DEA-Based Evaluation of the Oil Prices Effect on Industry: A Case Study of the Stock Exchange
الموضوعات : نشریه بینالمللی هوش تصمیمAli Taherinezhad 1 , Alireza Alinezhad 2
1 - PhD Candidate, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Oil Price, Stock Exchange, Performance Evaluation, Data Envelopment Analysis (DEA).,
ملخص المقالة :
This paper evaluates the impact of oil prices on the industry and the total index of the Tehran Stock Exchange. In addition, the impact of oil shocks on the Tehran Stock Exchange is also studied. The analyzed data are the weekly crude oil price data in the industry and the index of the Tehran Stock Exchange during the 10-year period from 2005 to 2015. For data analysis, this paper proposes an appropriate mathematical model based on the non-parametric technique of data envelopment analysis (DEA), which is a powerful mathematical tool for ranking decision-making units (DMUs). Ultimately, we used the proposed model to calculate the efficiency and ranking of 10 companies active in the food and beverage industry (except for the sugar group) and analyzed the results with the CCR model. Following that, the final ranking was done using the weighted average method. According to the results of the research, Mahram Production Group Company was ranked 1st in the weighted average method, and Shir Pegah West Azerbaijan was ranked second, and Nab Industrial Companies, Pars Livestock Feed, Georgian Biscuits, Iran Behnoosh, Pak Dairy, Behpak, Salemeen and Shir Pegah Isfahan ranked 3rd to 10th , respectively.
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International Journal of Decision Inelligence
Vol 1, Issue 3, Summer 2024, 23-33
DEA-Based Evaluation of the Oil Prices Effect on Industry:
A Case Study of the Stock Exchange
Ali Taherinezhada,1, Alireza Alinezhada
aDepartment of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Received 13 May 2024; Accepted 16 May 2024
Abstract
This paper evaluates the impact of oil prices on the industry and the total index of the Tehran Stock Exchange. In addition, the impact of oil shocks on the Tehran Stock Exchange is also studied. The analyzed data are the weekly crude oil price data in the industry and the index of the Tehran Stock Exchange during the 10-year period from 2005 to 2015. For data analysis, this paper proposes an appropriate mathematical model based on the non-parametric technique of data envelopment analysis (DEA), which is a powerful mathematical tool for ranking decision-making units (DMUs). Ultimately, we used the proposed model to calculate the efficiency and ranking of 10 companies active in the food and beverage industry (except for the sugar group) and analyzed the results with the CCR model. Following that, the final ranking was done using the weighted average method. According to the results of the research, Mahram Production Group Company was ranked 1st in the weighted average method, and Shir Pegah West Azerbaijan was ranked second, and Nab Industrial Companies, Pars Livestock Feed, Georgian Biscuits, Iran Behnoosh, Pak Dairy, Behpak, Salemeen and Shir Pegah Isfahan ranked 3rd to 10th, respectively. The rest of the results have been discussed and analyzed in detail. The results of this research can have a beneficial and profound effect on the managerial insights of policymakers in the fields of stock exchange and the oil industry.
Keywords: Oil Price, Stock Exchange, Performance Evaluation, Data Envelopment Analysis (DEA).
[1] * Corresponding Author. Email: atqiau@gmail.com
1.Introduction
Financial markets are one of the most basic markets in any country. The conditions of these markets strongly affect the real sectors of the economy and are strongly influenced by other sectors. One of the important components of financial markets is the stock exchange. The stock exchange is an organized and official market for buying and selling companies under specific rules and regulations. One of the tasks of these markets is to help make the price of securities fair and speed up transactions (Schwartz et al., 2015). On the one hand, the stock exchange is a center for collecting savings and liquidity of the private sector in order to finance long-term investment projects. The stock market is affected not only by the national economy, but also by the global economy. The 1997 crisis of Southeast Asian countries had an impact on the world economy, including on the economy of Iran, through the reduction of the demand of these countries for crude oil and the fall in oil prices. It can be seen that there is a significant relationship between stock market developments, recession, and economic prosperity (Neacșu & Georgescu, 2024).
Oil is one of the world's strategic goods and is considered one of the production inputs of every country. As a result, the extreme fluctuations in oil prices are the main source of turmoil for the economies of oil-producing countries. On
the other hand, since the oil revenues in Iran's economy are based on the principles of a single-product economy, it shows that the price of oil and its revenues are considered an exogenous factor and a driver of economic prosperity and recession in Iran. At the same time, the fluctuation outside the control of this factor causes most economic variables to fluctuate (Liu et al., 2023a).
