2468 Advances in Environmental Biology, 7(9): 2468-2478, 2013 ISSN 1995-0756
This is a refereed journal and all articles are professionally screened and reviewed ORIGINAL ARTICLE
Identification, comparison and ranking financial ratiosin Homogeneo usandnon- homogeneous Industriesusing degrees of the relative, absolute, and Synthetic of graymodel (GM)
1Saeed Nadi
1Department of Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran IntersectionBafYazd, Zip code: 8918685149, Yazd, Iran
Saeed nadi, Identification, comparison and ranking financial ratiosin Homogeneousandnon- homogeneous Industriesusingdegrees of the relative, absolute, and Synthetic of graymodel(GM)
ABSTRACT
Given the significance of the financial statements in any organization, there are various computerized systems developed for analyzing the financial statements. Using expert and Fuzzy systems in analyzing the financial statements and trying to develop a system by which the different ratios of the organization are compared, the average industry index might be used to improve the analyses effectively. One of the instruments for determining the financial position of an organization is to analyze the financial statements. This affair aims to examine the financial ratios, contribute the directors and investors and creditors in order to evaluate the financial performance and position along with the bankruptcy or profitability prediction. This study applies the gray model to identify, compare and rank the financial statements in terms of the liquidity, leverage and profitability degrees of gray model. For this purpose, five automobile companies from the homogenous industry and five companies from different non-homogeneous industries during the years from 2006 to 2010 have been selected. Ranking financial ratios in these industries in a parallel form showed that the quick ratio with the highest average ranking is the most effective ratio; while ROE with the least average ranking is the least effective financial ratio in identifying the performance of these companies in the gray model.
Key words: Gray Model, Financial Ratios, Performance, Ranking, Degrees.
Introduction cash flows and growth in the financial issues which leads to effective financial evaluation. It can be used Organization's financial performance analysis by firms to distinguish healthy companies from was conducted to evaluate pastperformancestatus of unhealthy ones [21]. Because of financial ratios companies and help to determine future strategies to application in different contexts of decision making identify potential problems that may be exchanged in and performance assessment, it is considered as one to severe problems in future. In fact, this issue is of the key areas of accounting research. The used to find company strengths in order to gain a importance of financial ratios has led to many competitive edge in various industries [23]. researchers to devote much of their research to this Moreover, corporate profitability is considered all the topic. Despite all these issues, every research is time by stakeholders in companies. Company's conducted to achieve specific goals. This study profitability in future and predictability can be very aimed to identify, compare, and rank financial ratios useful for stakeholders make decisions on buying and group of liquidity, leverage and profitability within selling stocks, giving credit to companies and also three degrees of relative, absolute, and synthetic gray warn the directors to improve their performance, etc. models. Generally, in the area of financial management, scientific techniques, methods and models are used 2. Research background: extensively to support wise decisions of companies and other organizations about financial issues. In this Koh and Yang(2008) study entitled" The use of regard, the financial ratios are the most important gray relational analysis for multi-criteria decision tools to predict the financial situation, stop financial problems" addressed this model. In this study, two crisis of companies and even help financial growth. case studies are solved using gray analysis and Evidence suggested that there is a linear combination obtained result compared with results obtained from of ratios in financing and capital as well as revenue, solvingthe sameproblem with data envelopment
