A FAHP AND VIKOR METHOD FOR EVALUATION OF THE FINANCIAL PERFORMANCE OF AGRICULTURE COMPANIES LISTED ON THE BELGRADE STOCK EXCHANGE Текст научной статьи по специальности «Экономика и бизнес»

A FAHP AND VIKOR METHOD FOR EVALUATION OF THE FINANCIAL PERFORMANCE OF AGRICULTURE COMPANIES LISTED ON THE BELGRADE STOCK EXCHANGE

Milorad Kovjanic1, Predrag Vukadinovic2, Snezana Knezevic3, Tijana Obradovic 4 Aleksandra Mitrovic 5, Aleksandar Grgur 6, Marko Milasinovic 7 *Corresponding author E-mail: snezana.knezevic@fon.bg.ac.rs

A R T I C L E I N F O

Original Article

Received: 02 November 2022

Accepted: 25 February 2023

doi:10.59267/ekoPolj2301185K

UDC

519.8:658.14(497.11Belgrade) Keywords:

financial performance, FAHP method, VIKOR method, agriculture, forestry, and fisheries, Belgrade Stock Exchange

A B S T R A C T

Decision-making can be improved by using different models by financial statements users for different purposes. In this paper, the FAHP model was implemented for financial performance evaluation of companies operating within the A-agriculture, forestry and fisheries sectors on the Belgrade Stock Exchange. In addition, we used the VIKOR method, for ranking companies against the results achieved. With all the constrains shown, the research presented in this paper, raises questions for insight and future development, through the possibilities of ranking the financial performance of the company.

JEL: C10, M40, Q12

1 Milorad Kovjanic, Ph.D student, Singidunum University, Danijelova Street no. 32, 11000 Belgrade, Serbia, E-mail: kovjanicmilorad64@gmail.com, ORCID ID (https://orcid. org/0009-0006-5545-4730)

2 Predrag Vukadinovic, Ph.D., Associate professor, Singidunum University, Danijelova Street no. 32, 11000 Belgrade, Serbia, E-mail: pvukadinovic@singidunum.ac.rs, ORCID ID (https://orcid.org/0000-0002-4859-7497)

3 Snezana Knezevic, Ph.D., Associate Professor, University of Belgrade, Faculty of Organizational Sciences, Jove Ilica street no. 154, 11000 Belgrade, Serbia, E-mail: snezana. knezevic@fon.bg.ac.rs, ORCID ID (https://orcid.org/0000-0003-0176-6107)

4 Tijana Obradovic, Ph.D., Associate Professor, University of Belgrade, Faculty of Organizational Sciences, Jove Ilica street no. 154, 11000 Belgrade, Serbia, E-mail: obradovic.tijana@fon.bg.ac.rs, ORCID ID (https://orcid.org/0000-0001-9613-318X)

5 Aleksandra Mitrovic, Ph.D., Associate Professor, University of Kragujevac, Faculty of Hotel Management and Tourism, Vojvodanska no. 5a, 36210 Vrnjacka Banja, Serbia, E-mail: aleksandra.stankovic@kg.ac.rs, ORCID ID (https://orcid.org/0000-0002-8302-0853)

6 Aleksandar Grgur, M.Sc., National Center for Corporate Education, Murska no. 14, 11000 Belgrade, Serbia, E-mail: grguraleksandar2@gmail.com, ORCID ID (https://orcid. org/0000-0002-1538-7055)

7 Marko Milasinovic, M.Sc., Teaching Assistant, University of Kragujevac, Faculty of Hotel Management and Tourism, Vojvodanska no. 5a, 36210 Vrnjacka Banja, Serbia, E-mail: marko.milasinovic@kg.ac.rs, ORCID ID (https://orcid.org/0000-0002-9346-9283)

Introduction

Agricultural activities are of great importance to any economy. Agricultural production systems are very complex. The argument for this claim lies in the fact that these systems are influenced by the interaction of social and environmental factors. Dimensioning these factors can often be difficult (Nkurunziza et al. (2020). Given the benefits it leads to, as well as the risks it faces, it is important to explore the efficiency of businesses in this area. In particular, it is significant to examine efficiency of publicly listed companies whose shares are traded on stock exchanges. In the case of the Republic of Serbia, this is the case of the A-agriculture, forestry and fisheries sectors and companies whose shares are listed on the Belgrade Stock Exchange. There are numerous internal and external factors that affect the work of these companies, and because of the importance of accounting information in agriculture for different, numerous users, the importance and needs of this research are perceived. In addition, the importance of applying different methods for evaluating financial performance in agriculture and thus further supporting decision-making is also emphasized. There is an obvious problem of obtaining reliable data, its comparison, which clearly imposes the need for new research and relevant conclusions in this area. Financially sustainable business is influenced by a number of very complex factors, both internal and external (Srebro et al., 2021).