The empirical literature on the impact of oil shocks and oil price fluctuations on the total index of the stock exchange can be divided based on developing and developed countries. In developing countries, economic growth is usually higher, and their demand for oil is increasing. On the other hand, energy consumption is more efficient in developed countries, and the production of goods and services in developing countries consumes more energy than developed countries. Hence, the changes in oil prices have a greater effect on the production situation of these countries and, as a result, the capital market returns and consequently the total index of their stock exchanges (Dhingra et al., 2024).
The importance of examining crude oil price fluctuations on Iran's economy as the second-largest producer of crude oil among the organization of crude oil producing countries in the world and due to the large volume of income from exports (more than 50% of the country's total exports) is very high to the extent that Iran has turned into a country dependent on crude oil revenues (more than 60% of the government's annual budget comes from crude oil exports) (Golkarami & Kord, 2024). So, the volatility in the world price of crude oil can have a great effect on Iran's economic structure. Although oil shocks can have a negative effect on the stock market due to the uncertainty they create in the financial markets, this issue depends on the nature of the shock (demand side or supply side). If the shock is from the demand side, the markets can have a positive response to this shock, and if the shock is from the supply side, the market response can be negative (Filis et al., 2011).
The necessity of checking and predicting oil price fluctuations using data envelopment analysis (DEA) comes from the fact that oil is a very important resource in the world economy, especially in Iran. Changes in oil prices and not paying attention to them can have significant effects on the economy of an oil-rich country. Therefore, in DEA, predicting oil price fluctuations is an important input in many decision-making processes (Ouenniche et al., 2014). DEA is a powerful method for calculating the efficiency of decision-making units (DMUs) such as companies, hospitals, universities, etc. In this method, the efficiency of DMUs is calculated using mathematical programming models. The most key feature of this method is that the DMUs under investigation are homogeneous and use inputs of the same type to produce outputs of the same type. This is the feature that makes the units comparable to each other (Taherinezhad & Alinezhad, 2022; Taherinezhad & Alinezhad, 2023).
The aim of the current research is to investigate oil price fluctuations via DEA models using recorded and definitive data in the Iranian stock market. This research shows its innovation and key contribution in the use of DEA models and Tehran stock exchange data in comparison with previous literature on the subject. The relevant organizations and bodies, having the innovative and practical results of this research, can take a smoother path to make correct decisions in this field at a wide and international level.
The rest of the paper is organized as follows: Section 2 reviews the most relevant recently published articles in this field. The research methodology and details of the collected data are presented and explained in Section 3. Section 4 includes computational results and discussion. Finally, in Section 5, conclusions, future research directions and suggestions are given.
2. Relevant Studies
In recent years, research in the field of decision science has accelerated and expanded greatly (Sarrafha et al., 2014; Alinezhad & Khalili, 2019a; Alinezhad & Khalili, 2019b; Alinezhad & Taherinezhad, 2020; Alinezhad et al., 2023). DEA is one of the important tools of decision science that has been widely used in various fields such as banking industry, medical industry, poultry industry, media industry, and other widely used industries (Alinezhad et al., 2007; Kiani Mavi et al., 2010; Alinezhad & Taherinezhad, 2021; Taherinezhad et al., 2023a; Taherinezhad et al., 2023b). In current study, if we look at DEA from a technical point of view, the oil industry and stock exchange market will be placed next to it as an application. Based on this, a comprehensive approach that simultaneously includes DEA, oil industry, and stock exchange market was used to review the literature. We limited the search for relevant articles to the last 15 years to examine novel innovations and contributions. In the following, the found studies are described in detail.
Ling et al. (2010) investigated the stock market in Malaysia with the DEA approach and measured the efficiency of companies in the Malaysian stock market. Finally, after the results were released, for each inefficient company, there was a set of optimal companies to be their reference company. Filis et al. (2011) investigated the relationship between crude oil prices and stocks in developed countries using the vector autoregressive (VAR) model. They conducted studies on the relationship between crude oil prices and stock indices in six member countries of the Persian Gulf Cooperation Council, whose study period was from 1999 to 2009. They used weekly data and showed that there is a negative relationship between the crude oil price and the stock index. Shahbazi et al. (2013) investigated the impact of crude oil price shocks on the stock market. They showed that the crude oil supply shock has no significant effect on the crude oil price. Only the shocks of crude oil demand and total demand are considered effective factors in stock returns on the Tehran stock exchange.
