How to cite this paper
APA: Ali, G. (2026). Enhancing project financial performance prediction: An explainable machine learning framework integrating frontier efficiency and super learner. Journal of Project Management, 11(1), 151-168.
Chicago/Turabian: Ali, G. 2026. "Enhancing project financial performance prediction: An explainable machine learning framework integrating frontier efficiency and super learner." Journal of Project Management 11, no. 1 (2026): 151-168.
AMA: Ali, G. Enhancing project financial performance prediction: An explainable machine learning framework integrating frontier efficiency and super learner. Journal of Project Management. 2026;11(1):151-168.
References
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Amirteimoori, A., & Allahviranloo, T. (2025). Performance and managerial ability analysis in banking sector: a fuzzy data envelopment analysis approach. Soft Computing. https://doi.org/10.1007/s00500-025-10556-5
Anand, V., Brunner, R., Ikegwu, K., & Sougiannis, T. (2019). Predicting profitability using machine learning. Available at SSRN 3466478.
Arno, H., Mulier, K., Baeck, J., & Demeester, T. (2025). Business Failure Prediction From Textual and Tabular Data With Sentence-Level Interpretations. Annals of Operations Research, 1–26.
Baik, B., Chae, J., Choi, S., & Farber, D. B. (2013). Changes in operational efficiency and firm performance: A frontier analysis approach. Contemporary Accounting Research, 30(3), 996–1026.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Borchert, P., Coussement, K., De Caigny, A., & De Weerdt, J. (2023). Extending business failure prediction models with textual website content using deep learning. European Journal of Operational Research, 306(1), 348–357.
Caves, R.E., & Barton, D.R. (1990). Efficiency in U.S. manufacturing industries. 194. https://mitpress.mit.edu/9780262031578/efficiency-in-u-s-manufacturing-industries/
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Chuan’Chewie’Ang, T., Azad, A. S. M. S., Pham, T. A. T., & Zhong, A. (2021). Firm efficiency and stock returns: Australian evidence. International Review of Financial Analysis, 78, 101935.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software (Vol. 2). Springer.
Dahunsi, O. J., & Soetan, O. R. (2022). Resource-use efficiency and firm performance in Nigeria’s manufacturing industry. Economy, 9(1), 1–8.
D’Amato, V., D’Ecclesia, R., & Levantesi, S. (2024). Firms’ profitability and ESG score: A machine learning approach. Applied Stochastic Models in Business and Industry, 40(2), 243–261. https://doi.org/10.1002/ASMB.2758
Dash, R. K., Nguyen, T. N., Cengiz, K., & Sharma, A. (2023). Fine-tuned support vector regression model for stock predictions. Neural Computing and Applications, 35(32), 23295–23309. https://doi.org/10.1007/S00521-021-05842-W/METRICS
Demerjian, P., Lev, B., & McVay, S. (2012). Quantifying managerial ability: A new measure and validity tests. Management Science, 58(7), 1229–1248.
Dey, R., & Mathur, R. (2023). Ensemble learning method using stacking with base learner, a comparison. International Conference on Data Analytics and Insights, 159–169.
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. ArXiv Preprint ArXiv:1810.11363.
Dudu, H., & Kilicaslan, Y. (2009). Concentration, profitability and (in) efficiency in large scale firms. In Productivity, efficiency, and economic growth in the Asia-Pacific region (pp. 39–58). Springer.
Erdebilli, B., & Devrim-İçtenbaş, B. (2022). Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey. Mathematics 2022, Vol. 10, Page 2466, 10(14), 2466. https://doi.org/10.3390/MATH10142466
Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity changes in Swedish pharamacies 1980–1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1), 85–101.
Geoffrey Love, E., & Nohria, N. (2005). Reducing slack: The performance consequences of downsizing by large industrial firms, 1977–93. Strategic Management Journal, 26(12), 1087–1108.
Greene, W. H., & Segal, D. (2004). Profitability and efficiency in the US life insurance industry. Journal of Productivity Analysis, 21, 229–247.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, Issue 1). Springer.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28, 1–26.
