Niklas Bayrle Institute of Technology and Process Management Ulm University. Leo Brecht Institute of Technology and Process Management Ulm University

Transcription

1 Operational Capability Analytic A Data Envelopent Analyi on fir fundaental data Nikla Bayrle Intitute of Technology and Proce Manageent Ul Univerity Leo Brecht Intitute of Technology and Proce Manageent Ul Univerity Abtract There are variou approache to ae a fir financial perforance. Typically, financial ratio or etric on upply chain perforance are calculated and evaluated to ae a fir financial perforance. Proble often arie when fir fro different indutrie, ize and countrie are evaluated becaue of the underlying linearity auption in bencharking coparion. To overcoe thee difficultie we ugget to eaure financial perforance differently, which we call Operational Capability Analytic. We define the operational capability a the potential to tranlate fir' aet etc. efficiently into revenue, earning etc.. We can how that the reulting efficiency core i highly correlated with a fir' financial perforance repreented by claical financial perforance indicator. In thi paper, the Banker, Charne & Cooper (1984) (BCC) and Charne, Cooper & Rhode (1978) (CCR) Data Envelopent Analyi (DEA) odel are ued to calculate thi operational capability. We ue ultiple input and output factor fro the balance heet, incoe and cah flow tateent. The advantage of our non-paraetric approach i that we can calculate the financial perforance of different fir without knowing a priori the relationhip between the input and output variable and their correponding weight. We are able to how that our ethodology can ae a fir financial perforance by deploying a correlation analyi to other perforance indicator. A well, we etablih a relationhip to the tock arket. An equity portfolio baed on bet perforing fir calculated by our Operational Capability Analytic how uch better return than portfolio copoed by the wort fir. The yield are even better in the long run. Therefore, our concluion i that operational capabilitie pay off over a id-ter tie horizon (> 1y). The arket acknowledge the perforance of the bet operating fir. Our reult can have ueful iplication for invetent anageent. The BCC DEA odel in general wa the ore valuable one in coparion with the CCR DEA odel in thi tudy. In the dicuion, we how the liitation of our tudie and how they can be olved and further developent. Keyword DEA; Data Envelopent Analyi; Fir Perforance; Operational Capability; Financial Perforance; Fundaental Analyi; Portfolio Selection I. INTRODUCTION The ain objective of thi paper i to find a ethodology to eaure a fir financial perforance. Norally you ue claical financial ratio for liquidity, profitability, leverage and aet utilization (ee [1] for further inforation) to ae a fir financial ituation repectively perforance. Thee ratio aue a linear relationhip between the variable. Thi i often not the cae a in[2]becaue of econoie of cale for exaple. Furtherore, it i often only poible to copare fir within the ae indutry and countrie. Thi paper trie to overcoe thee difficultie by uing the non-paraetric DEA, which can evaluatevariou fir baed onultiple input and output variable. It i crucial to find the bet variable for calculating a fir operational capability, which reflect a fir financial perforance. More detail about thi connection will follow in ection 3.DEA etablihe a betpractice relationhip between input and output paraeter. The bet fir pecify the efficient frontier againt which the other inefficient fir will beevaluated. In thi paper, we want to tet if DEA i applicable to reolve our ain objective and can be ued a a ethod for eauring a fir financial perforance. Further, we want to elect equity portfolio in the tock arket baed on our perforance evaluation reult and copare the bet fir of our evaluation againt the wort. Due to the efficient arket hypothei it hould not be poible to generate abnoral return with any given invetent trategy over a long tie horizon. On the other hand, unique inight and analytic like aeing the operational efficiency hould be rewarded (ee[1] for further detail) and could lead to higher return. In