sample be ,leer to zero Iherefore, the perfonnance of these models was evaluated based on how these models would ~neasurethe mean of the d~scret~onary accruals near to zero.

Pal* FranctJu120-21. 2015. 17(7) Pan VII

Fig 1 The Structuw. of h e MLP 1311

Fig. 2 The Shuchlre of GRNN 1291

A. Deroripriw Slolislics

Tables 11, Ill, and IV show the results of the sel models. Table I1 shows the summary information of th model for the designated years. Adjusted varies between 0.133 and 0.997. The maximum Adjusted belongs tothe year 2008, and fhe minimum Adjusted R2belongs to the year 2009. As table I1 shows, the Durbin Watson of the investigated variables is mar to 2, which indicates that serial cornlation problem does not exist among the designated variables. Table 111 shows the MLP results for each year. The data is: divided into three gmups: train data. validation data, and M' data. The percentages were 70%. IS%, and 15% respectively.t The number of neurons in the hidden layer were changed from two to six neurms. As table IU shows, the best performance; for 2003 occumd in a model with five neurons with the meanmeanmeani square a m r of 0.01225. In year 2004, the best perfomam-' occurred in the model with six neumns with the mean squan 5 error of 0.01 12. In tfie years from 2005 to 2007, the bst : perfonnance occurred in models with two neurons with the; mean square error of 0.0084, 0.0089, and 0.0093 respectively. !' The best prformance of the year 2008 occumd in a model ;. with three neumns with the mean square error of 0.0065. In :the year 2009, the model with two neurons had the best i performance with a mean square m r of 0.0061. In year 2010, the best performance occurred m a model with a six newons and the mean square m r of 0.0055. The model with a thne neumns and the mean square m o r of 0.0058, had the best performance in the year 20 1I Finally, the result of the GRNN is shown in table IV. The spread changed among 0.1 to I with the ~ntcrvalof 0.1. Thenfore, 90 models were built to obtain the results. As table IV indicates, the best model for all years is where the spread is 0.1.

Table I shows the demiptive statistics of the study. It discloses that the maximum amount of the variables in most years (2003, 2004, 2005, 2007, 2008, 2010, and 201 L), the minimum amount of the most years (2004,2007,2008,2009, xnd zom), and the m a x ~ m i u n t o f ~ d a r d d e v i a ~ for all years (except 2006) belongs to" changes in revenue divided by the last year total assets". This finding reveals the importance of the changes in revenue for examining earnings management.

show the results o f the selected re summary information of the LR ;led R' vanes between 0.133 and sled belongs to the year 2008, I R'belongs to the year 2009. As Watson of the ~nvest~gated h lndtcates that serial correlation Jng the des~gnatedvariables. .P results for each year. The data is train data. validation data. and ten e 70D/o. 15%. and 15% respect~veiy. the h~ddenlayer were changed from ble 111 shows. the best performance ~ d ew l ~ t hfive neurons w ~ t hthe mean In year 2004. the best performance th six neurons with the mean square years from ZOOS to 2007, the b e s ~ models with two neurons wtth the 184.0.0089. and 0.0093 respect~vely. the year 2008 occurred m a model the mean square ermr of 0.0065. In lei wrth two neurons had the best square e m of 0.0061. In year2010. :urred tn a model w ~ t ha six neurons ,r of 0 0055. The model w ~ t ha three .quare error of 0 0058. had the best !Ol 1 he GRNN IS shown In table IV. The 0.1 to I w t h the interval of 0.1. :re built to obtain the results. As table del for all yean IS where the spread is

TABLE I

. ... . 2003

Revenue I ITAl-lA

94

PPE I ~ A I CFO I fTA1-I TACC I ITAl-l

94

ITTAl-l

93 93 93 93 93 93 93 93 93 93 93

Rewnue a RAl-IA PPE I ITAI-I

CFO I ITAI-I TACC 1 iTAI-I IrTAl-I RMnuel fTAI-lA PPE I ITAl-I CFO I fTAt-l TACCI iTAl-l IRAt-I Revrnue 1ITA1-!A PPE liTA1-I CFO 1 ITAl-l TACC 1 fTAl-1 1fTAt-l Revmu* llTA1-IA PPE 1 KAI- I CFO t TTAI- I TACC I ITAI-l l ITAl- 1 R m n u e 1aA1-18

