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Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance Evaluation Model Article in International Journal of Fuzzy Systems · February 2016 DOI: 10.1007/s40815-016-0142-8
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Int. J. Fuzzy Syst. (2016) 18(2):248–262 DOI 10.1007/s40815-016-0142-8
Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance Evaluation Model Meysam Shaverdi1 • Iman Ramezani2 • Reza Tahmasebi3 • Ali Asghar Anvary Rostamy4
Received: 29 January 2015 / Revised: 30 December 2015 / Accepted: 5 January 2016 / Published online: 20 February 2016 Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016
Abstract Financial performance evaluation is a very crucial process for industries in current highly competitive environment. Therefore, designing an accurate and appropriate performance evaluation framework is beneficial for insiders and also shareholders of a company. To evaluate financial performance, we need to consider some financial indicators that reflect the competitiveness of a company. There are many financial indicators and criteria that are vague and can be regarded as a fuzzy multiple criteria decision-making (MCDM) problem. In this paper, we developed a new financial performance evaluation framework to rank the companies in Iranian petrochemical industry based on fuzzy MCDM approach. To achieve this aim, firstly, the main criteria are identified by comprehensive literature review and experts’ opinion. Then, a hierarchical financial performance evaluation model is structured using main financial criteria and their sub-criteria. We used fuzzy analytic hierarchy process to determine the weights of the criteria. Then companies are ranked using fuzzy AHP and fuzzy TOPSIS comparatively. The results indicate that the obtained ranks of the
& Ali Asghar Anvary Rostamy
[email protected] 1
Department of Finance and Management Science, Edwards School of Business, University of Saskatchewan, Saskatoon, SK S7N 5A7, Canada
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
3
Faculty of Management and Accounting, University of Tehran, Farabi Campus, Qom, Iran
4
Department of Accounting, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
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companies by these methods are almost the same with respect to their own sectors. Keywords Fuzzy AHP (FAHP) Fuzzy TOPSIS Multicriteria decision making (MCDM) Performance evaluation Financial ratios
1 Introduction Organizational performance is an indicator which measures how well an organization accomplishes its objectives [1]. One of the most important parts of organizational performance is financial performance. In the current competitive global market, assessing the financial performance of a firm or industry has a crucial importance not just for managers, creditors, and current/potential investors but also for the companies taking place in the similar industry. Organization or sector performance evaluation is generally defined within the area of financial analyses. As the term of financial performance is considered under various meanings like return, productivity, output, and economic growth, using the financial ratios in the performance evaluation process can be applicable for both companies and related sectors. Financial ratios extracted from the data in income statement and balance sheets are considered as crucial measurement tools in determining performance and financial assets of firms. For long time, a huge number of studies in the literature have indicated the benefits of the financial ratios [2, 3]. In various studies related to financial performance evaluation, different index and criteria have been introduced by large numbers of researchers during last years. In the financial performance review, many studies are generally considered defining the relationships among the financial index and also the impact of these
M. Shaverdi et al.: Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance…
index on the performance of firms that will be described in literature review section. For developing an applicable financial performance evaluation model, we need to define a suitable and reliable framework based on past studies and reviewed index and criteria. Consequently, evaluating the index required a reliable quantitative approach to rank and assess the index as well as the firms. Thus, multi-criteria decision-making (MCDM) models can be a very useful and effective tool for this aim to make a better judgment on index and accordingly a good performance evaluation of financial status of the firm. A good decision-making model requires to tolerate ambiguity that usually is a common characteristics in many decision-making problems [4]. Usually, decision makers provide uncertain answers compared to exact ones, so the transformation of qualitative preferences to point estimates may not be sensible. Analytical hierarchy process (AHP) that requires the selection of arbitrary values in pairwise comparison may not be sauciest and uncertainty should be considered in some or all pair-wise comparison values [4]. When dealing with group decision making, it is necessary to consider the diverse types of uncertainty. Fuzzy set theory and its natural ability to deal with uncertainty could provide the needed flexibility to handle the uncertainty factors in decision making [5]. Since the fuzzy linguistic approach can take the optimism/pessimism rating attitude of decision makers into account, linguistic values, whose membership functions are usually characterized by triangular fuzzy numbers, are recommended to assess preference ratings instead of conventional numerical equivalence method [6]. As a result, the fuzzy MCDM should be more appropriate and effective than conventional MCDM in real practice where an uncertain pair-wise comparison environment exists. The main objective of this study is to develop a new decision-making model, by enabling the decision makers to measure the performance of petrochemical firms by the application of multi-criteria decision making. This study suggests a framework for assessing the financial performance, which combines two main MCDM models: analytic hierarchy process (FAHP) and the technique for order performance by similarity to ideal solution (TOPSIS) based on fuzzy approach. The proposed model helps investors and financial analysts about evaluation and outcomes of those firms to make a better decision for their future financial investment. The new fuzzy MCDM approach ranks the index of proposed framework and then evaluates the main petrochemical companies regarding to the index. The proposed model is constituted based on comprehensive review of literature and extracting the main index and modifying the model based on experts’ opinions. The rest of this paper is organized as follows. Section 2 gives literature review of performance evaluation models.
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In the third section, financial performance measures are defined, and in the fourth section, firstly, basics of fuzzy number and fuzzy sets is defined, and then fuzzy AHP and fuzzy TOPSIS methodologies are explained, respectively. An application of proposed model is given in section five. Section six presents result and analysis of the finding and calculations. Finally, in section seven, results are presented and suggestions for the future studies are clarified. This section concludes the paper.
