Research Article
BibTex RIS Cite

Operasyonel ve Finansal Verimliliğin Havacılık Hisse Senedi Fiyatları Üzerindeki Etkisi: SHAP Yorumlanabilirliğine Sahip Bir Makine Öğrenme Modeli

Year 2025, Volume: 26 Issue: 1, 167 - 182, 21.01.2025

Ahmet Akusta

https://doi.org/10.37880/cumuiibf.1560514

Abstract

Bu çalışma, bir makine öğrenimi yaklaşımı kullanarak, operasyonel ve finansal verimlilik ölçütlerinin havacılık sektöründeki hisse senedi fiyatlarını nasıl etkilediğini incelemektedir. SHapley Additive exPlanations (SHAP) ile geliştirilmiş bir CatBoost regresyon modeli, 2015-2023 yılları arasında 65 küresel havacılık şirketinden toplanan veriler kullanılarak geliştirilmiştir. Model, Mevcut Koltuk Kilometre Başına Toplam Gelir (ASM), Yolcu Yük Faktörü, likidite oranları ve borç-varlık oranları dahil olmak üzere çeşitli operasyonel ve finansal göstergelere dayalı olarak hisse senedi fiyatlarını tahmin etmektedir. Bulgular, özellikle ASM başına Toplam Gelir ve Yolcu Yük Faktörü gibi operasyonel verimlilik ölçütlerinin havacılık sektöründeki hisse senedi fiyatlarının tahmininde önemli bir rol oynadığını göstermektedir. Hızlı oran ve borç varlık oranı gibi finansal ölçütler de modele katkıda bulunmakta ancak operasyonel faktörlere kıyasla ikincil bir etkiye sahip görünmektedir. SHAP değerleri, modelin tahminleri hakkında yorumlanabilir bilgiler sağlayarak farklı özelliklerin göreceli öneminin daha iyi anlaşılmasına olanak tanımıştır. Ayrıca çalışmanın bulguları, operasyonel ve finansal metriklerin hisse senedi fiyatlarına yansıdığını göstererek, Etkin Piyasa Hipotezi'nin (EPH) yarı-güçlü formunu desteklemektedir. Bu sonuçlar, finansal sağlık önemini korusa da, daha yüksek operasyonel verimlilik gösteren havacılık şirketlerinin olumlu borsa performansı için daha iyi konumlandırılabileceğini göstermektedir. Bu çalışma, operasyonel ve finansal ölçütleri bir makine öğrenimi çerçevesine entegre ederek havacılık sektöründe hisse senedi fiyat tahmini için kapsamlı ve yorumlanabilir bir model sunarak mevcut literatüre katkıda bulunmaktadır.

Keywords

Havacılık hisse senedi fiyatları makine öğrenimi SHAP değerleri operasyonel verimlilik CatBoost

