Non-Financial Key Performance Indicators and Quality of Earnings

Transcription

1 Non-Fnancal Key Performance Indcators and Qualty of Earnngs Alreza Dorestan Northeastern Illnos Unversty Zabhollah Rezaee he Unversty of Memphs We examne the assocaton between the extent of key performance ndcators (KPI) dsclosures and the qualty of earnngs measured by both the conventonal earnng response coeffcent and the E-loadng factor developed by Ecker et al. (2006). he E-loadng factor captures the senstvty of the frm s return to earnngs qualty, smlar to beta whch captures the senstvty of returns to market movements and s used as a proxy for the perceptons of nvestors about the earnngs qualty. he results ndcate a postve assocaton between non-fnancal KPI dsclosures and the qualty of earnngs only for companes n ol and gas ndustry, but the assocaton s manly non-lnear. INRODUCION Ample evdence shows that proper use of key performance ndcators (KPIs) mproves the company s performance (Marshall et al., 2000; Reck, 2001; Larcker et al., 2007; Jackson, 2008). KPI dsclosures are expected to affect busness practces that can result n better performance. Ratonal expectaton mples that more transparency through KPI dsclosure can mprove the percepton of nvestors because n compettve stock markets and lmted resources, nvestors scrutnze companes and nvest ther money n companes whch are more productve and more transparent. hs expectaton s consstent wth Cheung et al. (2010) who fnd a postve assocaton between more transparency and market valuaton n 100 major Chnese lsted companes. However, Smth et al (2009) conclude that ths assocaton vares among dfferent companes n dfferent countres (e.g. Japan, Sweden, and France). he mportance of KPI dsclosures n corporate reportng s underscored n the UK Companes Act of 1985 and the report of the Advsory Commttee on Improvements to Fnancal Reportng (ACIFR) to the Unted States Securtes and Exchange Commsson (SEC) n In ths paper, we examne the assocaton between the extent of KPI dsclosures and earnngs qualty (he mportance of earnngs qualty s dscussed by Caylor et al. (2007). he qualty of earnngs s measured by both the conventonal earnng response coeffcent (ERC) and the E-loadng factor developed by Ecker, Francs, Km, Olsson, and Schpper (2006). he E-loadng factor captures the senstvty of the frm s return to earnngs qualty, smlar to beta whch measures the senstvty of returns to market movements. As shown n Ecker et al. (2006), the calculatons of e-loadng factor are affected by fnancal KPIs, but no nonfnancal KPIs are nvolved n e-loadng calculatons. In other words, the e- loadng captures the perceptons of nvestors about the frm s earnngs qualty. We orgnally started wth a sample of 200 companes from each selected ndustry and ended up wth companes that had complete Journal of Accountng and Fnance vol. 11(3)

2 data for both 2006 and 2007 years. All companes n related populatons are numbered startng from 1 and we used a table of random numbers to select our sample companes. Our fnal samples nclude a random sample of 156 companes lsted on S&P 500, a random sample of 135 manufacturng companes lsted on the New York Stock Exchange (NYSE), and a random sample of 113 ol and gas companes lsted on NYSE. We choose a random sample of S&P500 companes because of the mportance of the economc mpacts of large companes and to facltate efforts n hand-collectng data on KPI dsclosure. Also the choce of manufacturng and ol and gas companes s based on the mportance of socal, envronmental, and sustanablty reportng n ths ndustry (e.g., Lane 2010; Johansen 2010). Gven the nature of hand collectng KPI data, the sample szes are ncreased enough both to be cost effectve and results to be generalzable. he overall results ndcate that for companes n the ol and gas ndustry there s a postve assocaton between non-fnancal KPI dsclosures and the qualty of earnngs, but the assocaton s manly non-lnear. hs study s expected to contrbute to the lterature n several ways. Frst, we nvestgate the assocaton between the extent of non-fnancal KPI reportng and the qualty of earnngs. o the best of our knowledge, ths s the frst study that addresses ths ssue. Second, usng the econometrcs technque of goodness of ft for model selecton, we have shown that a non-lnear model can better explan the assocaton between ERC and KPI dsclosure, so we queston the use of lnear models n the ERC and E-load.ng studes. Fnally, whle the SEC and the U.S. reasury Department have shown growng nterests n KPI dsclosures, no emprcal results are avalable to support such nterest. he polcy mplcaton of ths study n provdng emprcal evdence regardng the recommendatons made by the Advsory Commttee on Improvements to Fnancal Reportng (ACIFR) n 2008 s to encourage the Securtes and Exchange Commsson (SEC) and the Fnancal Accountng Standards Board (FASB) to defne specfc KPIs and requre companes n each ndustry to consstently report them. Detals and dfferent perspectves of KPI dsclosures are shown n Appendx A. he remander of the paper s organzed as follows. Secton II ncludes a dscusson of KPIs and ther relevance n fnancal and nonfnancal reportng. Our hypotheses development s presented n Secton III. Secton IV explans the sample desgn, data, and methodology. Secton V presents the results. Summary and mplcatons are dscussed n Secton VI. Relevance of KPIs radtonal fnancal statements provde hstorcal fnancal nformaton concernng an entty s fnancal postons and results of operatons as proxes for future busness performance. Investors demand forward-lookng fnancal and nonfnancal nformaton on key performance ndcators (KPIs) relevant to the entty s governance, economc, ethcal, socal, and envronmental actvtes. Parmenter (2008) defnes KPI as a set of measures focusng on factors that are most crtcal for the success of the organzaton. KPIs were frst ntroduced by Kaplan and Norton (1996) as balanced score cards and redefned by Norreklt (2003) and used n dfferent studes (e.g., Herath et al. 2009). KPIs nclude both fnancal and non-fnancal measures (Burton et al., 2006; Wersma, 2008; Veen-Drks and Van, 2009). Standard setters worldwde are consderng overhaulng fnancal reportng and restructurng fnancal statements by focusng on KPIs and provdng nformaton concernng how busnesses are actually run (Relly, 2007). he U.K. Companes Act 2006 sgnfcantly expands corporate responsblty reportng to nclude both fnancal and other KPIs concernng nformaton about the company s polces pertanng to envronmental matters, employee actvtes, and socal and communty ssues (UK Companes Act, 2006). Furthermore, the mportance of KPI reportng can be seen n the fnal, report of the Advsory Commttee on Improvements to Fnancal Reportng (ACIFR) to the Unted States Securtes and Exchange Commsson (SEC) n 2008.whch recommends the extensve use of KPIs. For mportance and relevance of more dsclosure and transparency see Hughes et al. (2001), Gordon et al. (2002), Ettredge et al. (2002), Arya et al. (2005), Reck and Wlson (2006), adesse (2006), and Kelton and Yang (2007). Investors demand forward-lookng fnancal and nonfnancal nformaton and companes have strved to provde such nformaton. radtonally, publc companes have focused on achevng ther prmary economc objectve of makng proft and enhancng shareholders wealth by engagng n operatng, 76 Journal of Accountng and Fnance vol. 11(3) 2011