2 Literature Review on Financial Performance Evaluation There is abundant literature on financial performance evaluation models using fuzzy MCDM models. Wang evaluated financial performance of domestic airlines in Taiwan with fuzzy TOPSIS method [7]. Lee et al. developed an approach based on the fuzzy analytic hierarchy process (FAHP) and balanced scorecard (BSC) to evaluate an IT department in the manufacturing industry in Taiwan [8]. The BSC concept was applied to define the hierarchy with four major perspectives (i.e., financial, customer, internal business process, and learning and growth), and performance indicators were selected for each perspective. An FAHP approach was then proposed in order to tolerate vagueness and ambiguity of information. An FAHP information system is finally constructed to facilitate the solving process. Gumus developed a two-step methodology to evaluate hazardous waste transportation firms containing the methods of fuzzy AHP and TOPSIS, a numerical example was presented to clarify the methodology [9]. Wu et al. proposed an FMCDM approach to evaluate banking performance [10]. By drawing on the four perspectives of a BSC, in their research they first summarized the evaluation indexes synthesized from the literature relating to banking performance. Then, to screen these indexes, 23 indexes fit for banking performance evaluation were selected through expert questionnaires. Furthermore, the relative weights of the chosen evaluation indexes were calculated by FAHP and the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were, respectively, adopted to rank the banking performance and improve the gaps with three banks as an empirical example. The analysis results highlight the critical aspects of evaluation criteria as well as the gaps to improve banking performance for achieving aspired/desired level. It shows that the proposed FMCDM evaluation model of banking performance using the BSC framework can be a useful and effective assessment tool. Sun developed an evaluation model based on FAHP process and TOPSIS by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and
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subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers [11]. Yu et al. developed an evaluation model-based AHP, fuzzy sets, and TOPSIS to rank e-commerce websites in e-alliance [12]. The AHP was applied to analyze the structure of ranking problem and to determine weights of the criteria, fuzzy sets were utilized to present ambiguity and subjectivity with linguistic values parameterized by triangular fuzzy numbers, and TOPSIS method is used to obtain final ranking. Yalcin et al. proposed a new financial performance evaluation approach using fuzzy multi-criteria decision-making methods for financial performance evaluation of Turkish manufacturing industries [3]. For doing the research they used accounting-based financial performance measures and value-based financial performance measures as financial performance measures. They used fuzzy AHP to construct evaluation criteria hierarchy and used TOPSIS and VIKOR as ranking tools [3]. Ishizaka and Nguyen [13] used fuzzy AHP method to facilitate selection of student bank accounts. Yilmaz and Konyar [14] used TOPSIS to evaluate the financial performance of 9 lodging companies in Istanbul stock exchange. Mandic et al. [15] suggested a fuzzy multi-criteria model that facilitated the evaluation of the financial performance of banks. The study was conduct of the banking sector in Serbia during 2005 and 2010. Wang developed an evaluation model of financial performance using fuzzy TOPSIS in Taiwan container shipping companies [7]. Shen and Tzeng [16] proposed an integrated two-stage inference system to predict the financial performance of banks. Malichova and Durisova [17] evaluate financial performance of enterprises operating in IT sector based on financial indicators. O’Neill et al. [18] consider quality management approaches and their impact on firms‘ financial performance. They found that firm quality management orientation provides a statistically significant financial performance advantage over those firms who do not engage in quality management.
3 Theoretical Background
International Journal of Fuzzy Systems, Vol. 18, No. 2, April 2016
3.1.1 Traditional Accounting-Based Financial Performance Measure In this section, traditional accounting-based financial performance measures such as ROA, ROE, EPS, and P/E will be explained. 3.1.1.1 Return on Assets (ROA) This measure determines the efficiency of applying resources for earning. It can be calculated using the following formula [19]: ROA ¼
Net income available to stockholders : Total assets
Another way to measure ROA is to multiply profit margin by total assets turnover so [20] ROA ¼ Profit margin Total assets turnover Net income Sales ¼ : Sales Total assets ROA indicates how much the profit an enterprise is able to generate for each dollar of assets invested [21]. As seen from the ROA formulation, the higher return means the better profit performance for a company. ROA gives an idea how efficient the management applies its assets to create earnings. In fact, ROA is an easy way of comparing a firm’s performance with that of other competitors [3]. 3.1.1.2 Return on Equity (ROE) ROE determines the profitability with the invested money of shareholders and it is used to measure the real cost of spending money [22]. ROE can be determined with different ways but the most common way to calculate ROE is as follows [23]: ROE ¼ ROA Equity multiplier Net income available to common stockholders ¼ : Stockholders equity ROE can be determined by multiplying the ROA by the equity multiplier which indicates the ratio of assets to common equity so the formula is as follows [20, 24]:
3.1 Financial Performance Measures
ROE ¼ ROA Equity multiplier Net income Total assets Net income ¼ ¼ : Total assets Common equity Common equity
Financial ratios and measures are very good ways to evaluate a company’s performance. In this paper, they were used as input criteria of decision-making methods. To evaluate performance of the companies, traditional and modern financial performance measures will be used to evaluate the performance of companies. In the following, the sub-criteria measures of each main-criterion are briefly defined.
Generally, relatively higher ROE rates indicate that companies sell at higher multiple of book value than companies with low returns. Because only the stockholder’s equity appears in the denominator, the measure is directly influenced by the amount of debt which a company is using to finance assets. The higher the ratio, the more efficient management of the equity base utilization and also the better return to its investors [23].