ection II we will preent forer cientific paper, which worked on iilar idea like our. In ection III will be an overview over fundaental analyi, DEA, our Operational Capability Analytic (OCA) ethod and the data collection. Subequently the reult will be hared in ection IV. A a lat point we will draw our ain concluion and we will give an outlook to future reearch in ection V. II. LITERATURE REVIEW During the pat decade, any acadeic paper applied DEA to fundaental analyi. Soe of thetried to overcoe the proble of the typical financial ratio, other wanted to tet the uability of DEA in the context of fundaental analyi and fir perforance analyi. One of the firt paper in thi doain i fro [3] who analyzed 47 pharaceutical fir baed on the contituent part of return on capital eployed. Thu, the elected variable were equity and average debt a input and tax, interet payent and earning available for hareholder a output. Reference [3] ued an input orientated BCC odel for hi aeent. The author i convinced that DEA i a very good tool for analyzing a fir, but i alo aware of data quality proble. Reference [4] evaluated 44 US coputer anufacture baed on variable fro the incoe tateent and balance heet. Hi Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 9

2 purpoe wa to analyze the coputer indutry in detail. He elected COGS, Capex, R&D, SG&A, nuber of eployee and PP&E a input and revenue, EBT and arket capitalization a output. The CCR input orientated odel and the Malquit Index were ued a DEA ethod. The author do not copletely explain their choice of variable. A very intereting contribution to our doain wa by [5]. In thi paper, the author aeed the Fortune 5 fir baed on the Fortune indicator. The bencharking wa done in a two tage procedure which i hown in Fig. 1. The firt tage aee the profitability of the given fir and the econd tage the arketability. An intereting reult i that the bet fir in ter of profitability are not copulory the bet fir in ter of arketability. The author ued the BCC and CCR odel and howed that the BCC odel i the better one for the given data becaue of the underlying variable returnto-cale property. Reference [6]conider 29 fir in the energy ector in their calculation. The input and output variable are deduced fro the DuPont Forula. They take total aet, equity and cot a input variable and revenue a an output variable. The reult how that a financial DEA core i better for aeing a fir financial perforance than iple financial ratio. Thi wa a quick overview about oe poible olution to eaure a fir financial perforance. There are other paper which did related reearch like [7], [8], [9] etc.. Reference [1] ued DEA for portfolio election. In the firt tep, the author calculated a fir efficiency (or financial perforance) with the input variable average equity, average aet and ale cot and with the output variable revenue, operating profit and net incoe. Taiwanee fir data wa ued and the CCR and BCC odel were ipleented. The author howed uperior return for portfolio baed on the DEA reult and ugget teting the reult in other arket. Figure 1: Input-output yte for Fortune 5 fir by Zhu (2). the input and output feature baed on the available data other tarted with caual dependency like in the DuPont forula. We could identify a reearch gap in the applicability of DEA in fundaental analyi for different indutrie and countrie. A well, we will preent an input-output-odel baed on caual reaoning. Beide that, only few author tried to validate their reult with a correlation analyi to other perforance indicator. In addition, our equity portfolio hall how if financial excellence pay off at the tock arket. III. DATA AND METHODOLOGY In the following ection, we will give an overview of fundaental analyi and data envelopent analyi. Succeeding we will deign our own input-output-odel for aeing a fir operational capability. In the concluding ubection, we will introduce the data et. A. Fundaental Analyi Fundaental analyi i ued for analyzing a fir expectation to deterine a proper tock price (ee [1]) baed on a fir fundaental data. Reference [12] how that you can get abnoral return by uing fundaental ignal. They concluded that the tronget