Predicting Tourism Firms' Performance Indicators Using the Decision Tree Algorithms

Year 2025, Issue: Advanced Online Publication

Yeşim Helhel , Yıldırım Yılmaz , Abdullah Akgün

https://doi.org/10.30519/ahtr.1782951

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.

Keywords

Decision trees , Financial performance , Stock return , Tourism , Macroeconomy

Ethical Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

• Agiomirgianakis, G. M., Magoutas, A. I., & Sfakianakis, G. (2013). Determinants of profitability in the Greek tourism sector revisited: The impact of the economic crisis. Journal of Tourism and Hospitality Management, 1(1), 12-17.
• Akgün, A., Çizel, B., & Ajanovic, E. (2022). Mining excursion tourist profile through classification algorithms. Quality and Quantity, 56(4), 2567–2588. https://doi.org/10.1007/s11135-021-01234-3
• Akinboade, O. A., & Braimoh, L. A. (2010). International tourism and economic development in South Africa: A Granger causality test. International journal of tourism research, 12(2), 149-163. https://doi.org/10.1002/jtr.743
• Al-Najjar, B. (2014). Corporate governance, tourism growth and firm performance: Evidence from publicly listed tourism firms in five Middle Eastern countries. Tourism Management, 42, 342–351. https://doi.org/10.1016/j.tourman.2013.09.008
• Andrikopoulos, A., Merika, A. A., & Merikas, A. G. (2016). Financial disclosure in the travel and leisure industry. International Journal of Tourism Research, 18(6), 612–619. https://doi.org/10.1002/jtr.2078
• Archer, B., & Fletcher, J. (1996). The economic impact of tourism in the Seychelles. Annals of Tourism Research, 23(1), 32-47. https://doi.org/10.1016/0160-7383(95)00041-0
• Arici, H. E., Arasli, H., Koseoglu, M. A., Saydam, M. B., & Olorunsola, V. O. (2024). Financial determinants of governance scores in hospitality and tourism enterprises. Quality & Quantity, 1-22. https://doi.org/10.1007/s11135-023-01820-7
• Arslanturk, Y., Balcilar, M., & Ozdemir, Z. A. (2011). Time-varying linkages between tourism receipts and economic growth in a small open economy. Economic Modelling, 28(1–2), 664–671. https://doi.org/10.1016/j.econmod.2010.06.003
• Assaf, A., & Cvelbar, K. L. (2011). Privatization, market competition, international attractiveness, management tenure and hotel performance: Evidence from Slovenia. International Journal of Hospitality Management, 30(2), 391–397. https://doi.org/10.1016/j.ijhm.2010.03.012
• Assaf, A. G., & Josiassen, A. (2012). Identifying and Ranking the Determinants of Tourism Performance: A Global Investigation. Journal of Travel Research, 51(4), 388–399. https://doi.org/10.1177/0047287511426337
• Balcilar, M., van Eyden, R., Inglesi-Lotz, R., & Gupta, R. (2014). Time-varying linkages between tourism receipts and economic growth in South Africa. Applied Economics, 46(36), 4381–4398. https://doi.org/10.1080/00036846.2014.957445
• Barrows, C. W., & Naka, A. (1994). Use of macroeconomic variables to evaluate selected hospitality stock returns in the US. International Journal of Hospitality Management, 13(2), 119–128. https://doi.org/10.1016/0278-4319(94)90033-7
• Ben Aissa, S., & Goaied, M. (2016). Determinants of Tunisian hotel profitability: The role of managerial efficiency. Tourism Management, 52, 478–487. https://doi.org/10.1016/j.tourman.2015.07.015
• Bogdan, S. (2019). Macroeconomic impact on stock returns in the Croatian hospitality industry. Zbornik Veleučilišta u Rijeci, 7(1), 53–68. https://doi.org/10.31784/zvr.7.1.2
