Evaluation of financial performance of Chemical and Petrochemical companies in Tehran Stock Exchange

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1 Evaluation of financial performance of Chemical and Petrochemical companies in Tehran Stock Exchange Morteza Bavaghar.1, Dr Mohammad Esmaeil Ezazi.2, Seyyed Mehdi Banihashemi Kahangi.3 1. Department of Accounting, Islamic Azad University, Bandar Jask Branch, Bandar Jask, Iran. 2. Department of Financial Management, University of Sistan and Baluchestan, Zahedan, Iran. 3. Department of Accounting, Islamic Azad University, Bandar Jask Branch, Bandar Jask, Iran. ABSTRACT This paper aims at designing an efficient model for evaluation of investment Chemical and Petrochemical companies' performance by using fuzzy data envelope analysis (FDEA). In so doing, this paper has selected 28 investment companies and compared function of DEA with FDEA in evaluating efficiency and ranking companies. Input variables include initial capital of companies, risk, and price to earnings ratio (P/E); and output variables consist of return on equity and earnings per share. Data pertaining to these variables have been gathered during 5 years. First by using DEA and variable returns to scale methods, companies' efficiency, surplus production factors, optimal values of inputs, and model companies for inefficient companies were calculated and introduced. Then fuzzy data was divided into three high, moderate, and low zones by triangulation method, and efficiency of the studied companies was calculated by fuzzy DEA method and based on mathematical modeling. Finally the efficiency calculated via FDEA method was compared with the one calculated by DEA method and it was revealed that there is a significant difference between them. It can be inferred from other statistical estimations of the model that DEA model is more efficient and has fewer errors in ranking companies and evaluating efficiency compared with FDEA model. - Key words : financial performance, Chemical and Petrochemical companies, Tehran Stock Exchange, Data envelope analysis method, fuzzy. INTRODUCTION Economic growth in today societies is regarded among macro strategic goals of governments and in the light of this matter, production cycles create new products. Thus capital is regarded as one of the main elements of production and the position of capital is more important than other factors. An efficient economy must collect and apply large and small capitals and provide these financial resources for those who need them. In economy, this role is played by capital market (Kapicka, M., 2013). By enjoying physical facilities and various financial instruments, capital market equips capital resources and collects resources and provides them for production sector and so both funds suppliers and funds consumers take benefit; also it creates welfare and economic development (Chari, A., et al., 2012). Investment companies are financial intermediaries which invest funds obtained from selling their stocks on a set of securities. It is about ninety years that these companies are present in the capital market and have influenced both investment and securities market. They mainly trade securities in the secondary markets and rarely finance new investments (CUZMAN, I., et al., 2012). Those who buy stocks of these companies expect their stocks earnings to be more than stocks of other companies or bonds or any other investment. The main goal of these companies is maximizing the returns. By enjoying the required knowledge and efficient experts, these companies make various portfolios and influence the trend of market movement highly (Dvorak, T., 2012). Efficiency is among the most important factors of optimal performance evaluation in the economic and services sectors. Multiple definitions have been offered for efficiency, yet their common point is that an enterprise is recognized efficient that obtains maximum output from combination of specific data. Thus the first step in improving efficiency is its measurement. To measure the efficiency, some methods have been innovated which are classified into parametric and non-parametric methods (IVORSCHI, R., 2012). This paper aims at investigating the performance of investment companies by using (fuzzy) data envelope analysis method among member companies in Tehran Stock Exchange.