indicator of on-year-ahead earning are ignal of relative change in inventorie, capital expenditure and effective tax rate. In general, fundaental analyi i baed on the underlying auption that every ecurity ha an intrinic value, which depend on the earning potential of the ecurity (ee [13]). With the help of fundaental data fro the balance heet, cah flow and incoe tateent and other ource, an analyt hould be able to predict if the actual price of the ecurity i above or below it intrinic value. In the following ubection, we will ue fundaental data to ae a fir financial perforance. B. Data Envelopent Analyi The DEA i a non-paraetric perforance or efficiency evaluation ethod baed on linear prograing. It wa firt introduced by [14] a a ethod to copare the efficiencie of different non-profit organization (or o-called deciion aking unit (DMU)) and expanded over the year. In thi paper, we ue the original CCR odel with contant returnto-cale and a odel baed on a variable return-to-cale auption called the BCC odel. Thee odel are ued a a ranking ethod for the fir in our data frae. DEA calculate a bet-practice frontier function againt which all the fir are evaluated. Reference [11] ued DEA for equity portfolio election. Three different DEA odel, the CCR, uper-efficiency and croefficiency odel calculate the core. They ue tock price, enterprie value-per-hare a input paraeter, and book value-per-hare, dividend-per-hare and EBITDA-per-hare a output paraeter. In addition, they ue a econd odel with a oentu indicator a an added output paraeter. In a third odel they ue only tock price a an input paraeter and the fourth one i iilar to the third one but the earning-per-hare i ubtituted for EBITDA-perhare. All portfolio elected on the four different inputoutput-odel ignificantly outperfor the arket portfolio and botto portfolio. Their reult how that DEA ethod are applicable for portfolio election. In our literature review, we have hown that there are different DEA olution for uing a fir fundaental data.furtherore, other author proved the applicability of DEA a a portfolio election ethod. Soe author elected 1) The Charne, Cooper & Rhode odel The o-called CCR odel fro [14] i the original DEA odel. The idea i to calculate for every Deciion Making Unit (DMU) an efficiency core baed on input and output. Reference [14] forulated thi efficiency core in precie fro a ax θ = i=1 r=1 r=1 i=1 u r y r v i x i v i x ij 1; j = 1,, n u r y rj v i, u r where v i and u r are the input repectively the output weight aigned to the input and output. θ i the (1) Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 1

3 efficiencycore of DMU under evaluation. It i poible to olve thi optiization proble in for of a fractional prograing odel (1) by uing Charne and Copper tranforation (ee [15]). The converted linear prograing odel can be written a (ee [16]): r =1 u r y ri ax θ = j =1 j =1 r=1 u r y r v j x j = 1 v j x ji, i {1,, n} v j, j {1,, } μ r, r {1,, } A DMU i called CCR efficient if it optial olution i θ = 1 otherwie it i called CCR inefficient. Solving thi kind of proble i till not perfectly poible under all circutance. The odel i tranfored to a dual linear prograing odel with an input or output orientation. The dual proble can be written in input orientation a (ee [16]): in θ (3) θ,λ θx Xλ Yλ y λ Input orientation ean that within the linear optiization the input are iniized. Output orientation i directly the oppoite, the input are hold fix and the output are axiized. 2) The Banker, Charne & Cooper odel Reference [17] extended the CCR odel (1) o that the contant return-to-cale auption wa replaced with a variable return-to-cale auption. The extended BCC odel i naed by their acrony. It can be written adual linear prograing odel with input orientation: in θ θ,λ θx Xλ Yλ y λ eλ = 1 The additional contraint eλ = 1 change the convexity auption o that variable return-to-cale can be aued. Suppleentary we ue the uper-efficiency odel to dicriinate between DEA efficient fir, for ore detail ee [18]. (4) C. Operational Capability Analytic The purpoe of thi paper i to develop a ethodology to evaluate a fir financial perforance. In order to achieve the