DEA-Based Evaluation of the Oil Prices Effect on Industry: A Case Study of the Stock Exchange
الموضوعات : نشریه بینالمللی هوش تصمیم
Ali Taherinezhad
1
(PhD Candidate, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
alireza alinezhad
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.
[1] Alinezhad, A., & Khalili, J. (2019). EDAS Method. New Methods and Applications in Multiple
Attribute Decision Making (MADM), 149-155.
[2] Alinezhad, A., & Khalili, J. (2019). MABAC method. New methods and applications in multiple
attribute decision making (MADM), 193-198.
[3] Alinezhad, A., & Taherinezhad, A. (2020). Control Chart Recognition Patterns Using Fuzzy RuleBased System. Iranian Journal of Optimization, 12(2), 149-160.
[4] Alinezhad, A., & Taherinezhad, A. (2021). Performance Evaluation of Production Chain using
Two-Stage DEA Method (Case Study: Iranian Poultry Industry). new economy and trade, 16(3),
105-130.
[5] Alinezhad, A., Heidaryan, L., & Taherinezhad, A. (2023). Ranking the Measurement System of
Auto Parts Companies via MSA–MADM Combinatorial Method under Fuzzy Conditions. Sharif
Journal of Industrial Engineering & Management, 38(2), 15-27.
[6] Alinezhad, A., Makui, A., & Mavi, R. K. (2007). An inverse DEA model for inputs/outputs
estimation with respect to decision maker’s preferences: The case of Refah bank of IRAN.
Mathematical Sciences, 1(1-2), 61-70.
[7] Bagirov, M., & Mateus, C. (2019). Oil prices, stock markets and firm performance: Evidence from
Europe. International Review of Economics & Finance, 61, 270-288.
[8] Behera, C., & Rath, B. N. (2024). The interconnectedness between crude oil prices and stock
returns in G20 countries. Resources Policy, 91, 104950.
[9] Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224-226.
[10] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making
units. European journal of operational research, 2(6), 429-444.
[11] Delgado, N. A. B., Delgado, E. B., & Saucedo, E. (2018). The relationship between oil prices, the
stock market and the exchange rate: Evidence from Mexico. The North American Journal of
Economics and Finance, 45, 266-275.
[12] Dhingra, B., Batra, S., Aggarwal, V., Yadav, M., & Kumar, P. (2024). Stock market volatility: a
systematic review. Journal of Modelling in Management, 19(3), 925-952.
[13] Filis, G., Degiannakis, S., & Floros, C. (2011). Dynamic correlation between stock market and oil
prices: The case of oil-importing and oil-exporting countries. International review of financial
analysis, 20(3), 152-164.
[14] Golkarami, A., & Kord, A. (2024). Geoeconomic Explanation of the Feasibility Study of Selling
Crude Oil and Gas Resources in the Governance of the Country's Progress. Geopolitics Quarterly,
20(73), 240-267.
[15] Hashmi, S. M., Ahmed, F., Alhayki, Z., & Syed, A. A. (2022a). The impact of crude oil prices on
Chinese stock markets and selected sectors: evidence from the VAR-DCC-GARCH model.
Environmental Science and Pollution Research, 29(35), 52560-52573.
[16] Hashmi, S. M., Chang, B. H., Huang, L., & Uche, E. (2022b). Revisiting the relationship between
oil prices, exchange rate, and stock prices: An application of quantile ARDL model. Resources
Policy, 75, 102543.
[17] Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial
independence of regression residuals. Economics letters, 6(3), 255-259.
[18] Kiani Mavi, R., Makui, A., Fazli, S., & Alinezhad, A. (2010). A forecasting method in data
envelopment analysis with group decision making. International Journal of Applied Management
Science, 2(2), 152-168.
[19] Ling, O. P., Kamil, A. A., Nain, M., & Awang, H. (2010). Data envelopment analysis for stocks
selection on Bursa Malaysia. Archives of Applied Science Research, 2(5), 11-35.
[20] Liu, F., Umair, M., & Gao, J. (2023a). Assessing oil price volatility co-movement with stock
market volatility through quantile regression approach. Resources Policy, 81, 103375.
[21] Liu, F., Xu, J., & Ai, C. (2023b). Heterogeneous impacts of oil prices on China's stock market:
Based on a new decomposition method. Energy, 268, 126644.
[22] Neacșu, M., & Georgescu, I. E. (2024). Analysing the Impact of Crises on Financial Performance:
Empirical Insights from Tourism and Transport Companies Listed on the Bucharest Stock
Exchange (during 2005–2022). Journal of Risk and Financial Management, 17(2), 80.
[23] Ouenniche, J., Xu, B., & Tone, K. (2014). Relative performance evaluation of competing crude
oil prices’ volatility forecasting models: a slacks-based super-efficiency DEA model. American
Journal of Operations Research, 4(4), 235-245.
[24] Sarrafha, K., Kazemi, A., & Alinezhad, A. (2014). A multi-objective evolutionary approach for
integrated production-distribution planning problem in a supply chain network. Journal of
Optimization in Industrial Engineering, 7(14), 89-102.
[25] Schwartz, R. A., Byrne, J. A., & Wheatley, L. (Eds.). (2015). The economic function of a stock
exchange. Springer International Publishing.
[26] Shahbazi, K., Rezaei, E., & Salehi, Y. (2013). The impact of oil price shocks on the stock returns
of Tehran Stock Exchange (TSE). Financial Knowledge of Securities Analysis, 6(18), 125-136.
[27] Silvapulle, P., Smyth, R., Zhang, X., & Fenech, J. P. (2017). Nonparametric panel data model for
crude oil and stock market prices in net oil importing countries. Energy economics, 67, 255-267.
[28] Taherinezhad, A., & Alinezhad, A. (2022). COVID-19 crisis management: Global appraisal using
two-stage DEA and ensemble learning algorithms. Scientia Iranica, (Article in press).
[29] Taherinezhad, A., & Alinezhad, A. (2023). Nations performance evaluation during SARS-CoV-2
outbreak handling via data envelopment analysis and machine learning methods. International
Journal of Systems Science: Operations & Logistics, 10(1), 2022243.
[30] Taherinezhad, A., Alinezhad, A., & Gholami, S. (2023a). An Application of Data Envelopment
Analysis and Game Theory for Appraisal of Balanced Scorecard Indexes in Media Industry.
Journal of Industrial System Engineering and Management, 2(2), 47-59.
[31] Taherinezhad, A., Alinezhad, A., Gholami, S., & Abdolvand, M. (2023b). A Network Data
Envelopment Analysis Approach for Efficiency Measurement of Poultry Industry Production
Chains. International Journal of Decision Intelligence, 1(2), 1-12.
[32] Xu, B., & Ouenniche, J. (2012). A data envelopment analysis-based framework for the relative
performance evaluation of competing crude oil prices' volatility forecasting models. Energy
Economics, 34(2), 576-583.
[33] Zhao, Y., & Morita, H. (2024). Estimating Malmquist-type indices with StoNED. Expert Systems
with Applications, 250, 123877