• Boz, G., Menéndez-Plans, C., & Orgaz-Guerrero, N. (2015). The Systematic-Risk Determinants of the European Accommodation and Food Services Industry in the Period 2003-2011. Cornell Hospitality Quarterly, 56(1), 41–57. https://doi.org/10.1177/1938965514559047
• Chambers, N., & Cifter, A. (2022). Working capital management and firm performance in the hospitality and tourism industry. International Journal of Hospitality Management, 102. https://doi.org/10.1016/j.ijhm.2022.103144
• Chen, M. H. (2007). Macro and non-macro explanatory factors of Chinese hotel stock returns. International Journal of Hospitality Management, 26(4), 991–1004. https://doi.org/10.1016/j.ijhm.2006.04.002
• Chen, M. H. (2010). The economy, tourism growth and corporate performance in the Taiwanese hotel industry. Tourism Management, 31(5), 665–675. https://doi.org/10.1016/j.tourman.2009.07.011
• Chen, M. H. (2011). The response of hotel performance to international tourism development and crisis events. International Journal of Hospitality Management, 30(1), 200–212. https://doi.org/10.1016/j.ijhm.2010.06.005
• Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272. https://doi.org/10.1016/j.eswa.2011.02.173
• Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220–228. https://doi.org/10.1016/j.econmod.2013.09.024
• Dağıstanlı, H. A., Özen, F., & Saraçoğlu, İ. (2024). Forecasting Sustainability Reports with Financial Performance Indicators using Random Forest for Feature Selection and Gradient Boosting for Learning. Savunma Bilimleri Dergisi, 20(2), 279-302. https://doi.org/10.17134/khosbd.1492365
• Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40(10), 3970–3983. https://doi.org/10.1016/j.eswa.2013.01.012
• Demir, E., Alıcı, Z. A., & Chi Keung Lau, M. (2017). Macro explanatory factors of Turkish tourism companies’ stock returns. Asia Pacific Journal of Tourism Research, 22(4), 370–380. https://doi.org/10.1080/10941665.2016.1271814
• De Schoenmaker, S., Van Cauwenberge, P., & Vander Bauwhede, H. (2014). Effects of local fiscal policy on firm profitability. Service Industries Journal, 34(16), 1289–1306. https://doi.org/10.1080/02642069.2014.942654
• Dimitriadou, A., Gogas, P., & Papadimitriou, T. (2025). Tourism and uncertainty: a machine learning approach. Current Issues in Tourism, 28(14), 2278-2298. https://doi.org/10.1080/13683500.2024.2370380
• Dimitrić, M., Tomas Žiković, I., & Arbula Blecich, A. (2019). Profitability determinants of hotel companies in selected Mediterranean countries. Economic Research-Ekonomska Istrazivanja , 32(1), 1977–1993. https://doi.org/10.1080/1331677X.2019.1642785
• Dritsakis, N. (2004). Tourism as a long-run economic growth factor: an empirical investigation for Greece using causality analysis. Tourism Economics, 10(3), 305-316. https://doi.org/10.5367/0000000041895094
• Durica, M., Frnda, J., & Svabova, L. (2019). Decision tree based model of business failure prediction for Polish companies. Oeconomia Copernicana, 3, 453–469.
• Gémar, G., Moniche, L., & Morales, A. J. (2016). Survival analysis of the Spanish hotel industry. Tourism Management, 54, 428–438. https://doi.org/10.1016/j.tourman.2015.12.012
• Gunduz, L., & Hatemi-J, A. (2005). Is the tourism-led growth hypothesis valid for Turkey? Applied Economics Letters, 12(8), 499–504. https://doi.org/10.1080/13504850500109865
• Gu, Z., & Kim, H. (2002). Determinants of restaurant systematic risk: A reexamination. The Journal of Hospitality Financial Management, 10(1), 1–13. https://doi.org/10.1080/10913211.2002.10653757
• Hadi, D. M., Irani, F., & Gökmenoğlu, K. K. (2022). External determinants of the stock price performance of tourism, travel, and leisure firms: Evidence from the United States. International Journal of Hospitality and Tourism Administration, 23(4), 679–695. https://doi.org/10.1080/15256480.2020.1842838