PPE I ~ A I - l

CFO I iTAl-l TACC 1 /TAl-l IiTAl-I RNenue 1 iTAl-lA PPE I ITAt-1 CFO I ~ A I - I TACC 1 fTAl-1 IiTAl-l Revenue I mt-IA

201 1

PPE I RAl-I CFO I IAI- I TACC t fTAl-l IITAI-I Rrvrnue I ITAI-IA PPE 1 ITAI-I CFO t /TAI-I TACC I ITAI-I

94 V4

93

93 93 93 92 92 92 91 92

0 2894 U 2941 0 1825 0 0759

OW00 0 1599 0 1948

0 1889 0 0563 0M)O 0 1929 0 2865 0 1156 0 1002 OWW 0 1195 0 2669 0 14.43 0 0565 O

W

0 1593 0 1616 0 1436 0 0622

93 93 93 93 93

ODOOO 0 7637

94 94 94 94 94

O W 04142

94 94

94 94 94 94

94 94 94 PI

0 2667 0 1454 02960 0.307 01315 001319 00000 0 0607 0.136 1 0 1 I63 0 0230 OOOOO 0 I116 0 394 0 1069 0 0332

Paris F m e Jul20-21. 2015. 17 (71 Pal VII

year 2003 2004 ZOO5 2006 2007 2008 2009 2010 2011

Yur 2003

2011

R 0806 0645 0 703 0.745 0.578 0999 0412 OW 0799

TABLE 11 TllE SVWMAR) ~K)RMATIOVOFMULTI-VANABLE REGRESSlOh k? Adjusled R' Smdud Enur 0649 0633 0 1341 0416 0389 0 11-11 0494 0471 0 1183 0 555 0535 0 I236 0334 0 303 0 I553 0998 0 997 0 1212 0133 0 1018 0170 0415 0 0889 0389 0638 0621 0 1183

Numberofneumns Perfomunccofuain dam Pdoormana of validation dam Performanceof tut dam Total Performanceofmodel Perfonnaacc oftra~ndam Pdamunce of val&t~on dala Perfomam of ten dam Tot4 P e r f o m ~ of e model Pdommnce oftram data Perfofnlldal~ondata Pcrfamnncc orten data T w l Performanceof model M ~ r m ~ofc mln e dma F m f ~ r m ~ ofval~datm ce dam Perfwmma of len d.D Toml P e r f m n c e ofmodel Pdmmsnce o f t n ~ & nu Performanceof~lldatlondam Pdonnance o f t s t dam TOMPnfomunce of model Fwfomumeeof m n dam Performance o f n l ~ d ~ d ~m on P ? & n ! a nof~tW c dam Total Perfornunee of model Perfoormance oftrun dam Performanceofvalld.11on d m Perfomce of tnt dam Total Perfomncc of ma*l Performanceoftraln data Performance ofvalhiatlon dam ~erformsnccof tcrl dam Total Performanceof madel Perfbrmanceo f v a ~ ndata RrformPnx ofv8l1da11ondam M-ce o i l m dam Toel Palwmurc ofmodel

TABLE 111 THE MEnu WAREERROR FOR MLP 2 3 0.0104 0.0222 0 0228 0 0399 00211 0 0322 0 0247 0.0155 00094 00169 0 0058 00145 00158 0 0273 OOl5l 00129 0 0093 0015 0 0076 00066 0 0084 00184 00129 00103 00137 0 0089 00152 0 0304 0 W89 0 0125 00132 00141 00197 0 0074 00104 00176 0 OW3 0 0532 00167 00159 0 0091 00101 00175 0 0265 0 OW5 011203 0 0122 0012 0011 00103 0 0071 00169 OW61 00134 0 0097 00117 0 0056 0 0068 0 144 0007 0 0086 00138 0 M)81 00071 00213 0 0074 0 0049 0 0L29 00314 00058 0 0233 0 0095

4 0.0156 00152 00193 OOl6l 0 0082 0 0097 0 0242 OOlO8 00114 0 0226 00168 00139 0 0097 00102 0 0273 00124 0 M)89 0 0086 0 0352 0 0129 00108 0 0086 00193 00117 00103 0 0058 OOlO2 0 0097 0 0049 00066 00107 0006 0 0078 0 0057 0 0274 00104