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M. Shaverdi et al.: Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance…
3.1.1.3 Earnings Per Share (EPS) The indicator of each outstanding share of a company is called EPS. The basic EPS can provide a measure of the interests of each ordinary share of a parent entity in the performance of the entity (Board [25]. EPS is used to answer the question of if a company is growing and it can be calculated by [26] EPS ¼
Net income available to shareholders : Number of outstanding shares
A good way to specify whether a company is growing would be to look at their EPS compared to previous years. It is often considered as the only most important index to determine a company’s profitability [3]. 3.1.1.4 Price Earnings Ratio (P/E) Under certainty and while there are perfect markets, the price of a security is equal to the present value of the future cash flows and under assumptions of 1. K: this variable represents constant dividend payout ratio; 2. g: this variable shows constant growth in earnings per share; and 3. r: it shows constant riskless rate (r), P/E can be calculated by Gordon–Shapiro valuation equation as follows [27]: P=E ¼
K : rg
But the above formula usually can be adjusted in the absence of further investment and consider permanent earnings. The P/E ratio indicates how much investors are willing to pay for buying shares per dollar of current earnings. It is the best index to analyze performance while there are other factors that an investor should consider them before making an investment decision. The P/E ratio can be calculated by the following formula [20]: P=E ¼
Price per share : Earnings per share
Since earnings per share are reported in the income statement, the market value per share of stock is not reported in the fiscal statements, but should be determined from financial new source. The main idea of P/E ratio is what the market is willing to pay for the firms’ profits [3]. 3.1.2 Modern Value-Based Performance Measures There are also some other criteria to measure financial performance and they are called modern value-based financial performance measures. Performance measures such as CFROI, EVA, and CVA are some of Modern Value-based Performance Measures. The Modern Valuebased financial performance measures are as follows: 3.1.2.1 Economic Value Added (EVA) Economic Value Added (EVA) is a developing concept to asses financial
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performance [28]. For the first time, Stern Stewart introduced concept of EVA (Services, [29]. It is the base for theory of evaluating enterprise value which received attention by many of researchers [30]. It is the difference between net operating income of a company after taxes and its cost of capital of both equity and debt and it was used by many of giant corporate such as Coca-Cola and AT & T that they were very satisfied with EVA which lead to sudden popularity of EVA [31]. It is a single period and accounting-based measure of corporate performance and there are some ways to calculate EVA that can be explained as follows [32]: One way for determining EVA for each year is multiplying company’s economic book value of capital C (Ct-1) at the beginning of the year by the difference between its return on Capital r (rt) and its cost of capital k (kt) and It can be written as follows: EVAt ¼ ðrt kt Þ Ct1 : Another way that may make more sense is to think that EVA is the difference between net operating profit of a firm after taxes (NOPATt) and its cost of capital: EVAt ¼ NOPATt ðkt Ct1 Þ: 3.1.2.2 Market Value Added (MVA) Market Value Added (MVA) is a market-generated number and it can be measured as follows [32]: It can be determined by subtracting the capital invested in a company C from the sum V of the total market value of the firm’s equity and the book value of its debt: MVAt ¼ Vt Ct : MVA is usually the present value of a series of EVA values [33] or it is a measure of value created by management while there is an excess of capital invested by shareholders [34]. Furthermore, MVA is the best external criteria to assess management performance in the long term and can be calculated as follows [35]: MVA ¼ Total market value Total capital employed: Theatrically, there is a direct connection between MVA and EVA [36]. MVA is closely related to the measure in that it is the present value of all expected future EVA and may be thought of as the net present value of the firm [37]. It is also believed that MVA is a reasonable proxy for the measurement of owner wealth maximization while taking into consideration the relative risk-based costs of doing so. 3.1.2.3 Cash Flow Return on Investment (CFROI) Cash flow return on investment (CFROI) is an internal rate of return and it can bring a consistent basis to evaluate companies regardless of their size and this characteristic
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makes it very popular among money management community to compare companies against each other to make investment decisions [38]. The following five-step process is used to determine CFROI [39]: 1. 2. 3. 4. 5.
Determine the average life of the firm’s assets. Determine gross cash flow. Determine gross cash investment. Determine sum of all non-depreciating assets such as land, working capital, and other assets. Solve the acquired equation for CFROI.
CFROI illustrates whether the firm has earned returns superior to its cost of capital and therefore created value for this shareholders. In this sense, it shows an important similarity to EVA. Both measures assume that management creates value by earning returns on invested capital greater than the cost of capital. For the owners of the company or shareholders, high CFROI is an advantage because less money should to be invested to create future growth. 3.1.2.4 Cash Value Added (CVA) Cash Value Added (CVA) is a measure for calculating the amount of cash a company generates through its operations. CVA can be measured as follows [22]: CVA ¼ Gross cash flows ðoperatingÞ Economic depreciation Capital charge: Capital expense allocates a cost for the usage of all capital the company is using, which is equal to the company’s cost of capital times the amount of gross capital invested. Another calculation method for CVA considers the company’s profitability measured as a difference between CFROI and WACC. It is named indirect calculation by BCG [40]. 3.1.2.5 Current Ratio Current ratio is calculated by dividing current assets by current liabilities [41]. 3.1.2.6 Quick Ratio Quick ration is a variation of the current ratio which has in the numerator those current assets of the firm that could convert quickly into cash [41]. 3.1.2.7 Debt Ratio Debt Ratio is used to measure the amount of liabilities usually long-term debt and can be calculated by dividing total liabilities by total assets [41]. 3.1.2.8 Inventory Turnover Ratio This measure indicates the number of times per period inventory was turned over (meaning that sold) and it can be found using the following equation: Inventory turnover ratio ¼
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Cost of goods sold : Average inventory
3.1.2.9 Total Asset Turnover Ratio It measures the ratio of total operation revenue in total assets: Total asset turnover ratio ¼
Total operating revenue : Total assets
3.1.2.10 Fixed Asset Turnover Ratio It measures the ratio of total operation revenue in total assets: Fixed asset turnover ratio ¼
Total operating revenue : Net total assets
It is one of activity ratios which measure how effectively, companies use their assets [42]. 3.1.2.11 Accounts Receivable Turnover Ratio It measures ability to collect debts for each company (Weygandt et al. [43]): Receivable turnover ratio =
Net receivable sales : Average net receivables
3.2 Fuzzy Set and Fuzzy Number The fuzzy set theory was introduced by Zadeh [44]. Fuzzy set theory provides a mathematical framework in which vague conceptual phenomena can be studied [45]. Fuzzy set theory is a suitable tool to reinforce the comprehensiveness and correctness of the decision-making stages. Fuzzy set theory is an important approach to provide a measure while there is uncertainty of concepts that are associated with human beings’ subjective judgments including linguistic terms, satisfaction level, and importance level that are often vague [46]. A variable whose values are not quantitative but phrases in a natural language is a linguistic variable. The concept of a linguistic variable is very beneficial in dealing with situations, which are too complicated or not well defined to be rationally described in usual quantitative expressions [45]. For example, some of lingual expressions are usually regarded as natural representations of preferences or judgments, human’s lingual expressions such as satisfied, fair, dissatisfied are among them. Herrera and Herrera-Viedma has shown that linguistic terms are intuitively more convenient to use when decision makers express the subjectivity and imprecision of their evaluation [47]. For these reasons, the fuzzy set theory is used accompanied with AHP in this paper. Fuzzy numbers are particular categories of fuzzy quantities that are equal to a simplification of a real number r. For each fuzzy quantity, M(x) indicates the closeness of M(x) estimator r. A fuzzy number is normally described [48]. A real number can normally be described with a fuzzy number in which each grade of membership values between 0 and 1. Depending on situation, different fuzzy numbers can be used and among different types of fuzzy numbers while triangular and trapezoidal fuzzy numbers are most common fuzzy numbers that are used. Triangular
M. Shaverdi et al.: Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance…
fuzzy numbers (TFNs) are usually used because of their calculation easiness in representing the linguistic variables, ~ ¼ ðl1 ; m1 ; u1 Þ is used to represent TFN and memhere M bership function lM~ ðxÞ : R ! ½0; 1 of TFN is represented as follows [48]: 8 xl > > lxm > > >m u : 0 otherwise where l and u stand for the lower and upper value, ~ and m is the mid-value of respectively, of the support of M, ~ M. The parameters l, m, and u that describe a fuzzy number indicate the smallest possible value, the most promising value, and the largest possible value, respectively. A tri~ is shown in Fig. 1 [48, 49]. angular fuzzy number M Figure 1 shows a triangular fuzzy number [48].
