ASSESSMENT OF FACTORS AFFECTING FINANCIAL PERFORMANCE OF TOURISM COMPANIES IN BIST BY MEANS OF DATA MINING ALGORITHMS IN FINANCIAL RATIOS
ASSESSMENT OF FACTORS AFFECTING FINANCIAL PERFORMANCE OF TOURISM COMPANIES IN BIST BY MEANS OF DATA MINING ALGORITHMS IN FINANCIAL RATIOS

Author(s): Duygu Arslanturk Collu, Ayaz Yusuf Altin, Leyla Akgün, Ecevit Eyduran
Subject(s): Economy, Financial Markets, Tourism
Published by: Udruženje ekonomista i menadžera Balkana
Keywords: Tourism companies;financial ratios;data mining algorithms;MARS;BIST;
Summary/Abstract: The present study was conducted on seven tourism companies in BIST Tourism Index inorder to describe continuous financial factors which affect net profit margin (NPM) as a continuousresponse variable through CART (Classification and Regression Tree), CHAID (Chi-Square AutomaticInteraction 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 weremeasured based on model goodness of fit criteria, viz. r (Pearson correlation coefficient between actualand 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 approximationerror (RAE), mean absolute deviation (MAD), Akaike’s information criterion (AIC) and thecorrected Akaike’s information criterion (AICc). In the study, financial factors used in the prediction ofNPM were current ratio (CR), acid-test ratio (ACTR), asset turnover ratio (ASTR), accounts receivableturnover 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), longterm liabilities to equity ratio (LOLER) and total debt to total assets ratio (TODTAR) as predictors. Inthe 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 significantorder 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 ExhaustiveCHAID 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 companieswith EQTR > - 0.2125 and ASTR < 0.0246 had the highest average NPM of 4.226, iii) the diagram ofCHAID tree-based algorithm revealed that the companies with TODTAR < 0.6145 and EQTR < 0.0311had the highest NPM with the average of 2.778. It is recommendable that data mining algorithms captureoptimal cut-off values of influential factors, which may ensure the highest NPM values.

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