A specific field of the very nature of agricultural production, and special treatment of financial statements from this industry, lead to increased interest in this field. According to Sun (2010) "the analytic hierarchy process (AHP) is a powerful method to solve complex decision problems based on an additive weighting process, in which several relevant attributes are represented through their relative importance". Therefore, it is of great importance to compare the financial performance of the companies quantitatively (Milojevic et al., 2021; Farrokh et al. 2016; Filgueira-Vizoso et al., 2023), through the results of FAHP models. It is considered significant that parts of the paper provide certain guidelines for improvement where this is possible. The above is the basis for adequate use of financial statements, all in synergy with the FAHP model, which as a mathematical model enables evaluation of the financial performance of the company. Therefore, the aim of this research is to evaluate the financial performance of companies listed in the sector of A-agriculture, forestry and fisheries in the Republic of Serbia using the FAHP framework.

At the beginning of the work, a review of literature was presented, followed by research methodology and theoretical basis of FAHP models and VIKOR models, as well as a literature overview of these models. The following section presents the results and discussion on the research.

Literature Review

Much of the research uses the AHP method in the first phase, to address the priority weights of criteria used by FAHP (Meixner, 2009, Knezevic et al., 2017), and in the second is specifically tailored to the objectives of the work in different fields of

observation. Various problems in agriculture were discussed using the AHP model (Bogdanovic & Hadzic, 2019; Ali et al., 2021; Veisi et al., 2022). One of the papers describes in detail the potential of the AHP method in choosing the best alternative in the decision tree with multiple criteria (see Brozova, 2004; Kong et al., 2005), especially emphasizing the importance of both the AHP method and other mathematical methods for decision-making processes in agricultural practice. In the work of Lu et al. (2014), the AHP weighting method is applied for the purpose of evaluating financial data for the sector that includes agriculture, forestry, animal husbandry and fisheries. The model relies on the analysis of profitability, solvency, capacity (operating & developmental), and liquidity. In doing so, two methods were combined - the analytical hierarchical process and the variance weighting method. Table 1 illustrates a short literature review emphasizing the main theoretical contributions related to application of the FAHP/AHP methods in the agricultural area.

Table 1. Theoretic and empirical contributions on FAHP/AHP methods in the agricultural area

Author Focus Methodology

Tashaoy et al. (2019) determining land suitability for a watershed FAHP

Sicat at al. (2005) determining land suitability classification AHP

Demirel et al. (2012) risk-based evaluation of agricultural strategies FAHP & FANP

Alphonce, C. B. (1997) identifying potential applications in agricultural decisions in developing countries AHP

Aktun & Samut (2013) evaluating agricultural performance of the provinces of country FAHP & VIKOR

Yang et al. (2019) optimization of the disassembly line balancing model for agricultural machinery FAHP

Choi et al. (2013) finding the best way of agricultural reservoir water resources assessment FAHP

Toloi et al. (2022) determination of factors that are relevant for decision-making on soybean production AHP

Source: Author's systematization

The AHP method was also used to evaluate criteria when evaluating agro-industrial projects (Din & Yunusova, 2016) and when formulating public policies related to family farms (Petrini et al., 2016).

Methodology

Existing knowledge of the financial performance of agricultural enterprises in the Republic of Serbia demonstrates the sense of using the FAHP method.

The research in the paper was conducted on a sample of 18 joint-stock companies listed on the Belgrade Stock Exchange within Sector A-Agriculture, Forestry and Fisheries. These are companies that are registered with the Agency for Business Registers under the activity code 0111 - Cultivation of cereal (except rice), legumes and oilseeds and that had made financial statements publicly available as of April 1, 2022. Thus, the research included the following companies: Agrobacka a.d. Backa Topola, Backa a.d. Sivac,

Bajinovac a.d. Bajina Basta, Borac a.d. Surjan, PP Feketic a.d. Sombor, Hajducica a.d. Hajducica, Irmovo a.d. Kisac, Jadran a.d. Nova Gajdobra, Lucic Prigrevica a.d. Novi Sad, Mitrosrem a.d. , Sremska Mitrovica, Nova Pescara a.d. Deliblata, Omoljica a.d. Omoljica, PTK Panonija a.d. Panonija, PP Miletic a.d. Sombor, Sloga a.d. Banatski Karlovac, Sloga a.d. Kac, Stari Tamis a.d. Pancevo and Vojvodina a.d. Sombor. For the purposes ofthe research, data from the individual financial reports ofthe aforementioned companies in the period from 2015 to 2020 were used.