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ability to properly consider the inherent uncertainty and carelessness of pair comparisons [53]. Fuzzy AHP is developed to resolve the expanded hierarchical issues. Scholars of decision making realized that distanced judgment is more compelling than rigid judgments. The reason is that individuals often cannot explicitly express their preferences because of fuzzy nature of comparison process [51]. The relative importance specified by the AHP decision makers is oral and it is vague. Decision makers often like to use oral presentation of their preferences rather than numerical values and the nature of pair comparisons makes they cannot explicitly express their opinions about priorities. In such conditions, the best solution is making decisions based upon multiple goals and conditions to gain a rather suitable level of achievement. These deficiencies show that the nature of decision making is full of complexities and ambiguities in most minor to most major cases, most decisions are made in a fuzzy environment. So, using this method can reduce ambiguities of decisions made [54].
4 Methodology
4.1.2 Methodology of Fuzzy AHP
In this section, the methodology of this paper will be presented; at first, a review on basic concepts of fuzzy numbers and fuzzy sets will be done, then the fuzzy AHP and fuzzy TOPSIS methodologies will be done.
In this study, the extent of FAHP which was originally introduced by Chang is applied, [55]. Considering that X ¼ fx1 ; x2 ; . . .; xn g is an object set, and G ¼ fg1 ; g2 ; . . .; gn g is a goal set, based on the method of Chang’s extent analysis, each object is taken and extent analysis for each goal is performed mutually. Therefore, m extent analysis values for each object can be gained, with the following signs:
4.1 Fuzzy AHP 4.1.1 Fuzzy AHP Literature Review
Mg1i ; Mg2i ; . . .; Mgmi ;
Analysis Hierarchical Process (AHP) is a tool for making decision based on multi-criteria decision making and it was first proposed by Saaty [50]. Since introduction of AHP, it has been one of the most useful multi-criteria decision-making tools for decision making and researchers. Although it records knowledge in complex form, the conventional AHP is unable to reflect the way human thinks [51]., the conventional AHP becomes confusing while it uses an exact yardstick for comparing the opinions of decision makers [52]. Some researchers criticize AHP for using lopsided judgmental scales and lack of
In which Mgji ðj ¼ 1; 2; . . .; mÞ all are TFNs. The steps of Chang’s extent analysis is in the following [55]:
i ¼ 1; 2; . . .; n:
Step 1 The value of fuzzy synthetic extent of i.th objective is defined as " #1 n n X m X X sk ¼ mgj i mgj i : ð2Þ j¼1
i¼1 j¼1
Pm j To calculate value of j¼1 Mgi , the fuzzy addition operation of m extent analysis values for a special matrix is performed such as " # m m m m X X X X ð3Þ Mgij ¼ ; mj ; uj j¼1
j¼1
and to obtain
j¼1
hP
j¼1
m j¼1
;
Pm
j¼1
fuzzy addition operation of performed such as n X m X
Fig. 1 Triangular fuzzy function
i¼1 j¼1
Mgij
¼
n X i¼1
li ;
n X i¼1
mj ;
Pm
Mgji ðj
mi ;
n X
j¼1
uj
i1
, at the first the
¼ 1; 2; . . .; mÞ values is ! ui :
ð4Þ
i¼1
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International Journal of Fuzzy Systems, Vol. 18, No. 2, April 2016
After it the inverse of the above vector is calculated as follows: " #1 n X m X 1 1 1 j Mgi ¼ Pn ; Pn ; Pn : ð5Þ i¼1 ui i¼1 mi i¼1 li i¼1 j¼1
Step 4 Using normalization, the normalized weight vectors are
Step 2 While M1 ¼ ðl1 ; m1 ; u1 Þ and M2 ¼ ðl2 ; m2 ; u2 Þ are two triangular fuzzy numbers, the degree of probability of M2 ¼ ðl2 ; m2 ; u2 Þ M1 ¼ ðl1 ; m1 ; u1 Þ is defined as VðM2 M1 Þ ¼ sup minðlm1 ðxÞ; lm2 ðyÞÞ ð6Þ
4.2 Fuzzy TOPSIS
and it can be presented as follows: VðM2 M1 Þ ¼ hgtðM1 \ M2 Þ ¼ lM2 ðdÞ VðM2 M1 Þ ¼ hgtðM1 \ M2 Þ ¼ lM2 ðdÞ 8 1 > < 0 ¼ l1 u2 > : ðm2 u2 Þ ðm1 l1 Þ
ð7Þ m2 m1 l 1 u2
:
otherwise ð8Þ
Figure 2 [55] illustrates Eq. (6) in which d is the ordinate of the highest intersection point D between lM1 and lM2 . To compare M1 and M2, we need both the values of VðM1 M2 Þ and VðM2 M1 Þ. Step 3 For a convex fuzzy number the degree possibility to be greater than k convex fuzzy Mi ði ¼ 1; 2; . . .; kÞ numbers can be defined by VðM M1 ; M2 ; . . .; Mk Þ ¼ V ½ðM M1 Þ and ðM M2 Þ and . . .ðM Mk Þ : ¼ minVðM Mi Þ;
ð9Þ
i ¼ 1; 2; 3; . . .; k
Suppose that dðAi Þ ¼ minVðSi Sk Þ for k ¼ 1; 2; . . .; n; k 6¼ i then the weight vector can be given by W 0 ¼ ðd 0 ðA1 Þ; d 0 ðA2 ; . . .; d0 ðAn ÞÞÞT :
ð10Þ
In the above equation, Ai ¼ ði ¼ 1; 2; . . .; nÞ are n elements.