References

  • Aldabbas, M., Arabyat, Y. A., Al-Shawawreh, T. B., Alfalayeh, G. A., & Alqudah, M. Z. (2023). The role of information technology in raising the efficiency of Amman Stock Exchange mediated by the behavior of the stock prices. WSEAS Transactions on Business and Economics, 20, 1129–1143. https://doi.org/10.37394/23207.2023.20.101
  • Alici, A., Alici, A., & Sevil, G. (2022). Analysis of sector-specific operational performance metrics affecting stock prices of traditional airlines. Independent Journal of Management & Production, 13(2), 488–506. https://doi.org/10.14807/ijmp.v13i2.1777
  • Amankwah-Amoah, J. (2018). Why are so many African companies uncompetitive on the global stage? Insights from the global airline industry. Africa’s Competitiveness in the Global Economy, 195–216. https://doi.org/10.1007/978-3-319-67014-0_8
  • Arp, L., Baratchi, M., & Hoos, H. H. (2022). VPint: Value propagation-based spatial interpolation. Data Mining and Knowledge Discovery, 36(5), 1647–1678. https://doi.org/10.1007/s10618-022-00843-2
  • Astrup, R., Coates, K. D., & Hall, E. (2008). Finding the appropriate level of complexity for a simulation model: An example with a forest growth model. Forest Ecology and Management, 256(10), 1659–1665. https://doi.org/10.1016/j.foreco.2008.07.016
  • Babu, B. S., Venkat, K. M., Reddy, J. M., & Sai, K. R. (2022). Airline ticket price prediction. International Journal for Research in Applied Science and Engineering Technology.
  • Bhargav, B., & Prabu, R. T. (2023). Airline passenger satisfaction prediction using novel hybrid random forest model comparison with K-nearest neighbour model. 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 1–6.
  • Bissessur, A., & Alamdari, F. (1998). Factors affecting the operational success of strategic airline alliances. Transportation, 25(4), 331–355. https://doi.org/10.1023/A:1005081621754
  • Briec, W., & Kerstens, K. (2004). A Luenberger-Hicks-Moorsteen productivity indicator: Its relation to the Hicks-Moorsteen productivity index and the Luenberger productivity indicator. Economic Theory, 23(4), 925–939. http://www.jstor.org/stable/25055794
  • Choudhary, A., Jagadeesh, R., Girija, E., Madhuri, M., & Shravani, N. (2023). Flyhigh: Machine learning-based airline fare prediction model. 2023 6th International Conference on Information Systems and Computer Networks (ISCON), 1–8.
  • De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48. https://doi.org/10.1016/j.neucom.2015.12.114
  • Dewikristi Siallagan, S., & Prijadi, R. (2020). The impact of operational and financial hedging to the airline operating performance. KnE Social Sciences. https://doi.org/10.18502/kss.v4i6.6635
  • Dorogush, A. V., Ershov, V., & Gulin, A. (2017). CatBoost: Gradient boosting with categorical features support. Workshop on ML Systems at NIPS 2017. Retrieved from https://github.com/
  • Evans, A., & Schäfer, A. W. (2014). Simulating airline operational responses to airport capacity constraints. Transport Policy, 34, 5–13. https://doi.org/10.1016/j.tranpol.2014.02.013
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Gimerská, V., Šoltés, M., & Mirdala, R. (2023). Improving operational efficiency through Quality 4.0 tool: Blockchain implementation and subsequent market reaction. Quality Innovation Prosperity, 27(2), 16–32. https://doi.org/10.12776/qip.v27i2.1877
  • Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: an interdisciplinary review. Journal of big data, 7(1), 94.
  • Hong, A. C. Y., Khaw, K. W., Chew, X., & Yeong, W. C. (2023). Prediction of US airline passenger satisfaction using machine learning algorithms. Data Analytics and Applied Mathematics (DAAM).
  • Huang, G. (2021). Missing data filling method based on linear interpolation and LightGBM. Journal of Physics Conference Series, 1754(1), 012187. https://doi.org/10.1088/1742-6596/1754/1/012187
  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
  • Konar, J., Khandelwal, P., & Tripathi, R. (2020). Comparison of various learning rate scheduling techniques on convolutional neural network. 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS 2020). https://doi.org/10.1109/SCEECS48394.2020.94
  • Köse, Y., & Yılmaz, E. (2023). An empirical research on the factors affecting profitability in air transportation. Sosyoekonomi, 31(58), 43–60. https://doi.org/10.17233/sosyoekonomi.2023.04.02
  • Kumar, D. S., Prem, S., Ashfaq, S. M., Rafiq, P. M., & Varma, C. N. (2023). Airline fare prediction using machine learning.
  • Labantová, L., & Begera, V. (2014). Financial, operational and economic indicators of airline operators. Faculty of Aeronautics, Technical University of Košice, 16(2).
