Abstract
Predicting EPS (earning per share) with financial ratios is of high practical value. In this study, we applied two methods of data mining to predict EPS and compared their performance. We used three classifiers to find rules for predicting EPS from financial ratios, and compared the difference of prediction performance with and without data discretization. The experimental data set was extracted from the online financial database of Taiwan Economic Journal (TEJ) and collected from the 2009–2013 financial statements of six different industries. 26 condition attributes were selected from financial statements of listed public companies. The decision attribute, EPS, was classified into two and three classes. The result shows that there are three key determinants: Operating income per share, Times interest earned, and Total assets growth rate, and the method of with data discretization is of better prediction accuracy.
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