Kunapuli, G. (2023). Ensemble Methods for Machine Learning. Simon and Schuster.
Kwak, S. K., & Kim, J. H. (2017). Statistical data preparation: management of missing values and outliers. Korean Journal of Anesthesiology, 70(4), 407. https://doi.org/10.4097/KJAE.2017.70.4.407
Ladds, M. A., Thompson, A. P., Kadar, J.-P., J Slip, D., P Hocking, D., & G Harcourt, R. (2017). Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry. Animal Biotelemetry, 5, 1–9.
Lee, S., Nguyen, N., Karamanli, A., Lee, J., & Vo, T. P. (2023). Super learner machine‐learning algorithms for compressive strength prediction of high performance concrete. Structural Concrete, 24(2), 2208–2228.
Leibenstein, H. (1966). Allocative efficiency vs." X-efficiency". The American Economic Review, 56(3), 392–415.
Lukason, O., & Laitinen, E. K. (2019). Firm failure processes and components of failure risk: An analysis of European bankrupt firms. Journal of Business Research, 98, 380–390.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2), 757–774. https://doi.org/10.1016/J.JKSUCI.2023.01.014
Montesinos, A. (2022). Profit prediction based on financial statements using deep neural network. 2022 IEEE World AI IoT Congress (AIIoT), 533–537.
Mousa, G. A., Elamir, E. A. H., & Hussainey, K. (2022). Using machine learning methods to predict financial performance: Does disclosure tone matter? International Journal of Disclosure and Governance, 19(1), 93–112. https://doi.org/10.1057/s41310-021-00129-x
Naimi, A. I., & Balzer, L. B. (2018). Stacked generalization: an introduction to super learning. European Journal of Epidemiology, 33, 459–464.
Nandy, A., & Singh, P. K. (2020). Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India. Journal of Cleaner Production, 267, 122106. https://doi.org/10.1016/J.JCLEPRO.2020.122106
Nguyen, G. X., & Swanson, P. E. (2009). Firm characteristics, relative efficiency, and equity returns. Journal of Financial and Quantitative Analysis, 44(1), 213–236.
Nguyen, X. C., Nguyen, T. T. H., La, D. D., Kumar, G., Rene, E. R., Nguyen, D. D., Chang, S. W., Chung, W. J., Nguyen, X. H., & Nguyen, V. K. (2021). Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam. Resources, Conservation and Recycling, 167, 105381. https://doi.org/10.1016/J.RESCONREC.2020.105381
Önder, E., & Altintaş, A. T. (2017). Financial performance evaluation of Turkish construction companies in Istanbul Stock Exchange (BIST). International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(3), 108–113.
Peng, Z., Zhang, Y., & Sun, T. (2025). Big Data-Driven Carbon Trading and Industrial Firm Value Based on DEA and DID. Journal of Theoretical and Applied Electronic Commerce Research 2025, Vol. 20, Page 43, 20(1), 43. https://doi.org/10.3390/JTAER20010043
Perroni, M. G., Veiga, C. P. da, Forteski, E., Marconatto, D. A. B., da Silva, W. V., Senff, C. O., & Su, Z. (2024). Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction. SAGE Open, 14(2). https://doi.org/10.1177/21582440241257800/ASSET/96C69FFD-5A2F-4D8C-A3DF-91DCF440DB31/ASSETS/IMAGES/LARGE/10.1177_21582440241257800-FIG10.JPG
Phillips, R. V, Van Der Laan, M. J., Lee, H., & Gruber, S. (2023). Practical considerations for specifying a super learner. International Journal of Epidemiology, 52(4), 1276–1285.
