ASSESSMENT OF FACTORS AFFECTING FINANCIAL PERFORMANCE OF TOURISM COMPANIES IN BIST BY MEANS
OF DATA MINING ALGORITHMS IN FINANCIAL RATIOS
Duygu Arslantürk Çöllü, Assistant Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
Ayaz Yusuf Altin, Assistant Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
Leyla Akgün, Assistant Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
Ecevit Eyduran, Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
DOI: https://doi.org/10.31410/tmt.2018.425
OF DATA MINING ALGORITHMS IN FINANCIAL RATIOS
Duygu Arslantürk Çöllü, Assistant Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
Ayaz Yusuf Altin, Assistant Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
Leyla Akgün, Assistant Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
Ecevit Eyduran, Professor
Iğdır University, Faculty of Economics and Administrative Sciences,
Department of Business Administration, Iğdır, Turkey
DOI: https://doi.org/10.31410/tmt.2018.425
3rd International Thematic Monograph - Thematic Proceedings: Modern Management Tools and Economy of Tourism Sector in Present Era, Belgrade, 2018, Published by: Association of Economists and Managers of the Balkans in cooperation with the Faculty of Tourism and Hospitality, Ohrid, Macedonia; ISBN 978-86-80194-14-1; Editors: Vuk Bevanda, associate professor, Faculty of Business Studies, Megatrend University, Belgrade, Serbia; Snežana Štetić, full time professor, The College of Tourism, Belgrade, Serbia
Abstract: The present study was conducted on seven tourism companies in BIST Tourism Index in
order to describe continuous financial factors which affect net profit margin (NPM) as a continuous
response variable through CART (Classification and Regression Tree), CHAID (Chi-Square Automatic
Interaction Detector), Exhaustive CHAID and MARS (Multivariate Adaptive Regression Splines) algorithms.
In the present study, the data of these companies from the period 2011-2017 were evaluated.
Predictive performances of CART, CHAID, Exhaustive CHAID and MARS in predicting NPM were
measured based on model goodness of fit criteria, viz. r (Pearson correlation coefficient between actual
and predicted values in NPM), coefficient of determination (R2), adjusted coefficient of determination
(Adj.R2), standard deviation ratio (SDRATIO), root of mean square error (RMSE), global relative approximation
error (RAE), mean absolute deviation (MAD), Akaike’s information criterion (AIC) and the
corrected Akaike’s information criterion (AICc). In the study, financial factors used in the prediction of
NPM were current ratio (CR), acid-test ratio (ACTR), asset turnover ratio (ASTR), accounts receivable
turnover ratio (ACRTR), equity turnover ratio (EQTR), short term liabilities to total assets ratio (SHTLTAR),
long term liabilities to total assets ratio (LOTLTAR), total assets to equity ratio (TOAER), long
term liabilities to equity ratio (LOLER) and total debt to total assets ratio (TODTAR) as predictors. In
the prediction of the NPM and the description of the influential financial factors influencing the NPM,
the highest predictive accuracy was obtained by MARS algorithm (r=0.980) and the statistically significant
order was found as MARS (r=0.980) > Exhaustive CHAID (r=0.915) = CART (r=0.873) = CHAID
(r=0.868) algorithms.
In conclusion, the achieved results indicated that, i) the regression tree diagram constructed by Exhaustive
CHAID algorithm displayed that tourism companies with LOTLTAR < 0.3715 and EQTR <
0.0311 had the highest average NPM of 2.778, ii) CART tree-based algorithm showed that the companies
with EQTR > - 0.2125 and ASTR < 0.0246 had the highest average NPM of 4.226, iii) the diagram of
CHAID tree-based algorithm revealed that the companies with TODTAR < 0.6145 and EQTR < 0.0311
had the highest NPM with the average of 2.778. It is recommendable that data mining algorithms capture
optimal cut-off values of influential factors, which may ensure the highest NPM values.
Keywords: Tourism companies, financial ratios, data mining algorithms, MARS, BIST
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