Corresponding Author Saeed Nadi, Department of Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran Intersection BafYazd, Zip code: 8918685149, Yazd, Iran 2469 Adv. Environ. Biol., 7(9): 2468-2478, 2013 analysis, TOPSISmethod and SAW method. Grey thestructure ofsystem and uncertainty in the relational analysis method for ranking alternatives decision-making system inputs. Moreover, it is a and the results of TOPSIS is more close to the simple simple functional model that at the same time weighted average [16]. includes the above mentioned methods. In this Koh and Liang conducted study , in seven method, no detailed information is required and international airports in North-East Asia, provided uncertain information can be used[4]. an effective approach for evaluating service quality in the airport. With regard to this fact that evaluation 2.1. Theoretical framework of gray system theory: of service quality as a multi-criteria decision-making approach is too difficult, a combination of gray There are many different various systems in the correlation analysis method (Weiker) is used. The real world, each of which have their specific proposed model considers decision makers' attitudes components and subsystems for the understanding of and priorities of the customer to determine the which their components, structures and relationship weights of each criterion. Results of this study between should be identified. If the system obvious indicate that this approach is an effective tool for or unknown information to be represented by white multi-criteria decision-making problems in a fuzzy and black colors respectively, then information on environment [15]. the nature of most information systems are not white Chang study (2006) on the rating of commercial (completely known) or black (completely unknown) , banks in Taiwan was conducted using gray systems rather a mixture of the two is shown in gray color. approach. In this study, financial ratios were used as Such systems are called gray systems. The main indicators to evaluate and rank the studies banks. The character of this system is incomplete information next step in this research was to study characteristics about the system [7]. Gray system theory in recent influencing banks performance. The results indicate years has been introduced as a highly effective that the gray systems approach is better than technique for solving problems by discrete and conventional statistical methods such as regression incomplete data[24]. "Deng" in late 1960 conducted analysis, multivariate statistical methods to evaluate various studies on economic and fuzzy systems the performance of the banks, because there's no control and prediction and was faced with the system restriction on evaluation of a large amount of data high uncertainty. The system characteristics were not [5]. easily described by fuzzy mathematics, probability Dabaghi and Malek study entitled " A method and statistics. In general, fuzzy mathematics deals for evaluating and ranking the company's prospects" with problems in which the uncertainty can be used a mixed research to provide an appropriate expressed by experts using discrete / continuous prospect of important task for managers and functions. We need understanding ofthe principles of strategic planning in the organizations. relevantdistribution This study combined qualitative research methods of functionsorlargesamplerequiredto achieve the "legal group interviews" and quantitative method of required reliability. In such a case what can be done "gray possibility degree" (new techniques of decision if there is low number of experts in problem and making and mathematical evaluation of vague and experience level without possibility of obtaining uncertain data) to provide an approach to assess and membership or having a few samples? Professor rank organizations prospect. Results show that the Deng published an article entitled "problems gray qualitative research methods of a gray possibility systems control" in International Journal of Systems grade is designed along methods such as fuzzy multi- & Control Letters in 1982 for optimal solution of criteria decision making method, multi-objective gray systems in such a situation. He introduced the decision making method, multi-attribute utility gray system theory and its applications that are today theory anddata envelopment analysis method to be classified in five areas of evaluation, modeling, used in various quantitative evaluations and decision- prediction, control and