Porter, M. E. (1981). The Contributions of Industrial Organization to Strategic Management. The Academy of Management Review, 6(4), 609. https://doi.org/10.2307/257639
Sánchez, J. C. M., Mesa, H. G. A., Espinosa, A. T., Castilla, S. R., & Lamont, F. G. (2025). Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting. Smart Agricultural Technology, 100791.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
Thabet, H. H., Darwish, S. M., & Ali, G. M. (2024). Measuring the efficiency of banks using high-performance ensemble technique. Neural Computing and Applications, 1–19. https://doi.org/10.1007/S00521-024-09929-Y/TABLES/5
Thien, N. H., Asif, J., Kweh, Q. L., & Ting, I. W. K. (2024). Firm efficiency and corporate performance: the moderating role of controlling shareholders. Benchmarking: An International Journal, 31(8), 2602–2623.
Ting, I. W. K., Asif, J., Kweh, Q. L., & Phuong, T. T. K. (2024). Mediating effect of firm efficiency on the controlling shareholdings–firm performance nexus: evidence from public listed firms in Malaysia. Financial Innovation, 10(1), 1–20. https://doi.org/10.1186/S40854-023-00518-X/TABLES/8
Tutcu, B., Kayakuş, M., Terzioğlu, M., Ünal Uyar, G. F., Talaş, H., & Yetiz, F. (2024). Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Applied Sciences, 14(17), 7459.
Van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1).
Vukovic, D. B., Spitsina, L., Gribanova, E., Spitsin, V., & Lyzin, I. (2023). Predicting the performance of retail market firms: regression and machine learning methods. Mathematics, 11(8), 1916.
Williamson, O. E. (2008). The economic institutions of capitalism. The Political Economy Reader: Markets as Institutions, 27.
Wong, J., Manderson, T., Abrahamowicz, M., Buckeridge, D. L., & Tamblyn, R. (2019). Can hyperparameter tuning improve the performance of a super learner?: a case study. Epidemiology, 30(4), 521–531.
Ylinen, M., & Ranta, M. (2024). Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach. Accounting & Finance, 64(1), 247–276. https://doi.org/10.1111/ACFI.13146
Yu, Y.-S., Barros, A., Tsai, C.-H., & Liao, K. H. (2014). A comparison of ratios and data envelopment analysis: Efficiency assessment of Taiwan public listed companies. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(1), 212–219.
Zhou, M., Liu, H., & Hu, Y. (2022). Research on corporate financial performance prediction based on self‐organizing and convolutional neural networks. Expert Systems, 39(9), e13042.
Amirteimoori, A., & Allahviranloo, T. (2025). Performance and managerial ability analysis in banking sector: a fuzzy data envelopment analysis approach. Soft Computing. https://doi.org/10.1007/s00500-025-10556-5
Anand, V., Brunner, R., Ikegwu, K., & Sougiannis, T. (2019). Predicting profitability using machine learning. Available at SSRN 3466478.
Arno, H., Mulier, K., Baeck, J., & Demeester, T. (2025). Business Failure Prediction From Textual and Tabular Data With Sentence-Level Interpretations. Annals of Operations Research, 1–26.
Baik, B., Chae, J., Choi, S., & Farber, D. B. (2013). Changes in operational efficiency and firm performance: A frontier analysis approach. Contemporary Accounting Research, 30(3), 996–1026.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Borchert, P., Coussement, K., De Caigny, A., & De Weerdt, J. (2023). Extending business failure prediction models with textual website content using deep learning. European Journal of Operational Research, 306(1), 348–357.
Caves, R.E., & Barton, D.R. (1990). Efficiency in U.S. manufacturing industries. 194. https://mitpress.mit.edu/9780262031578/efficiency-in-u-s-manufacturing-industries/
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Chuan’Chewie’Ang, T., Azad, A. S. M. S., Pham, T. A. T., & Zhong, A. (2021). Firm efficiency and stock returns: Australian evidence. International Review of Financial Analysis, 78, 101935.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software (Vol. 2). Springer.
Dahunsi, O. J., & Soetan, O. R. (2022). Resource-use efficiency and firm performance in Nigeria’s manufacturing industry. Economy, 9(1), 1–8.