2 Literature Review According to the experiences of industrial countries, efficiency measurement is so important that establishment and implementation of a performance evaluation system may increase productivity by 5 to 10 percent even without any changes in the organization or investment. So in our time, efficiency must be the biggest goal of managers and the most precious destination of all firms. Endeavor to increase efficiency is the most serious challenge of management on the threshold of 21 st century (Paradi, J. C., et al., 2013). Theoretical Bases One of the efficient tools that have actualized this matter is data envelope analysis method which is supported by over 2500 valid theoretical and empirical references and is based on mathematical models (Tu- Chuan, C., et al., 2013). However since this method and other methods of performance evaluation work with certain data and in the real world there is no certain data in practice, the necessity of applying fuzzy logic becomes undeniable. In the following, the words used in this paper are defined. Internal efficiency: it is a kind of efficiency that raises optimality of the enterprise actions by assuming input constraints. That is, how an enterprise acts in the current conditions (Emami Meibodi, A., 2005). Social efficiency: it is a kind of efficiency that pertains to the optimality of the effects of the enterprise activities (Woo, W. C., 2013). Technical efficiency: it is the maximum possible production gained by certain production factors (Frances, N.O., 2012). Allocative efficiency: it represents dimensions of optimal allocation of the existing factors with the minimum costs for the enterprise (Färe, R., et al., 2012). Economic efficiency: it is the enterprise ability to obtain the maximum possible profit considering the price and levels of inputs and it is obtained through multiplying technical efficiency by allovative efficiency (ENESCU, M., et al., 2012). Total factor productivity: it represents value of using employees and capital facilities or time to the raw materials, parts and services purchased which is deducted from total value of outputs (Azar Adel, 2003). Total productivity: it shows the relation between system output and all consumed resources applied to produce that output. The obtained index specifies how the whole resources are utilized (Suphannachart, W., 2013). Effectiveness: it is the degree of achieving the determined goals pertaining to the performance and providing human satisfaction from endeavors that are qualitative (BEGEÇ, S., 2013). Research Background (Hsu, J. et. al, 2013), this paper uses Risk-Adjusted Return on Capital (RAROC) to assess the performance of 14 financial holding companies (FHCs) in Taiwan. (Hua, L. et. al, 2012), they suggest the central take the imperfect substitutability or energy structure changes into consideration. We also discuss the difficulties and deficiencies when using Divisia aggregate approach. (Nduagu, E. et. al, 2013) this study presents the energy and environmental performance assessment of producing magnesium hydroxide (Mg(OH)2) from Mg silicates for CO2 mineral sequestration applied to a natural gas combined cycle (NGCC) power plant. (Steve, M. et. al, 2013) contrasting approaches to assessing the performance of public services highlight important issues for policy-makers and future research. (Frank A. G. den Butter, et. al, 2013) this article assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. Research Methodology This paper is regarded as an applied research in terms of objective, a comparative inferential research in terms of research method, and a documentary research in terms of data collection. It uses mathematical modeling and software to analyze the results. Statistical Universe, Sample, and Sampling Method The statistical universe is Chemical and Petrochemical companies in Tehran Stock Exchange. With regard to the limited number of sample, 28 companies are studied in this paper. Data Collection Documentary library research method is used for data collection. Observing and using financial and nonfinancial documents of the studied companies are tools of data collection. So in this paper, data pertaining to the theoretical literature will be gathered through referring to the related papers, books, studies, theses, and so on. Also data related to the research variables will be inferred by referring to Stock indicators, library of Stock Exchange, financial documents and reports of Stock companies, data of the accounting programs "Tadbir Pardaz" and "Rahavard Novin", and

3 So, data is collected through documentary library method. Data Analysis In this paper, performance, efficiency and rank of investment companies are evaluated by using data envelope analysis and fuzzy DEA methods and the results of these two models are compared to determine the optimal model. Conceptual Model In this paper, all calculations are carried out through two basic models of DEA method namely, fuzzy constant returns to scale (FCRS) and fuzzy variable returns to scale (FVRS). Also both input-oriented and output-oriented approaches are estimated in these models. Figure 1 depicts the research conceptual model. Figure 1- Research conceptual model Findings Here, different forms of efficiency (i.e. managerial, scale, and technical) in the investment companies are calculated in variable returns to scale and inputoriented states. Optimal values of variables are calculated for maximizing efficiency in each company, reference unit(s) is introduced to the inefficient companies, and surplus production factors are calculated in the inefficient companies. Specialized programs of DEA method, i.e. Version 2.1 DEAP and EXCEL are used for Computations. Due to constraints of constant returns to scale assumption (e.g. it is only useful when enterprises act at an optimal scale and this is very far-fetched) and for advantages of variable returns to scale approach and providing more accurate and complete results, the above outputs will be calculated in the variable returns to scale (VRS) state. In the following, the efficiency estimation model is designed by fuzzy data envelope analysis and then in SAS.V1.9.3 software, efficiency values for any three states (managerial, scale, and technical) are estimated. Finally based on the results, accuracy of research hypotheses will be investigated. Efficiency Calculation by Variable Returns to Scale DEA Here, the results of different types of efficiency, values of target input and output, reference companies and their weights and surplus production factors based on minimizing (input-oriented) production factors are presented in table 1. Company Name Table 1- The companies' efficiency calculated by DEA method Technical Managerial Scale Mean Efficiency Efficiency Efficiency Efficiency Type of Returns Persian Gulf Petrochemical Irs Kharg Petrochemical Irs Poly Acryl Iran Investment in Oil and Irs Gas Supply Farabi Petrochemical Irs The floor Irs Gulf Chemical Industries