decision [22]. making issues[3]. This theory consists of five main sections: gray Mohammadi and Mowla'ii conducted a research prediction, gray relation, gray decision, gray entitled" Application of gray multi-criteria decision programming and gray control. Most prediction in evaluating the performance of companies" using methods require a large number of historical data and Shannon entropy method to rank Mother statistical methods used to study the properties of the SpecializedandInvestmentCompaniesListed system. In addition, due to the confusion caused by inTehranStock Exchange based on financial complex interactions between the system and outside indicators and ratios.Theyusedthegray concept to the system and its environment, systems evaluation overcometheadverseconditionscaused bylack of will be crucial. Gray prediction model as main core information. The resultsshowed thatthe of gray system theory consists advantageous of grayrelationalanalysisformulti-criteria decision creating a model with low uncertain data which is considers inputs as number intervals in addition to considered as a perfect tool for prediction systems examiningrelationshipbetweenvariousparameters with complex, uncertain and irregular structure [20]. andoptions. In fact, it represents both uncertaintyin
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Gray prediction models, compared with the box Jenkins models and Artificial Intelligence techniques The present study is a practical research because requiring a lot of time and effort for identifying and it's expected results can be used to enhance the modeling to determine various processes and potential investors' insight. The statistical population parameters, are more functional and simpler. The of this study included a case of listed companies on gray prediction model uses a differential equation to the stock exchange in the automotive and parts describe uncertain system with a few data. This industry, machinery andequipment and food model is suitable for smoothing static data rather processingin dustry exceptsugar. Given the scope of than data with high random changes [14]. this study from the beginning of 2006 until the end of 2010, the selected companies must have been worked 2.2. Purpose of gray systems theory: at least for five years. Moreover, because of relatively complex mathematical formulas and tables The gray system theory purpose and its and in order to save time, 9 firms were selected as application is to build a bridge between social judgment statistical population among which sixU U science and natural science in which gray means lack companies were bankrupt and threeU U companies were of information, incomplete information and non-bankrupt. It should bementioned that Saipa uncertainty[11]. Deng in ChinaUniversity ofScience Diesel Company was classified in both homogeneous and Technology published an article entitled and heterogeneous groups at the same time. "problems gray systems control" in 1982 after which gray system theory was introduced [9]. 4. Research variables: The basic idea of gray theory can be briefly stated that it focuses on partial or limited information Each study has a number of independent and available about a system that is trying to visualize the dependent variables. Independent variables in this overall picture. study include: liquidity ratios, debt or leverage ratios, and profit ratios of listed companies of Iran Stock 2.3. Grey relational analysis: Exchange. Dependent variable of the present study includes the bankruptcy syndrome condition of the There are several factors affecting the general companies during 2006-2010. Dependent variable system the interaction of which influence the system includes ratings of these ratios in homogenous and status and trend of development. Most systems heterogeneous industries. analyses are an effort to identify the most important S1= (Performance indicator) = factors in any system, but there are always some lackofbankruptcies ratio (Total assets divided by unknown or less known factors. One of methods used total liabilities) to deal with such systems is the gray relation analysis S2= (Current Assets) - (inventory) divided by considered as an important component of gray current liabilities system. The basic idea of gray analysis as a method S3= Total current assets divided by current of quantitative analysis is based on the fact that close liabilities relation and