target we ugget calculating the operational capability of a fir. We uppoe that a fir ha capabilitie to tranfor it aet (input) into output like revenue, earning, cah flow etc. Norally the production function of each fir i unknown o we ue the non-paraetric DEA approach to calculate an efficient frontier function. The baic idea of the operational capability analytic (OCA) i hown in Fig. 2.A fir ue input like aet, equity, eployee etc. and tranfor the into output like ale, earning etc. In thi odel, a fir can iniize it input with fixed output or it can axiize it output with fixed input. Therefore, our uggeted Operational Capabilitie Analytic identifie an efficient fir when it i uing a iniu of input at fixed output or produce a axiu of output with fixed input. Thee are the two general poibilitie of aeing the fir operational capabilitie. Regularly it i difficult to copare fir fro different indutrie, ize and countrie. To overcoe thee difficultie we ue DEA to analyze the fir operational capability. DEA can cobine input and output without knowing a-priori the relationhip between the variable and the correponding weight. In thi paper, we choe the input orientated CCR and BCC odel for evaluating our Input-Output-Model. The odel were introduced in ubection 2. B. We choe the input orientation becaue of the dataet characteritic. Chooing the correct input and output feature i critical for every DEA. Reference [18] ugget only including variable, which ake practical ene for the etting under invetigation. Reference [2] ephaizethat raw data and ratio hould not be ixed when it i poible. Reference [21] recoend toonly uing variable, which can be clearly identified a input and output and not a a ixture. Input (e.g. aet) Manageent operation Operational Capabilitie External effect Output (e.g. ale) Fig. 2: Syte overview of a fir' input and output. Own preentation baed on Bogetoft and Otto (211, p.14). In addition, a variable hould be contributing to the objective of the analyi.in our OCA input-output-odel we chooe the DuPont forula given in [22] a a tarting point for variable election: ROE = aet equity ale aet after tax interet + net incoe ale net incoe after tax interet + net incoe Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 11

4 quantity of fir quantity of fir In the next tep we breakdown aet to current aet and fixed aet. Cah & arketable ecuritie (CMS), account receivable (AR) and inventorie (INV) are part of the current aet. We take property, plant & equipent (PPE) a an intruental variable for the fixed aet. Continuing thi approach we breakdown net incoe to ale inu cot of good old (COGS), Selling, general, and adinitrative expene (SGA), Depreciation (DA) etc.. In addition to the Du Pont Forula, we add variable fro the cah flow tateent (ee [23]). The free cah flow i defined a: Free Cah Flow = Operating Cah Flow Capex We pick Free Cah Flow (FCF) a an output variable and Capex a an input variable. A working capital i part of the operating cah flow we add account payable (AP) a an input variable. Other variable of the cah flow tateent are already included through the other reource. All our variable are lited in Tab. 1. In order to control for ize effect we include the nuber of eployee a an input variable. In the following tep we will how the collected data and will calculate the fir operational capabilitie with our OCA ethodology. Succeeding we will analyze the OCA reult and copare the to other indicator. The ai i to how that the OCA core i a validated ethod for aeing fir perforance. TABLE 1: Lit of input and output variable. Variable Input INV, CMS, AR, AP, SGA, COGS, Capex, DA, PPE, Equity, Eployee Output Sale, EBIT, FCF IV. RESULTS In the following ection, we will hare our reult. A. Reult fro different aple year The reult of the BCC DEA odel are hown in Fig. 3 and were calculated with the linear prograing ethod given in forula (3). A you can ee, there are any efficient fir each year. A fir i aid to be efficient at core point. For thi reaon, we calculated the uper-efficiency (core fro to ) for ranking bcc-efficient fir.the reult are relatively table over the five year period. The OCA Score calculated by the CCR