FAHP

Van Laarhoven (1983) proposed a method of fuzzy judgment by comparison to the triangular fuzzy number. Chang (1996) proposed the principle for comparison between the elements of the fuzzy numbers (see Zhu et al., 1999), and published it later in 1996. This work is considered the first, maOking Chang the inventor of the FAHP method. Decision-making using the Fuzzy AHP method enabled the development of different approaches, and one of them is a fuzzy expanded AHP method based on fuzzy triangle numbers (Chang, 1996). Like all methods, this one has critics, but even so, its widespread prevalence and application in different decision-making areas is noticeable.

X = {xj, x2,..., xn} is a set of objects, and G = {g1, g2,..., gn} is a set of goals. The extended analysis methodology Chang (1996) for each object taken provides an

extended analysis of the goal gi. Extended Analysis Values m for each object can be presented as follows:

Ml,Ml,...,MI,i = 1,2,...,n, (1)

whereMj ,(j = 1,2,...,m) are fuzzy triangular numbers. This is the analysis of the following steps:

Step 1: The values of fuzzy extensions for the i-th object in Expressions (2):

^ = VMi

j=1

i=1 j=l

(2)

In order to obtain the expression

YZMj

_ i =1 j=1

fuzzy operations with values of the extended analysis, which is represented by Expressions (3), (4):

, it is necessary to perform additional

V M'

Au ii

j=i

m m

V1 ' V m, V ui

j=i j=i j=i

(3)

YLK

i=i j=i

m m

j=i j=i j=i

(4)

In addition, to calculate the inverse vector using Expression (5):

f \

H« i

i=i j=i

1

i

i

m 5 m 5 m 1

> u. > m. > l

(5)

'l=i 1 y

Step 2: The degree of possibility for M2 = (l2, m2, u2) and Mj = (l, mx, ) is defined by Expression (6):

V(M2 > M1) = y > x [min(>M1 (x), (y))] (6) It can be represented in the following manner by Expression (7):

V (M > m 2) = hgt (M n M 2) ^(d ) V(M2 >Mj) = hgt(Mx nM2) = MM2(d) (7)

i 0

li — u 2

(m2 - mi) - (mi - li)

m2 > mi /i >^2 otherwise

Where d is the ordinate of the highest intersection point/) between juMi Mmi and /lixj (Figure 1). In order to compare Mx and M,, values of both V(Ml >M,) VW1 > M2Dand F(M2 >M,)№ > Ml) are needed.

Figure 1. The intersection between M1 and M2

Source: Chang (i996)

Step 3: The degree of possibility for a convex fuzzy number to be greater than the k & convex numbers Mi (i = 1,2,..., k) can be defined by Expression (8):

V(M >M„M2,...,Mk)

V = V((M > Mj )and(M > M2), ...,(M > Mk )

V = minV(M >Mt),i = 1,2,3,...,k (8) Let us assume that Expression (9) is true:

cl(Ai ) = m\x\ V(SI > Sk) (9)

for k = 1,2,..., n\ k ^ / ^ = 1,2,... ft; k =j£ i The weight vector is obtained by Expression (10):

W = (d'(4),d'(4,...,d'(An))T (10) Where Ai (i = 1,2,..., n) consists of n elements.

Step 4: Through normalization, the weight vectors are reduced to Expression (11):

W = (d(4),d(A2,...,d(An))T (11) where W does not represent a fuzzy number.