correlation between the two factors is S4=Earnings before interest and tax deduction growing in a dynamic process that should be divided by the value of the company measured based on the similarity of curves. Higher S41= company value = cash - total debt + market similarity shows a higher degree of correlation value between the series and vice versa [16]. S42 = Market value= number of issued shares* Gray relation degree is used to measure the current value per share degree of the similarity. According to definition, by S5=Earnings before interest and tax deduction considering the assumption of m +1, following divided by interest expense behavior series of a system will be available:[10]. S6 = Net income divided by net sales S7 = Net income divided by shareholders' equity = (1), (2), β¦ , ( ) , = 1,2, β¦ , S8 = Net income divided by total assets[2].
ππ ππ ππ ππ 3.ππ ResearchοΏ½π₯π₯ scopeπ₯π₯ and π₯π₯methodologyππ οΏ½ ππ : ππ 5. Description of studied companies
Table 1: Companies' classification in the homogeneous industries Non-bankrupt without accumulated Industry Company Name Bankrupt losses Saipa * Saipa Diesel * Automotive and Parts Iran Khodro * Iran Khodro Diesel * Pars Khodro * Total 5
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Table 2: Companies' classification in the heterogeneous industries Non-bankrupt without Industry Company Name Bankrupt accumulated losses Automotive and Parts Saipa Diesel * Equipment Absal * Food products Mahram Production Co. * Machinery Tractorsazi Iran Co. * Equipment Iran Casting Co. * Total 5
1 6. Steps for calculation of relative, absolute and 1 0 β² 0 synthetic gray degrees: = ππβ β² ( )+ β² ( ) 2 =2 ππ 1 ππ At the start of the ranking process, the financial οΏ½π π οΏ½ οΏ½οΏ½ π₯π₯ ππ 1 π₯π₯ 0ππ ππ οΏ½ β² ππ 0 = ππβ β² ( )+ β² ( ) ratio was calculated by gray model in 5 different time 2 periods for each of the companies. Next, items =2 ππ ππ οΏ½π π οΏ½ οΏ½οΏ½ π₯π₯ ππ π₯π₯ ππ ππ οΏ½ related to the degree of gray incidence and its ππ calculation to determine Q decision matrix for each The initial values of β² calculated as follows. homogeneous and heterogeneous industries Equation (6) π₯π₯ππ presented in the following five steps. β² 1 2 3 β² β² β² First step: absolute degree of gray incidence of = , , = ( 1 , 2 , 3 ) 1 1 1 π₯π₯ ππ π₯π₯ ππ π₯π₯ ππ was measured to calculate the correlation between ππ ππ ππ ππ π₯π₯ οΏ½ ππ ππ ππ οΏ½ π₯π₯ π₯π₯ π₯π₯ the sequences of , without the involvement of Andπ₯π₯ representsπ₯π₯ π₯π₯ the zero starting point as follows: βππππ other factors. Equation (7) ππ ππ 0 β² β² β² β² β² β² β² β² β² ππ ππ β² = 1 1 , 2 1 , 3 1 = ( 11, 21, 31) Equation (1) ππ ππ ππ ππ ππ ππ ππ 1 + | | + π₯π₯ CharacteristicsοΏ½π₯π₯ βπ₯π₯ π₯π₯ βofπ₯π₯ theπ₯π₯ relativeβπ₯π₯ οΏ½ degreeπ₯π₯ π₯π₯ of π₯π₯gray = incidence of :[13]. 1 + | | + ππ + ππ π π οΏ½π π οΏ½ 1. 0 < 1 βππππ ππππ π π ππ οΏ½π π ππ οΏ½ οΏ½π π ππ β π π ππ οΏ½ Third ππstep: The general relationship of Equation (2) ππππ 1 proximity betweenππ β€ the sequences of , calculated 0 1 0 | | = ππβ X ( )+ ( ) using the synthetic degree of gray incidence of . 2 ππππ ππππ =2 = ΞΈ + (1 ΞΈ) π π ππ οΏ½οΏ½1 ππ ππ π₯π₯ππ ππ οΏ½ ππππππ ππ 0 1 0 = ππβ X ( )+ ( ) ππππ ππππ ππππ 2 ππ Absoluteβ degreeβ ππ characteristic of gray incidence =2 οΏ½π π ππ οΏ½ οΏ½οΏ½ ππ ππ π₯π₯ππ ππ οΏ½ of [18]. ππ 0 < 1 Equation (3) 1.ππππ 1 ππAfter calculating the synthetic degree of gray ππππ 0 0 1 0 = ππβ [X ( ) X ( )] + [ ( ) incidence for ππeachβ€ company, synthetic gray matrix is 2 =2 formed in the fourth step. οΏ½π π ππ β π π ππ οΏ½ οΏ½οΏ½ ππ ππ β ππ ππ π₯π₯ππ ππ Finally, in the fifth step, Q Decision Matrix was ππ X0( )] formed to analyze the level of intensity (weak or strong) and identify the function of each of the ππ β ππ οΏ½ companies. [17]. Some weights were assigned to each gray degree Characteristics of the absolute degree of gray incidence in synthetic gray matrix in the following incidence of : five steps. 1. 0 < 1 Step 1: Calculation of the weight ratio. βππππ Step 2: Formation of computationaltable of Q Second step : the relative degree of gray βππππ β€ matrix (weighted) incidence of to calculate the relative changes of Step 3: Assigning weights to each of the , relative toππππ the starting pointwas calculated synthetic gray matrix degrees based on the positive ππ ππ ππ and negative factors ππ ππEquation (4) Step 4: Formation of Q matrix using specific 1 + β² + β² weights to synthetic gray degrees for each company = 1 + β² + β² + β² β² This matrix is formed after calculation of οΏ½π π πποΏ½ οΏ½π π ππ οΏ½ ππππ synthetic degree of gray incidence ( for each of ππ ππ ππ ππ ππ EquationοΏ½π π οΏ½ (5)οΏ½ π π οΏ½ οΏ½π π β π π οΏ½ the companies). ππππππ
