Stock selection using data envelopment analysis–discriminant analysis

Discriminant Analysis (DA) is a statistical method that can predict the category of a newly sampled observation. Some investigations involving Data Envelopment Analysis (DEA) have conducted discriminant analysis using DEA, an approach called DEA–DA. Based on both the DEA–DA method of Sueyoshi and Sueyoshi & Hwang and the concepts of investment decision, this study measures from the financial indices of Taiwanese banks to construct a discriminant function that allow investors to distinguish between superior and inferior stocks in terms of stock returns for the upcoming year. Analytical results demonstrate that a 100% hit rate for DEA–DA and leave-one-out cross validation with DEA–DA attains an accuracy rate of 85% confirming that DEA–DA is a highly effective method of classifying stocks into groups to help investors to select stocks.