Durb~n-Wawn 1 996 1 6% 1163 2 161 1909 1 903 2341 1 765 2 021

5 0.0127 0 029 00122 00315 00129 00183 00165 00142 OOlOl 0 0086 0 0324 00132 0 0239 00178 00212 0 0226 00191 00167 0 0562 0 0244 0 0078 0 0232 00197 00119 0 W77 0 0092 00121 0 0086 00061 0 0045 00133 0 0069 OOlSl 00111 0 032 1 0017

6 0.0156 00119 0 0428 00191 0 0092 0 0095 OOll2 00095 00361 0 0307 0 OZ44 0 0335 00101 00146 0 0284 00135 0 0055 0015 00111 0 0078 0 0076 0 02 00103 00099 00102 00143 00147 00115 0 0067 00101 0 0055 OW7 0 0087 00531 00133 0016

I

i

A h obtahm

'; research

-

hypothe

square error, the > mult of the

At f m t , the I investigated by Shapiro-Wilk tesi

Since the sign1 0.05, tbe normal~t

C.Hypotheses l

Consequently, ' one-way ANOVA the first hypotheMLP and GRNN accepted. Hence. Jlan the precis, nlanagement. Table Vlll shu indcatu that the slgmficant and t h

Paris France Ju120-21.

2015. 17171Pan VII

TABLE IV THE RESULT OFGRNNMODEL

ZESSIOL

Durbin-Watson 1.996

Mear

After obtaining related data for the designated models. research hypotheses were investigated. The less the mean square error, the more precise is the model. Table V shows the result of the different models for detecting earnings management. TABLE V

THEMTAN SWARE ERROR FOR THE RESEARCH MODELS Y W

2003

Lmeu rtgmrion

0018

MLP 0.01225

GRNN

0.0046

At first, the normality of the mean square error was investigated by conducting Kolmogrov-Smimov test and Shapiro-Wilk test. Table VI posits the results. Since the s~gnificancelevel of every model was above the 0.05, the normality of the data were finalized. C. Hypotheses Testing Conscquenrly, for accepting or rejecting the hypotheses. the one-way ANOVA was exerted. Table VI1 shows the result of the fust hypothesis. .It indicates that h e difference between MLP and GRNN were insignificant, and therefore H 1 was nol accepted. Hence, the precision of the GRNN was not greater than the precision of the MLP in predicting earnings management. Table Vlll sl:o.:.s the :i.,ult uT the second hypot!iasis. It ir:,: ~ a t e slbat Ihc 2ifferel:ic bet,..cn MLP and LR model is b:.!jliiicant 2nd thc~cfore,1;' was accrpt~rl.In other woi:ls, the

precision of the MLP was greater than the prec~s~on of the LR model in predicting earnings management Table IX shows the result of the third hypothesis. It indicates that the difference between GRRN and LR model is significant. By considenng the mean square ermr of the GRNN (0.00536). which was less than the mean square error of the linear regression model (0.0150). H3 was accepted. Hence, the precision of GRNN was greater than the precision of the LR model in predicting earnings management

Paris France Jul10-21. 201.5. 17 (7) Fan VII

TABLE .. - - - VI

TllE NOR~IALITYTEST K o l m o p -SrnirnovTSI

Model Linear Rcgmsion MLP

GRNN

natirlic 0.191 0.ZW 0.139

Depor slg

freedom

0.253 0.200 0.200

9 9 9

Shapiro-U'ilt Tesl natinic 0.928 0.911 0.976

freedom DCvC Of

sis

9

0.459

9

0.342

9

0.44 I (I] VWU

TABLE VII

muY8 -arc

PI

-3.

wmn'

Val. 51 [3]

Vsriable I MLP

Vwbk ? Linear Regression

TABLE \'Ill THE ANOVA RESL'LT Variabk I. variable 1 -0.00678

E-l

Maail:

Smnhrdm 0.00330

sie

0.017

251.

PI C W ~ ' 151 ..

TABLE IX GRNN

Variablr 2 Linear Regmsion

Variable I -variable ? 4.00964

Accour Wr6blr or Enm DiRe

TUE A M ~ ARESULT

Variable I

I.De.Ln

Sundard enor 0.00330

rig 0.M

Teorn,

I61 H t l m [7)