4 Shiraz Petrochemical Irs Inorganic salts of Goltash Irs Isfahan Petrochemical Irs Industrial and chemical color Irs Petrochemical Investment Irs Paxan Irs Iran's investment in the chemical industry Raw material for synthetic fiber Irs Labyran Irs Chemical industry Sinai Irs Chemical barking Primrose Irs Abadan Petrochemical Irs Pars International Irs Nyrvklr Drs Tolypers Irs Carbon black bark Irs Carbon Iran Drs Kermanshah Petrochemical Irs Petrochemical shazand Irs Petrochemical Fanavaran Irs The average efficiency Note: irs means increasing returns to scale and drs means decreasing returns to sale. Diagrams of scale, managerial, and technical efficiencies are presented in figures 1 to 3. Except for poli akril, fars, amlah and Iran's investment in the chemical industry Companies that are regarded among quite efficient companies with 100 percent efficiency means, the rest companies are classified into inefficient group; so optimal values of their inputs and outputs must be determined and surplus production factors of all companies must be calculated. Below companies can be classified into the group of companies with high and acceptable efficiency mean. Persian Gulf Petrochemical, Oil & Gas and Petrochemical Investment supply Farabi Petrochemical, petrochemical Isfahan and carbon Iran companies with mean efficiencies higher than 80% encounter 20% inefficiency at most, and by a little care and optimization they will close their reference company and get efficient. it. The weakest efficiencies Goltash Company (0.33) and Paksan Company (0.35). The rest 18 companies have moderate efficiencies (between 30 to 80 percent). The average scale efficiency, managerial efficiency, and technical efficiency equal respectively 0.74, 0.79, and Since managerial efficiency of all studied companies is near 80%, one can claim that investment companies have a relatively good situation in terms of managerial efficiency, a situation between average and good in terms of technical efficiency, and are getting well in terms of scale efficiency. Efficiency Calculation by Variable Returns to Scale FDEA Here, efficiency was calculated by using mathematical programming in SAS environment and the results are presented in table 2. As analysis of this table is similar to table 1, we refrain from mentioning Table 2- The companies' efficiency calculated by FDEA method Company Name Technical Efficiency Managerial Efficiency Scale Efficiency Mean Efficiency

5 Persian Gulf Petrochemical Kharg Petrochemical Poly Acryl Iran Investment in Oil and Gas Supply Farabi Petrochemical The floor Gulf Chemical Industries Shiraz Petrochemical Inorganic salts of Goltash Isfahan Petrochemical Industrial and chemical color Petrochemical Investment Paxan Iran's investment in the chemical industry Raw material for synthetic fiber Labyran Chemical industry Sinai Chemical barking Primrose Abadan Petrochemical Pars International Nyrvklr Tolypers Carbon black bark Carbon Iran Kermanshah Petrochemical Petrochemical shazand Petrochemical Fanavaran Source: research findings Research Hypotheses This paper presents below hypotheses. For more validity, the results of variable return state are compared with fuzzy values. First hypothesis (FDEA model is more efficient than DEA model) To test the research hypotheses, paired samples test in SPSS software was used. Tables 3 to 6 present the output results. Table 3- The results of paired samples test of mean efficiencies in both methods (first hypothesis) Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 FDEA DEA Paired Samples Correlations Pair 1 FDEA & DEA N Correlation Sig Paired Samples Test Paired Differences t Sig. (2-tailed) Mean Std. Deviation Std. Error Mean Mean Std. Error Mean