In order to address the main deficiency of the classic AHP method, which is an insufficiently large scale of comparison, different comparison scales have been developed based on fuzzy triangle numbers. One of them, through which it is easier to evaluate the importance of criteria or alternatives, is Chang scale (Chang, 1996). Assessing the significance between pairs means that after ranking all correlation coefficients within the indicators, weight coefficients are determined (see Knezevic et al., 2019; Mitrovic et al., 2015; Mitrovic et al., 2021)

VIKOR METHOD

The VIKOR method ("VlseKriterijumska Optimizacija I Kompromisno Resenje") is recognizable by its frequent use in multi-criteria ranking and its usefulness when it comes to finding solutions for various variants of decision-making problems (Rajkovic et al., 2020). The application of the VIKOR method for multi-criteria ranking is based

on the Qi metric presented as follows:

s, - s* R. - R* „ s. - s* , R. - R*

Q = -,R = ,Q = ^ + (1 -,i = 1,2,m. (12) s - s R - R s - s R - R

Otherwise, this method is distance-measure-based. The closest solution to an ideal is called a compromise solution or a viable solution. Further, according to the formula:

i

LP (F* F) = £^ f - fj (x)p ]} p ,1 < p <a (13)

is pointed to the distance between ideal point F* F'the point fix), in the criterion function space (Opricovic, 1986).

First of all, it is pointed out that for each action there is a value of Qi, in order to determine afterwards which action is characterized by the smallest value, because that action is chosen. The next step is to calculate the measure for the multicriteria ranking of the i-th action (Qi) as follows:

Q = p QSi (1 - pí QR (14)

where

s i - s* R i - R nn 2 = = (15) s - s R - R

Minimizing the mentioned metric leads to finding a compromise solution. By applying the FAHP method, the weighting coefficients for the observed financial ratio (which represent the technique of financial analysis) in the financial performance segment (Mandic et al., 2014) are identified, so that in the next step, the VIKOR method is applied. The purpose of the VIKOR method is to evaluate companies using four rating criteria, with the ultimate goal of determining which company has the best financial performance. It is especially emphasized that the VIKOR method has a high utility value when it comes to decision-making problems related to conflicted and incommensurable criteria or when quantitative or qualitative criteria are considered (Muñoz-Medina et al., 2021).

Results and Discussion

At the beginning of presenting the results of the research, the weight coefficients are shown according to the types of financial ratios (Knezevic et al., 2019), specifically for each of them (Table 2).

Table 2. Weight coefficients by types of ratio indicators.

Type of ratio Name Weight coefficient

Cash-coverage ratio 0.496

Liquidity ratios Acid test ratio 0.292

Working capital ratio 0.118

Net working capital 0.094

Type of ratio Name Weight coefficient

Profitability ratios ROA 0.441

ROE 0.280

Profit margin ratio 0.112

Operating margin ratio 0.168

Solvency ratios Debt-to-Equity ratio 0.541

Interest coverage ratio 0.224

Debt ratio 0.131

Equity ratio 0.104

Activity ratios Collection period 0.339

Days' sales in cash 0.409

Days payable outstanding 0.126

Total asset turnover 0.125

Source: Author's analysis

The next step determines the values of liquidity, profitability, solvency and activities ratios from 2015-2020, and according to the years of observed companies. Some of the companies in the particular year and particular indicators could not be ranked because they did not have a profit at the end of the year, or had no debts, so certain ratios could not be calculated. After receiving value results for all indicators for all 6 years, it was necessary to determine the rankings of companies for each specific indicator. Table 3 shows the rankings of companies by liquidity ratios.

Table 3. Company rankings according to liquidity indicator.

Company name 2015 2016 2017 2018 2019 2020

Agrobacka a.d. Backa Topola 2 3 2 2 1 1

Jadran a.d. Nova Gajdobra 1 1 1 1 2 2

Omoljica a.d. Omoljica 14 14 13 14 5 3

Lucic Prigrevica a.d. Novi Sad 6 6 4 4 4 4

Mitrosrem a.d. Sremska Mitrovica 17 17 5 10 6 5

Sloga a.d. Kac 3 2 3 3 3 6

Stari Tamis a.d. Pancevo 8 7 7 7 8 7

Borac a.d. Surjan 9 10 11 9 10 8

PTK Panonija a.d. Panonija 11 11 10 8 13 9

Vojvodina a.d. Sombor 7 8 8 6 11 10

PP Miletic a.d. Sombor 10 9 9 5 12 11

Hajducica a.d. Hajducica 13 13 15 15 7 12

Nova Pescara a.d. Deliblato 5 5 12 11 14 13

PP Feketic a.d. Sombor 12 12 14 13 15 14

Irmovo a.d. Kisac 15 15 16 16 16 15

Backa a.d. Sivac 18 18 6 12 9 16

Bajinovac a.d. Bajina Basta 16 16 17 17 17 17

Sloga a.d. Banatski Karlovac 4 4 18 18 18 18

Source: Author's analysis

In the listed liquidity indicator table, the company Jadran a.d. Nova Gajdobra has the best rankings in all 6 years observed. In the following table there is a ranking of observed companies for profitability indicators.