  • Lee, B. L., Wilson, C., Pasurka, C. A., Fujii, H., & Managi, S. (2017). Sources of airline productivity from carbon emissions: An analysis of operational performance under good and bad outputs. Journal of Productivity Analysis, 47(3), 223–246. https://doi.org/10.1007/s11123-016-0480-4
  • Lee, J. (2019). Effects of operational performance on financial performance. Management Science Letters, 9(1), 25–32.
  • Lee, K. (2023). Airline operational disruptions and loss-reduction investment. Transportation Research Part B: Methodological, 177, 102817. https://doi.org/10.1016/j.trb.2023.102817
  • Lee, S., & Park, S.-B. (2013). A study on the association between operating leverage and risk: The case of the airline industry. International Journal of Economics and Finance, 6(3), 120. https://doi.org/10.5539/ijef.v6n3p120
  • Lee, S., Kim, H., & Lee, N. (2019). A comparative analysis of financial and operational performance pre-and post-IPO: With a focus on airline companies. Academy of Accounting and Financial Studies Journal, 23(3), 1–14.
  • Lopes, I. F., & Beuren, I. M. (2017). Comportamento dos custos e sua relação com medidas de eficiência operacional em companhias aéreas. BASE - Revista de Administração e Contabilidade Da Unisinos, 14(1), 30–46. https://doi.org/10.4013/base.2017.141.03
  • Lopes, I. T., Ferraz, D. P., & Rodrigues, A. M. G. (2016). The drivers of profitability in the top 30 major airlines worldwide. Measuring Business Excellence, 20(2), 26–37. https://doi.org/10.1108/mbe-09-2015-0045
  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 30 (pp. 4765–4774). Curran Associates, Inc.
  • Lundberg, S., Erion, G., & Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. ArXiv Preprint. https://doi.org/10.48550/arxiv.1802.03888
  • Mantin, B., & Wang, J.-H. (2012). Determinants of profitability and recovery from system-wide shocks: The case of the airline industry. Journal of Airline and Airport Management, 2(1), 1–33. https://doi.org/10.3926/jairm.2
  • McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
  • McLean, D. (2005). Operational efficiency of commercial transport aircraft. Measurement and Control, 38(8), 243–248. https://doi.org/10.1177/002029400503800803
  • Nagesh, P., Naidu, K. B. J., Kowshik, P., & Sekhar, P. C. (2023). Airline ticket price prediction model. International Journal for Research in Applied Science and Engineering Technology.
  • Nohara, Y., Matsumoto, K., Soejima, H., & Nakashima, N. (2021). Explanation of machine learning models using Shapley additive explanation and application for real data in hospitals. ArXiv Preprint. https://doi.org/10.48550/arxiv.2112.11071
  • Okur, M., & Gurbuz, A. O. (2015). Behavioral finance in theory and practice. In Banking, Finance, and Accounting: Concepts, Methodologies, Tools, and Applications (pp. 18). IGI Global. https://doi.org/10.4018/978-1-4666-6268-1.ch016
  • Pamungkas, D. P., & Suhadak, S. (2017). The effect of jet fuel price and macroeconomics variables on profitability of airline industry in Asia (Study at airline companies in Indonesia, India, and China period 2006–2015). Jurnal Ekonomi Bisnis dan Kewirausahaan, 50(3), 208–217.
  • Panigrahi, R., Patne, N. R., Pemmada, S., & Manchalwar, A. D. (2022). Regression model-based hourly aggregated electricity demand prediction. Energy Reports, 8, 16–24. https://doi.org/10.1016/j.egyr.2022.10.004
  • Piranti, M. (2021). The impact of fuel price fluctuation and macroeconomic variables to airlines performance. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 5486–5494.
  • Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.
  • Putra, R. A. K., Persada, S. F., Kumalasari, R. D., Herdina, A. M., Sekardhani, M., & Razif, M. (2024). Operational cost efficiency and profitability effects on companies' distribution stock prices during COVID-19. Jurnal Manajemen Teknologi, 23(1), 76–90. https://doi.org/10.12695/jmt.2024.23.1.5
  • Rossi, M. (2015). The efficient market hypothesis and calendar anomalies: A literature review. International Journal of Managerial and Financial Accounting, 7(3/4), 285. https://doi.org/10.1504/IJMFA.2015.074905
  • Sachdeva, T. (2020). Managing shareholders in turbulent times: Evidence from Indian stock market. International Journal of Business and Globalisation, 25(3), 265. https://doi.org/10.1504/IJBG.2020.109005
  • Şahinbaş, K. (2022). Price prediction model for restaurants in Istanbul by using machine learning algorithms. Ekonomi İşletme ve Maliye Araştırmaları Dergisi, 4(2), 159–171.
