Price-earning Ratio Estimation Based On Data Classification Methods

Price-earning ratio model is one of the most frequently used valuation model.While using price-earning ratio model,the most important step is to estimate the reasonable PE value of the target company.Investors normally use the mean PE value of the target company ' s industry as the estimator,without paying attention to the difference of prospects and operating situation between companies,a practice which will definitely cause remarkable error.This paper attempts to use data classification methods and financial ratios to improve accuracy of the PE value estimation.We have selected the data of real estate industry from 2013 to 2015 and 16 categorical attributes for this research.Real estate companies are classified into several categories by order of their PE values.We use decision tree algorithms and logistic regression to decide which category a target company belongs to,and the median PE value of the category as the estimator.The result shows that it is effective using data classification method to estimate PE value.