Table 4. Company rankings according to profitability indicator.

Company name 2015 2016 2017 2018 2019 2020

Agrobacka a.d. Backa Topola 17 15 17 17 17 16

Jadran a.d. Nova Gajdobra 16 16 16 15 16 17

Lucic Prigrevica a.d. Novi Sad 1 6 2 3 4 9

Mitrosrem a.d. Sremska Mitrovica 12 7 9 13 9 3

Sloga a.d. Kac 3 13 14 12 15 15

Stari Tamis a.d. Pancevo 9 5 3 4 6 5

Borac a.d. Surjan 11 1 7 5 2 4

PTK Panonija a.d. Panonija 7 10 8 10 3 1

Vojvodina a.d. Sombor 6 9 6 6 10 11

PP Miletic a.d. Sombor 4 3 1 2 11 10

Hajducica a.d. Hajducica 10 8 5 7 12 2

Nova Pescara a.d. Deliblato 14 11 10 9 8 12

PP Feketic a.d. Sombor 5 2 4 8 7 8

Irmovo a.d. Kisac 2 4 13 1 1 7

Backa a.d. Sivac 15 14 11 11 14 14

Sloga a.d. Banatski Karlovac 8 17 15 16 5 6

Omoljica a.d. Omoljica / 12 / 14 13 13

Bajinovac a.d. Bajina Basta / / / 0 0 0

Source: Author's analysis

Lucic Prigrevica a.d. Novi Sad has the best rankings in the table for the profitability indicator. Omoljica a.d. Omoljica and Bajinovac a.d. Bajina Basta companies cannot be taken into account, because during some years they did not have sales revenues. Table 5 shows ranking of observed companies for activity indicators.

Table 5. Company rankings according to activity indicator.

Company name 2015 2016 2017 2018 2019 2020

Agrobacka a.d. Backa Topola 16 16 15 15 15 14

Jadran a.d. Nova Gajdobra 15 14 16 16 16 15

Lucic Prigrevica a.d. Novi Sad 11 9 10 10 8 10

Mitrosrem a.d. Sremska Mitrovica 2 6 9 11 10 6

Sloga a.d. Kac 12 15 14 14 14 12

Stari Tamis a.d. Pancevo 13 13 13 13 13 1

Borac a.d. Surjan 9 4 5 5 4 8

PTK Panonija a.d. Panonija 10 10 8 9 9 7

Vojvodina a.d. Sombor 6 5 4 4 3 5

PP Miletic a.d. Sombor 4 7 6 7 6 4

Hajducica a.d. Hajducica 7 11 12 8 11 2

Nova Pescara a.d. Deliblato 14 12 11 12 12 13

PP Feketic a.d. Sombor 3 1 2 2 1 3

Irmovo a.d. Kisac 5 2 1 1 2 9

Backa a.d. Sivac 8 8 7 6 7 11

Sloga a.d. Banatski Karlovac 1 3 3 3 5 16

Omoljica a.d. Omoljica / 17 / 16 16 16

Bajinovac a.d. Bajina Basta / / / 16 16 16

Source: Author's analysis

Table 5 shows that PP Feketic a.d. Sombor has the best rankings for activity indicators. Omoljica a.d. Omoljica and Bajinovac a.d. Bajina Basta companies cannot be taken into account, because during some years they did not have specific parameters for calculating activity ratios. Table 6 shows the company's solvency indicator rankings.

Table 6. Company rankings according to solvency indicator.