• Helhel, Y., & Akgun, E. (2024). Examining the Relationship Between Tourism Index Return and Financial, Macroeconomic and Tourism Industry Development Indicators: An Application of MS-VAR Models. Politicka Ekonomie, 72(4), 626–652. https://doi.org/10.18267/j.polek.1424
• Hezam, Y., Luong, H., & Anthonysamy, L. (2025). Machine learning in predicting firm performance: A systematic review. China Accounting and Finance Review, 27(3), 309-339. https://doi.org/10.1108/CAFR-03-2024-0036
• Hooi, T. S., Habibullah, M. S., Choon, S. W., & Mohamed, A. (2019). “Doing Well” by “Doing Good”? Evidence from tourism-related firms in four South Asia countries. Jurnal Pengurusan, 55, 13–22. https://doi.org/10.17576/PENGURUSAN-2019-55-02
• Irani, F., Katircioglu, S. & Gokmenoglu, K.K. (2021). Effects of business and finance conditions on tourism firms’ financial performances: Evidence from major tourist destinations. Sage Open, 1-13. https://doi.org/10.1177/21582440211040120
• Kang, K. H., Lee, S., & Huh, C. (2010). Impacts of positive and negative corporate social responsibility activities on company performance in the hospitality industry. International Journal of Hospitality Management, 29(1), 72–82. https://doi.org/10.1016/j.ijhm.2009.05.006
• Karas, M., & Reznakova, M. (2017). Predicting the bankruptcy of construction companies: A CART-based model. Engineering Economics, 28(2), 145–154. https://doi.org/10.5755/j01.ee.28.2.16353
• Kim, M. C., & Kim, Y. H. (2014). Corporate social responsibility and shareholder value of restaurant firms. International Journal of Hospitality Management, 40, 120–129. https://doi.org/10.1016/j.ijhm.2014.03.006
• Kraus, A., & Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. The Journal of Finance, 28(4), 911-922.
• Kuzey, C., Karaman, A. S., & Akman, E. (2019). Elucidating the Impact of Visa Regimes: A Decision Tree Analysis. Tourism Management Perspectives, 29, 148–156. https://doi.org/10.1016/j.tmp.2018.11.008
• Lee, J. S., & Jang, S. C. (Shawn). (2007). The systematic-risk determinants of the US airline industry. Tourism Management, 28(2), 434–442. https://doi.org/10.1016/j.tourman.2006.03.012
• Lee, S. K., & Jang, S. C. S. (2012). The real estate risk of hospitality firms: Examining stock-return sensitivity to property values. International Journal of Hospitality Management, 31(3), 695–702. https://doi.org/10.1016/j.ijhm.2011.09.005
• Lee, S., & Park, S. Y. (2009). Do socially responsible activities help hotels and casinos achieve their financial goals? International Journal of Hospitality Management, 28(1), 105–112. https://doi.org/10.1016/j.ijhm.2008.06.003
• Li, W. W., Ma, X. L., & Yu, H. (2019). Is the domestic tourism cycle synchronized with the economic cycle? Evidence from Mainland China and Taiwan. Asia Pacific Journal of Tourism Research, 24(9), 944–964. https://doi.org/10.1080/10941665.2019.1653332
• Lim, S.(E) & Ok, C., M. (2021). A meta-analytic review of antecedents of hospitality and tourism firms’ performance: A cross-cultural comparison. Tourism Management, 86, 104325. https://doi.org/10.1016/j.tourman.2021.104325
• Manogna, R.L., & Mishra, A. K. (2022). Measuring financial performance of Indian manufacturing firms: application of decision tree algorithms. Measuring Business Excellence, 26(3), 288–307. https://doi.org/10.1108/MBE-05-2020-0073
• Marti, C. P., Rovira-Val, M. R., & Drescher, L. G. J. (2015). Are firms that contribute to sustainable development better financially? Corporate Social Responsibility and Environmental Management, 22(5), 305–319. https://doi.org/10.1002/csr.1347