6 There is a significant difference between efficiency values calculated by these two methods (Sig 0.05). Also based on the results, the average efficiency in DEA method is 0.69 and in FEDA is Efficiency of DEA method is clearly more than efficiency calculated by FDEA method. Thus FDEA model has less efficiency compared to Stock Exchange model and the first hypothesis is rejected. Second hypothesis (mean technical efficiency of FDEA has a significant difference with mean technical efficiency of DEA method) The results of paired samples test of technical efficiency are presented in table 4. Table 4- The results of paired sample test of mean technical efficiencies in both methods (second hypothesis) Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 FDEA DEA Paired Samples Correlations Pair 1 FDEA & DEA N Correlation Sig Paired Samples Test Paired Differences T Sig. (2-tailed) Mean Std. Deviation Std. Error Mean Mean Std. Error Mean There is a significant difference between technical efficiency values calculated by these two methods (sig 0.05). Also based on the results, the average efficiency in DEA method is 0.59 and in FDEA method is Technical efficiency of DEA method is clearly more than efficiency calculated by FDEA method. Thus FDEA model has less efficiency compared to DEA model and the second hypothesis is rejected. Third hypothesis (mean managerial efficiency of FDEA method has a significant difference with mean managerial efficiency of DEA method) The results of paired samples test of mean managerial efficiencies are presented in table 5. Table 5- The results of paired sample test of mean managerial efficiencies in both methods (third hypothesis) Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 FDEA DEA Paired Samples Correlations Pair 1 FDEA & DEA N Correlation Sig Paired Samples Test Paired Differences t Sig. (2-tailed) Mean Std. Deviation Std. Error Mean Mean Std. Error Mean There is a significant difference between managerial efficiency values calculated by these two methods (sig 0.05). Also based on the results, the average efficiency in DEA method is 0.78 and in FDEA method is Managerial efficiency of DEA method is clearly more than efficiency calculated by FDEA method. Thus FDEA model has less managerial efficiency compared to DEA model and the third hypothesis is rejected. Fourth hypothesis (mean scale efficiency of FDEA method has a significant difference with mean scale efficiency of DEA method) The results of paired samples test of mean scale efficiencies are presented in table 6. Table 6- The results of paired sample test of mean scale efficiencies in both methods (fourth hypothesis) Paired Samples Statistics 649

7 Mean N Std. Deviation Std. Error Mean Pair 1 FDEA DEA Paired Samples Correlations Pair 1 FDEA & DEA N Correlation Sig Paired Samples Test Paired Differences t Sig. (2-tailed) Mean Std. Deviation Std. Error Mean Mean Std. Error Mean There is a significant difference between scale efficiency values calculated by these two methods (sig 0.05). Also based on the results, the average efficiency in DEA method is 0.73 and in FDEA method is Scale efficiency of DEA method is clearly more than efficiency calculated by FDEA method. Thus FDEA model has less scale efficiency compared to DEA model and the fourth hypothesis is rejected. Conclusions and Suggestions In DEA method, by using a set of data pertaining to the final products and all effective factors and inputs applied in the process of production or service providing, and based on the performance of model enterprises, a standard limit for production is estimated and the relative efficiency of other units are compared with it. In this method that enjoys stronger theoretical bases compared with other methods of efficiency measurement and evaluation including index method, production function method, SFA method, AHP method and so on, total factor productivity is calculated by distance function, production factors, and Malmquist Index by separating its components namely, managerial efficiency, technological efficiency, scale efficiency, and technical efficiency. In this paper, first the main model of DEA method, i.e. variable returns to scale, was analyzed and equations of FDEA method were presented. With respect to the constraints of constant returns to scale model and that it is only applied when enterprises act at an optimal scale which is approximately impossible, variable returns to scale model was used for analysis. The main result of this paper is that DEA model is more efficient compared to FDEA model. Also mean technical, managerial, and scale efficiencies of DEA method are more than FDEA method. Suggestions As regards different types of efficiency, managers of companies are suggested to improve technical efficiency by adopting proper and optimal strategies (through using technical and technological equipments and facilities and human resources) and then undertake some measures for improving scale efficiency (through using tools of gaining public confidence) and managerial efficiency (by holding educational courses and establishing reward system based on managers competency, reviewing human resources and efficient managers maintenance system, developing a meritbased hiring system, designing related research plans, etc.). As regards return to scale factor, to increase efficiency in the form of production factors including capital, new human resources, area, fixed assets, etc., it is suggested to apply budget, resources proper allocation, and efficient management to improve efficiency of the inefficient companies. Also surplus factors must be removed from the operational cycle and be allocated to the inefficient units. This paper has been carried out regarding evaluation of efficiency of Tehran Stock Exchange companies. Organizations are recommended to use the same method for their internal units so as to increase the whole company efficiency by increasing units' efficiency. Suggestion for Future Research Ranking subsidiary institutions of each company through DEA approach. Analyzing sensitivity of input and output variables and calculating the role of each variable in efficiency. Investigating the effect of any input and output variable on the efficiency of different regions by econometrics models. Using DEA/AHP, DEA/GP, DEA/SFA, and DEA/BSC methods for evaluating efficiency and performance and comparing them with DEA method. References 1- Adel Azar (2003). "Designing Mathematical Model of Performance Evaluation by using Data Envelope Analysis Method", Human Sciences Modares Journal, period 6, No BEGEÇ, S., (2013), Effective Diversity Management Initiatives, Econ-journals, journal of International

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