In recent years, the number of quantitative models available for predicting crude oil price fluctuations has grown widely. This makes evaluating the relative performance of models to find the most suitable model with low error a vital work. Xu and Ouenniche (2012) reviewed the relevant literature and found that most studies tend to compare models using a single criterion at a time. The gap that emerges here is: which forecasting model performs best when considering all criteria? Focusing on this research gap, Ouenniche et al. (2014) developed a slacks-based super-efficiency DEA model to evaluate the relative performance of competing crude oil prices’ volatility forecasting models. Silvapulle et al. (2017) use an innovative nonparametric panel data approach to model the long-run relationship between the monthly oil price index and the stock market indices of ten major oil importing countries (i.e., the United States, Japan, China, South Korea, India, Germany, France, Singapore, Italy and Spain) introduced. Generally, the results showed that the non-parametric panel data model better captures the way in which the underlying oil-stock price relationship has evolved over time compared to the point estimates of the parametric counterpart.
Delgado et al. (2018) analyzed the variables of oil price, exchange rate and stock market index to explain how they interact with each other in the Mexican economy. The period examined in this study included monthly data from January 1992 to June 2017. They implemented a VAR model that included the oil price, nominal exchange rate, Mexican stock market index, and consumer price index. Their results showed that the price of oil is statistically significant against the exchange rate. Finally, they came to the conclusion that the increase in the price of oil causes the increase in the exchange rate. Bagirov and Mateus (2019) designed a three-step study with the aim of expanding the understanding of the relationship between oil prices, stock markets, and financial performance of oil and gas companies. First, they studied the impact of oil price fluctuations on stock markets in Europe. In the second step, they examined the volatility spillover between oil and European stock markets. Finally, in the third step, the impact of crude oil price changes on the financial performance of oil and gas companies, both listed and non-listed, in the Western Europe region was investigated. Their findings confirmed the existence of a relationship between oil and European stock markets and proved that crude oil prices significantly and positively affect the performance of listed oil and gas companies in Western Europe.
In another study, Hashmi et al. (2022a) investigated the impact of crude oil prices on the Chinese stock market and selected industries using the VAR-DCC-GARCH (short for vector autoregressive, dynamic conditional correlation, and generalized autoregressive conditional heteroskedasticity) model in the period from December 26, 2001 to April 30, 2019. Their experimental results also showed that the effect of Brent crude oil price on the composite index of Shanghai and selected industries is significant. Following this research, Hashmi et al. (2022b) in another study investigated the interactions between oil prices, exchange rates and stock returns in Pakistan using quarterly data from January 2000 to December 2019. They demonstrated the use of quantile autoregressive distributed lag (ARDL) model in this research. Empirical findings showed that the effect of oil price and exchange rate on stock prices is different in bullish, bearish and normal conditions of the stock market.
Liu et al. (2023b) studied the heterogeneous effects of oil prices on the Chinese stock market based on a new decomposition method. They used the GAO (short for generalized additive outlier) method to control potential factors. In line with the trend of previous studies, Behera and Rath (2024) investigated the relationship between crude oil prices and stock returns in G20 countries. Using the dynamic connectedness approach and dataset from March 24, 2014 to December 15, 2023, they found volatility transmission between stock returns and crude oil prices. Their research findings provided important implications for investors and policymakers related to this field.
As we found out from reviewing the articles, the issue of the impact of oil prices on stock indices is one of the trending research issues in the world today, which is of particular importance. According to the reviewed articles, no research has been done that is completely similar to the present research. In the present study, we use the DEA technique to investigate the effect of oil prices on the Tehran stock exchange. For analysis, we consider a set of food industry companies as DMUs. From another point of view, the innovation and novelty of the current research are defined by the use of DEA in the specific case study. So that the data used in this study has not been tested in another study with the same approach.