3 nvestng, and fnancng actvtes to provde and dstrbute goods and servces. hs narrow focus on achevng economc performance has been crtczed for gnorng other socal, ethcal, and envronmental responsbltes of corporatons (Rezaee, 2007). he multple bottom lnes (MBL) objectves of economc, socal, ethcal, and envronmental (ESEE) performance have been advocated by global busness and nvestment communtes (GRI, 2002). Wth the MBL objectves, the prmary goal s to acheve economc performance of creatng shareholders value whle gvng proper consderaton to other measures of performance ncludng socal, ethcal and envronmental measures. Dscussons of envronmental accountng and the effects and mportance of envronmental measures can be found n Patten (2002), Vllers and Staden (2006), Burnett and Habsen (2008), Lohmann (2s 009), Hopwood (2009), and Veen- Drks and Van (2009). Furthermore, Rley et al. (2003) examne the value relevance of nonfnancal nformaton n Arlne ndustres and conclude that nonfnancal nformaton are more value relevant compare to tradtonal accountng metrcs. Ghalayn and Noble (1996) post that the objectve of performance measurement has changed and the tradtonal performance measures based on productvty are no longer applcable to the new global compettve market. New measures are beng developed based on combnaton of a varety of performance measures. hey revew and analyze the lmtatons of tradtonal measures and dscuss the characterstcs of the new performance measures. Furthermore, Epsten and Roy (2001) dscuss the ncreasng trend n recognton of the mportance of corporaton socal responsblty and present a framework to evaluate the drvers of corporate socal performance and actons that management can take to affect performance. hey argue that wth the knowledge of drvers of socal responsblty and ther effects on stockholders, managers can make sgnfcant contrbutons to ther companes and the socety. hey provde a framework, whch ncludes factors that they clam can change the culture of an organzaton by presentng a new drecton that mproves both socal and fnancal performance. Moreover, there has been a growng trend n nternatonal nterest n multple bottom lnes performance reportng, whch ncludes envronmental, socal, and governance ssues. It s beleved that reportng these bottom lnes performances can affect the performance of portfolos, so they must be properly managed and reported. Because of the mportance of ths ssue, the Unted Natons Secretary General n 2005 nvted a group of representatves of 20 nvestment organzatons from 12 dfferent countres and asked them to establsh a set of global best practce prncples for responsble nvestment (Unted Natons 2005). Also, the UK Carbon Reducton Commtment (CRC), whch requres companes to measure and report ther carbon emssons from energy use, soon became effectve and close to 10,000 companes were affected by ths requrement. he complance wth ths regulaton wll have sgnfcant effects on companes cash flows. In short, organzatons wth 500,000 Brtsh Sterlng or more are requred to: (1) measure ther energy sources, (2) report the usage to the government, and (3) pay for ther polluton. In short, KPIs help an organzaton defne ts goals. After an organzaton dentfes ts goals and ts stakeholders, the organzaton needs to measure ts performance n achevng the organzaton's sustanable goals. he preparaton and use of KPIs both provde management wth nformaton needed for mprovng performance to acheve organzatonal goals and help nvestors to evaluate management performance. he ACIFR report recommends that the SEC should encourage publc companes to use KPIs n ther busness reports. he commttee recommends that the SEC should encourage prvate sectors to dsclose understandable, consstent, relevant, and ndustry-specfc KPIs n ther Management Dscussons and Analyses (MD&A) and other companes dsclosure. he commttee clams that KPIs wll provde ncremental nformaton beyond what s tradtonally provded n conventonal fnancal statements reportng and can provde more transparency about a company to ts stockholders. hey argue that more transparency reduces the cost of captal and mproves the market effcency. However, the commttee does not provde any evdence to support the recommendatons. he results presented n ths paper provde some prelmnary evdence to support the commttee s recommendaton. In ths study the transparency s used to ndcate the extent to whch companes reveal nformaton that fnancal statements users would lke to know. KPIs are ntegral components of strategc decsons and sustanablty reportng, and they are relevant to the operatonal performance of organzatons of any Journal of Accountng and Fnance vol. 11(3)

