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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
Cited By: 1

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

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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
Cited By: 1

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

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  • 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
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  • 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.
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  • 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.
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  • 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).
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  • 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
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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 2025 Volume: 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

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POLİNOM REGRESYON KONTROLLÜ GERİLİM KİPLİ FLYBACK DÖNÜŞTÜRÜCÜ
Uludağ University Journal of The Faculty of Engineering
https://doi.org/10.17482/uumfd.1587544
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