D’Amato, V., D’Ecclesia, R., & Levantesi, S. (2024). Firms’ profitability and ESG score: A machine learning approach. Applied Stochastic Models in Business and Industry, 40(2), 243–261. https://doi.org/10.1002/ASMB.2758
Dash, R. K., Nguyen, T. N., Cengiz, K., & Sharma, A. (2023). Fine-tuned support vector regression model for stock predictions. Neural Computing and Applications, 35(32), 23295–23309. https://doi.org/10.1007/S00521-021-05842-W/METRICS
Demerjian, P., Lev, B., & McVay, S. (2012). Quantifying managerial ability: A new measure and validity tests. Management Science, 58(7), 1229–1248.
Dey, R., & Mathur, R. (2023). Ensemble learning method using stacking with base learner, a comparison. International Conference on Data Analytics and Insights, 159–169.
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. ArXiv Preprint ArXiv:1810.11363.
Dudu, H., & Kilicaslan, Y. (2009). Concentration, profitability and (in) efficiency in large scale firms. In Productivity, efficiency, and economic growth in the Asia-Pacific region (pp. 39–58). Springer.
Erdebilli, B., & Devrim-İçtenbaş, B. (2022). Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey. Mathematics 2022, Vol. 10, Page 2466, 10(14), 2466. https://doi.org/10.3390/MATH10142466
Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity changes in Swedish pharamacies 1980–1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1), 85–101.
Geoffrey Love, E., & Nohria, N. (2005). Reducing slack: The performance consequences of downsizing by large industrial firms, 1977–93. Strategic Management Journal, 26(12), 1087–1108.
Greene, W. H., & Segal, D. (2004). Profitability and efficiency in the US life insurance industry. Journal of Productivity Analysis, 21, 229–247.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, Issue 1). Springer.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28, 1–26.
Kunapuli, G. (2023). Ensemble Methods for Machine Learning. Simon and Schuster.
Kwak, S. K., & Kim, J. H. (2017). Statistical data preparation: management of missing values and outliers. Korean Journal of Anesthesiology, 70(4), 407. https://doi.org/10.4097/KJAE.2017.70.4.407
Ladds, M. A., Thompson, A. P., Kadar, J.-P., J Slip, D., P Hocking, D., & G Harcourt, R. (2017). Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry. Animal Biotelemetry, 5, 1–9.
Lee, S., Nguyen, N., Karamanli, A., Lee, J., & Vo, T. P. (2023). Super learner machine‐learning algorithms for compressive strength prediction of high performance concrete. Structural Concrete, 24(2), 2208–2228.
Leibenstein, H. (1966). Allocative efficiency vs." X-efficiency". The American Economic Review, 56(3), 392–415.
Lukason, O., & Laitinen, E. K. (2019). Firm failure processes and components of failure risk: An analysis of European bankrupt firms. Journal of Business Research, 98, 380–390.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2), 757–774. https://doi.org/10.1016/J.JKSUCI.2023.01.014
Montesinos, A. (2022). Profit prediction based on financial statements using deep neural network. 2022 IEEE World AI IoT Congress (AIIoT), 533–537.
Mousa, G. A., Elamir, E. A. H., & Hussainey, K. (2022). Using machine learning methods to predict financial performance: Does disclosure tone matter? International Journal of Disclosure and Governance, 19(1), 93–112. https://doi.org/10.1057/s41310-021-00129-x
Naimi, A. I., & Balzer, L. B. (2018). Stacked generalization: an introduction to super learning. European Journal of Epidemiology, 33, 459–464.
Nandy, A., & Singh, P. K. (2020). Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India. Journal of Cleaner Production, 267, 122106. https://doi.org/10.1016/J.JCLEPRO.2020.122106
Nguyen, G. X., & Swanson, P. E. (2009). Firm characteristics, relative efficiency, and equity returns. Journal of Financial and Quantitative Analysis, 44(1), 213–236.