4 type and sze. Havng predetermned KPIs as ther goals, companes can better drect ther operatons to acheve these preset goals. If KPIs are to be used for judgment and decson makng, they must be properly defned and consstently appled. he research queston addressed n ths paper s whether there s an assocaton between the extent of non-fnancal KPI dsclosure and the qualty of earnngs. HYPOHESIS DEVELOPMEN here s no requrement n the Unted States for dsclosure of nonfnancal KPIs, and even though n the Unted Kngdom the UK Companes Act of 1985 requres the publcaton of certan KPIs n accordance wth the EU Accounts Modernzaton Drectve for all except small companes, the Act only requres the publcaton of fnancal KPIs. Anecdotal evdence suggests that companes n general do not voluntarly dsclose non-fnancal nformaton because a) there s not enough external pressure from regulatory and accountng standard settng bodes, nvestors, and other stakeholders for dsclosure of nonfnancal nformaton, b) management does not perceve that the benefts from dsclosng non-fnancal nformaton exceeds ts mplcaton costs, and c) management does not consder the non-fnancal nformaton (e.g., socal responsblty and sustanablty reportng) to be of a crtcal mportance to companes (Delotte 2007). Nonetheless, the recent nterest n and move toward busness sustanablty of trple bottom lne reportng of socal, envronmental and economc performance should encourage busnesses to dsclose KPIs. Corporate socal responsblty and sustanablty reportng and ther relevance to corporate reportng are dscussed n detals by Cooper et al. (2007) and Gray (2010). Pror studes such as Copeland, Koller, Murrn, and Foote (2000), Dowlng (2006), and Zhang and Rezaee (2009) follow a four stage model to lnk fnancal and non-fnancal nformaton to corporatons both fnancal and market performance. Consstent wth ths model, McGure (1998), Ruf et al. (1972), Moskowtz (1972), Smpson and Kohers (2002), and Verschoor (1998) have establshed a lnk between corporaton socal responsblty and fnancal performance, Zhang and Rezaee (2009) document a lnk between the credblty of frms and a hgher earnngs qualty, and Pnnuck and Potter (2009) dscuss the mportance of earnngs n measurng the economc performance of Australan local governments. Furthermore, Dedman et al. (2008) and Chan et al. (2009) show how voluntary dsclosure and the qualty of accountng nformaton affect the stock prces for a sample of U.K. companes. A study by the Hackett Group (2006) shows that proper use of KPI reportng helps the company s fnance department to decrease costs and mprove productvty of ts operatons. In another study, Lambert et al. (2005) examne the assocaton between accountng nformaton and dsclosure, as well as the cost of captal and conclude that the qualty of nformaton can both drectly and ndrectly mpact the cost of captal. We argue that KPIs can be ntegrated nto accountng dsclosures and reportng systems to mprove performance and to provde nvestors wth nformaton to meet ther needs. he overall qualty of the management nformaton system, whch ncludes both fnancal and non-fnancal nformaton, can postvely affect the performance of the frm. Furthermore, we argue that frms algn ther fnancal and non-fnancal nformaton to postvely nfluence ther performance. hat s, we post that there s a postve assocaton between non-fnancal KPIs and qualty of earnngs. We test the followng overall hypothess: H1: he extent of non-fnancal KPI reportng s assocated wth earnngs qualty. o measure the qualty of earnngs, we use a metrc, e-loadng factor, developed by Ecker et al. (2006), and the conventonal earnng response coeffcent (ERC). Ecker et al. (2006) provde an nnovatve metrc for measurng the senstvty of the frm s return to earnngs qualty n a specfc perod of tme as short as a quarter. her metrc postulates that the coeffcent on the earnng qualty factor, called e-loadng, captures the senstvty of the frm s return to earnng qualty, smlar to beta whch captures the senstvty of returns to market movements. In other words, the e-loadng captures the perceptons of nvestors about the frm s earnngs qualty. Ecker et al. (2006) show that e-loadngs vary cross- sectonally wth other characterstcs of earnngs qualty. hey also show that nvestors consder the 78 Journal of Accountng and Fnance vol. 11(3) 2011

5 lower ERC to be related to hgher e-loadng factor. Based on pror studes as mentoned earler, we argue that the perceved hgher earnngs qualty s a leadng drver of nvestors postve market reactons. E- loadng can be measured for frms that have lmted tme-seres accountng data, whch usually s requred for estmatng earnngs qualty usng other accountng-based measures, so an mportant advantage of the e-loadng factor s ts ablty to ncrease the samplng power and the generalzablty (external valdty) of the results. hs argument, together wth our dscussons n the prevous paragraphs, leads to the followng hypotheses: H1a: he extent of non-fnancal KPI dsclosure s assocated wth fnancal reportng qualty measured by the e-loadng factor. Assumng that KPIs are accurate and approprate, they wll reduce uncertanty, so we also hypothesze that: H1b: he extent of non-fnancal KPI dsclosure s assocated wth fnancal reportng qualty measured by the earnngs response coeffcent. he above hypotheses are used to test the possble assocaton between the extent of non-fnancal KPI reportng and the qualty of earnngs. hat s, an mprovement n earnngs qualty s expected to be assocated wth the extent of KPI dsclosure. SAMPLE DESIGN, DAA, AND MEHODOLOGY Sample Desgn We have orgnally started wth a sample of 200 companes from each selected ndustry to facltate hand-collecton efforts and ended up wth companes that had complete data for both 2006 and 2007 years. All companes n related populatons are numbered startng from 1 and we used a table of random numbers to select our sample companes. Our fnal samples nclude a random sample of 156 companes lsted on S&P 500, a random sample of 135 manufacturng companes lsted on the New York Stock Exchange (NYSE), and a random sample of 113 ol and gas companes lsted on NYSE. We choose a random sample of S&P500 companes because of the mportance of the economc mpacts of large companes. Also the choce of manufacturng and ol and gas companes s based on the mportance of socal, envronmental, and sustanablty reportng n ths ndustry (e.g., Lane 2010; Johansen 2010). Gven the nature of hand collectng KPI data, the sample szes are ncreased enough both to be cost effectve and results to have external valdty. Contrary to pror research such as Lambert and Larcker (1987) and Ittner and Larcker (1998), whch have used a cross-sectonal regresson model wth only one year observaton, we have looked at a two year perod, 2006 and 2007, wth hand collectng data for about 400 companes wth 800 observatons. Furthermore, n some cases, to calculate the change n lags and varances of some varables, we have extracted data for three to fve years. Data We have collected our data from companes webstes, the Research Insght database, CRSP database, and 10-Ks fled wth the SEC. We have collected our sample companes from the Research Insght database and searched on the LexsNexs Academc Busness lbrary database for 10-K flngs durng fscal year endng on December 31, 2006 and We then examned the sample companes webstes, 10-K flngs, the MD&A and other nformaton dsclosed n these documents and search for dsclosures of factors that pror studes consder the crtcal success factors beyond conventonal fnancal reportng. Usng the detaled nformaton lsted on Appendx A, we determned the extent of both fnancal and nonfnancal KPI dsclosure on the followng eght KPI perspectves: 1) nvestor perspectve, 2) employee Journal of Accountng and Fnance vol. 11(3)

