Document Type : Research Paper


Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.



The purpose of this research is to evaluate and rank the efficiency of pharmaceutical companies in creating operational cash flows in line with the objectives of financial reporting. The research method for collecting theoretical bases and research data is library studies. In this research, in order to evaluate the efficiency of pharmaceutical companies in creating operational cash flow, the Data Envelopment Analysis (DEA) model with weight limit is used. The results of this research show that Farabi pharmaceutical company has the highest efficiency score in creating Operating Cash Flows (OCFs) and Loqman pharmaceutical company has the lowest efficiency score. The findings of this research confirm that DEA is a suitable technique for evaluating the performance of companies in creating operational cash flow. Also, this technique, along with traditional financial analysis, can be considered a useful instrument for deciding and evaluating the performance and efficiency of companies. This article can make analysts more familiar; financial and accounting researchers with DEA applications in financial and accounting analysis. Also, this research can expand the use of scientific models in financial and accounting research.


Main Subjects

[1]     Lee, K. H., & Saen, R. F. (2012). Measuring corporate sustainability management: A data envelopment analysis approach. International journal of production economics, 140(1), 219–226.
[2]     Orpurt, S. F., & Zang, Y. (2009). Do direct cash flow disclosures help predict future operating cash flows and earnings? The accounting review, 84(3), 893–935.
[3]     Billings, B. K., & Morton, R. M. (2002). The relation between SFAS No. 95 cash flows from operations and credit risk. Journal of business finance & accounting, 29(5–6), 787–805.
[4]     Dechow, P. M., & Dichev, I. D. (2002). The quality of accruals and earnings: The role of accrual estimation errors. The accounting review, 77(s-1), 35–59.
[5]     Kousenidis, D. (2006). A free cash flow version of the cash flow statement: a note. Managerial finance, 32(8), 645–653.
[6]     Bradbury, M. (2011). Direct or indirect cash flow statements? Australian accounting review, 21(2), 124–130.
[7]     Tucker, J. W., & Zarowin, P. A. (2006). Does income smoothing improve earnings informativeness? The accounting review, 81(1), 251–270.
[8]     Das, M. C., Sarkar, B., & Ray, S. (2013). On the performance of Indian technical institutions: a combined SOWIA-MOORA approach. Opsearch, 50, 319–333.
[9]     Demerjian, P., Lev, B., & McVay, S. (2012). Quantifying managerial ability: A new measure and validity tests. Management science, 58(7), 1229–1248.
[10]   Casey, C., & Bartczak, N. (1985). Using operating cash flow data to predict financial distress: Some extensions. Journal of accounting research, 23(1), 384–401.
[11]   Carslaw, C. A., & Mills, J. R. (1991). Developing ratios for effective cash flow statement analysis. Journal of accountancy, 172(5), 63.
[12]   Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European accounting review, 13(3), 465–497.
[13]   Dechow, P. M. (1994). Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals. Journal of accounting and economics, 18(1), 3–42.
[14]   Lee, J. E., Glasscock, R., & Park, M. S. (2017). Does the ability of operating cash flows to measure firm performance improve during periods of financial distress? Accounting horizons, 31(1), 23–35.
[15]   Rahmawati, R., & Narsa, I. M. (2020). Operating cash flow, profitability, liquidity, leverage and dividend policy. International journal of innovation, creativity and change, 11(9), 121–148.
[16]   Bernardin, D. E. Y., & Tifani, T. (2019). Financial distress predicted by cash flow and leverage with capital intensity as moderating. E-journal appreciation of economics, 7(1), 18–29.
[17]   Nguyen, H., & Nguyen, T. (2020). The prediction of future operating cash flows using accrual-based and cash-based accounting information: Empirical evidence from Vietnam. Management science letters, 10(3), 683–694.
[18]   Bowlin, W. F. (1999). An analysis of the financial performance of defense business segments using data envelopment analysis. Journal of accounting and public policy, 18(4–5), 287–310.
[19]   Khajavi, S., Ghayomi, A., & Jafari, M. (2010). Data envelopment analysis technique: ‌a complementary method for traditional ‌analysis of financial ratios. Accounting and auditing review, 17(2), 41-56. (In Persian).