  • Saranga, H., & Nagpal, R. (2016). Drivers of operational efficiency and its impact on market performance in the Indian airline industry. Journal of Air Transport Management, 53, 165–176. https://doi.org/10.1016/j.jairtraman.2016.03.001
  • Satter, S., Kwon, T.-H., & Kim, K.-D. (2023). A comparative analysis of various machine learning algorithms to improve the accuracy of HbA1c estimation using wrist PPG data. Sensors, 23(16), 7231. https://doi.org/10.3390/s23167231
  • Seufert, J. H., Arjomandi, A., & Dakpo, K. H. (2017). Evaluating airline operational performance: A Luenberger-Hicks-Moorsteen productivity indicator. Transportation Research Part E: Logistics and Transportation Review, 104, 52–68. https://doi.org/10.1016/j.tre.2017.05.006
  • Singh, J., Sharma, S. K., & Srivastava, R. (2019). What drives Indian airlines' operational expense: An econometric model. Journal of Air Transport Management, 77, 32–38. https://doi.org/10.1016/j.jairtraman.2019.03.003
  • Slater, P. J. (2023). Mind the GAAP: Determinants of U.S. airline operating cash flows. Journal of Academy of Business and Economics, 23(1), 74–84. https://iabe.org/IABE-DOI/article.aspx?DOI=JABE-23-1.6
  • Subramanian, R., Murali, M. S., Deepak, B., Deepak, P., Reddy, H. N., & Sudharsan, R. R. (2022). Airline fare prediction using machine learning algorithms. 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 877–884.
  • Subramanyam, K. R., & Wild, J. J. (2016). Financial statement analysis (11th ed.). McGraw-Hill Education.
  • Sumerli Sarıgül, S., Ünlü, M., & Yaşar, E. (2023). Financial performance analysis of airlines operating in Europe: CRITIC-based MAUT and MARCOS methods. International Journal of Business and Economic Studies, 5(2), 76–97. https://doi.org/10.54821/uiecd.1257488
  • Sun, Y., Wong, A. K., & Kamel, M. S. (2009). Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence, 23(04), 687-719.
  • Tanrıverdi, G., Merkert, R., Karamaşa, Ç., & Asker, V. (2023). Using multi-criteria performance measurement models to evaluate the financial, operational and environmental sustainability of airlines. Journal of Air Transport Management, 112, 102456. https://doi.org/10.1016/j.jairtraman.2023.102456
  • Wang, W.-K., Lu, W.-M., & Tsai, C.-J. (2011). The relationship between airline performance and corporate governance amongst U.S. listed companies. Journal of Air Transport Management, 17(2), 148–152. https://doi.org/10.1016/j.jairtraman.2010.06.005
  • Woo, K.-Y., Mai, C., McAleer, M., & Wong, W.-K. (2020). Review on efficiency and anomalies in stock markets. Economies, 8(1), 20. https://doi.org/10.3390/economies8010020
  • Wulansari, T., Suwardi, F., Firdaus, F., Purwanto, D., Hibatullah, Z., & Marganis, V. C. (2024). The influence of liquidity, productive asset quality and operational efficiency on company stock prices: Study of banks going public for the 2016–2020 period. International Journal of Economics and Management Sciences, 1(2), 19–28. https://doi.org/10.61132/ijems.v1i2.18
  • Xu, Y., & E, X. (2020). ARIMA based on linear interpolation for SO2 monitoring data’s calibration. Journal of Physics Conference Series, 1616(1), 012044. https://doi.org/10.1088/1742-6596/1616/1/012044
  • Yadav, D. K., & Goriet, M. O. (2022). An illustrative evaluation of external factors that affect performance of an airline. Journal of Aerospace Technology and Management, 14, 1122. https://doi.org/10.1590/jatm.v14.1253
  • Yilmaz, E., & Köse, Y. (2023). An empirical research on the factors affecting profitability in air transportation. Sosyoekonomi, 31(58), 43–60. https://doi.org/10.17233/sosyoekonomi.2023.04.02
  • Yongxin, L. (2009). Discussing on trend to efficient market hypothesis of securities and futures market. 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, 3, 149–152.
  • Yüksel, B. (2023). Zaman serilerinde talep tahmini (Demand prediction in time series). YBS Ansiklopedi, 11(2), 1–18.
  • Zhang, Q., Koutmos, D., Chen, K., & Zhu, J. (2021). Using operational and stock analytics to measure airline performance: A network DEA approach. Decision Sciences, 52(3), 720–748. https://doi.org/10.1111/deci.12363
  • Zhao, Q., Wang, H., Luo, J.-C., Luo, M., Li, L., Yu, S.-J., Li, K., Zhang, Y., Sun, P., Tu, G.-W., & Luo, Z. (2021). Development and validation of a machine-learning model for prediction of extubation failure in intensive care units. Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.676343
  • Zou, L., Yu, C., & Dresner, M. (2015). Fleet standardization and airline performance. Journal of Transport Economics and Policy, 49(1), 149–166. https://www.jstor.org/stable/jtranseconpoli.49.1.0149

Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability

Year 2025, Volume: 26 Issue: 1, 167 - 182, 21.01.2025

Ahmet Akusta

https://doi.org/10.37880/cumuiibf.1560514

Abstract

Using a machine learning approach, this study examines how operational and financial efficiency metrics influence stock prices in the aviation industry. A CatBoost regression model enhanced with SHapley Additive exPlanations (SHAP) was developed using data from 65 global aviation companies collected between 2015 and 2023. The model predicts stock prices based on various operational and financial indicators, including Total Revenue per Available Seat Mile (ASM), Passenger Load Factor, liquidity ratios, and debt-to-assets ratios. The findings suggest that operational efficiency metrics, particularly Total Revenue per ASM and Passenger Load Factor, play a significant role in predicting stock prices within the aviation sector. Financial metrics, such as the Quick Ratio and Debt-to-Assets Ratio, also contribute to the model but appear to have a secondary influence compared to operational factors. SHAP values provided interpretable insights into the model's predictions, allowing for a better understanding of the relative importance of different features. Furthermore, the study's findings offer support for the semi-strong form of the Efficient Market Hypothesis (EMH), demonstrating that operational and financial metrics are reflected in stock prices. These results indicate that aviation companies demonstrating higher operational efficiency may be better positioned for favorable stock market performance, although financial health remains important. This study contributes to the existing literature by integrating operational and financial metrics into a machine learning framework, offering a comprehensive and interpretable model for stock price prediction in the aviation industry.

Keywords

Aviation stock prices machine learning SHAP values operational efficiency CatBoost