3. Methodologies
In this paper, after reviewing the existing literature, hypotheses were proposed, and after determining the sample size and appropriate sampling method, data was collected, and then analysis was done using statistical techniques, and finally, the hypotheses were tested. In a general summary, it can be said that this research is a descriptive-survey type of research. In addition, since the results of this research can be used practically, this research contains an applied case study. In the field of applied studies (with a case study), analogical and inductive methods can be used to advance research. The method used in this article is also the deductive-inductive method. In this way, the theoretical framework and the background of the research have been collected through the library, articles and the Internet in a comparative manner. After that, information is collected to reject or confirm the hypotheses in an inductive format. Based on this, we used field studies to collect data and information with the aim of predicting cash and price returns using the DEA method. Based on the purpose of the research and by examining the theoretical framework of the subject, the hypothesis of the research is explained as follows: "It is possible to obtain the relationship between the changes in the price of crude oil and the total price index of the Tehran stock exchange using DEA". Plus, the details of the thematic, spatial and temporal domains of the current study are as follows: This research is thematically in the field of financial management science and the analysis of forecasting cash return and stock price return using DEA in companies admitted to the Tehran stock exchange. Spatial domain includes companies in the food and beverage industry (except the sugar group) in the Tehran stock exchange. Additionally, the time period chosen to collect data and test the hypotheses is 10 years between 2005-2015.
3.1. Sampling and Data Collection
As mentioned earlier, the statistical population of this research is all the companies accepted on the Tehran stock exchange, which are active in the industry of all kinds of food and beverage products (except the sugar group). Generally, there are common methods for sampling in humanities research, including simple random sampling, systematic random sampling, stratified sampling, cluster sampling, and multi-stage sampling (Berndt, 2020). The sampling method in this research is simple random sampling, so that all members of the statistical community have an equal chance of selection. In fact, in this research, the elimination random sampling method has been used. For sampling, first, the parent investment companies (holdings) in the industries mentioned above have been removed from the statistical population, and then the remaining companies have been selected as members of the sample if they meet the following conditions. Therefore, among all the companies active in the four investigated industries, the companies that met the following criteria for a period of 10 years (2005-2015) were selected as a statistical sample:
· The companies must be admitted to the Tehran Stock Exchange by the end of 2004.
· The financial history of companies must end at the end of March every year.
· Companies should not have changed their financial year during the periods in question.
· Companies should not stop their activities during the intended periods.
· The information they need should be available in the variable definition section.
· The accounting procedures used for the financial period should be the same.
There are a total of 22 companies active in the food and beverage industry (except sugar) on the Tehran stock exchange. The number of examined samples from this industry is 10 companies, so 45% of the companies in this industry have been investigated, and their names are as follows: 1) Behpak; 2) Iran Behnoosh; 3) Georgian Biscuits; 4) Pars Livestock Feed; 5) Salemeen; 6) Shir Pegah Isfahan; 7) Shir Pegah West Azerbaijan; 8) Nab Industrial Companies; 9) Mahram Production Group Company; 10) Pak Dairy.
The initial collection of data was done through library studies, and then it was finalized by receiving the financial information of the last few years from the companies admitted to the Tehran Stock Exchange. The financial data is extracted from the audited financial statements included in the Research, Development and Islamic Studies Management site of the Stock Exchange Organization (www.rdis.ir) and the stock reporting software, Tadbirpardaz and Rahevard Novin. At the same time, authentic domestic and foreign books and articles on the subject, the database of companies, and the stock exchange have also been used.
3.2. Variable Selection
3.2.1. Price Index & Cash Yield
Before defining the variables, consider the following notations:
· 
: The price of the 
th company at time 
.
