International Journal of Business and Applied Social Science

ISSN: 2469-6501 (Online)

DOI: 10.33642/ijbass
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  • Call for Papers: VOL: 5, ISSUE: 10, Submission Deadline October 31, 2019


VOLUME; 5, ISSUE; 6, June 2019

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Author(s): Burcu Sakız, Ph.D., Günay Deniz Dursun

The growing demand for airline transportation in recent years has increased the importance of airline passenger and cargo operations and aviation sector globally. Aviation sector is a sector that has unique properties like high fixed costs, cyclical demand, intense competition, and vulnerability to external shocks like terrorist attacks, disasters, global financial crises especially after deregulation in 1978. Air transport industry is responsible for connecting the global economy, providing a lot of jobs and making modern quality of life possible. Under high competition, it is crucial for airline companies to evaluate and analyze which core business areas are essential for them to prevent bankruptcy and to reach sustainable success. Initially developed in 1968 and evaluated by Altman in time, Altman’s Z score model remains a commonly used tool for evaluating the financial health. Altman Z” score has been well accepted, widely used models of predicting survivals and failures. This model is one of the most frequently used risk early warning models. As one of the biggest player of aviation sector, Turkish airline industry is affected by many different social, political, economic and legal factors on both national and international level as well as other airlines. It is very important to forecast the companies that may gone bankrupt and determine underlying causes. Therefore, this paper evaluates the Altman's Z” score model for predicting the bankruptcy risk of Turkish Airlines inc. which is the Turkey’s flag carrier via financial performance ratios taken from financial statements for the years between 2002 to 2016. Within the scope of the research, both the theoretical information and the applied method details are held. Also for next three years (2017-2019) Z” score values are predicted using artificial intelligence neural network algorithms.
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