References

  • Aldabbas, M., Arabyat, Y. A., Al-Shawawreh, T. B., Alfalayeh, G. A., & Alqudah, M. Z. (2023). The role of information technology in raising the efficiency of Amman Stock Exchange mediated by the behavior of the stock prices. WSEAS Transactions on Business and Economics, 20, 1129–1143. https://doi.org/10.37394/23207.2023.20.101
  • Alici, A., Alici, A., & Sevil, G. (2022). Analysis of sector-specific operational performance metrics affecting stock prices of traditional airlines. Independent Journal of Management & Production, 13(2), 488–506. https://doi.org/10.14807/ijmp.v13i2.1777
  • Amankwah-Amoah, J. (2018). Why are so many African companies uncompetitive on the global stage? Insights from the global airline industry. Africa’s Competitiveness in the Global Economy, 195–216. https://doi.org/10.1007/978-3-319-67014-0_8
  • Arp, L., Baratchi, M., & Hoos, H. H. (2022). VPint: Value propagation-based spatial interpolation. Data Mining and Knowledge Discovery, 36(5), 1647–1678. https://doi.org/10.1007/s10618-022-00843-2
  • Astrup, R., Coates, K. D., & Hall, E. (2008). Finding the appropriate level of complexity for a simulation model: An example with a forest growth model. Forest Ecology and Management, 256(10), 1659–1665. https://doi.org/10.1016/j.foreco.2008.07.016
  • Babu, B. S., Venkat, K. M., Reddy, J. M., & Sai, K. R. (2022). Airline ticket price prediction. International Journal for Research in Applied Science and Engineering Technology.
  • Bhargav, B., & Prabu, R. T. (2023). Airline passenger satisfaction prediction using novel hybrid random forest model comparison with K-nearest neighbour model. 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 1–6.
  • Bissessur, A., & Alamdari, F. (1998). Factors affecting the operational success of strategic airline alliances. Transportation, 25(4), 331–355. https://doi.org/10.1023/A:1005081621754
  • Briec, W., & Kerstens, K. (2004). A Luenberger-Hicks-Moorsteen productivity indicator: Its relation to the Hicks-Moorsteen productivity index and the Luenberger productivity indicator. Economic Theory, 23(4), 925–939. http://www.jstor.org/stable/25055794
  • Choudhary, A., Jagadeesh, R., Girija, E., Madhuri, M., & Shravani, N. (2023). Flyhigh: Machine learning-based airline fare prediction model. 2023 6th International Conference on Information Systems and Computer Networks (ISCON), 1–8.
  • De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48. https://doi.org/10.1016/j.neucom.2015.12.114
  • Dewikristi Siallagan, S., & Prijadi, R. (2020). The impact of operational and financial hedging to the airline operating performance. KnE Social Sciences. https://doi.org/10.18502/kss.v4i6.6635
  • Dorogush, A. V., Ershov, V., & Gulin, A. (2017). CatBoost: Gradient boosting with categorical features support. Workshop on ML Systems at NIPS 2017. Retrieved from https://github.com/
  • Evans, A., & Schäfer, A. W. (2014). Simulating airline operational responses to airport capacity constraints. Transport Policy, 34, 5–13. https://doi.org/10.1016/j.tranpol.2014.02.013
  • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
  • Gimerská, V., Šoltés, M., & Mirdala, R. (2023). Improving operational efficiency through Quality 4.0 tool: Blockchain implementation and subsequent market reaction. Quality Innovation Prosperity, 27(2), 16–32. https://doi.org/10.12776/qip.v27i2.1877
  • Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: an interdisciplinary review. Journal of big data, 7(1), 94.
  • Hong, A. C. Y., Khaw, K. W., Chew, X., & Yeong, W. C. (2023). Prediction of US airline passenger satisfaction using machine learning algorithms. Data Analytics and Applied Mathematics (DAAM).
  • Huang, G. (2021). Missing data filling method based on linear interpolation and LightGBM. Journal of Physics Conference Series, 1754(1), 012187. https://doi.org/10.1088/1742-6596/1754/1/012187
  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
  • Konar, J., Khandelwal, P., & Tripathi, R. (2020). Comparison of various learning rate scheduling techniques on convolutional neural network. 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS 2020). https://doi.org/10.1109/SCEECS48394.2020.94
  • Köse, Y., & Yılmaz, E. (2023). An empirical research on the factors affecting profitability in air transportation. Sosyoekonomi, 31(58), 43–60. https://doi.org/10.17233/sosyoekonomi.2023.04.02
  • Kumar, D. S., Prem, S., Ashfaq, S. M., Rafiq, P. M., & Varma, C. N. (2023). Airline fare prediction using machine learning.
  • Labantová, L., & Begera, V. (2014). Financial, operational and economic indicators of airline operators. Faculty of Aeronautics, Technical University of Košice, 16(2).
  • Lee, B. L., Wilson, C., Pasurka, C. A., Fujii, H., & Managi, S. (2017). Sources of airline productivity from carbon emissions: An analysis of operational performance under good and bad outputs. Journal of Productivity Analysis, 47(3), 223–246. https://doi.org/10.1007/s11123-016-0480-4