Table 1
Descriptive statistics of the collected dataset
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Variable | Mean | Median | Maximum | Minimum | Standard Deviation | ||||||||||||||
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TEDPIX | 2.816 | 2.907 | 4.994 | 0.250 | 1.219 | ||||||||||||||
| 40.065 | 40.009 | 48.409 | 30.479 | 4.142 | ||||||||||||||
| 11.211 | 11.143 | 15.953 | 7.256 | 1.795 | ||||||||||||||
| 40.002 | 39.537 | 49.829 | 30.583 | 4.813 | ||||||||||||||
| 6.767 | 6.838 | 10.302 | 3.368 | 1.705 | ||||||||||||||
| 24.535 | 24.609 | 29.449 | 16.465 | 3.001 | ||||||||||||||
| 0.453 | 0.431 | 0.936 | 0.094 | 0.166 | ||||||||||||||
| 4.952 | 5.171 | 6.596 | 2.868 | 0.888 | ||||||||||||||
| 2.959 | 2.666 | 6.149 | 1.663 | 1.020 | ||||||||||||||
| 3.047 | 2.854 | 5.086 | 0.057 | 1.119 | ||||||||||||||
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Variable | Sample Size | J-B | P-Value | ||||||||||||||||
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TEDPIX | 50 | 1.086 | 0.581 | ||||||||||||||||
| 50 | 0.958 | 0.619 | ||||||||||||||||
| 50 | 0.097 | 0.953 | ||||||||||||||||
| 50 | 0.879 | 0.644 | ||||||||||||||||
| 50 | 1.335 | 0.513 | ||||||||||||||||
| 50 | 1.658 | 0.436 | ||||||||||||||||
| 50 | 2.490 | 0.288 | ||||||||||||||||
| 50 | 2.011 | 0.366 | ||||||||||||||||
| 50 | 9.438 | 0.059 | ||||||||||||||||
| 50 | 0.143 | 0.931 | ||||||||||||||||
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Company
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DMU | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |||||||||
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2006 | 0.19761 | 0.85159 | 0.05606 | 0.08319 | 1 | 0.73983 | 1 | 0.2283 | 1 | 1 | |||||||||
2007 | 0.72768 | 1 | 1 | 0.40092 | 1 | 0.60216 | 1 | 1 | 1 | 0.84501 | |||||||||
2008 | 0.83236 | 1 | 1 | 0.71614 | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||
2009 | 1 | 1 | 0.62553 | 0.46413 | 1 | 1 | 1 | 0.35805 | 0.05593 | 0.51758 | |||||||||
2010 | 1 | 0.33558 | 1 | 0.79099 | 1 | 1 | 0.01035 | 0.23993 | 0.54018 | 0.3605 | |||||||||
2011 | 0.49625 | 1 | 1 | 1 | 0.62237 | 0.4599 | 1 | 1 | 1 | 1 | |||||||||
2012 | 1 | 0.97895 | 1 | 1 | 0.40007 | 1 | 1 | 1 | 0.03809 | 1 | |||||||||
2013 | 1 | 0.16244 | 0.87511 | 0.58687 | 1 | 1 | 1 | 1 | 0.19727 | 1 | |||||||||
2014 | 1 | 1 | 1 | 1 | 0.86638 | 1 | 0.77722 | 1 | 1 | 1 | |||||||||
2015 | 0.59001 | 1 | 1 | 0.73439 | 1 | 1 | 1 | 0.68093 | 1 | 1 | |||||||||
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Number of effective years | 5 | 6 | 7 | 3 | 7 | 7 | 8 | 6 | 6 | 7 | |||||||||
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Rank | 4 | 3 | 2 | 5 | 2 | 2 | 1 | 3 | 3 | 2 | |||||||||
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Table 4
Ranking of companies based on the weighted average method (
values)
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Company
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DMU | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |||||||||
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2006 | 0.100 | 0.408 | 0.031 | 0.080 | 0.570 | 0.106 | 1.985 | 0.146 | 2.986 | 0.308 | |||||||||
2007 | 0.167 | 0.584 | 0.788 | 0.209 | 0.615 | 0.118 | 3.802 | 0.480 | 4.086 | 0.319 | |||||||||
2008 | 0.105 | 0.461 | 0.337 | 0.097 | 0.527 | 0.185 | 2.103 | 0.354 | 1.272 | 0.475 | |||||||||
2009 | 0.898 | 0.661 | 0.254 | 0.250 | 0.311 | 0.837 | 1.423 | 0.156 | 0.112 | 0.378 | |||||||||
2010 | 0.517 | 0.099 | 0.690 | 0.254 | 0.755 | 0.165 | 0.021 | 0.067 | 0.359 | 0.110 | |||||||||
2011 | 0.157 | 0.690 | 1.124 | 0.738 | 0.191 | 0.095 | 2.957 | 0.991 | 5.688 | 0.088 | |||||||||
2012 | 0.528 | 0.121 | 0.269 | 1.657 | 0.047 | 0.405 | 1.280 | 1.010 | 0.032 | 0.391 | |||||||||
2013 | 0.268 | 0.039 | 0.258 | 0.169 | 0.202 | 0.381 | 1.308 | 0.718 | 0.315 | 0.667 | |||||||||
2014 | 1.438 | 0.996 | 0.835 | 1.592 | 0.128 | 0.632 | 0.853 | 1.451 | 4.374 | 1.483 | |||||||||
2015 | 0.097 | 0.780 | 0.417 | 0.127 | 0.519 | 0.212 | 1.307 | 0.073 | 1.552 | 0.443 | |||||||||
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Where 
(
) are the input characteristic weights and 
is the 
th input weight belonging to the 
th unit and 
(
) is the efficiency of the th unit obtained from the CCR model. In this method, the characteristic weight is an average of the input weights, and the contribution of each weight in this ratio is the efficiency. As a result, any weight that corresponds to more efficiency will have a greater effect on the characteristic weight. Similarly, we obtain the set of joint weights of the outputs from Equation 10:
Where 
(
) is the characteristic weight of the 
th output, 
is the weight of the th output of the th unit, which is obtained from the CCR model and ( 
) is the efficiency of the th unit obtained from the CCR model.
