Performance evaluation of electricity generation companies traded on BIST according to the financial parameters through the application of TOPSIS method

Electrical energy is essential to the economic and social development and will improve the quality of life in any nation. Recently, a great deal of attention has been received and a fairly large body of literature has been focused on the TOPSIS method. In this study, the financial performance of 6 electricity generation companies whose shares are traded in the BIST are evaluated with multi decision making analysis by using financial ratios.  The study   conducted .NET framework coded in C# programming language.


Introduction
Energy is one of the most important components in developing countries.Energy plays a critical role in the economic growth, progress, and development, as well as poverty alleviation and security of any nation.Energy exists in many forms but the most important form is the electrical energy.Electrical energy can be easily converted into other forms of energy and transmitted from one place to another with the help of conductors.Electrical energy is essential to the economic and social development and will improve the quality of life in any nation.
The Turkish electricity sector is going through a rapid growth, mainly due to rising demand.Similar to the electricity sector, both consumption and generation of electricity is increasing as well.As reported by the Turkish Electricity Transmission Company (Turkiye Elektrik İletim A.S., TEIAS), the installed capacity in Turkey in 2014 was 69519,8 MW, while this figure rose by 5,2% in 2015, reaching 73146,7 MW.Turkey's installed power capacity stands today at around 75 GW and the aim is to have 100 GW of installed capacity by 2023.The total installed capacity is made up of The Electricity Generation Company (EUAS), independent power producers, the build-own-operate power plants, the build-own-operate-transfer power plants, the auto-producers and companies operating under transfer of operating rights agreements.
Power generation capacity has increased significantly.In 2015, total net electricity generation increased by 3% compared to 2014.The Turkish electricity generation is largely dominated by natural gas/LNG powered electric plants (37,8%), coal (lignite, imported coal and hard coal) powered plants (30%) and hydroelectric plants (25,8).Wind power plants constitute about 4,4% of total electricity generation in Turkey.Geothermal power plants accounted for 1,3% and biogas power plants 0,6%.The nuclear energy is not used yet as power generation resource in Turkey.But, Turkey intends to have it first nuclear power plant operating until 2023.Distribution of The Turkish electricity generation according to the sources is given in Figure 3 (TEIAS, 2015).Recently, a great deal of attention has been received and a fairly large body of literature has been focused on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.Financial ratio analysis is one of the most widely used fundamental analysis techniques.Feng and Wang (2000) investigated a performance evaluation process for Taiwan's five major airlines with financial ratios.They used the grey relation analysis to select the representative indicators and used the TOPSIS method.Yurdakul and İç (2003) used the TOPSIS method for the financial performance evaluation of five large-scale public companies that operates in the Turkish automotive industry.Wang (2008) implemented fuzzy multi-criteria decision making approach to evaluate financial performance of domestic airlines in Taiwan.Ertuğrul and Karakaşoğlu (2009) used a fuzzy multi-criteria decision making model for performance evaluation of Turkish cement firms by using some of the traditional accounting-based financial ratios.Dumanoğlu ve Ergül (2010) analyzed financial performances of 11 technology firms whose shares are traded on the Istanbul Stock Exchange (ISE) with the TOPSIS method by using financial statements for the period 2006-2009. Bülbül and Köse (2011) evaluated the financial performance of 19 Turkish food companies whose shares are traded on the ISE with multi decision making analysis by using financial ratios.See also Meydan et al. (2016), where authors evaluated financial performance of food companies traded on the ISE using Grey Relational Analysis.Uygurtürk and Korkmaz (2012) analyzed financial performances of 13 basic metal industry enterprises whose shares are traded on the ISE with the TOPSIS method by using financial statements for the period 2006-2010.An application of evaluating the performance of 23 Greek manufacturing sectors with the use of financial data has been provided by Halkos and Tzeremes (2012).Mandic et al. (2014) proposed a fuzzy multi-criteria model that will facilitate the assessment of the financial performance of Serbian banks for the period between the years 2005 and 2010.In addition, financial performances of 7 tourism firms traded in the Borsa Istanbul (BIST) have been evaluated as to eight financial ratios including liquidity, leverage, profitability and activity indicators between the period 2010-2014 by Özçelik and Kandemir (2015).The main objective of this study is to evaluate the financial performance of 6 electricity generation companies whose shares are traded in the BIST with multi decision making analysis by using financial ratios.For this purpose, the TOPSIS method is used in this study.
The paper is organized as follows: The next section presents introduction of 6 electricity generation companies.The main idea of the TOPSIS method is outlined in section 3. Section 4 presents the financial performance of 6 electricity generation companies.Finally, the last section concludes the paper.