  • Lee, J. (2019). Effects of operational performance on financial performance. Management Science Letters, 9(1), 25–32.
  • Lee, K. (2023). Airline operational disruptions and loss-reduction investment. Transportation Research Part B: Methodological, 177, 102817. https://doi.org/10.1016/j.trb.2023.102817
  • Lee, S., & Park, S.-B. (2013). A study on the association between operating leverage and risk: The case of the airline industry. International Journal of Economics and Finance, 6(3), 120. https://doi.org/10.5539/ijef.v6n3p120
  • Lee, S., Kim, H., & Lee, N. (2019). A comparative analysis of financial and operational performance pre-and post-IPO: With a focus on airline companies. Academy of Accounting and Financial Studies Journal, 23(3), 1–14.
  • Lopes, I. F., & Beuren, I. M. (2017). Comportamento dos custos e sua relação com medidas de eficiência operacional em companhias aéreas. BASE - Revista de Administração e Contabilidade Da Unisinos, 14(1), 30–46. https://doi.org/10.4013/base.2017.141.03
  • Lopes, I. T., Ferraz, D. P., & Rodrigues, A. M. G. (2016). The drivers of profitability in the top 30 major airlines worldwide. Measuring Business Excellence, 20(2), 26–37. https://doi.org/10.1108/mbe-09-2015-0045
  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 30 (pp. 4765–4774). Curran Associates, Inc.
  • Lundberg, S., Erion, G., & Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. ArXiv Preprint. https://doi.org/10.48550/arxiv.1802.03888
  • Mantin, B., & Wang, J.-H. (2012). Determinants of profitability and recovery from system-wide shocks: The case of the airline industry. Journal of Airline and Airport Management, 2(1), 1–33. https://doi.org/10.3926/jairm.2
  • McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
  • McLean, D. (2005). Operational efficiency of commercial transport aircraft. Measurement and Control, 38(8), 243–248. https://doi.org/10.1177/002029400503800803
  • Nagesh, P., Naidu, K. B. J., Kowshik, P., & Sekhar, P. C. (2023). Airline ticket price prediction model. International Journal for Research in Applied Science and Engineering Technology.
  • Nohara, Y., Matsumoto, K., Soejima, H., & Nakashima, N. (2021). Explanation of machine learning models using Shapley additive explanation and application for real data in hospitals. ArXiv Preprint. https://doi.org/10.48550/arxiv.2112.11071
  • Okur, M., & Gurbuz, A. O. (2015). Behavioral finance in theory and practice. In Banking, Finance, and Accounting: Concepts, Methodologies, Tools, and Applications (pp. 18). IGI Global. https://doi.org/10.4018/978-1-4666-6268-1.ch016
  • Pamungkas, D. P., & Suhadak, S. (2017). The effect of jet fuel price and macroeconomics variables on profitability of airline industry in Asia (Study at airline companies in Indonesia, India, and China period 2006–2015). Jurnal Ekonomi Bisnis dan Kewirausahaan, 50(3), 208–217.
  • Panigrahi, R., Patne, N. R., Pemmada, S., & Manchalwar, A. D. (2022). Regression model-based hourly aggregated electricity demand prediction. Energy Reports, 8, 16–24. https://doi.org/10.1016/j.egyr.2022.10.004
  • Piranti, M. (2021). The impact of fuel price fluctuation and macroeconomic variables to airlines performance. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 5486–5494.
  • Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.
  • Putra, R. A. K., Persada, S. F., Kumalasari, R. D., Herdina, A. M., Sekardhani, M., & Razif, M. (2024). Operational cost efficiency and profitability effects on companies' distribution stock prices during COVID-19. Jurnal Manajemen Teknologi, 23(1), 76–90. https://doi.org/10.12695/jmt.2024.23.1.5
  • Rossi, M. (2015). The efficient market hypothesis and calendar anomalies: A literature review. International Journal of Managerial and Financial Accounting, 7(3/4), 285. https://doi.org/10.1504/IJMFA.2015.074905
  • Sachdeva, T. (2020). Managing shareholders in turbulent times: Evidence from Indian stock market. International Journal of Business and Globalisation, 25(3), 265. https://doi.org/10.1504/IJBG.2020.109005
  • Şahinbaş, K. (2022). Price prediction model for restaurants in Istanbul by using machine learning algorithms. Ekonomi İşletme ve Maliye Araştırmaları Dergisi, 4(2), 159–171.
  • Saranga, H., & Nagpal, R. (2016). Drivers of operational efficiency and its impact on market performance in the Indian airline industry. Journal of Air Transport Management, 53, 165–176. https://doi.org/10.1016/j.jairtraman.2016.03.001
  • Satter, S., Kwon, T.-H., & Kim, K.-D. (2023). A comparative analysis of various machine learning algorithms to improve the accuracy of HbA1c estimation using wrist PPG data. Sensors, 23(16), 7231. https://doi.org/10.3390/s23167231