The advantages of this method are that we obtain the common set of weights without solving a new model and using the results of the CCR model. The common set of weights in this method considers the proper proportion of weight and efficiency factors. In this method, a weight is neither omitted nor rounded, but a common weight with the closest feature to all units is considered. After obtaining the common set of weights, we need to calculate the efficiency of the decision units by Equation 11:
Table 4 shows the calculation results of 
for each company between the time period of 2005-2015. After calculating for DMUs, the ranking of units is changed and only one unit is selected as the best efficient unit. This changes in ranking can be seen in Table 4. Table 5 also reflects the ranking comparison of the CCR method and the weighted average method. According to table 5, the CCR method assigns the first rank to company number 7 and the weighted average method assigns the first rank to company number 9.
Table 5
The final ranking of companies based on the CCR and the weighted average methods
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Rank | CCR Method | Weighted Average Method |
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1 | Company No. 7 | Company No. 9 |
2 | Company No. 3, 5, 6, and 10 | Company No. 7 |
3 | Company No. 2, 8, and 9 | Company No. 8 |
4 | Company No. 1 | Company No. 4 |
5 | Company No. 4 | Company No. 3 |
6 | - | Company No. 2 |
7 | - | Company No. 10 |
8 | - | Company No. 1 |
9 | - | Company No. 5 |
10 | - | Company No. 6 |
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Fig. 3. Radar chart for efficiency values of Mahram Production Group and Shir Pegah West Azerbaijan
5. Conclusion
In this research, we evaluated and investigated the effect of oil price in the industry using DEA technique in the companies admitted to the Tehran stock exchange. By using CCR and weighted average models and considering the time factor as DMU, dissimilar units can be compared with each other in a special way. The mentioned comparison, practically, has provided the basis for evaluating the performance of different companies with each other. According to the results of the research, Mahram Production Group Company ranked 1st in the weighted average method, and Shir Pegah West Azerbaijan Company ranked 1st in the CCR method. The reason for this difference in the CCR method and the weighted average method is that company number 9 had an efficiency close to 1 in all years between 2005 and 2015, or, in other words, it was on the frontier of efficiency. On the other hand, Shir Pegah West Azerbaijan Company has been efficient in eight years of the investigated period, but in the remaining two years, it was very far from the efficiency limit. This difference is so great that in the weighted average method, this distance is completely felt, and it has caused Shir Pegah West Azerbaijan Company to be ranked second in the final ranking (see Figure 3). According to this inference, we rank all companies based on the weighted average method, so the ranks number 3 to 10 are given to Nab Industrial Companies, Pars Feed, Georgian Biscuits, Behnoosh Iran, Pak Dairy, Behpak, Salemeen, and the milk of Pegah Isfahan is reserved, respectively.
Based on the results obtained and considering the effect of the oil price on the industry of the companies listed on the Tehran stock exchange in this research, it is suggested to the users of the financial statements of the companies listed on the stock exchange that they should consider this in their investment decisions. Pay attention to the subject. Also, it is suggested to the managers of the companies admitted to the Tehran stock exchange to pay attention to this issue in order to make the capital market as efficient as possible and to provide practical measures and the necessary platform for this.
It seems that the following ideas can complement the current research:
· Performance evaluation of safety processes in gas refineries using DEA.
· Measuring the productivity of companies admitted to the Tehran stock exchange using DEA.
· Evaluating the performance of listed companies in the Tehran stock exchange under uncertainty using fuzzy method, robust optimization, etc., because using these techniques can produce more reliable results and a more accurate model of the real world.
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