The electricity generation companies
In this paper, financial performances of 6 electricity generation companies whose shares are traded in the BIST are analyzed with the TOPSIS method by using financial ratios for 2015.The information of financial ratios calculation used was derived from the annual financial statements published on BIST (2016), Public Disclosure Platform (KAP, 2016) and Bigpara (2016) websites.The electricity generation companies studied are shown in Table 1.

TOPSIS
The TOPSIS is a multi-criteria decision technique.This technique is developed by Hwang & Yoon (1981).According to this method solution is calculated using Euclidian distance formula between positive and negative ideal solution of criteria.Performance of alternative is determined with nearest to positive ideal solution and farthest to negative ideal solution.The positive ideal solution has the maximum value of benefit criteria, minimum value of cost criteria (Equation 5).The negative ideal solution has the maximum value of cost criteria; minimum value of benefit criteria (Equation 6) Table 2 shows the structure of a type of multiple criteria decision problem.Flow chart of TOPSIS method's calculation steps are shown in Figure 2. Alternative whose performances are to be compared are defined.The criteria of alternatives are defined.A matrix of alternative and criteria are formed and data is set (Equation1).Then matrix is normalized with each of values in column are divided to sum of respective matrix column (Equation2, 3).İlkuçar, M., Çifci, A. (2016).Performance evaluation of electricity generation companies traded on BIST according to the financial parameters through the application of TOPSIS method.International Journal of Social Sciences and Education Research, 2 (3), 815-824.
Weight values of criteria are assigned by an expert.Weight values must be between 0 and 1 (0<w j <1).Sum of criteria weight values must be 1.Equation 4 is acquired by multiplying weight values and respective criteria of alternative ( () =  ( *  () ).
The positive ideal value ( ( B ) is maximum value of benefit criteria, minimum value of cost criteria (Equation 5).The negative ideal solution is maximum value of cost criteria, minimum value of benefit criteria (Equation 6).
Distance of alternative from positive and negative ideal solution is calculated using Euclidean Distance Formula (Equation 7, 8).Performances of alternatives are determined according to relative proximity to positive and negative ideal solution (Equation 9).D i being greater indicates that the relevant alternative is closer to positive ideal solution (Aktaş et al., 2015).If D i =1 then alternate is point of positive ideal solution and if D i =0 then alternate is point of negative ideal solution (Özdağoğlu, 2013).

𝑫 𝒊
B is the deviation from positive ideal and,   O is the deviation from negative ideal solution.
In Table 8, distance calculations of ideal positive and negative solutions are done.Later in the study, relative distance from ideal positive and negative solution is calculated (Equation 9) and performance values are acquired.Performance is calculated different weight values (Table 9).Two different weight's performance values of Table 10 is illustrated in descending order in Table 11.According to this order, company with the best performance is AKSEN, worst is AKENR.AYEN close to AKSEN, and AKSUE close to AKENR.

Conclusions and recommendations
Companies engaged in electricity energy generation, traded in the Borsa İstanbul, are evaluated according to the acknowledged evaluation criteria.TOPSIS performance evaluation method is used since this is a multi-criteria decision problem.Six companies are listed in descending order according to 2015 economic data criteria and company performance.As a result, company with the best performance was AYEN, and the company with the worst performance was AKENER.

Table 1 .
The electricity generation companies.

Table 2 .
Table indicate that alternative which is denoted i=1,2,…,m; criteria j:1,2,…n; x ij value demonstrate the criteria performances of alternative(Yu et all., 2011).A type of multiple-criteria decision problem.

Table 3 .
Description of criteria

Table 4 .
Performance criteria values of Energy Production Companies transacted in BIST

Table 5 .
Performance criteria values of Energy Production Companies transacted in BIST

Table 7 .
Vector table which is weighted according to w j weight value

Table 8 .
Positive and negative ideal values of criteria according to benefit/cost features İlkuçar, M., Çifci, A. (2016).Performance evaluation of electricity generation companies traded on BIST according to the financial parameters through the application of TOPSIS method.International Journal of Social Sciences and Education Research, 2 (3), 815-824.

Table 9 .
The weight of criteria

Table 10 .
Distance value of alternative from ideal positive and negative

Table 11 .
Performance order of alternatives different weight according to proportional values of positive and negative ideal