[20]   Hajiha, Z., & Ghilavi, M. (2012). Using the technique of data coverage analysis to measure the efficiency of manufacturing companies listed on Tehran Stock Exchange using a model based on financial reporting. Financial engineering and portfolio management, 3(12), 111-130. (In Persian).
[21]   Mohammadi, A., & Dastyar, H. (2013). Evaluating efficiency of pharmaceutical companies and their ranking via data envelopment window analysis. Journal of health accounting, 2(3), 23-39. (In Persian).
[22]   Neukirchen, D., Engelhardt, N., Krause, M., & Posch, P. N. (2022). Firm efficiency and stock returns during the COVID-19 crisis. Finance research letters, 44, 102037.
[23]   Alinezhad Sarokolaei, M., & Saati, S. (2017). Presenting of time driven data envelopment analysis model in financial statements analysis of listed firms in Tehran stock exchange. Journal of operational research in its applications (applied mathematics), 13(4), 55-65. (In Persian).
[24]   Yousefi, S., Farzipoor Saen, R., & Seyedi Hosseininia, S. S. (2019). Developing an inverse range directional measure model to deal with positive and negative values. Management decision, 57(9), 2520–2540.
[25]   Saqafi, A., Osta, S., Amiri, M., & Barzideh, F. (2018). A model for performance assessment of the investment companies‎ with data envelopment analysis approach and principal component‎ segregation method. Financial accounting research, 10(1), 75-94. (In Persian).
[26]   Shaban, R., Banimahd, B., Hosseinzadeh Lotfi, F., & Nikoumaram, H. (2020). Evaluate the efficiency of audit firms using data envelopment analysis. Journal of decisions and operations research, 5(3), 402-413. (In Persian).
[27]   Liu, H., Zhang, R., Zhou, L., & Li, A. (2023). Evaluating the financial performance of companies from the perspective of fund procurement and application: New strategy cross efficiency network data envelopment analysis models. Energy, 269, 126739.
[28]   Lahouel, B. Ben, Zaied, Y. Ben, Song, Y., & Yang, G. (2021). Corporate social performance and financial performance relationship: A data envelopment analysis approach without explicit input. Finance research letters, 39, 101656.
[29]   Wanke, P., Azad, M. A. K., Emrouznejad, A., & Antunes, J. (2019). A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking. International review of economics & finance, 61, 52–68.
[30]   Hedayat Mazhari, R., Khoramabadi, M., & Lashgar Ara, S. (2021). Assessingefficiency using data envelopment analysis method and its relation to financial ratios. Financial accounting research, 13(3), 89–110. DOI: 10.22108/far.2022.129532.1785
[31]   Bagheri Mazraeh, N., Rostami Mal Khalife, M., & Varzi, M. (2022). A comparison of super-efficiency through data envelopment analysis technique and financial ratios in Iranian stock exchange banks. Journal of decisions and operations research, 6(Spec. Issue), 1-16. (In Persian). DOI: 10.22105/dmor.2021.236731.1163
[32]   Raei, R., Bajalan, S., & Saedi, Z. (2022). The time-scale effect of volatility of asset market on the efficiency of the country’s banking network with emphasis on regime change. Journal of decisions and operations research, 7(1), 55–76.
[33]   Batir, T. E., Volkman, D. A., & Gungor, B. (2017). Determinants of bank efficiency in Turkey: Participation banks versus conventional banks. Borsa istanbul review, 17(2), 86–96.
[34]   Štefko, R., Horváthová, J., & Mokrišová, M. (2021). The application of graphic methods and the DEA in predicting the risk of bankruptcy. Journal of risk and financial management, 14(5), 220.
[35]   Nkambule, N. A., Wang, W. K., Ting, I. W. K., & Lu, W. M. (2022). Intellectual capital and firm efficiency of US multinational software firms. Journal of intellectual capital, 23(6), 1404–1434.
[36]   Kamel, M. A., Mousa, M. E. S., & Hamdy, R. M. (2021). Financial efficiency of commercial banks listed in Egyptian stock exchange using data envelopment analysis. International journal of productivity and performance management, 71(8), 3683–3703.
[37]   Rahimi, H., Minouei, M., & Fathi, M. (2022). Financial distress of companies listed on the Tehran stock exchange using the dynamic worst practice frontier-based DEA model. Advances in mathematical finance and applications, 7(2), 507–525.
[38]   Li, X. B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European journal of operational research, 115(3), 507–517.
[39]   Podinovski, V. V. (2017). Returns to scale in convex production technologies. European journal of operational research, 258(3), 970–982.