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11 12 β¦ 1 = 1, β¦ , 2 22 β¦ 2 = ππ N= Total number of obvious symptoms s m ππ β¦ππβ¦ β¦ β¦ππ ππ ππ consideredππ πΉπΉ in the level of company. ππ 1ππ 2 β¦ ππ οΏ½ππππππ οΏ½ β οΏ½ οΏ½ F = the total number of surveyed companies 1 2 β¦ represents the degree of synthetic π π π π π π π π Step 5: Diagram and analysis of companies gray incidenceππ ofππ S comππ pany's operators[6]. π π π π π π π π performance. Afterππ ππ imposingππ all weights proportional to In the fifth stage, Q matrix numbers basically numbers forming synthetic gray matrix, strong generated in the fourth step were plotted for levels matrix for each for each symptom obtained homogeneous and heterogeneous companies, as follows[6]. respectively. ππ 11 β¦ 1 = β¦ β¦ Grey conceptual model: ππ ππ 1 β¦ ππ ππππ=ππ 1, β¦οΏ½ , β± οΏ½ πΉπΉ πΉπΉπΉπΉ ππ ππ ππ ππ
S
S
7. A summary of the results of identification, First step: Formation of gray degree ranking comparison, and ranking of financial ratios: diagrams for each of the companies
In order to identify the most desirable to least An example of synthetic gray degree rating in Saipa effective financial ratios of each gray degrees Diesel Company : (absolute, relative, synthetic), performance of these companies was calculated as follows: Using the described formula, synthetic degree of gray incidence in the third step calculated as follows:
Table 1: Calculation of Ο1j using equation 3 Ο1j Ο12 Ο13 Ο14 Ο15 Ο16 Ο17 Ο18 Ο19 0.769206 0.7363417 0.7844646 0.7259432 0.6445979 0.605996 0.6666482 0.6989541
Table 1 indicates thatinSaipa Diesel Co., recognition, in terms of synthetic degree of gray synthetic degree of 14 with gray number of incidence) (Figure (4-3)). As a result, following 0.7844646 is optimal factor (in the sense that S4 is equation can be obtained to rank synthetic gray too important in performanceππ recognition in terms of factors of the company: synthetic degree of gray incidence) while synthetic degree of 17 with gray number of 0.605996 is 14 > 12 > 13 > 15 > 19 > 18 > 16 > 17 considered as the least effective factor (in the sense that S7 hasππ low level of importance in performance ππ ππ ππ ππ ππ ππ ππ ππ
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Second step: assigning the importance level of Ranking of financial ratings in terms of gray degrees gray degree ranks. in the homogeneous companies: Third step: assigning important level ratio ( A ) for each ranking column. Table 2 represents significance level of Fourth step: assigning equal importance for homogeneous companies factors (financial ratios) for each degree identification of the most favorable financial ratios of Fifth step: formation of the frequency table for companies in terms of synthetic gray degrees in the financial ratios in each column. performance evaluations. All pre-ranked numbers in Sixth step: calculate the weight of each of the the synthetic gray degrees of homogeneous financial ratios companies were used to form this table. The Seventh step: calculating weighted ratings for frequencyof eachfinancial ratioineach column was each financial ratio counted to find most desirable factor. Final step: formationof mean comparative table for ratings of financial ratios
Table 2: Identification of the most suitable financial ratio in terms of synthetic gray degrees in homogeneous companies Eight Seven Six Five Four Three Two One Rank/ Title Saipa Diesel Saipa Iran Khodro Iran Khodro Diesel Pars Khodro 1 2 3 4 5 6 7 8 Significance level A=significance level/maximum 0.125 0.25 0.375 0.5 0.625 0.75 0.875 1 significance level B=A/Number of companies 0.025 12:05 0.075 0.1 0.125 0.15 0.175 0.2
F FR W Rank 1 0 1 1 1 1 0 0 s9 0.48 4 0 1 2 2 0 0 0 0 s8 0.4 5 2 1 1 0 1 0 0 0 s7 0.3 7 0 2 1 1 0 0 0 0 s6 0.28 8 2 1 0 0 2 0 0 0 s5 0.35 6 0 0 0 0 1 1 1 2 s4 0.85 2 0 0 0 0 0 3 2 0 s3 0.8 3 0 0 0 0 0 0 2 3 s2 0.95 1
For weight calculation of significance level in Rank column in Table 1 represents the overall each financial ratios, the following equation was ranking of most desirable to least effective financial used: [1]. ratios to identify the performance of synthetic gray = degrees( diagram 1).