Nguyen, X. C., Nguyen, T. T. H., La, D. D., Kumar, G., Rene, E. R., Nguyen, D. D., Chang, S. W., Chung, W. J., Nguyen, X. H., & Nguyen, V. K. (2021). Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam. Resources, Conservation and Recycling, 167, 105381. https://doi.org/10.1016/J.RESCONREC.2020.105381
Önder, E., & Altintaş, A. T. (2017). Financial performance evaluation of Turkish construction companies in Istanbul Stock Exchange (BIST). International Journal of Academic Research in Accounting, Finance and Management Sciences, 7(3), 108–113.
Peng, Z., Zhang, Y., & Sun, T. (2025). Big Data-Driven Carbon Trading and Industrial Firm Value Based on DEA and DID. Journal of Theoretical and Applied Electronic Commerce Research 2025, Vol. 20, Page 43, 20(1), 43. https://doi.org/10.3390/JTAER20010043
Perroni, M. G., Veiga, C. P. da, Forteski, E., Marconatto, D. A. B., da Silva, W. V., Senff, C. O., & Su, Z. (2024). Integrating Relative Efficiency Models with Machine Learning Algorithms for Performance Prediction. SAGE Open, 14(2). https://doi.org/10.1177/21582440241257800/ASSET/96C69FFD-5A2F-4D8C-A3DF-91DCF440DB31/ASSETS/IMAGES/LARGE/10.1177_21582440241257800-FIG10.JPG
Phillips, R. V, Van Der Laan, M. J., Lee, H., & Gruber, S. (2023). Practical considerations for specifying a super learner. International Journal of Epidemiology, 52(4), 1276–1285.
Porter, M. E. (1981). The Contributions of Industrial Organization to Strategic Management. The Academy of Management Review, 6(4), 609. https://doi.org/10.2307/257639
Sánchez, J. C. M., Mesa, H. G. A., Espinosa, A. T., Castilla, S. R., & Lamont, F. G. (2025). Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting. Smart Agricultural Technology, 100791.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
Thabet, H. H., Darwish, S. M., & Ali, G. M. (2024). Measuring the efficiency of banks using high-performance ensemble technique. Neural Computing and Applications, 1–19. https://doi.org/10.1007/S00521-024-09929-Y/TABLES/5
Thien, N. H., Asif, J., Kweh, Q. L., & Ting, I. W. K. (2024). Firm efficiency and corporate performance: the moderating role of controlling shareholders. Benchmarking: An International Journal, 31(8), 2602–2623.
Ting, I. W. K., Asif, J., Kweh, Q. L., & Phuong, T. T. K. (2024). Mediating effect of firm efficiency on the controlling shareholdings–firm performance nexus: evidence from public listed firms in Malaysia. Financial Innovation, 10(1), 1–20. https://doi.org/10.1186/S40854-023-00518-X/TABLES/8
Tutcu, B., Kayakuş, M., Terzioğlu, M., Ünal Uyar, G. F., Talaş, H., & Yetiz, F. (2024). Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Applied Sciences, 14(17), 7459.
Van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1).
Vukovic, D. B., Spitsina, L., Gribanova, E., Spitsin, V., & Lyzin, I. (2023). Predicting the performance of retail market firms: regression and machine learning methods. Mathematics, 11(8), 1916.
Williamson, O. E. (2008). The economic institutions of capitalism. The Political Economy Reader: Markets as Institutions, 27.
Wong, J., Manderson, T., Abrahamowicz, M., Buckeridge, D. L., & Tamblyn, R. (2019). Can hyperparameter tuning improve the performance of a super learner?: a case study. Epidemiology, 30(4), 521–531.
Ylinen, M., & Ranta, M. (2024). Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach. Accounting & Finance, 64(1), 247–276. https://doi.org/10.1111/ACFI.13146
Yu, Y.-S., Barros, A., Tsai, C.-H., & Liao, K. H. (2014). A comparison of ratios and data envelopment analysis: Efficiency assessment of Taiwan public listed companies. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(1), 212–219.
Zhou, M., Liu, H., & Hu, Y. (2022). Research on corporate financial performance prediction based on self‐organizing and convolutional neural networks. Expert Systems, 39(9), e13042.