• Mishra, R. K., Jothi, J. A. A., Urolagin, S., & Irani, K. (2023). Knowledge based topic retrieval for recommendations and tourism promotions. International Journal of Information Management Data Insights, 3(1), 100145. https://doi.org/10.1016/j.jjimei.2022.100145
• Molina-Azorin, J. F., Pereira-Moliner, J., & Claver-Cortés, E. (2010). The importance of the firm and destination effects to explain firm performance. Tourism Management, 31(1), 22–28. https://doi.org/10.1016/j.tourman.2009.02.009
• Mucharreira, P. R., Antunes, M. G., Abranja, N., Justino, M. R. T., & Quirós, J. T. (2019). The relevance of tourism in financial sustainability of hotels. European Research on Management and Business Economics, 25(3), 165–174. https://doi.org/10.1016/j.iedeen.2019.07.002
• Myers, S. C. (1984). Capital Structure Puzzle, The Journal of Finance, 39(3), 574-592.
• Nunez, J. C. S., Gomez-Pulido, J. A., & Ramirez, R. R. ( 2024). Machine learning applied to tourism: A systematic review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(5), e1549. https://doi.org/10.1002/widm.1549
• Pacheco, L. (2016). RevPAR determinants of Portuguese hotels: The global and the local factors. Tourism: An International Interdisciplinary Journal, 64(2), 219-230.
• Paci, R., & Marrocu, E. (2014). Tourism and regional growth in Europe. Papers in Regional Science, 93(S1), S25–S50. https://doi.org/10.1111/pirs.12085
• Parte-Esteban, L., & Alberca-Oliver, P. (2016). Tourist Flow and Earnings Benchmarks: Spanish Hotel Industry. Journal of Hospitality and Tourism Research, 40(1), 58–84. https://doi.org/10.1177/1096348013491595
• Roudi, S., Arasli, H., & Akadiri, S. Saint. (2019). New insights into an old issue–examining the influence of tourism on economic growth: evidence from selected small island developing states. Current Issues in Tourism, 22(11), 1280–1300. https://doi.org/10.1080/13683500.2018.1431207
• Sahin, Y., Bulkan, S., & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 40(15), 5916–5923. https://doi.org/10.1016/j.eswa.2013.05.021
• Sawangarreerak, S., & Thanathamathee, P. (2021). Detecting and analyzing fraudulent patterns of financial statement for open innovation using discretization and association rule mining. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 128. https://doi.org/10.3390/joitmc7020128
• Seera, M., Lim, C. P., Kumar, A., Dhamotharan, L., & Tan, K. H. (2024). An intelligent payment card fraud detection system. Annals of Operations Research, 334(1–3), 445–467. https://doi.org/10.1007/s10479-021-04149-2
• Singal, M. (2012). Effect of consumer sentiment on hospitality expenditures and stock returns. International Journal of Hospitality Management, 31(2), 511–521. https://doi.org/10.1016/j.ijhm.2011.07.009
• Töglhofer, C., Eigner, F., & Prettenthaler, F. (2011). Impacts of snow conditions on tourism demand in Austrian ski areas. Climate Research, 46(1), 1–14. https://doi.org/10.3354/cr00939
• Weerathunga, P. R., Xiaofan, C., & Samarathunga, M. (2019). Firm-specific and contextual determinants of sri lankan corporate hotel performance. International Journal of Economics and Financial Issues, 9(3), 213–224. https://doi.org/10.32479/ijefi.7880
• Yang, Z., & Cai, J. (2016). Do regional factors matter? Determinants of hotel industry performance in China. Tourism Management, 52, 242–253. https://doi.org/10.1016/j.tourman.2015.06.024
• Zheng, Q., & Yanhui, J. (2007, August). Financial distress prediction based on decision tree models. In 2007 IEEE International Conference on Service Operations and Logistics, and Informatics, 1-6. IEEE.