odel are hown in Fig. 4were calculated with the linear prograing ethod given in forula (4). In coparion to the BCC reult, there are le efficient fir now. Thi i due to the ore dicriinating power of the CCR odel (ee [2]). The BCC efficient frontier i ore cloely to the data therefore you get ore fir, which are efficient. BCC Score 211 BCC Score 212 BCC Score 213 BCC Score 214 BCC Score Fig. 3: OCA Score with the BCC odel. OCA Score with BCC odel CCR Score 211 CCR Score 212 CCR Score 213 CCR Score 214 CCR Score 215 D. Data collection and aple creation The data wa retrieved fro Blooberg and we collected the needed data for every fir in Wetern Europe with a arket capitalization higher than io. Euro at the end of the year 215. The financial and inurance indutry were excluded fro our dataet becaue of the different capital tructure. For the 2.6 identified fir we had coplete dataet of 447 fir in 215, 428 in 214, 415 in 213, 412 in 212 and 399 in 211 for all variable. We alo had to control for nonnegative Input. Uing negative input i not poible with our choen DEA odel with input orientation OCA Score with CCR odel Fig. 4: OCA Score with CCR odel. TABLE 2: Thi table how the pearan correlation between the CCR, BCC Score and the elected financial ratio. *, **, and *** denote ignificance at the 1%, 5%, and 1% level, repectively. A two-tailed tet wa ued. CCR Score BCC Score ROE ROA PM CR QR CTR CF/Capex Z CCC CCR 211 1,,83***,26***,23***,11* -,16*** -,17*** -,18***,26***,2*** -,28*** BCC 211,83*** 1,,24***,21***,11* -,14*** -,21*** -,2***,2***,16*** -,33*** CCR 212 1,,84***,34***,33***,22*** -,17*** -,2*** -,22***,27***,24*** -,23*** BCC 212,84*** 1,***,29***,28***,18*** -,18*** -,24*** -,23***,26***,21*** -,24*** CCR 213 1,,89***,28***,26***,17*** -,21*** -,2*** -,24***,32***,25*** -,28*** BCC 213,89*** 1,***,22***,2***,13** -,19*** -,24*** -,24***,26***,17*** -,28*** CCR 214 1,,85***,26***,23***,11* -,19*** -,22*** -,25***,28***,29*** -,29*** BCC 214,85*** 1,***,23***,19***,11* -,14*** -,25*** -,22***,22***,19*** -,29*** CCR 215 1,,85***,27***,23***,12** -,18*** -,21*** -,24***,25***,27*** -,3*** BCC 215,85*** 1,***,21***,16***,9 -,15*** -,27*** -,25***,15***,14*** -,29*** CCR Ø 1,,86,29,26,15 -,18 -,2 -,23,28,25 -,27*** CCR SD,2,4,5,5,2,2,3,3,3,3*** BCC Ø,85 1,,24,21,12 -,16 -,24 -,23,22,18 -,28*** CCR SD,2,,3,4,4,2,2,2,5,3,3*** Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 12

5 On the other hand, the BCC odel fit better to the variable return-to-cale property of the dataet. The relatively table core ditribution over the five year tell u that our odel i not enitive to different data et. A well, any of the betbencharked fir are the bet one in the following period.in concluion, we ugget the hypothee that a high efficiency core i poitive correlated with other financial perforance eaure and that an efficient fir ha a high probability to be efficient in the following period. B. Verfication by correlation analyi In thi ubection, we want to verify our reult by a correlation analyi. Thi hould anwer our hypothei that a fir with a high OCA Score i alo poitive correlated to other financial perforance indicator. Therefore, we chooe everal eaure like Return on Equity (ROE), Return on Aet (ROA), Profit Margin (PM), Cah Ratio (CR), Quick Ratio (QR), Current Ratio (CTR), CF/Capex, Altan Z-Score (Z), and Cah Converion Cycle (CCC).A it can be een in Fig. 3 and Fig. 4, the OCA Score are not norally ditributed o hence we chooe the Spearan rank order correlation. The correlation reult are lited in Tab. 2. In addition, the Spearan correlation i not o enitive to outlier in coparion to the pearon correlation. Our OCA Score how a very trong correlation between each other. The core i lightly ore poitive correlated with the ROE than the ROA. That could be due to the fact, that equity i included a an input variable and the total aet are divided into different Coponent in our odel. We interpret thee correlation a poitive for our core. If a fir ha a high core it hould alo ha a higher ROE and ROA than other fir, thi i conitent with our reult. Further, we have a weaker poitive correlation between the core and the PM. Thi poitive