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A Hybrid Two-Stage DEA and Deep Learning Framework for Efficiency Evaluation of Iranian Stock Exchange Companies
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Abstract
This study investigates the efficiency of Iranian stock exchange-listed companies (2007–2023) using a hybrid approach integrating two-stage Data Envelopment Analysis (DEA) with Deep Learning (DL). Traditional DEA evaluates efficiency but struggles with nonlinear patterns and noisy data. By combining DEA with Long Short-Term Memory (LSTM) and TabNet models, this research addresses these limitations. Results reveal that LSTM outperforms TabNet in predicting efficiency scores (MSE: 0.0025 vs. 0.0203), demonstrating its superiority in capturing temporal dependencies in financial data. The hybrid framework enhances accuracy in identifying inefficiencies, optimizing resource allocation, and informing strategic decisions. This methodology bridges DEA’s multi-input/output assessment with Artificial Intelligence (AI)’s predictive power, offering transformative insights for financial analytics.
Keywords
Two-stage data envelopment analysis Deep learning Long short-term memory TabNetReferences (19)
- [1] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
- [2] Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
- [3] Sutton, R. S. (1998). Reinforcement learning, a Bradford book. MIT press. https://mitpress.mit.edu/9780262193986/reinforcement-learning/
- [4] Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business horizons, 63(2), 227–243. https://doi.org/10.1016/j.bushor.2019.12.002
- [5] Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central european business review, 8(2), 46-55. https://www.ceeol.com/search/article-detail?id=775030
- [6] Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., … ., & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The bottom line, 33(2), 183–200. https://doi.org/10.1108/BL-03-2020-0022
- [7] Pitt, C., Paschen, J., Kietzmann, J., Pitt, L. F., & Pala, E. (2023). Artificial intelligence, marketing, and the history of technology: Kranzberg’s laws as a conceptual lens. Australasian marketing journal, 31(1), 81–89. https://doi.org/10.1177/18393349211044175
- [8] Bouasabah, M. (2024). A performance analysis of machine learning algorithms in stock market prediction, compared to traditional indicators. Information systems and technological advances for sustainable development (pp. 264–271). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-75329-9_29
- [9] Mehtab, S., & Sen, J. (2020). A time series analysis-based stock price prediction using machine learning and deep learning models. International journal of business forecasting and marketing intelligence, 6(4), 272–335. https://doi.org/10.1504/IJBFMI.2020.115691
- [10] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of ambient intelligence and humanized computing, 13(7), 3433–3456. https://doi.org/10.1007/s12652-020-01839-w
- [11] Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023). Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications. International journal of financial studies, 11(3), 94. https://doi.org/10.3390/ijfs11030094
- [12] Jamshidi, M., Sanei, M., & Mahmoodirad, A. (2022). An uncertain allocation model for data envelopment analysis: A case in the Iranian stock market. Scientia iranica, 29(6), 3434–3454. https://doi.org/10.24200/sci.2020.55047.4046
- [13] Pourmahmoud, J., & D, N. B. (2022). Evaluation of general two-stage network systems in the presence of undesirable and non-discretionary data. Iranian journal of operations research, 13(2), 83–95. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://iors.ir/journal/article-1-800-en.pdf
- [14] Amin, G. R., & Hajjami, M. (2021). Improving DEA cross-efficiency optimization in portfolio selection. Expert systems with applications, 168, 114280. https://doi.org/10.1016/j.eswa.2020.114280
- [15] Bencová, T., & Boháčiková, A. (2022). DEA in performance measurement of two-stage processes: Comparative overview of the literature. Economic studies, 31(5), 111-129. https://www.ceeol.com/search/article-detail?id=1048505
- [16] Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia cirp, 99, 650–655. https://doi.org/10.1016/j.procir.2021.03.088
- [17] Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial intelligence review, 53(8), 5929–5955. https://doi.org/10.1007/s10462-020-09838-1
- [18] Arik, S. Ö., & Pfister, T. (2021). Tabnet: Attentive interpretable tabular learning. Proceedings of the AAAI conference on artificial intelligence (pp. 6679–6687). PKP Publishing Services Network. https://doi.org/10.1609/aaai.v35i8.16826
- [19] Shah, C., Du, Q., & Xu, Y. (2022). Enhanced TabNet: Attentive interpretable tabular learning for hyperspectral image classification. Remote sensing, 14(3), 716. https://doi.org/10.3390/rs14030716