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

Ahmet Akusta

doi:10.37880/cumuiibf.1560514

EN TR

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

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

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

Öz

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.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Finans

Bölüm

Araştırma Makalesi

Yazarlar

Ahmet Akusta ^*
0000-0002-5160-3210
Türkiye

Erken Görünüm Tarihi

19 Ocak 2025

Yayımlanma Tarihi

21 Ocak 2025

Gönderilme Tarihi

3 Ekim 2024

Kabul Tarihi

29 Aralık 2024

Yayımlandığı Sayı

Yıl 2025 Cilt: 26 Sayı: 1

DOI

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

IZ

https://izlik.org/JA65PU99XB

Kaynak Göster

RIS / Bibtex

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

Cited By

POLİNOM REGRESYON KONTROLLÜ GERİLİM KİPLİ FLYBACK DÖNÜŞTÜRÜCÜ

Uludağ University Journal of The Faculty of Engineering

https://doi.org/10.17482/uumfd.1587544