6 perspectve, 3) customer perspectve, 4) suppler perspectve, 5) socal perspectve, 6) nternal perspectve, 7) nnovaton perspectve, and 8) envronmental perspectve. hen, to calculate KPI varables, we have used a content analyss n whch the KPI ndex s calculated as a rato of the total number of KPI key words dsclosed to total words ncluded n management dscusson and analyss (MD&A) of the sample companes. he use of content analyss for analyzng nonfnancal nformaton has been extensvely used n accountng lterature (e.g., Unerman, 2000; Furrer, homos, and Goussevskaa, 2008; Adams and Frost, 2008; Damrel and Bozcuk, 2009). We used the mywordcoun software to count total words n MD&A as well as total words pertanng to KPIs. he words were grouped nto fnancal and non-fnancal KPIs and we focus on non-fnancal KPIs prmarly because dsclosures of fnancal KPIs are typcally regulated and standardzed. he nonfnancal KPI scores of each frm n our three samples (all frms, manufacturng frms and frms n the ol and gas ndustry) are computed based on the rato of the total KPI related words to total words ncluded n MD&A. hs method of content analyss s the smplest, the most dependable, and the least subjectve way of analyzng qualtatve nformaton. E-loadng data are extracted from the database provded by Professor Olsson and other faculty members n the Duke Unversty, and data requred for runnng the Fama and French three-factor model are extracted from the database lnked to ther webste. All analysts earnngs forecasts and abnormal earnngs are collected from the latest edton of the Insttutonal Brokers' Estmate System (I/B/E/S). Fnancal data are manly extracted from the Research Insghts database and the Center for Research n Securty Prces (CRSP). Methodology We use a metrc, e-loadng factor, developed by Ecker et al. (2006), and the conventonal earnng response coeffcent (ERC) as proxes for earnngs qualty. Ecker et al. (2006) consder earnngs qualty as a metrc of nformaton rsk and defne earnngs qualty wth respect to the mappng of current accruals nto last, current, and next perod cash flows. Ecker et al. (2006) follows Dechow and Dchev (2002) to call ths mappng accrual qualty. As mentoned by Ecker et al. (2006), e-loadngs are smlar to s- loadng and h-loadng and do not follow any theoretcal foundatons. Only the beta, rsk factor, s based on the captal asset prcng model (CAPM) and theoretcal foundatons such as the Effcent Market Hypothess (EMH). Ecker et al. (2006) provde emprcal evdence to show that e-loadng can be measured for frms that lack tme-seres data. hey show that a larger e-loadng mples greater senstvty to poor earnngs qualty. In other words, the e-loadng captures the senstvty of stock returns to earnngs qualty. S-loadng s the coeffcent of SMB (Small mnus Bg), whch shows the senstvty of stock returns to a frm s sze. SMB s the dfference between the average return on three small and three bg portfolos (French, 2009). o test the assocaton between the extent of non-fnancal KPI reportng and the qualty of earnngs, frst we use the followng model: Ch _ ELOD 0 1DNFKPI 2SIZE 3Ch _ ROE 4Ch _ MKtoBK Ch _ BEA 6Sale _ Growth 7 AGE 8Ch _ LVRG 9Ch _ CASH 5 Ch _ PROFI K j11 INDS j j e (1) Defntons of all varables are shown n Appendx B. he dependent varable, Ch_ELOD, s an e-loadng varable reflectng the fnancal reportng qualty determned based on Ecker et al. (2006). he process of calculatng the e-loadng varable s dscussed next. Consstent wth the hypothess of ths study, we expect the coeffcent of change n nonfnancal KPI to be sgnfcant and negatve, ndcatng that companes wth hgh qualty of earnngs provde more extensve non-fnancal dsclosure. 80 Journal of Accountng and Fnance vol. 11(3) 2011

7 We look at e-loadng nstead of stock returns, because a change n stock returns s a reflecton of many factors and we are only nterested n the porton that can be attrbuted to the mpact of KPI reportng. E-loadng s a metrc that solates the mpact of KPI reportng on earnngs qualty, whch n turn s expected to result n a postve market reacton, a lower cost of captal and hgher stock return (Lambert et al. 2005). Followng McNchols (2002) and Ecker et al. (2006), we have used the followng modfed verson of Dechow and Dchev s model: CA 0, j 1, jcfo 1 2, jcfo 3, jcfo 1 4, j Re (2) 5, j PPE u v Where: CA CA CL Cash SDEB otal current accruals n year. We estmate equaton (2) n annual ndustry cross-sectons for Fama and French (1997) ndustres wth suffcent ndustry-year observaton to calculate resduals, u j,. Accordng to Ecker et al. (2006), the earnngs qualty metrc for frm j n year s the standard devaton of frm j s resduals over the last fve years. hen we calculate the accrual qualty varable as: AQfactor j u ), for = -5,, -1. (3), ( hen, we form a dynamc portfolo by formng decles based on the value of AQ avalable on the frst day of each month, wth the smallest AQ values placed on the frst decle and the largest AQ on the tenth decle. Next, we calculate the average daly return for each decle. We then calculate the AQ factormmckng portfolo, AQfactor, whch s the dfference between the daly returns of the largest four AQ decles (decles 7-10) and smallest four decles (decles 1-4). hs process results n a tme-seres of daly returns for each decle. hen, we correlate the AQ factor wth the returns of each frm to determne the exposure of the frm to the poor earnngs qualty n a smlar way as we correlate a frm s returns wth the market rsk premum to determne the exposure of the frm to market rsk (BEA). Consstent wth Ecker et al. (2006), we estmate the followng three-factor asset prcng model modfed for e-loadng factors: R ( s SMB h HML AQfactor (4) t RF, t 0, 1, RM, t RF, t ) t t t t In ths model, the estmates of other coeffcents capture the frm s exposure to return representatons of market rsk, sze, and book to market, respectvely, n year. he man fnancal data used n ths study are collected from companes 10-K fllngs to the SEC, CRSP, Compustat Research Insght databases, companes webstes, the Fama-French webste, and E-loadng database. he SMB and HML data are collected from the Ken French webste. Furthermore, to test the assocaton between the extent of KPI reportng and qualty of earnngs usng the ERC (H1b), we examne ERCs at the earnngs announcement dates to assess the dfference n share prce reactons n companes wth dfferent KPI dsclosure levels. he dfference n share prce reactons provdes evdence about the nformaton content of the KPI dsclosure. Our regresson model to test ths hypothess (H1b) s derved from models used by Collns and Kothar (1989), Dhalwal et al. (1991), and Dhalwal and Reynolds (1994): Journal of Accountng and Fnance vol. 11(3)

