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Journals / Advances in Hospitality and Tourism Research (AHTR) / 2026 / Volume: 14 Issue: 3

Predicting Tourism Firms' Performance Indicators Using the Decision Tree Algorithms

Yeşim Helhel(AKDENİZ ÜNİVERSİTESİ, TURİZM FAKÜLTESİ)0000-0002-1293-6450
Yıldırım Yılmaz(AKDENİZ ÜNİVERSİTESİ, TURİZM FAKÜLTESİ)0000-0002-6537-7111
Abdullah Akgün(AKDENİZ ÜNİVERSİTESİ)0000-0001-9949-1417
Pages
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Publication Date
2026-09-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that the Random Forest algorithm achieves the strongest predictive performance for ROA, ROE, and stock return performance, recording the highest scores across all key metrics.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study explores how well the most popular decision tree algorithms can predict the financial and stock return performances of tourism firms listed on the stock exchange in an emerging economy.
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Key Findings & Data:The models incorporate tourism growth metrics, macroeconomic variables, and firm-level financial indicators as predictors.
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Conclusion & Impact:The results indicate that the Random Forest algorithm achieves the strongest predictive performance for ROA, ROE, and stock return performance, recording the highest scores across all key metrics.
🔬Design:Modeling & Simulation
📍Scope:2012 Verileri
⚙️Variables:Decision trees · Financial performance · Stock return

Abstract

This study explores how well the most popular decision tree algorithms can predict the financial and stock return performances of tourism firms listed on the stock exchange in an emerging economy. The models incorporate tourism growth metrics, macroeconomic variables, and firm-level financial indicators as predictors. To this end, four widely used decision tree algorithms are applied to quarterly data spanning from the first quarter of 2012 to the fourth quarter of 2024. The results indicate that the Random Forest algorithm achieves the strongest predictive performance for ROA, ROE, and stock return performance, recording the highest scores across all key metrics. The firms’ liquidity level (quick ratio), asset size, and real exchange rate as a macroeconomic variable are the most significant determinants of ROA and ROE. Regarding stock return performance as a market-based measurement, liquidity level and the real exchange rate remain critical, while tourism expenditures and the macroeconomic indicator of M2 money supply also prove to be significant determinants.

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Cite This Article

Helhel, Y., Yılmaz, Y., Akgün, A. (2026). Predicting Tourism Firms' Performance Indicators Using the Decision Tree Algorithms. Advances in Hospitality and Tourism Research (AHTR), Volume: 14 Issue: 3. https://doi.org/10.30519/ahtr.1782951

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