W ¼ ðdðA1 Þ; dðA2 ; . . .; dðAn ÞÞÞT ;
ð11Þ
where W is a non-fuzzy number.
Assigning a precise performance rating to an alternative for the criteria under consideration is often difficult for a decision maker. Fuzzy is used to assign the relative importance of the criteria using fuzzy numbers instead of precise numbers [56]. Here, Fuzzy Topsis is used for final ranking and the following section described the fuzzy TOPSIS methodology [57, 58]. Every MCDM problem can be expressed in matrix format as Eqs. (12) and (13). 3 2 A1 x~11 x~12 x~13 x~1n 7 A2 6 6 x~21 x~22 x~23 x~2n 7 6 A3 6 x~31 x~32 x~33 x~3n 7 ð12Þ 7; .. 6 .. .. 7 .. .. .. . 4 . . 5 . . . Am x~m1 x~m2 x~m3 x~mn ~ ¼ ½w ~1 ; w ~2 ; w ~3 ; . . .; w ~n ; W
ð13Þ
where x~ij ; i ¼ 1; 2; . . .; m; j ¼ 1; 2; . . .; n, Ai, i = 1, m ~j ; j ¼ 1; 2; . . .; n are represent m alternatives and w weighting factors of criteria and linguistic triangular fuzzy numbers are denoted by xij ¼ ðaij ; bij ; cij Þ. Note that xij is the performance rating of the ith alternative, Ai, with ~j represents the weight respect to the jth criterion, Cj and w of the jth criterion, Cj. The normalized Fuzzy decision matrix is denoted by R~ and it is shown as Eq. (14): R~ ¼ r~ij mn : ð14Þ The weighted Fuzzy normalized decision matrix is shown in Eq. (15): 2 3 2 3 ~n r~1n ~2 r~12 w ~1 r~11 w v~11 v~12 v~1n w 6 v~21 v~22 v~m1 v~2n 7 6 w ~n r~2n 7 ~2 r~22 w 6 7 6 ~1 r~21 w 7 6 .. .. 7 ¼ 6 .. .. 7: .. .. .. .. 4 . 4 5 . . 5 . . . . . ~1 r~m1 w ~1n r~mn ~12 r~m2 w v~m1 v~n2 v~m1 v~mn w ð15Þ
Fig. 2 Intersection point between M1 and M2
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The advantage of using Fuzzy numbers is assigning the relative importance of the criteria using Fuzzy numbers instead of precise numbers. This method can be used to solve the group decision maker problem under Fuzzy environment. Given the above Fuzzy theory, the proposed Fuzzy TOPSIS procedure is then defined as follows: The weighted Fuzzy normalized decision matrix is shown in Eq. (15): Step 1 Choose the appropriate linguistic variables ðwj ; j ¼ 1; 2; . . .; nÞ for the weight of the criteria and the linguistic
M. Shaverdi et al.: Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance…
ratings ð~ xij Þ i = 1, 2, …, m; j = 1,2, …, n for alternatives with respect to criteria. As other fuzzy numbers in the Fuzzy linguistic rating ð~ xij Þ the ranges of normalized triangular fuzzy numbers belong to [0,1]; so, there is not any requirement for a normalization procedure. Step 2 Construct the weighted normalized Fuzzy decision matrix. The weighted normalized value V~ is calculated by Eq. (15). Step 3 Determine values of positive ideal (A ) and negative ideal (A ) solutions. The Fuzzy positive ideal solution (FPIS, A*) and the Fuzzy negative ideal solution (FNIS, A ) are shown in Eqs. (16) and (17): A ¼ v~ 1 ; v~ 2 ; . . .; v~ n ¼ ðmax v~ij ji ¼ 1; 2; . . .; mÞ; j ¼ 1; 2; . . .; n ; ð16Þ i
~ ~ A ¼ v~ 1 ;v 2 . . .; v n ¼ ðmin v~ ij ji ¼ 1; 2; . . .; mÞ; i
j ¼ 1; 2; . . .; n :
ð17Þ
While using max and min operations does not give triangular fuzzy number but it is possible to describe approximated values of min and max as triangular fuzzy numbers, since we know that the elements v~ij 8i; j are normalized positive triangular fuzzy numbers and their ranges belong to the closed interval [0,1]. Thus, we can define the fuzzy positive ideal solution and the negative ideal as v~ j ¼ ð1; 1; 1Þ and v~ j ¼ ð0; 0; 0Þ; j ¼ 1; 2; . . .; n. Step 4 Compute separation measures. The distance of each alternative from A and A can be currently calculated using Eqs. (18) and (19). n X dð~ vij ; v~ ij Þ; i ¼ 1; 2; . . .; m; ð18Þ d ¼ j¼1
d ¼
n X
dð~ vij ; v~ ij Þ;
i ¼ 1; 2; . . .; m:
255
2003–2013. Since we find that the main solution to evaluate financial index is to develop an evaluation index framework, our study emphasizes on the designing financial performance evaluation system with reasonable and objective factor weights. Specifying the rank of a factor would be a multiple criteria decisionmaking problem, and the decision makers usually have more confidence giving linguistic variables rather than expressing their judgments in the form of numerical figures. Thus, fuzzy set theory is a widely used tool to deal with uncertain and imprecise dataset. Whereas, AHP, proposed by Satty [50], is a very useful decisionmaking method, being an extension of AHP, fuzzy AHP is able to solve the hierarchical fuzzy decision-making problems with better and more reliable results. The fuzzy AHP method has been widely used in different studies to solve various decision-making problems and it is extensively reviewed in literature review section. The past studies revealed the high applicability of fuzzy MCDM models for solving practical decision-making problems for achieving better judgment on complicated set of criteria and sub-criteria. Thus, fuzzy AHP as well as fuzzy TOPSIS are suitable techniques for determining the financial index weights in our financial performance evaluation system. The main steps of performance evaluating in this study are shown in Fig. 3. As indicated in Fig. 3, the first step is determining the financial ratios to be used in the decision-making process. Then, value of financial ratios is determined for each firm. After constructing the evaluating criteria hierarchy model, the FAHP was used to obtain the fuzzy weights of the financial ratios. Then, two MCDM analytical tools, FAHP and fuzzy TOPSIS are used to evaluate the performance of companies
ð19Þ
j¼1
Step 5 Compute the closeness coefficient to ideal solution. This step solves the closeness coefficient to an ideal solution by Eq. (20): CCi ¼
di : di þ di
ð20Þ
Step 6 Rank preference order. Select an alternative with maximum CCi or rank alternatives based upon the CCi in descending order [56].