Company name 2015 2016 2017 2018 2019 2020

Agrobacka a.d. Backa Topola 16 15 14 16 16 17

Jadran a.d. Nova Gajdobra 7 7 8 4 4 8

Lucic Prigrevica a.d. Novi Sad 1 2 6 7 8 7

Mitrosrem a.d. Sremska Mitrovica 11 10 9 9 10 12

Sloga a.d. Kac 10 8 7 8 9 10

Stari Tamis a.d. Pancevo 3 3 1 1 1 3

Borac a.d. Surjan 6 5 4 3 5 4

PTK Panonija a.d. Panonija 4 4 2 2 2 2

Vojvodina a.d. Sombor 15 14 16 15 14 15

PP Miletic a.d. Sombor 5 1 12 13 13 13

Hajducica a.d. Hajducica 2 13 15 11 3 1

Nova Pescara a.d. Deliblato 13 11 11 12 11 11

PP Feketic a.d. Sombor 8 9 13 14 14 14

Irmovo a.d. Kisac 12 12 10 10 12 9

Backa a.d. Sivac 14 16 17 17 17 16

Sloga a.d. Banatski Karlovac 9 6 3 6 6 5

Omoljica a.d. Omoljica / 0 / 5 7 6

Bajinovac a.d. Bajina Basta / / / 0 0 0

Source: Author's analysis

In Table 6 for the solvency indicator, Stari Tamis a.d. Pancevo has the best ranking. Omoljica a.d. Omoljica and Bajinovac a.d. Bajina Basta companies cannot be taken into account, because for some years they did not have specific parameters for calculating solvency ratios. The AHP method can be combined with the VIKOR method when we want to adapt AHP method to changes in the environment. The ratio numbers used in the AHP method are used to determine the significance of certain ratio numbers within a specific indicator. After the significance of ratio numbers and indicators have been obtained by using the AHP method, the VIKOR method is used to rank companies against the achieved results. As the significance of the indicator differs according to the user of the financial statements' information, this research uses the creditor's perspective. In this sense, the significance of the indicators can be seen in the following table.

Table 7. Significance of indicators.

Significance of indicators Indicator

0.427 Liquidity

0.326 Profitability

0.156 Solvency

0.093 Activity

Source: Author's analysis

In the end, we get the following table that shows us, according to the values of the indicators, which companies have the highest rankings in relation to each other.

Table 8. Company rankings.

Company name 2015 2016 2017 2018 2019 2020

Stari Tamis a.d. Pancevo 6 4 3 4 3 1

Mitrosrem a.d. Sremska Mitrovica 17 16 5 12 4 2

PTK Panonija a.d. Panonija 10 12 6 6 5 3

Hajducica a.d. Hajducica 14 15 16 15 6 4

Borac a.d. Surjan 8 3 7 5 2 5

PP Miletic a.d. Sombor 4 2 2 2 15 6

Vojvodina a.d. Sombor 5 6 4 3 8 7

Lucic Prigrevica a.d. Novi Sad 2 1 1 1 1 8

PP Feketic a.d. Sombor 7 5 9 9 13 9

Irmovo a.d. Kisac 13 13 17 10 7 10

Sloga a.d. Kac 1 7 10 7 11 11

Agrobacka a.d. Backa Topola 16 14 13 13 12 12

Jadran a.d. Nova Gajdobra 11 10 12 8 10 13

Omoljica a.d. Omoljica 15 17 14 17 9 14

Nova Pescara a.d. Deliblato 12 8 15 11 18 15

Backa a.d. Sivac 18 18 8 16 16 16

Bajinovac a.d. Bajina Basta 9 9 11 14 14 17

Sloga a.d. Banatski Karlovac 3 11 18 18 17 18

Source: Author's analysis

According to the analysis shown in the paper, and to the data from the financial statements, in years 2015-2020, the best companies were Stari Tamis a.d. Pancevo, PP Miletic a.d. Sombor and Lucic Prigrevica a.d. Novi Sad.

Conclusions

Different models are used for decision-making, and their application is usually limited by the display of information in financial statements. The FAHP and VIKOR methods help users to make a decision and can be very useful for ranking and evaluating companies on that basis. The aim of this paper was to explore and rank the financial performance of companies operating within the A-agriculture, forestry and fisheries sector on the Belgrade Stock Exchange, using FAHP and VIKOR methods.

In today's turbulent and competitive environment, a company's performance evaluation and its comparison with other companies is an important issue for various interest groups and for various reasons. It is about reaching the various investment goals of investors and creditors, with a focus on long-term sustainable business.

The research and analyses presented in this work contribute to the expansion of existing literature for a number of reasons. The ranking of companies operating within the A-agriculture, forestry and fisheries sector listed on the Belgrade Stock Exchange

is presented and analysed, which can be used for further analysis of companies listed on this stock exchange, as well as for analysis of the entire Sector A. The results of the survey outspread previous research in this field. In addition, the work and research done in this paper have certain limitations, which also represent the possibilities of further research. Namely, the research was limited to one sector observed, in this case agriculture, forestry and fisheries, which can be extended to more or even all companies whose shares are listed on the Belgrade Stock Exchange. Likewise, the sample can be extended to all companies from sector A in the Republic of Serbia. Finally, further research may include more spatial and time-consuming samples.