8 CAR 0 1UE 2UE * LOSS 3UE * Ch _ MKtoBK 4UE * Ch _ BEA UE * LnMKE UE * DNFKPI 5 6 j7 K 6 INDS Defntons of all varables are provded n Appendx B. All ndependent varables are for fscal years 2006 and hose that have not been prevously defned are LOSS, whch s a dummy varable equal to 1 f ncome before extraordnary tems s negatve for frm, and zero otherwse, and LnMKE, whch s the natural log of market value of equty at the end of year Fnally, the dependent varable, cumulatve abnormal return (CAR), s a three-day cumulatve abnormal return for frm n year 2007, whch s the resdual obtaned from a market model estmated over a three-day perod around the earnngs announcement day. We use the most recent I/B/E/S medan analysts earnngs-per-share avalable pror to each earnngs announcement date as the expected earnngs to calculate the unexpected earnngs as: ( AE EE ) UE (6) P hat s, the unexpected earnngs are equal to the dfference between actual earnngs (AE) and expected earnngs (EE) n 2007 dvded by stock prce (P) at the end of the year. Consstent wth our second hypothess (H1b), we expect 6 to be sgnfcant and postve. ANALYSES AND RESULS able 1 provdes descrptve statstcs for dependent and ndependent varables. he frst two columns of able 1 show descrptve statstcs for a sample of 156 companes lsted n the S&P 500 ndex, the second two columns show descrptve statstcs for a sample of 135 manufacturng companes lsted on the NYSE, and the last two columns show descrptve statstcs for a sample of 113 ol and gas companes lsted on the NYSE. able 2 presents the correlaton matrces for these three samples. ABLE 1 DESCRIPIVE SAISICS All ndustres Manufacturng Ol and Gas Std. Std. Std. Varable Mean Devaton Mean Devaton Mean Devaton Ch_ELOAD DFKPI DNFKPI SIZE Ch_ROE Ch_MKtoBK Ch_Beta Sale_Growth AGE Ch_LVRG Ch_CASH j j e (5) 82 Journal of Accountng and Fnance vol. 11(3) 2011

9 Ch_ELOAD 1.00 ABLE 2 CORRELAION MARICES Panel A: Random sample of 156 companes from all ndustres (S&P 500 Index) Ch_ELOAD DFKPI DNFKPI SIZE Ch_ROE Ch_MKtoBK Ch_Beta Sale_Growth AGE Ch_LVRG Ch_CASH DFKPI DNFKPI *** 1.00 SIZE Ch_ROE *** 1.00 Ch_MKtoBK Ch_Beta Sale_Growth ** AGE * ** 0.57*** 1.00 Ch_LVRG ** -0.23*** -0.46*** ** 0.22*** 1.00 Ch_CASH Ch_PROFI ** 0.76*** ** -0.15* -0.15* *, **, ***, sgnfcance at.01,.05, and.10 level, respectvely. (Ch_PROFI and DFKPI are dropped from analyss because of ther hgh correlaton wth other ndependent varables) Panel B: Random sample of 135 companes from manufacturng ndustry (lsted on NYSE) Ch_ELOAD 1.00 Ch_ELOAD DFKPI DNFKPI SIZE Ch_ROE Ch_MKtoBK Ch_Beta Sales_Growth AGE Ch_LVRG Ch_CASH DFKPI DNFKPI *** 1.00 SIZE Ch_ROE Ch_MKtoBK *** 1.00 Ch_Beta 0.17** Sales_Growth ** AGE *** Ch_LVRG ** -0.26*** Ch_CASH *** ** -0.18** 1.00 Ch_PROFI *** 0.12 *, **, ***, sgnfcance at.01,.05, and.10 level, respectvely. (Ch_PROFI and DFKPI are dropped from analyss because of ther hgh correlaton wth other ndependent varables) Journal of Accountng and Fnance vol. 11(3)

10 Panel C: Random sample of 113 companes from ol and gas ndustry (lsted on NYSE) Ch_ELOAD 1.00 Ch_ELOAD DFKPI DNFKPI SIZE Ch_ROE Ch_MKtoBK Ch_Beta Sales_Growth AGE Ch_LVRG Ch_CASH DFKPI DNFKPI *** 1.00 SIZE Ch_ROE ** 1.00 Ch_MKtoBK *** 1.00 Ch_Beta * Sales_Growth * 0.31*** AGE * *** 1.00 Ch_LVRG *** -0.26*** * Ch_CASH * ** -0.18* *** Ch_PROFI -0.26*** *** 0.71*** ** *** 0.53*** *, **, ***, sgnfcance at.01,.05, and.10 level, respectvely. (Ch_PROFI and DFKPI are dropped from analyss because of ther hgh correlaton wth other ndependent varables) All panels of able 2 show that fnancal and non-fnancal KPIs are hghly correlated wth each other. Evdence collected n ths study shows that companes wth more fnancal KPI dsclosure tend to dsclose more non-fnancal KPIs; therefore, to avod the multcollnearty problem, we have dropped the fnancal KPIs and focused only on the dsclosure of non-fnancal KPIs. For the same reason, we have dropped returns on equty from our models. he results of testng the frst hypothess (H1a) are provded n able 3. he frst two columns of ths table show that, after controllng for dfferent ndustres grouped by the frst dgt SIC ndustry code, the coeffcent of non-fnancal KPIs s not statstcally sgnfcant at any conventonal levels. he nablty to detect sgnfcance can be attrbuted to: frst, the cross sectonal analyss conducted n ths study whch focuses only on data for two years, 2006 and he lack of KPI tme seres data, the lkelhood that confoundng events nfluence the results are among possble reasons for not havng the sgnfcance. Other possble reasons wll be dscussed later n ths paper. he second two columns of able 3 show the results of estmatng the model usng data from a sample of manufacturng companes. As these two columns show, the coeffcent of change n KPIs s not sgnfcant, provdng no support for H1a when a sample of manufacturng companes s used. Fnally, the last two columns of able 3 show the results of estmatng the model usng data from a sample of ol and gas companes. As these two columns show, the coeffcent of change n KPI s not sgnfcant; therefore, the results do not support the frst hypothess (H1a) when the sample s lmted to companes from ol and gas ndustry. As mentoned earler, the goodness of ft technques shows that for all three sample data the use of lnear models s not approprate. Dagnostc tests of the resduals of these regressons provde some evdence that the use of lnear regresson for these e-loadng models s not approprate, as there s robust evdence that the assocaton between the change n the e-loadng varable and change n KPI ndex, as well as, other ndependent varables are non-lnear. Applcaton of non-lnear models s not uncommon n accountng lterature; the most commonly used non-lnear models are Logt models, n whch the dependent varable s a dchotomous varable (.e., Stone and Rasp 1991; Barnv and McDonald 1999; Jones and Hensher 2004, 2007; Ge and Whtmore 2005; Baxter et al. 2007). Examples of other non-lnear models can be found n studes conducted by Km and Mcleod (1999), Kohn (2003), Freeman and se (1992), Subramanyan (1996), and Stone and Rasp (1991). Km and Mcleod (1999) show how, compared to smple lnear models, non-lnear models better explan factors that affect the accuracy of the predcton by experts. 84 Journal of Accountng and Fnance vol. 11(3) 2011