ππππππ οΏ½ πΉπΉππππ β π΅π΅ππππ
9S 8 2S 6 8S 4 2 3S 0 7S Rank
4S 6S 5S
Diagram 1: Identification of the most appropriate financial ratio in terms of synthetic gray degrees in homogeneous companies
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8 8 7 77 6 6 6 6 6 5 5 5 44 4 4 333 2 1 222 111 0 9S 8S 7S 6S 5S 4S 3S 2S
absolute ranking Relative ranking synthetic ranking
Diagram 2: Comparative representation of gray degrees to identify the most appropriate and least effective financial ratios
Table 3: A comparative average value of financial ratings of homogeneous companie Average rating Synthetic rating Relative ranking Absolute rating 1.00 1 1 1 S2 3.00 3 3 3 S3 2.00 2 2 2 S4 5.67 6 5 6 S5 6.00 8 6 4 S6 6.33 7 7 5 S7 4.33 5 7 1 S8 4.67 4 4 6 S9
10 5.67 6 6.33 4.334.67 5 3 1 2 0 2s 3s 4s 5s 6s 7s 8s 9s
1Series
Diagram 3: Final representation of rankings compared with financial ratios to identify homogeneous companies' performance
As shown in diagram 3, S2 ((Current Assets) - (inventory) divided by current liabilities) with Table4 in Appendix B representsthe average ranking of 1 is the most effective financial significance level (financial ratios) of heterogeneous ratio and S7 (Net income divided by shareholders' companies to identify the most favorable financial equity) with an average rating of 6.33 is lowest ratios in terms of synthetic gray degree to investigate effective in terms of financial ratio to identify the the performance of mentioned companies. All pre- financial performance of homogeneous companies. ranked numbers in the synthetic gray degrees of Appendix A represents comparative diagrams of heterogeneous companies were used to form this relative and synthetic degree of gray incidence to table. The frequencyof eachfinancial ratioineach identify the least effective financial ratios in column was counted to find most desirable homogeneous companies. factor(Diagram 4).
9. Financial ratio ratings in terms of gray degree in heterogeneous companies
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9S 8 2S 6 8S 4 2 3S 0 7S Rank
4S 6S 5S
Diagram 4: Identification of the most appropriate financial ratio in terms of synthetic gray degree in heterogeneous companies
10
5
0 9S 8S 7S 6S 5S 4S 3S 2S
absolute ranking Relative ranking synthetic ranking
Diagram 5: A comparative average value of financial ratings to identify the most favorable to least effective financial ratios
Table 5: A comparative average value of financial ratings of heterogeneous companies Average rating Synthetic rating Relative ranking Absolute rating 1.33 1 1 2 S2 2.33 2 2 3 S3 4.00 3 5 4 S4 5.33 6 4 6 S5 4.67 5 7 2 S6 6.00 7 6 5 S7 3.67 4 6 1 S8 5.67 7 3 7 S9
6 5.33 5.67 6 4.67 4 3.67 4 2.33 1.33 2 0 2s 3s 4s 5s 6s 7s 8s 9s
1Series
Diagram 6: Final representation of rankings compared with financial ratios to identify heterogeneous companies' performance
As shown in diagram5, S2 ((Current Assets) - shareholders' equity) with an average rating of 6 is (inventory) divided by current liabilities) with lowest effective in terms of financial ratio to identify average ranking of 1.33 is the most effective the financial performance of heterogeneous financial ratio and S7 (Net income divided by companies.