Conflict of interests

The authors declare no conflict of interest.

References

1. Aktan, E., Samut, P. K. (2013). Agricultural performance evaluation by integrating fuzzy AHP and VIKOR methods, International Journal of Applied Decision Sciences, 6(4), 324-344, https://doi.org/10.1504/IJADS.2013.056865

2. Ali, E. B., Agyekum, E. B., & Adadi, P. (2021). Agriculture for sustainable development: A SWOT-AHP assessment of Ghana's planting for food and jobs initiative. Sustainability, 13(2), 628. https://doi.org/10.3390/su13020628

3. Alphonce, C. B. (1997). Application of the analytic hierarchy process in agriculture in developing countries, Agricultural Systems. 53, 1, 97-112

4. Bogdanovic, S., & Hadzic, M. (2019). Strategic multicriteria decision-making process in agriculture. Economic of Agriculture, 66(1), 89-106. https://doi. org/10.5937/ekoPolj1901089B

5. Brozova, H. (2004). The analytic hierarchy process for the decision tree with multiple criteria. Agricultural Economics, 50(2), 77-82. http://dx.doi.org/10.17221/5170-AGRICECON

6. Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655.

7. Choi, Eun-Hyeok, Bae, Sang Soo, & Jee, Hong Kee. (2013). Prioritization for Water Storage Increase of Agricultural Reservoir using FAHP Method. Journal of Korea Water Resources Association, 46 (2), 171-182, https://doi.org/10.3741/ JKWRA.2013.46.2.171

8. Demirel, N. £., Yucenur, G. N., Demirel, T., & Mu§dal, H. (2012). Risk-Based Evaluation of Turkish Agricultural Strategies using Fuzzy AHP and Fuzzy ANP. Human and Ecological Risk Assessment: An International Journal, 18(3), 685702. doi:10.1080/10807039.2012.672902

9. Din, G. Y., & Yunusova, A. B. (2016). Using AHP for evaluation of criteria for agro-industrial projects. International Journal of Horticulture and Agriculture, 7(1), 6. http://dx.doi.org/10.15226/2572-3154/1A/00104

10. Farrokh, M., Heydari, H., & Janani, H. (2016). Two comparative MCDM approaches for evaluating the financial performance of Iranian basic metals companies. Iranian Journal of Management Studies, 9, 359-382.

11. Filgueira-Vizoso, A., Cordal-Iglesias, D., Puime-Guillén, F., Lamas-Galdo, I., Martínez-Rubio, A., Larrinaga-Calderón, I., & Castro-Santos, L. (2023). Sensitivity study of the economics of a floating offshore wind farm. The case study of the SATH® concrete platform in the Atlantic waters of Europe. Energy Reports, 9, 2604-2617.

12. Knezevic, S. P., Mandic, K., Mitrovic, A., Dmitrovic, V., & Delibasic, B. (2017). An FAHP-TOPSIS framework for analysis of the employee productivity in the Serbian electrical power companies.Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, 22(2), 47-60.

13. Knezevic, S., Mitrovic, A., Vujic, M. & Grgur, A. (2019). Analiza finansijskih izvestaja. Samostalno izdanje autora, Beograd. [in English: Knezevic, S., Mitrovic, A., Vujic, M. & Grgur, A. (2019). Financial statement analysis. Self-published by the author, Belgrade]

14. Kong, F., & Liu, H. (2005). Applying fuzzy analytic hierarchy process to evaluate success factors of e-commerce. International Journal of Information and Systems Sciences, 7(3-4), 406-412.

15. Lin, C. N. (2020). A fuzzy analytic hierarchy process-based analysis of the dynamic sustainable management index in leisure agriculture. Sustainability, 72(13), 5395. https://doi.org/10.3390/su12135395

16. Lu, H., Cheng, Y., Gao, H., Zhou, C., & Wang, Q. (2014). Evaluation of Growth of Agricultural Listed Companies Based on AHP Weighting Method. Asian Agricultural Research, 6(12), 23-25.

17. Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37. https://doi.org/10.1016/j. econmod.2014.07.036

18. Meixner, O. (2009). Fuzzy AHP group decision analysis and its application for the evaluation of energy sources. In Proceedings of the 10th International Symposium on the Analytic Hierarchy/Network Process (pp. 2-16), Pittsburgh, PA, USA

19. Milojevic, S., Adamovic, M., Grgur, A. (2021). Evaluation of the Financial Performance of Serbian Dairy Companies Using the Integrated AHP and VIKOR Methodology for Sustainable Competition. International Academic Journal, 2(2), 50-67.