11 herefore, n the followng, we rerun our models usng the optmal scalng regresson to control for the observed non-lnearty ssue. Although we re-perform the test for each of our three samples, we focus on the samples from the manufacturng and ol and gas ndustres, to reduce ndustry effects and mprove the power of our tests. ABLE 3 RESULS FROM E-LOADING ESS USING LINEAR REGRESSION Ch _ ELOD 0 1DNFKPI 2SIZE 3Ch _ ROE 4Ch _ MKtoBK Ch _ BEA 6Sale _ Growth 7 AGE 8Ch _ LVRG 9Ch _ CASH 5 Ch _ PROFI K j11 INDS j j e Sample of: All ndustres Manufacturng Ol and Gas Coeff. t-stat Coeff. t-stat Coeff. t-stat (Constant) **.045 DNFKPI *.064 SIZE Ch_ROE Ch_MKtoBK Ch_Beta **.014 Sale_Growth AGE Ch_LVRG Ch_CASH **.031 SIC_ SIC_ SIC_ SIC_ SIC_ SIC_ SIC_ Adj. R-squared F-stat ** ***, **, * Sgnfcance at the 0.01, 0.05, and 0.10 level, respectvely. (hs test ncludes a random sample of 156 companes from all ndustres, a random sample of 135 companes from manufacturng ndustry, and a random sample of 113 companes from ol and gas ndustry, respectvely) he results of runnng the non-lnear verson of the above models are shown n able 4. he frst two columns of able 4 show the results of estmatng the model usng data from a sample of all ndustres. As these two columns show, the coeffcent of change n KPIs s not sgnfcant, thereby not supportng the frst hypothess of ths paper. he sgnfcant coeffcents are those of SIZE (negatve), Change n ROE (postve), market to book rato (postve), change n BEA (postve), sales growth (negatve), and lqudty (negatve). he second two columns of ths table show the results of estmatng the model usng data from a sample of manufacturng companes. As these two columns show, the coeffcent of change n KPIs s not sgnfcant, thereby not supportng the frst hypothess, whch s consstent wth the result of the lnear model. Other sgnfcant coeffcents are those of SIZE (negatve), leverage (postve), and sales growth (negatve). he last two columns of able 4 show the results of estmatng the model usng data from the sample of ol and gas companes. As these two columns show, the coeffcent of KPIs s hghly sgnfcant, whch provdes support for the frst hypothess of ths study. Other sgnfcant coeffcents are Journal of Accountng and Fnance vol. 11(3)

12 those of change n ROE (negatve), change n market to book rato (postve), change n BEA (postve), sales growth (negatve), and change n lqudty (negatve). ABLE 4 RESULS FOR E-LOADING ESS USING OPIMAL SCALING REGRESSIONS Ch _ ELOD f ( DNFKPI, SIZE, Ch_ ROE, GRW, Ch_ BEA, Sale _ Growth, AGE, Ch _ LVRG, Ch _ CASH, Ch _ PROFI ) Sample of: All ndustres Manufacturng Ol and Gas Coeff. F-stat Coeff. F-stat Coeff. t-stat DNFKPI ** SIZE -.22*** *** Ch_ROE.218*** ** Ch_MKtoBK.168** * Ch_Beta.175** *** Sale_Growth -.25*** ** ** AGE Ch_LVRG * Ch_CASH -.28*** ** Adj. R-squared F-stat 1.75** ** ***, **, * Sgnfcance at the 0.01, 0.05, and 0.10 level, respectvely. (hs test ncludes a random sample of 156 companes from all ndustres, a random sample of 135 companes from manufacturng ndustry, and a random sample of 113 companes from ol and gas ndustry, respectvely) he overall results show that only for companes n ol and gas ndustry there s a postve assocaton between non-fnancal KPI dsclosure and the qualty of earnngs measured by e-loadng factor, but the assocaton as shown on the last two tables (ables 3 and 4) s non-lnear. he results of testng the second hypothess H1b, usng all three prevously explaned samples, are shown n able 5. he frst two columns of able 5 show that, after controllng for dfferent ndustres, the coeffcent of nteracton of change n KPIs and unexpected earnngs s not statstcally sgnfcant when the sample ncludes 156 companes from all ndustres. he results show that only the coeffcents of nteracton of unexpected earnngs and market to book value of equty as well as some ndustry sector codes are sgnfcant, ndcatng that cumulatve abnormal returns vary by both the growth level of the company and the ndustry. he next two columns of able 5 show the results of estmatng the model usng data from a sample of 135 manufacturng companes. hese two columns show that the coeffcent of nteracton between change n KPIs and unexpected earnngs s not statstcally sgnfcant. No other coeffcents n for ths sample companes s statstcally sgnfcant. Fnally, the last two columns of able 5 provde the results of estmatng the model usng data from the sample of ol and gas companes. he results for the sample of ol and gas companes show that the nteracton between unexpected earnngs and the dsclosure of non-fnancal KPIs s not sgnfcant, provdng no support for the second hypothess of ths study. he only sgnfcant coeffcent s the coeffcent of nteracton between unexpected earnngs and change n market to book rato (postve). 86 Journal of Accountng and Fnance vol. 11(3) 2011