  • Seufert, J. H., Arjomandi, A., & Dakpo, K. H. (2017). Evaluating airline operational performance: A Luenberger-Hicks-Moorsteen productivity indicator. Transportation Research Part E: Logistics and Transportation Review, 104, 52–68. https://doi.org/10.1016/j.tre.2017.05.006
  • Singh, J., Sharma, S. K., & Srivastava, R. (2019). What drives Indian airlines' operational expense: An econometric model. Journal of Air Transport Management, 77, 32–38. https://doi.org/10.1016/j.jairtraman.2019.03.003
  • Slater, P. J. (2023). Mind the GAAP: Determinants of U.S. airline operating cash flows. Journal of Academy of Business and Economics, 23(1), 74–84. https://iabe.org/IABE-DOI/article.aspx?DOI=JABE-23-1.6
  • Subramanian, R., Murali, M. S., Deepak, B., Deepak, P., Reddy, H. N., & Sudharsan, R. R. (2022). Airline fare prediction using machine learning algorithms. 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 877–884.
  • Subramanyam, K. R., & Wild, J. J. (2016). Financial statement analysis (11th ed.). McGraw-Hill Education.
  • Sumerli Sarıgül, S., Ünlü, M., & Yaşar, E. (2023). Financial performance analysis of airlines operating in Europe: CRITIC-based MAUT and MARCOS methods. International Journal of Business and Economic Studies, 5(2), 76–97. https://doi.org/10.54821/uiecd.1257488
  • Sun, Y., Wong, A. K., & Kamel, M. S. (2009). Classification of imbalanced data: A review. International journal of pattern recognition and artificial intelligence, 23(04), 687-719.
  • Tanrıverdi, G., Merkert, R., Karamaşa, Ç., & Asker, V. (2023). Using multi-criteria performance measurement models to evaluate the financial, operational and environmental sustainability of airlines. Journal of Air Transport Management, 112, 102456. https://doi.org/10.1016/j.jairtraman.2023.102456
  • Wang, W.-K., Lu, W.-M., & Tsai, C.-J. (2011). The relationship between airline performance and corporate governance amongst U.S. listed companies. Journal of Air Transport Management, 17(2), 148–152. https://doi.org/10.1016/j.jairtraman.2010.06.005
  • Woo, K.-Y., Mai, C., McAleer, M., & Wong, W.-K. (2020). Review on efficiency and anomalies in stock markets. Economies, 8(1), 20. https://doi.org/10.3390/economies8010020
  • Wulansari, T., Suwardi, F., Firdaus, F., Purwanto, D., Hibatullah, Z., & Marganis, V. C. (2024). The influence of liquidity, productive asset quality and operational efficiency on company stock prices: Study of banks going public for the 2016–2020 period. International Journal of Economics and Management Sciences, 1(2), 19–28. https://doi.org/10.61132/ijems.v1i2.18
  • Xu, Y., & E, X. (2020). ARIMA based on linear interpolation for SO2 monitoring data’s calibration. Journal of Physics Conference Series, 1616(1), 012044. https://doi.org/10.1088/1742-6596/1616/1/012044
  • Yadav, D. K., & Goriet, M. O. (2022). An illustrative evaluation of external factors that affect performance of an airline. Journal of Aerospace Technology and Management, 14, 1122. https://doi.org/10.1590/jatm.v14.1253
  • Yilmaz, E., & Köse, Y. (2023). An empirical research on the factors affecting profitability in air transportation. Sosyoekonomi, 31(58), 43–60. https://doi.org/10.17233/sosyoekonomi.2023.04.02
  • Yongxin, L. (2009). Discussing on trend to efficient market hypothesis of securities and futures market. 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, 3, 149–152.
  • Yüksel, B. (2023). Zaman serilerinde talep tahmini (Demand prediction in time series). YBS Ansiklopedi, 11(2), 1–18.
  • Zhang, Q., Koutmos, D., Chen, K., & Zhu, J. (2021). Using operational and stock analytics to measure airline performance: A network DEA approach. Decision Sciences, 52(3), 720–748. https://doi.org/10.1111/deci.12363
  • Zhao, Q., Wang, H., Luo, J.-C., Luo, M., Li, L., Yu, S.-J., Li, K., Zhang, Y., Sun, P., Tu, G.-W., & Luo, Z. (2021). Development and validation of a machine-learning model for prediction of extubation failure in intensive care units. Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.676343
  • Zou, L., Yu, C., & Dresner, M. (2015). Fleet standardization and airline performance. Journal of Transport Economics and Policy, 49(1), 149–166. https://www.jstor.org/stable/jtranseconpoli.49.1.0149
There are 68 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Makaleler
Authors

Ahmet Akusta 0000-0002-5160-3210

Early Pub Date January 19, 2025
Publication Date January 21, 2025
Submission Date October 3, 2024
Acceptance Date December 29, 2024
Published in Issue Year 2025Volume: 26 Issue: 1

Cite

APA Akusta, A. (2025). Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability. Cumhuriyet Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 26(1), 167-182. https://doi.org/10.37880/cumuiibf.1560514
 Download Cover Image

Cumhuriyet University Journal of Economics and Administrative Sciences is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).