20. Mitrovic, A., Knezevic, S., & Milasinovic, M. (2021). Profitability analysis of hotel companies in the Republic of Serbia. Hotel and Tourism Management, 9(1), 121-134. https://doi.org/10.5937/menhottur2101121M

21. Mitrovic, A., Knezevic, S., & Velickovic, M. (2015). Ratio analysis specifics ofthe family dairies financial statements. Economics of Agriculture, 62(4), 1061-1078. https://doi.org/10.5937/ekoPolj1504061M

22. Muñoz-Medina, B., Ordóñez, J., Romana, M., & Lara-Galera, A. (2021). Typology Selection of Retaining Walls Based on Multicriteria Decision-Making Methods. Appl. Sci., 11 (4), 1457. https://doi.org/10.3390/app11041457

23. Nkurunziza, L., Watson, C. A., Oborn, I., Smith, H. G., Bergkvist, G., Bengtsson, J. (2020). Socio-ecological factors determine crop performance in agricultural systems, Scientific Reports, 10, 4232. https://doi.org/10.1038/s41598-020-60927-1

24. Opricovic, S. (1986). Multicriteria Optimization. Naucna Knjiga, Belgrade.

25. Petrini, M. A., Rocha, J. V., Brown, J. C., & Bispo, R. C. (2016). Using an analytic hierarchy process approach to prioritize public policies addressing family farming in Brazil. Land Use Policy, 51, 85-94. https://doi.org/10.1016/). landusepol.2015.10.029

26. Rajkovic, J., Postin, J., Konjikusic, M., Jagodic-Rusic, A., & Nikolic, M. (2020). Multi-criteria ranking of available forms of promotional activities: A case analysis. Anali Ekonomskog Fakulteta u Subotici, 56(43), 153-169. https://doi.org/10.5937/ AnEkSub2001153R

27. Sicat, R. S., Carranza, E. J. M., Nidumolu, U. B. (2005). Fuzzy modeling of farmers' knowledge for land suitability classification. Agricultural Systems, 83, 49-75.

28. Srebro, B., Mavrenski, B., Bogojevic Arsic, V., Knezevic, S., Milasinovic, M., & Travica, J. (2021). Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies. Sustainability, 13(14): 7712. https://doi. org/10.3390/su13147712.

29. Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 77457754. doi:10.1016/j.eswa.2010.04.066

30. Tashayo, B., Honarbakhsh, A., Azma, A., Akbari, M., (2020). Combined Fuzzy AHP-GIS for Agricultural Land Suitability Modeling for a Watershed in Southern Iran. Environmental Management, 66, 364-376, https://doi.org/10.1007/s00267-020-01310-8

31. Toloi, R. C., Reis, J. G. M. D., Toloi, M. N. V., Vendrametto, O., & Cabral, J. A. S. P. (2022). Applying analytic hierarchy process (AHP) to identify decision-making in soybean supply chains: a case of Mato Grosso production. Revista de Economia e Sociologia Rural, 60(2), https://doi.org/10.1590/1806-9479.2021.229595

32. Van Laarhoven, V., & Pedrycz, W. (1983). A fuzzy extention of saaty' s priority theory.fuzzy sets and systems. Fuzzy Sets and Systems, 77(1-3), 229-241. https:// doi.org/10.1016/S0165-0114(83)80082-7

33. Veisi, H., Deihimfard, R., Shahmohammadi, A., & Hydarzadeh, Y. (2022). Application of the analytic hierarchy process (AHP) in a multi-criteria selection of agricultural irrigation systems. Agricultural Water Management, 267, 107619. https://doi.org/10.1016lj.agwat.2022.107619

34. Wang, Y. J. (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Systems with Applications, 34(3), 1837-1845. https://doi.org/10.10167j.eswa.2007.02.029

35. Yang, Y., Yuan, G., Zhuang, Q., & Tian, G. (2019). Multi-objective low-carbon disassembly line balancing for agricultural machinery using MDFOA and Fuzzy AHP. Journal of Cleaner Production. doi: 10.1016/j.jclepro.2019.06.035

36. Zhu, K. J., Jing, Y., & Chang, D. Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European journal of operational research, 776(2), 450-456. https://doi.org/10.1016/S0377-2217(98)00331-2