5 Application The aim of this study is to evaluate the performance of 7 Iranian petrochemical firms in the Tehran Stock Exchange (TSE) based on their financial ratios during
Fig. 3 Fuzzy AHP ranking
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International Journal of Fuzzy Systems, Vol. 18, No. 2, April 2016
based on the weight of their financial ratios. Figures 3 and 4 indicate the fuzzy AHP and fuzzy TOPSIS steps. Also, Fig. 4 shows the procedure of fuzzy TOPSIS ranking. Firstly, the hierarchy financial performance evaluation model is designed by considering the past literature. The proposed model is denoted in Fig. 5. The model is categorized into 5 main criteria, namely, liquidity ratios, financial leverage ratios, activity ratios, profitability ratios, and growth ratios. As indicated in Fig. 5, each criteria has different sub-criteria. Based on the opinion of decision makers from different areas, importance of ratios are calculated with the help of questionnaires. FAHP is utilized for determining the weights of main and sub-criteria and it can reduce the uncertainty and vagueness in the decision process. Finally, Fuzzy TOPSIS method is suggested to evaluate the performance of the petrochemical companies and do final ranking by considering financial ratios and weights of the criteria. By this way, the ranking of the firms according to their general performance is obtained. Fig. 4 Fuzzy TOPSIS ranking C11: Current ratio (0.58429)
Liquidity Ratios (C1) C12: Quick ratio (0.475239)
C21: Debt ratio (0.0798) C22: Long term debt/ shareholder’s equity
Performance evaluaon
Financial leverage Ratios (C2)
C23: EBIT/ Interest expense (0.6864) C24: Long term debt/ Total asset (0.029)
C31: Inventory turnover ratio (1.0899) C32: Total asset turnover ratio (0.2026)
Activity Ratios (C3) C33: Fixed asset turnover ratio (1.4145) C34: Receivable accounting turnover ratio
C41: Net profit margin (0.1117)
Profitability Ratios (C4)
C42: ROI (0.0841) C43: ROE (0.6864) C51: Asset Growth (3.4011)
C52: Operating profit Growth (1.7801)
Growth Ratios (C5) C53: Sale Growth (1.1655) C54: shareholder’s equity Growth (0.8085)
Fig. 5 Hierarchical structure of performance evaluating model
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257
Table 1 Fuzzy comparison matrix C1
C2
C3
C4
C5
C6
C7
C8
C9
C1
1
1
1
1
3
9
1
1
9
1
2
9
1
4
9
1
5
9
1
6
9
1
5
9
1
5
9
C2
0.1
0.3
1
1
1
1
1
3
9
1
5
9
1
6
9
1
3
9
1
2
9
1
3
9
1
3
9
C3
0.1
1
1
0.1
0.3
1
1
1
1
1
4
9
1
4
9
1
6
9
1
3
9
1
6
9
1
6
9
C4
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
1
1
1
1
3
9
1
2
9
1
5
9
1
2
9
1
2
9
C5
0.1
0.3
1
0.1
0.2
1
0.1
0.3
1
0.1
0.3
1
1
1
1
1
3
9
1
4
9
1
2
9
1
2
9
C6
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
1
1
1
1
2
9
1
5
9
1
6
9
C7
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
0.1
0.2
1
0.1
0.3
1
0.1
0.5
1
1
1
1
1
4
9
1
2
9
C8
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
1
1
1
1
3
9
C9
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
1
1
1
C10
0.1
0.2
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
C11
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.2
1
C12
0.1
0.1
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.2
1
C13
0.1
0.2
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
0.1
0.2
1
C14
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
0.1
0.3
1
0.1
0.2
1
C15
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
0.1
0.2
1
0.1
0.3
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
C16
0.1
0.2
1
0.1
0.2
1
0.1
0.2
1
0.1
0.5
1
0.1
0.3
1
0.1
0.3
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
C17
0.1
0.5
1
0.1
0.5
1
0.1
0.3
1
0.1
0.5
1
0.1
0.5
1
0.1
0.2
1
0.1
0.3
1
0.1
0.2
1
0.1
0.5
1
To acquire the factor and sub-factor weights, a group of decision makers, including academic and professional experts, is formed. Questionnaires were provided to get their viewpoints by using pair-wise comparisons, which were derived from their assessments on the relative importance of one factor over another, were used to form the comparison matrices of each decision maker. Consider a group of K decision makers involved in the research: they make pair-wise comparisons of n elements. As a result of the pair-wise comparisons, we get a set of K matrices, aijk = {lijk, mijk, uijk} represents a relative importance of element i to j, as assessed by the expert k. The triangular fuzzy numbers in the group judgment matrix can be obtained by using the following equation: lij ¼ minfaijk g; k
mij ¼
k 1X bijk ; k k¼1
uij ¼ maxfdijk g: k
The representative comparison matrix of the group acquired when making pair-wise comparisons of the factors are shown in Tables 1 and 2. After finding the weights of the criteria, we see that receivable accounting turnover ratio has the highest weight.