correlation wa alo expectable. When we look at the liquidity ratio, we ee negative correlation. The reaon could be that it i not preferable for a fir to hold to uch cah and other arketable ecuritie. The CTR i highly connected to the Working Capital, if it i lower the Working Capital i lower and thi can preferable in oe cae (ee [24]for ore detail). Our lat correlation i between our core and the CCC. Thi i a negative one and thi wa expected. Reference [25] docuented a negative relationhip between CCC and corporate profitability. With thi ubection, we verified and validated our core. The BCC and CCR odel how ore or le iilar reult in our correlation analyi. We conclude that the OCA Score provide a eaureent of a fir financial perforance. Following we want to tet our econd hypothei if a fir with a high OCA core will tend to have a high OCA Score in the following period. In Tab. 3, the correlation of the OCA Score calculated by the CCR odel are lited. Evidently, the core are poitive correlated to the core of the following year. For the CCR odel we can accept our hypothei. In Tab 4. the correlation between the OCA Score baed on the BCC odel are lited. We can note that the correlation are lightly higher for the BCC odel than for the CCR odel. In concluion it i recognizable that our OCA Score of year t i poitive correlated with year t+1. That ean a copany with a high OCA Score will have in general a higher core in the following year. C. Portfolio election and tock arket perforance Baed on the reult of ubection A and B we want to elect equity portfolio to how of that operational capabilitie are alo rewarded by the tock arket. At firt, the relationhip between the OCA Score and arket data of the fir will be analyzed. A a tarting point we look for claical value and growth indicator. Table 3: Correlation between the CCR OCA Score of the teted year. All correlation are ignificant at 1% level ,,88,75,74,69 212,88 1,,83,8,74 213,75,83 1,,91,85 214,74,8,91 1,,89 215,69,74,85,89 1, Table 4: Correlation between the BCC OCA Score of the teted year. All correlation are ignificant at 1% level ,,89,81,79,73 212,89 1,,88,86,79 213,81,88 1,,9,83 214,79,86,9 1,,9 215,73,79,83,9 1, Table 5: Thi table how the pearan correlation between the CCR, BCC Score and the elected value key figure.*, **, and *** denote ignificance at the 1%, 5%, and 1% level, repectively. A two-tailed tet wa ued. 3-YRG 5-YRG 5-Y EBIDTA G ROIC ROIC / WACC CCR 211,21***,13**,13**,29***,29*** BCC 211,2***,12*,11*,27***,29*** CCR 212,3,16***,16***,38***,4*** BCC 212 -,2,14**,13*,32***,37*** CCR 213,17***,19***,17***,33***,35*** BCC 213,1*,13**,1*,27***,3*** CCR 214,21***,15***,4,29***,3*** BCC 214,14***,8,1,25***,25*** CCR 215,14***,17***,18***,29***,33*** BCC 215,7,9*,12*,21***,24*** CCR Ø,15,16,14,32,34 CCR SD,7,2,6,4,4 BCC Ø,1,11,9,26,29 BCC SD,8,3,5,4,5 For [26] revenue growth (RG) and return on inveted capital (ROIC) are the ain value driver. Thee two key figure deterine how revenue are converted to cah flow. To utain it value a fir ha to exceed it weighted average cot of capital (WACC) with it future cah flow. Following thi idea we take the ROIC/WACC ratio a another indicator for our correlation analyi. To conider M&A effect we look at the three and five year average revenue growth rate (3- YRG/5-YRG). In addition, we add the five year EBIDTA growth (5-YEBIDTAG) a an indicator. The relationhip between the OCA Score and indicator are hown in Tab. 5. In alot every year, the OCA Score are poitive and ignificant correlated to the 3-YRG and the 5-YRG. Therefore, fir which where bencharked higher indicate in general a higher revenue growth. Thi i the firt evidence, that the OCA Score can be ued for equity portfolio election. Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 13