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616641 models in a developing (TSE) market .It also indicates tlr VI. CONCLUSION AND DISCUSSION earnings management could be analyzed more accurately $ [I' R ~ The major purpose of this smdy was comparing the considering nonlinear relationships among the designatd C. precision ofthe ~i~~~ ~~~~~~~i~~(LR) with ~ ~ i f i~~~~~l ~ i ~ l variables. In addition. it provides a useful tool and means fa I91 T-i Pi"'SrL Sys~ms ~~~~~k (ANN)models in p d i c t i n g earnings management in the investors. financial analysts and capital mark6 participants. and helps them to predict earnings managema 1101w r . Tehran Stock Exchange (TSE) Market. the Vlc. -rhe result of investigating the fint hypothesis showed (hat more accurately. Since the greatest loss to the capi~almark* there was not a significant difference between the precision of UP lhe present lime. has been as a result of the earning the Generalized Regmsion Neural Networks (GRNN) and management- it would Prevent the occurrence of thr [ll] J in-r Multi-Layer Percepron Neural Networks (MLP) in predicting unfavorable effects of earnings management. By applyiry earnings management, -j-his finding is the opposite of the neural networks models. market participants can predict thc Upper S: Hoplund's (2012) conclusions. One reason for the discrepancy management. and are by th [I31 H&u. 1 Lnplica!~ between the findings, might be due the number of opportunistic behavior of the managers. On the other hand, lhe ixhavior will- 1141 observations, public nature of the firms listed in TSE. and.the Ina"gers s w n c r or later, find out that capital market participants wiU efficiency of the TSE market. account^, ~h~ result of investigating the hypothesis showed a b o w the fact. and therefore will become more conservative to implement earnings management This will ultimately lead to (''1 'Wm ,,erformance of fie MLP in contran with the LR Earnings model. This superiority may lie on the nonlinear relation a lower rate of exercising e a r n i n g management b 51 1-529. which exists between the indmndent and denendent ma"gement. [I61 N a bE Smwuul' variables. The acquired results are in accordance with findings Slock b VII. SUGGESTIONS 4. pp. 8-i. of the [6] and [32] studies. Based upon the resells of the study. the following 11'1 ~ ~ ~ ; The research results also showed the acceptance of the third Vol IJIZ hypoll~csis.Therfore. a svperior performance of the GKNN sue_~cstionsare r?lncic: nver :he LR mod-! was shr:-n. T I C reaso!t might alho hc due I ) Since tlic best ~csultsia acquil.cd frol:~adopting th? GRNN 1181 Wsmer. I and MLP. it is sug;ested [hat rcccarclicn apply Ihea S'a'isliica to the nonlinearity relationship between the indepclident and i~lodcls of ncural networks lo ohtain Inore accuratc [I9' dependent variables This c.zsult is consistelii with the Huillund piediction results. (Edr.). ). (2012) ~csearch. Chicni+: In sum. this rescarch showed that neural neh\.orks models 2 ) Tehran Stock Exchange companies ;141d fi11:incial analysls should use ncural nctwo~hs trc!-iiques for dctecti,,? IZ0' (GRNW and MLP) could more accurately predict earnings cantings managemvt. FIOW is managciltent than the linear regression mom l (LRI lnodrl. a This finding is the opposite of [l7]. [!?I. andji91 stti:!ics .It's 7 , Due to the intlw~.tancv thc earnin$:- mana:;cnent and a!>. 3-5. improving the ietho(r.; of detecting varni~i@ ii~lplicat!onis \cry impo;ra-t since ; I provide.. an e!!ii ;ice: moll-;cmcnt. the Ioiia\ving qige:.:. 'ns arc :lade: , \ . i d e r concc:::ing I I W :!...G;liics . )f thr ..: ural ~ ; ~ . i v o r h