13 CAR UE ABLE 5 RESULS FOR ERC ESS USING LINEAR REGRESSIONS UE UE * LOSS 3 * Ch _ MKtoBK 4 UE * LnMKE UE * DNFKPI 5 6 j7 K 6 INDS j j e UE * Ch _ BEA Sample of: All ndustres Manufacturng Ol and Gas Coeff. t-stat Coeff. t-stat Coeff. t-stat (Constant) * * UE UE_LOSS UE_Ch_MKtoBK 2.29** *** UE_BEA UE_LnMKE UE_DNFKPI SIC_ SIC_ SIC_ SIC_4 3.63** SIC_ SIC_ * SIC_ * Adj. R-squared F-stat * ***, **, * Sgnfcance at the 0.01, 0.05, and 0.10 level, respectvely. (hs test ncludes a random sample of 156 companes from all ndustres, a random sample of 135 companes from manufacturng ndustry, and a random sample of 113 companes from ol and gas ndustry, respectvely) However, the resduals obtaned from the above models and other dagnostc analyses reveal that the use of lnear regresson for these models s not approprate. here s robust evdence that the assocaton between the cumulatve abnormal return (CAR) and change n KPI dsclosure and other ndependent varables s not lnear. Furthermore, the overall model usng a random sample of manufacturng companes s not sgnfcant, ndcatng that the lnear model used n ths study s not capable to deal wth many complextes of manufacturng companes. Usng the econometrcs technque of goodness of ft, we provde evdence that the assocaton between ERC and KPI s non-lnear. he results of runnng the non-lnear verson of the above models are provded n able 6. he frst two columns of able 6 show the results of estmatng the model usng data from a sample companes lsted n the S&P 500 ndex from varous ndustres. As these two columns show, the coeffcent of nteracton of unexpected earnngs and change n KPI s not sgnfcant, provdng no support for the second hypothess of ths study. Sgnfcant coeffcents for ths sample are those of nteracton between unexpected earnngs and loss (postve), nteracton between unexpected earnngs and market to book value of equty (negatve), nteracton between unexpected earnngs and change n BEA (postve), and nteracton between unexpected earnngs and natural log of market value of equty (negatve). he next two columns show the results of estmatng the model usng data from a sample of manufacturng companes. In short, when the study s lmted to a sample of manufacturng companes, the complexty nvolved n manufacturng companes cannot be captured wth the models used n ths study. As a result, the evdence obtaned from testng manufacturng companes provdes no support for the second hypothess of ths study. Journal of Accountng and Fnance vol. 11(3)

14 Fnally, the last two columns of able 6 show the results for a sample of ol and gas companes. he results show that for ol and gas companes there s a sgnfcant assocaton between unexpected earnngs and change n non-fnancal KPI dsclosure, supportng the second hypothess of ths study. hs result s consstent wth the result obtaned usng e-loadng factors, so we can argue that the assocaton between non-fnancal KPIs and earnngs qualty s postve and robust. Other sgnfcant coeffcents are those of nteracton between unexpected earnngs and loss (negatve), and the nteracton between unexpected earnngs and market to book value of equty. ABLE 6 RESULS FOR ERC ESS USING OPIMAL SCALING REGRESSION CAR g( UE, UE * LOSS, UE * Ch _ MKtoBK, UE * Ch _ BEA, UE LnMKE, UE * DNFKPI ) * Sample of: All ndustres Manufacturng Ol and Gas Coeff. F-stat Coeff. F-stat Coeff. F-stat UE UE_LOSS.31** *** UE_Ch_MKtoBK -.38*** *** UE_Ch_BEA.82*** UE_LnMKVEQ -.69*** * UE_DNFKPI *** Adj. R-squared F-stat 2.98*** *** ***, **, * Sgnfcance at the 0.01, 0.05, and 0.10 level, respectvely. (hs test ncludes a random sample of 156 companes from all ndustres, a random sample of 135 companes from manufacturng ndustry, and a random sample of 113 companes from ol and gas ndustry, respectvely) CONCLUSIONS he results presented n ths paper provde some evdence for the assocaton between KPI dsclosure and the qualty of earnngs for companes n the ol and gas ndustry. When the conventonal ERC or the more recently developed e-loadng factor s used for the analyss, we show that the use of conventonal lnear approach may not be approprate n all cases, and there s a need for the use of a non-lnear approach. Usng a non-lnear approach, we document a sgnfcant assocaton between the change n KPI dsclosure and the qualty of earnngs only for companes n the ol and gas ndustry, but we found no assocaton for the other two samples. Usng both ERC and the e-loadng approach, we show that for companes n the ol and gas ndustry the drecton of assocaton s consstent wth our hypotheses when usng ether non-lnear ERC or non-lnear e-loadng approach. herefore, to mprove transparency of the fnancal statements especally durng the recent fnancal crses, we at least call for the compulsory publcaton of nonfnancal KPIs n the ol and gas ndustry. hs study s expected to contrbute to the lterature n several ways. Frst, we nvestgate the assocaton between the change n KPI reportng and qualty of earnngs usng both e-loadng and ERC approaches for companes n the ol and gas ndustry. o the best of our knowledge, ths s the frst study addressng ths ssue. Second, we extend the lterature on the relevance of corporate voluntary dsclosures. Fnally, the UK Companes Act of 1985, the SEC and the US reasury department have shown ther nterests n dsclosng KPI nformaton, but no emprcal results are avalable to support such nterest. he polcy mplcaton of ths study n provdng emprcal evdence regardng the recommendatons made by the ACIFR n 2008 s to encourage the SEC or FASB to defne ndustry specfc KPIs and requre companes n each ndustry to report them on a consstent bass. 88 Journal of Accountng and Fnance vol. 11(3) 2011