Table 3 shows the weights of criteria from maximum to minimum. Regarding to Table 3, receivable accounting turnover ratio and debt/total asset has the maximum and minimum weights, respectively. By applying FAHP, the weights of sub-criteria of financial ratios are determined for each company. Table 4 shows liquidity ratios for 7 companies, for both current ratio and the quick ratio, Shiraz has the highest value while for the current ratio, Abadan, and for quick ratio, Arak has the minimum values. Table 5 shows financial leverage ratios, including debt ratio, long-term debt/stakeholder’s equity and long-term debt/total asset for the companies. Regarding to the results, Farabi and Isfahan has the highest values in debt ratio and long-term debt/shareholder’s equity ratio, respectively. For EBIT/Interest expense ratio and long-term debt/total asset ratio, Shiraz has the highest values. Table 6 shows profitability ratios for the companies. Net profit margin, ROI, and ROE are measured for evaluating profitability. Shiraz, Farabi, and Abadan have the highest values for net profit margin, ROI, and ROE, respectively.
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Table 2 Fuzzy comparison matrix C10
Table 2 continued C11
C14
C15
C1
1
6
9
1
3
9
C11
1
2
9
1
3
9
C2
1
2
9
1
6
9
C12
1
5
9
1
5
9
C3
1
2
9
1
2
9
C13
1
4
9
1
3
9
C4
1
5
9
1
3
9
C14
1
1
1
1
6
9
C5
1
4
9
1
5
9
C15
0.1
0.2
1
1
1
1
C6
1
2
9
1
3
9
C16
0.1
0.5
1
0.1
0.3
1
C7
1
2
9
1
6
9
C17
0.1
0.2
1
0.1
0.5
1
C8
1
5
9
1
2
9
C9
1
4
9
1
5
9
C10
1
1
1
1
4
9
C1
1
5
9
1
2
9
1
C2
1
3
9
1
5
9
1
C3
1
6
9
1
4
9
1
2
9
1
2
9
C11 C12
0.1 0.1
0.3 0.5
1 1
1 0.1
1 0.3
C16
C17
C13
0.1
0.5
1
0.1
1
1
C4
C14
0.1
0.5
1
0.1
0.5
1
C5
1
3
9
1
2
9
1 1
C6
1
4
9
1
5
9
C7
1
2
9
1
4
9
1
C8
1
2
9
1
6
9
C9
1
5
9
1
2
9
C15 C16 C17
0.1 0.1 0.1
0.5 0.3 0.3
1 1 1
C12
0.1 0.1 0.1
0.3 0.3 0.2
C13
C10
1
4
9
1
3
9
C1
1
7
9
1
6
9
C11
1
3
9
1
5
9
C2
1
2
9
1
2
9
C12
1
5
9
1
5
9
C3
1
5
9
1
2
9
C13
1
4
9
1
3
9
C4
1
4
9
1
5
9
C14
1
2
9
1
6
9
C5 C6
1 1
5 3
9 9
1 1
3 6
9 9
C15 C16
1 1
3 1
9 1
1 1
2 3
9 9
C7
1
6
9
1
2
9
C17
0.1
0.3
1
1
1
1
C8
1
2
9
1
3
9
C9
1
6
9
1
6
9
C10
1
2
9
1
2
9
C11
1
3
9
1
1
9
C12
1
1
1
1
3
9
C13
0.1
0.3
1
1
1
1
C14
0.1
0.2
1
0.1
0.3
1
C15
0.1
0.2
1
0.1
0.3
1
C16
0.1
0.2
1
0.1
0.3
1
C17
0.1
0.2
1
0.1
0.3
1
C14
C15
C1
1
5
9
1
6
9
C2
1
3
9
1
2
9
C3
1
6
9
1
3
9
C4
1
2
9
1
5
9
C5
1
2
9
1
4
9
C6
1
5
9
1
2
9
C7
1
4
9
1
5
9
C8
1
3
9
1
3
9
C9
1
6
9
1
6
9
C10
1
2
9
1
2
9
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Table 7 shows growth ratios for petrochemical companies. As calculated in Table 7, for sales growth and operating profit growth, Shiraz has the highest values. Moreover, for asset growth and shareholder’s equity growth Isfahan and Farabi have the highest values, respectively. Table 8 indicates activity ratios for the petrochemical companies. For inventory turnover ratio, Arak has the highest value, for total asset turnover ratio, Farabi has the highest value, for fixed asset turnover ratio, Arak has the highest value, and for the receivable accounting turnover ratio, Shiraz has the highest value. After ranking and applying fuzzy AHP, the final results in Table 9 shows that Arak petrochemical company has the highest weight with 0.144851 and also has the best financial performance, whereas Shiraz petrochemical company has the minimum value and also the lowest financial performance.
M. Shaverdi et al.: Combining Fuzzy AHP and Fuzzy TOPSIS with Financial Ratios to Design a Novel Performance…
259
6 Results and Analysis
Table 3 Financial ratios ranking by FAHP FAHP output for criteria Receivable accounting turnover ratio
In the proposed method, FAHP was used as a tool for determining weights of financial ratios and fuzzy TOPSIS was used to determine the rank of petrochemical companies. Arak petrochemical industry was chosen as the best company and it had the best financial performance measures. The contributions of this paper are as follows:
3.570038264
Asset growth
3.401088484
Operating profit growth
1.780102706
Fixed asset turnover ratio
1.41450564
Sale growth
1.165505663
Inventory turnover ratio
1.089920915
Shareholder’s equity growth
0.808473229
EBIT/interest expense
0.686381098
ROE
0.686381098
Current ratio
0.584290222
Quick ratio Total asset turnover ratio
0.475238688 0.202565038
Net profit margin
0.111688857
Long-term debt/shareholder’s equity
0.084081716
ROI
0.084081716
Debt ratio
0.0798
Long-term debt/total asset
0.029027422
1.
2.
3.