6 Total Return in % Total Return in % Siilar to the revenue indicator the 5-YEBTIDAG i alot poitive and ignificant correlated to our OCA Scorein every year. Furtherore, the ROIC and ROIC/WACC ratio how poitive and ignificant correlation. Suarizing we can ay, that the OCA Score hould be a validated benchark for portfolio election TOP 3 BCC POOR 3 BCC TOP 3 CCR POOR 3 CCR MSCI TR The target for the future would be to find better feature variable and frontier function to dicriinate better the well and poor perforing fir TOP 3 BCC TOP 3 CCR POOR 3 BCC POOR 3 CCR Figure 6: Portfolio coparion of the different TOP and POOR portfolio baed on 211 data. The portfolio were generated with Blooberg. Figure 5: Portfolio coparion of the different rebalanced TOP and POOR portfolio. The portfolio were generated with Blooberg. The correlation for the BCC core are lightly lower than for the CCR core. Thi could be an indication for a better applicability of the CCR core for portfolio election. For our portfolio, we take the 3 bet (which we call TOP 3) and wort fir (which we call POOR 3) of our benchark every year and copare the portfolio to the MSCI Europe Total Return Index. The portfolio will be adjuted every year on June 1 t. The logic in that i, that the financial data i releaed each year between March and May for the preceding year. After the releae of the financial data, we calculated our OCA Score and ued the for portfolio election. The total return of each rebalanced portfolio i hown in Fig. 5. The TOP 3 CCR portfolio i obviouly the bet perforing portfolio with a cuulated total return of 152.8% during the period under review.the econd bet portfolio i the TOP 3 BCC portfolio with a cuulated total return of 9.3%. Proinent i that the POOR 3 CCR portfolio (total return of 9.%) i perforing only lightly wore that the TOP 3 BCC portfolio. The POOR 3 BCC portfolio i perforing wort over the given tie period with a total return of 52.6%. To conolidate thee reult we can ee, that our OCA Score baed on the CCR odel perfor uch better when conidering the bet fir. In contrat, the portfolio baed on the POOR 3 BCC perfor uch wore than any other portfolio. We ee thi firt portfolio tet a a tarting point for further reearch. Obviouly, you can get very well perforing portfolio under the given circutance. When we look at fir over a long tie horizon, in thi cae 4 year, the difference between high and low ranked fir are becoing larger (ee Fig. 6). The total return for invetor of the different portfolio are lited in Tab. 6. That could be an evidence for our hypothei that the tock arket acknowledge financial excellence in the long run and the difference between the fir are even plainer after one year. To validate thi hypothei different period and year ut be conidered in future work. Table 6: Total Return of the yearly adjuted portfolio and the long run portfolio baed on 211 data. The tiefrae i 1 t June 212 to 1 t Juli 216. yearly adjuted Portfolio total return long run portfolio (211) total return TOP 3 BCC 9,33% 96,49% POOR 3 BCC 52,58% 45,59% TOP 3 CCR 152,85% 174,13% POOR 3 CCR 88,96% 7,97% MSCI 58,8% 58,8% The wort fir of our OCA Score hould perfor uch wore than the coparion index. On the poitive ide, we can how that a high financial perforance pay off and i acknowledge by the arket. CONCLUSIONS Uing DEA, we howed that it i poible to ae a fir financial perforance with our OCA odel. Our OCA core i conitently correlated to other financial perforance indicator. Overall, we found a valid core, which can be ued a an equity portfolio election tool. The bet ranked fir howed uch higher return than the wort ranked fir. In the long run thi difference i becoing larger and larger. Operational capabilitie pay off and are acknowledged by the arket over a longer tie horizon. The CCR odel generated the better reult although the underlying data et ha variable return-to-cale propertie. The reaon for thi could be in the ore dicriinant power of the CCR frontier. A we can ee in Fig. 3 &4, there are any efficient fir and thee fir can only be dicriinated by the uper-efficiency calculation. Norally le feature lead to le efficient fir. Due to the le poibilitie of reaching an efficiency value of 1, there are le efficient fir with le variable. In future reearch, it would be great to invetigate different variable and odel. On the one hand decreaing nuber of variable could enhance the reult. On the other hand, increaing nuber of fir could lead to better reult. Another future reearch doain would be to analyze a fir lack baed on the OCA reult. Thi could lead to anagerial iplication and could iprove overall fir perforance. Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 14