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REFERENCES

Vhdu A. B. 8ad D, D. Cuzdriommh (20141. '&lening earnings muup-t: Inrights fmm Ihe last decade kading jourrulr publirhcd d" p d Economicsand Fhnce. Vol. IS. pp. 695-703. m . 1. R. . b e d y J. S., and D. A. Shrrkelfonl. 12012). "Research in landard Error Sig P] a-ling for hnm laxes''. J o m l or Accounting and Economics. 0.00330 0.391 Vol. 53. pp. 4 12434. [3] Janren. I.P.; h n w l h . S. and T. L. Yohn (2012) "A DDlapnortic lor EMlinlp M w e r n m l Using Changer in Assel Tunover and Profit M*." Contsmpornry Asawnling Rcrenrch. Vol. 29. No. I. pp. 221251. ilnndnrd m o r rig C.pkm,V. (2011). "Book Review." The In~ernmionalJolansl of 11 . 0.00130 0.M7 ~ccomtinp.VOI.46. No. 2. pp. 236-237. 151 Wrdbkwrki D. June. Jod 1. and S. Callaa (20141. 'The Dcvelopnent orEaminpr MiMgemenl llcearch: A Review o f LilmNn from Thrrc DflParpsliva" Theoretical J o m J o f A ~ o m l i n g(Zepyly Tmrrtycme Rrhunkowoki). Vol. 79. pp. 135-177. .land& ermr sag [6] HClwd. K R012). "DLcecIing Earninp Management wiIh Neural NenvorLr." Expen Systems wiIh Applications. VoI. 39, pp. 956-1-9570. 0 00330 0006 [7] Hbglmd. H. (2013). "Fuzzy Lincar Regression-Based Llelmion of E.mings Mana8mwu." Expm Syrlnns wilh Appliutions. Vol. 40. pp. 6166-6172. p i n g (TSE) market .I1 also indicates tbr n t c o u l d be analyzed m o r r accurately b~ PI D e T i i e . K. 0.. DeTie~e.D. H.. and S. A. Jorhi 12003). "Neural Networks as Swirtiml Tools for Business Researched' Organiiiaml %r relationships among the designad Resmxb M&ods. ~. Vol. 6121. m.236265. n. it provides a useful tool and 191 ~ w iC. i F. and Y. J. ~ h i o " ( 2 6 1 "Earnings . Mnna-nt Pdiclion: A ancia) analysts and capital Pilor Qudy afCnmbining Neural Nctwoitr and Dccision Tmi." Expen Syrtem wilh Applinlionr. Vol. 36. pp. 7183-7191. IPS them to predict earnings mUIagemeIi 1101 W n i r . M. and k Nuemi 120061."Eaminnr Mmazemenl: Reconcilinn IIIC 'views of Acownting~cadunics. ~ & t i u o n c k and FtegularG Ice the _matest loss l o the capital ma&& Review of Iranian S l u d W Jouml O f Accounting bowledge and me, has been as a result of earnin@ S ~ d y Vol. . l I. pp. 12-18. 'auld prevent the occurrence o f 1111 ~ooe. J. and V. ~uri (20081 Earnings Management: Emergjng lnrighs o f e a m i n i z ~ management. Bv in Theorv. PRE~~CL and R e r u ~ . hSorinucr. " -,annlvim -rr.....= ~r - . the USA. - . ~ [I21 % a William . R (2009). F i m c i a l Accounring Thmry.Fifth Edilion. dels. mark;t participants can th Upper W e River NJ: Prenlie Hall. ent. and are n o t mislead b y tfu 1131 Hribar, P. and D. Collins (2001). " h nin mimaring Accruals: Implintions for Emoirical Restarch." Journal of Accounlim renesreh Or o f the managers. On the other hand gW~hpp.105-134-' f o l l o w the opp&nistic behavior will; wiU [I41 Hcab. P. M. and J. M. Wahlen (1999). "A Review of lhc Earning out that capital markel Mansgemrnt Litenme and Ilr Implications for S l ~ d a r dSming." erefore w i l l become moR conservative M Acewnlin~Horimns. Vol. 13. PP. 365-38). mnagement, hi^ willultimately lead m [I51 Bergsl&r. D. a d T. Philippunb (2006). "CEO Incentives and Earnings M e a n g ~ ~ n Jounral ~.* o f FiMncial Economics. Vol. SO. pp. exercising earninss management by s1 1-99. [I]

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I161 N I l n d M. and E. Khamalar 1201I1. "An Investigsion of The Income Smmhing Behavior of Gmwth and Value Firms (Case Study: Tehran Lock Exchange Marku)." lnlemalional Buriners Research. Vol. I . No. Vll. SUGGESTIONS 4. pp. 84-93, results of the study. rhc followinl [17] D m t ~ " J.. W. 119951. "How Good am Neural Nelworks for Causal r'ortcau;:~_p?"u,urno! o i Business F.mcastiri~hielhod. ..nd Svric~w. Val. 14121. pp. 17-20, Its is ~ C ~ L I : - et du r n adoptins l h c GRNN [I?.: Warner. B.: and M. Mirm 11996). ..'::,derrrnl,ding Nv;.,~:Nel\vu;kr as SWlisIicalToo:." The Amcrican S~atlrlician.\%I. m41. p ~284-293. . suggested tllat rcsearcllcrs apply thcsc [I91 .Maqu=r. L.. I1::I. T.. \\'nnhley. I