15 Results presented n ths study should be nterpreted wth care because of the followng potental lmtatons. Frst, ths paper nvestgates only the potental assocaton between KPIs dsclosure and qualty of earnngs and as such does not establsh any causal relatonshp between the varables of nterest. Second, a two year analyss conducted n ths paper focuses only on data for the two years of 2006 and Because of the lack of tme seres KPI data, ths study does not nclude a tme-seres analyss of KPI dsclosure. hrd, there exsts the lkelhood that confoundng events nfluence the results. Future research s encouraged to focus on multple year observatons of KPI reportng as well as research on comparng mandatory versus voluntary KPI dsclosure. REFERENCES Adams, C. A. & Frost, G. R. (2008). Managng Socal and Envronmental Performance: Do Companes have Adequate Informaton? Australan Accountng Revew, 17, (43), Advsory Commttee on Improvements to Fnancal Reportng to SEC. (2008). Fnal Report of the Advsory Commttee on Improvement to Fnancal Reportng to the Unted States Securtes and Exchange Commsson, Arya, A., Glover, J., Mttendorf, B. & Narayanamoorthy, G. (2005). Unntended Consequences of Regulatng Dsclosures: he Case of Regulaton Far Dsclosure. Journal of Accountng and Publc Polcy, 24, (3), Barnv, R. & McDonald, J. B. (1999). Revew of Categorcal Models for Classfyng Issues n Accountng and Fnance. Revew of Quanttatve Fnance and Accountng, 13, (1), Baxter, R. A., Gawler, M. and Ang, R. (2007). Predctve Model of Insolvency Rsk for Australan Corporatons. ACM Internatonal Conference Proceedngs, 311, Burnett, R. D. & Hansen, D. R. (2008). Ecoeffcency: Defnng a Role for Envronmental Cost Management. Accountng, Organzaton and Socety, 33, (6), Burton, F. G., Coller, M. & uttle, B. (2006). Market Responses to Qualtatve Informaton from a Group Polarzaton Perspectves. Accountng, Organzaton and Socety, 31, (2), Caylor, M., Lopez,. & Rees, L. (2007). Is the Value Relevance of Earnngs Condtonal on the mng of Earnngs Informaton? Journal of Accountng and Publc Polcy, 26, (1), Chan, A.M., Lee, E. & Ln, S. (2009). he Impact of Accountng Informaton Qualty on the Msprcng of Accruals: he Case of FRS3 n the UK. Journal of Accountng and Publc Polcy 28, (3), Cheung, Y., Jang, P. & an, W. (2010). A ransparency Dsclosure Index Measurng Dsclosures: Chnese Lsted Companes. Journal of Accountng and Publc Polcy, 29, (3), Collns, D.W. & Kothar, S. P. (1989). An Analyss of Internatonal and Cross-Sectonal Determnants of Earnngs Response Coeffcents. Journal of Accountng and Economcs, (11), Cooper, S. M. & Owen, D. L. (2007). Corporate Socal Responsblty and Stakeholder Accountablty: he Mssng Lnk. Accountng, Organzaton and Socety, 32, (7-8), Copeland,., Koller,., Murrn, J. & Foote, W. (2000). Valuaton: Measurng and Managng the Value of Companes, 3rd Edton, John Wley & Sons, New York. Journal of Accountng and Fnance vol. 11(3)

16 Dechow, P. & Dchev, I. (2002). he Qualty of Accruals and Earnngs: he Role of Accrual Estmaton Errors. he Accountng Revew, (77) (supplement), Dedman, E., Ln, S., Prakash, A & Chang. C. (2008). Voluntary Dsclosure and ts Impact on Share Prces: Evdence from the UK Botechnology Sector. Journal of Accountng and Publc Polcy, 27, (3), Delotte. (2007). A Mne of Informaton: An Analyss of Sustanable Development Reportng n the Mnng Industry. Energy, Infrastructure and Utltes. Accessble at: Dhalwal, P. W., Lee, K. & Fargher, N. (1991). he Assocaton Between Unexpected Earnngs and Abnormal Securty Returns n the Presence of Fnancal Leverage. Contemporary Accountng Research, (7), Dhalwal, P.W. & Reynolds, S. (1994). he Effect of the Default Rsk of Debt on the Earnngs Response Coeffcent. he Accountng Revew (69): Dowlng, G. (2006). How Good Corporate Reputatons Create Corporate Value. Corporate Reputaton Revew, (9), Ecker, F.,Francs, J., Km, I., Olsson, P. & Schpper, K. (2006). A Return-Based Representaton of Earnngs Qualty. he Accountng Revew, (81), Epsten, M.J. & Roy, M. J. (2001). Sustanablty n Acton: Identfyng and Measurng the Key Performance Drvers. Long Range Plannng, 34, (5), Ettredge, M., Rchardson, V. & Scholz, s. (2002). Dssemnaton of Informaton for Investors at Corporate Web Stes. Journal of Accountng and Publc Polcy, 21, (4-5), Freeman, R. N. & se, S. (1992). A Non-Lnear Model of Securty Prce Responses to Unexpected Earnngs. Journal of Accountng Research, 30, (2), Fama, E. & French, K. (1992). he Cross-Secton of Expected Stock Returns. Journal of Fnance, (47), French, K. (2009). Descrpton of Fama/French Benchmark Factors. Avalable at: Furrer, O., homas, H. & Goussevskaa, A. (2008). he Structure and Evoluton of the Strategc Management Feld: A Content analyss of 26 Years of Strategc Management Research. Internatonal Journal of Management Revews, 10, (1), 1-23 Ge, W. & Whtmore, G. A. (2005). Bnary Response and Logstc Regresson n Recent Accountng Research Publcatons: A Methodologcal Note. Workng paper, McGll Unversty. Ghalayn, A. & Noble, J. (1996). he Changng Bass of Performance Measurement. Internatonal Journal of Operatons & Producton Management, 16, (8), Journal of Accountng and Fnance vol. 11(3) 2011