The proposed model presented a mixed multi-criteria decision-making models that applied simultaneously, two techniques to compare the result and ranking of companies to have a better and more precise judgment on each company’s performance. The proposed performance evaluation model is based on financial index. As many past studies pointed, overwhelming majority of evaluation models were based on other indexes not just financial criteria. While the past literature review shows that some performance evaluation models have been based on financial ratio but they were not complete. The current
Table 4 Liquidity ratios of companies Arak
Fanavaran
Khark
Abadan
Isfahan
Farabi
Shiraz
Current ratio
0.711618572
0.96915229
2.720482592
0.611797737
1.308979028
0.949359287
4.701829292
Quick ratio
0.38753971
0.822823112
2.296970269
0.482338372
0.867329295
0.641973901
3.714210526
Table 5 Financial leverage ratios Arak
Fanavaran
Khark
Abadan
Isfahan
Farabi
Shiraz
Debt ratio
0.638424
0.588587
0.320089
0.637287
0.679485
0.878689
0.340159
Long-term debt/ shareholder’s equity
0.118762376
0.373948055
0.074684578
0.280808938
0.663256587
0.137928416
0.333300168
EBIT/interest expense Long-term debt/total asset
19.28106956
14.09046777
0.042941679
6863.423009
0.153847149
23.13208795
0.050778845
0.101853061
43.53430738
-0.387975519
0.21258359
0.016732277
1732.600583 0.219924228
Table 6 Profitability ratios Arak
Fanavaran
Khark
Abadan
Isfahan
Farabi
Shiraz
Net profit margin
0.096867
0.638424
0.588587
0.320089
0.637287
0.679485
0.878689
ROI
0.085491
0.118762376
0.373948055
0.074684578
0.280808938
0.663256587
0.137928416
ROE
0.236439
19.28106956
14.09046777
68.423009
23.13208795
43.53430738
-0.387975519
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International Journal of Fuzzy Systems, Vol. 18, No. 2, April 2016
Table 7 Growth ratios Arak
Fanavaran
Khark
Sales growth
37.55959556
5.445713141
Asset growth
14.1056342
6.40143666
Shareholder’s equity growth Operating profit growth
-2.382609776
-39.70698271
Abadan
8.006127826 10.217908
-15.01243224
3.228111317
-38.70656639
-1.080534308
Isfahan
10.80260023
-8.396712952
28.3204579
40.4123215
-1.874075697
-9.631401703
-23.87572287
-63.86339807
Farabi -18.12822041 19.5786119 -57.51951655
-115.4126585
Shiraz 98.42234592 20.354391 23.32780635
1664.690047
Table 8 Activity ratios Arak
Fanavaran
Khark
Abadan
Isfahan
Farabi
Shiraz
Inventory turnover ratio
4.236905694
4.059101859
1.132567041
8.762013445
2.726269316
7.473338554
1.829235365
Total Asset turnover ratio
0.882559863
0.519611007
0.676670331
0.763193886
0.642971931
1.847232373
0.429277108
Fixed Asset turnover ratio
2.005236527
0.927536502
2.543624013
1.285087973
1.765918351
Receivable accounting turnover ratio
6.716224345
1.804131315
7.899930242
Table 9 Final weights of companies based on Fuzzy TOPSIS
Companies
Final weights
Arak
0.144851
Fanavaran
0.144232
Khark
0.143018
Abadan
0.144507
Isfahan
0.142228
Farabi
0.142009
Shiraz
0.139155
suggested model tries to identify new financial ratios and present a more comprehensive evaluation model.
17.46194469
5.394377196
10.82907598 6.282608015
1.057694857 26.79439916
financial performance measures, further researches can consider both quantitative and qualitative financial performance index. For future studies, researcher can work on other useful mathematical-based decision-making models such as PROMETHE and VIKOR, which can be applied with these proposed methods to prepare a precise result. Another suggestion is to combine balanced scorecard (BSC) approach with MCDM models for designing a more comprehensive evaluation model as BSC combines customer index, process index, learning index as well as financial index. The next proposed future study can be using DEA model based on current financial framework approach. In future researches, the proposed model can be applied to other industries.
7 Conclusion In current global economy, one of the most important competitive advantages of companies would be the financial situations that are generally assessed by the financial ratios. Financial ratios are useful quantitative financial information so companies can be evaluated over time and within a special sector. By comprehensive review of the past literature that applied MCDM models, we found that only the traditional financial ratios have been used. Thus, our proposed model is different from other studies because it uses not only the traditional financial measures but also the modern financial measures together in a MCDM environment. Since our suggested study has quantitative
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Meysam Shaverdi is a graduate student in finance at the Edwards School of Business, University of Saskatchewan, Canada. He received bachelor and master degrees in industrial management at IKIU and the University of Guilan, respectively. His research interests are applications of OR models in finance, fuzzy multi-criteria decision making (MCDM), sustainable and green supply chain management
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International Journal of Fuzzy Systems, Vol. 18, No. 2, April 2016 (SCM), and operations/finance interface. He has published some papers and book chapters in international journals and publishers in the aforementioned areas. Iman Ramezani holds a master degree in Socio-economic systems engineering from Sharif University of Technology and a bachelor’s in industrial engineering from Hormozgan University in Iran. He has some publications in fuzzy multi-criteria decision making (MCDM), sustainable SCM, multi-objective and simulation-based optimization, stochastic optimization, applied statistics, big-data as well as applications of OR in finance. Reza Tahmasebi is assistant professor of organization theory and organizational behavior at University of Tehran, Farabi Campus. He obtained his master degree in human resource management and PhD in organizational behavior, both from The University of Tehran. His research focused on performance management and talent management. He has published several articles in the area of organizational performance and human resource management. He is also working as human resource management consultant in many organizations. Ali Asghar Anvary Rostamy obtained his bachelor and MBA from The University of Tehran and PhD in Multiple Criteria Financial and Investment Decisions field from the Osaka University. He worked as full professor from September 2011 at Tarbiat Modares University (TMU). His research interest is on financial and investment decision making, organizational performance evaluation and management, and business decision making. He has extensively published more than 110 full papers at national and international high-ranked refereed journals and conferences.