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9S 8 2S 6 8S 4 2 3S 0 7S Rank
4S 6S
5S
Diagram 7: Identification of the most favorable financial ratios in terms of gray relative degree in homogeneous companies
9S 6 2S 4 8S
2 3S 0 7S Rank
4S 6S
5S
Diagram 8: Identification of the most suitable financial ratios in terms of gray absolute degree in homogeneous companies
9S 10 2S 8S 5
3S 0 7S Rank
4S 6S 5S
Diagram 9: Identification of the most favorable financial ratios in terms of the relative gray degree in heterogeneous companies
10. Discussion and conclusion: financial ratio while S7 (ROE ) with the lowest mean rating is the least effective financial ratio in After analyzing the performance of identification of companies performance by gray homogeneous and heterogeneous firms using the model. Therefore, since the most important ratios in gray model, least desirable and the most effective the automotive industry to identify company financial performance of the companies were ranked performance are immediate and current ratios with regard to identificaton of each absolute, (working capital)respectively, then it is suggested relevant, and synthetic degrees of gray ranking. The that interested investors to invest in this industry to results of financial ratio ranking in these industries in allocate more weight to theses two ratios in their a parallel form showed that S2 (immediate ratio) analysis. Moreover, some suggestions are proposed with the highest average rating is the most effective to expand the use of this theory for future researches.
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In one research to rank financial ratio of companies, ofdecisions made bythe managerordecision-makers the gray incidence degree of delta model was used are a part of the company's financial affairs. But and Markov chain gray model used to evaluate the other parts are related to external factors out of financial health of the company. Finally, the reachof managers. One of the external factors is limitation of this study is that inappropriate results inflation. In this study, the effects of inflation on may be obtained in financial analysis using financial financial ratios are ignored. ratios if inflationary pressures exist. Consequences
9S 8 2S 6 8S 4 2 3S 0 7S Rank
4S 6S
5S
Diagram 10: Identification of the most favorable financial ratios in terms of the absolute gray degree in heterogeneous companies
Table 4: Identification of the most favorable financial ratios in terms of synthetic gray degree in heterogeneous companies Eig Sev Si F Fo Thr T O Rank / Title ht en x ive ur ee wo ne Absal Iran Tractor Mahram Iran Casting Co Saipa Diesel 1 2 3 4 5 6 7 8 Significance level 0.1 0.2 0.3 0. 0.6 0.8 A= Significance level/ 0.75 1 25 5 75 5 25 75 Maximum significance level 0.0 12: 0.0 0. 0.1 0.1 0 0.15 B=A/ Number of companies 25 05 75 1 25 75 .2 F FR W Rank 1 2 0 1 0 1 0 0 s9 0. 7 38 0 0 2 1 1 1 0 0 s8 0. 4 53 2 1 1 0 0 0 0 1 s7 0. 7 38 0 1 2 0 2 0 0 0 s6 0. 5 45 1 0 0 1 1 1 0 0 s5 0. 6 4 0 1 0 2 1 0 0 1 s4 0. 3 58 0 0 0 0 0 1 3 1 s3 0. 2 88 0 0 0 0 0 1 2 2 s2 0. 1 9
References 2. Jahankhany, A., A. Parsaeian, 2005. Volume I, Financial Management, Tenth Edition, Studying 1. Ahadianpoor, D., 2005. bankruptcy prediction and collection of universities humanities (Samt) using artificial neural network model and its 3. Dabaghi, A., A. Malek, 2010. Providing a comparison withAltmanmultiplediscriminant method for evaluating and ranking organizations analysismodel, MS Thesis, Islamic Azad perspective using an integrated method of University, Science and Research Branch. research, Industrial Management Magazine, No 4.