7 Reference [1] Bodie, Zvi; Kane, Alex; Marcu, Alan J. (211): Invetent and portfolio anageent. 9. ed., global ed. New York, NY: McGraw-Hill/Irwin. [2] Whittington, G. (198), SOME BASIC PROPERTIES OF ACCOUNTING RATIOS. Journal of Buine Finance & Accounting, 7: [3] Sith, P. (199): Data envelopent analyi applied to financial tateent. In: Oega 18 (2), S [4] Thore, Sten; Kozetky, George; Phillip, Fred (1994): DEA of financial tateent data. The U.S. coputer indutry. In: J Prod Anal 5 (3), S [5] Zhu, Joe (2): Multi-factor perforance eaure odel with an application to Fortune 5 copanie. In: European Journal of Operational Reearch 123 (1), S [6] Feroz, E. H.; Ki, S.; Raab, R. L. (23): Financial tateent analyi. A data envelopent analyi approach. In: J Oper Re Soc 54 (1), S [7] Friedan, Lea; Sinuany-Stern, Zilla (1998): Cobining ranking cale and electing variable in the DEA context. The cae of indutrial branche. In: Coputer & Operation Reearch 25 (9), S [8] Lin, Weng-Cheng; Liu, Ching-Feng; Chu, Ching-Wu (25): Perforance Efficiency Evaluation of the Taiwan' Shipping Indutry: an Appliaction of Data Envelopent Anayli. In: Proceeding of the Eatern Aia Society for Tranportation Studie (5), S [9] Chen, Ter Yieth; Chen, Ling hua (27): DEA perforance evaluation baed on BSC indicator incorporated. In: Int J Productivity & Perf Mgt 56 (4), S [1] Chen, Yao; Iqbal Ali, Agha (24): DEA Malquit productivity eaure. New inight with an application to coputer indutry. In: European Journal of Operational Reearch 159 (1), S [11] Pätäri, Eero; Leivo, Tio; Honkapuro, Sauli (212): Enhanceent of equity portfolio perforance uing data envelopent analyi. In: European Journal of Operational Reearch 22 (3), S [12] Abarbanell, Jeffery S.; Buhee, Brian J. (1998): Abnoral Return to a Fundaental Analyi Strategy. In: The Accounting Review 73 (1), S [13] Faa, Eugene F. (1995): Rando Walk in Stock Market Price. In: Financial Analyt Journal 51 (1), S [14] Charne, A.; Cooper, W. W.; Rhode, E. (1978): Meauring the efficiency of deciion aking unit. In: European Journal of Operational Reearch 2 (6), S [15] Charne, A. and Cooper, W. W. (1962): Prograing with linear fractional functional. Naval Reearch Logitic, 9: [16] Cooper, Willia W.; Ruiz, Joé L.; Sirvent, Inaculada (27): Chooing weight fro alternative optial olution of dual ultiplier odel in DEA. In: European Journal of Operational Reearch 18 (1), S [17] Banker, R. D.; Charne, A.; Cooper, W. W. (1984): Soe Model for Etiating Technical and Scale Inefficiencie in Data Envelopent Analyi. In: Manageent Science 3 (9), S [18] Anderen, Per; Peteren, Niel Chritian (1993): A Procedure for Ranking Efficient Unit in Data Envelopent Analyi. In: Manageent Science 39 (1), S [19] Cook, Wade D.; Tone, Kaoru; Zhu, Joe (214): Data envelopent analyi. Prior to chooing a odel. In: Oega 44, S [2] Dyon, R. G.; Allen, R.; Caanho, A. S.; Podinovki, V. V.; Sarrico, C. S.; Shale, E. A. (21): Pitfall and protocol in DEA. In: European Journal of Operational Reearch 132 (2), S [21] Golany, B.; Roll, Y. (1989): An application procedure for DEA. In: Oega 17 (3), S [22] Brealey, Richard A.; Myer, Stewart C.; Allen, Franklin (211): Principle of corporate finance. 1. ed., global ed. New York, NY: McGraw-Hill Irwin. [23] Zülch, Henning; Hendler, Matthia (215): International financial reporting tandard 215. Weinhei: WILEY. [24] Akta, Nihat; Croci, Ettore; Peteza, Diitri (215): I working capital anageent value-enhancing? Evidence fro fir perforance and invetent. In: Journal of Corporate Finance 3, S [25] Enqvit, Juliu; Graha, Michael; Nikkinen, Jui (214): The ipact of working capital anageent on fir profitability in different buine cycle. Evidence fro Finland. In: Reearch in International Buine and Finance 32, S [26] Koller, Ti; Copeland, Thoa E. (211): Valuation workbook. Step-by-tep exercie and tet to help you ater Valuation. 5th ed. Hoboken, N.J: John Wiley & Son Inc (Wiley Finance, v.496). Proceeding of International Conference on Art, Science & Technology, Dubai, 2-22 Deceber 217 Page 15