2478 Adv. Environ. Biol., 7(9): 2468-2478, 2013
4. Mohammadi, A., N. Mowla'ii, 2010.Application making model with grey numbers evaluations, of Gray Multi-criteria decision making model in Expert system with Applications, 35(4): 1638- the assessment of corporate performance, 1644. Journal of Industrial Management, No. 15. Kuo, M.S., G.S. Liang, 2011. Combining 5. Chang ping chang, 2006. Managing business VIKOR with GRA techniques to evaluate attributes and performance for commercial servicequality of airports under fuzzy namks, Journal of American Academy of environment, Expert systems with applications, Business, 9(1): 104-109. 38: 1304-1312. 6. Che-Tsung Tung, Yu-Je Lee. and C. Simion, 16. Kuo, Y., T. Yang, G. Huang, 2008. The use of 2010. The innovative performance evaluation grey relational analysis in solving multiple model of grey factor analysis: A case studyof attribute decision making problems, computers listed biotechnology corporations in Taiwan, & industrial engineering, 55: 80-93. Expert Systems with Applications, Vol. 37. 17. Liu, S., B. Guo and Y. Dang, 1999. Grey system 7. David, K., 1994. Grey system and grey relational Theory and applications, Scientific Press. model. ACM SIGICE Bulletin, 20: 1-9. 18. Liu, S., Y. Lin, 2006. Grey information theory 8. Delcea, C. and C. Simion, 2011. βThe incidence and practical applications, london, springer. of bankruptcyβs syndrome ο¬rmβson current 19. Ping, Y. and H. yang, 2004. Using hybrid grey situation and future evolutionβ, Economic model to achieve revenue assurance of Computation and Economic Cybernetic Studies telecommunication companies, Journal of grey and Research, pp: 45. system, 7: 39-50. 9. Deng, J., 1988. Introduction to grey system 20. Qiao-Xing Li, Si-Feng Liu, 2008.βThe theory, The Journal of Grey Systems, 1: 1-24. foundation of the grey matrix and the grey 10. Diyar Akay, Mehmet Atak, 2006. "Grey inputβoutputanalysisβ, Applied Mathematical prediction with rolling mechanism for Modelling, 32: 267-291. electricity demand forecasting of Turkey", 21. VIRGINIA, m and C. Delcea, 2012. HOW WE Energy. CAN DIAGNOSE THE FIRMSβ DISEASES 11. Dong, G., D. Yamaguchi and M. Nagai, 2006. A USING GREY SYSTEMS THEORY, Journal of grey-based decision- making approach to the grey system, 7: 39-50. supplier selection problem Mathematical and 22. Wang Wei, Liu-ZhuangZhi, 2007. Contractors Computer Modeling , 46: 573-581. Selection based on the Grey Decision Model, 12. Fang, M. and G. Tzeng, 2004. Combining grey Huazhong University of Science and relation and TOPSIS concepts for selecting an Technology, 5501-5504.30-52. expatriate host country, Mathematical and 23. Zhang, S.J., Y. He, 2001. A GreyβMarkov computer Modeling, 46: 1473-1490. forecasting model for forecasting the total power 13. Guo-Dong Li, Daisuke Yamaguchi, 2006. requirement of agricultural machinery in Masatake Nagai; "A GM(1,1)βMarkov chain Shangxi Province. J Shanxi Agric Univ (Nat Sci combined model with an application to Edi) 21(3): 299-302. predict the number of Chinese international 24. Wang, Z.L., S.F. Liu, 2005. Extension of grey airlines", Technological Forecasting & Social superiority analysis, IEEE Trans. Syst. Man Change. Cybern. Conf., 1: 616-621. 14. Huang Lin Yong, Pin-Chan Lee and Hsin- I Ting, 2008. Systematic multi- atteibute decision