Parametric prediction on default risk of Chinese listed tourism companies by using random oversampling, isomap, and locally linear embeddings on imbalanced samples

This research pioneers the default risk parametric prediction of Chinese tourism companies with random oversampling and manifold learning for parametric modelling on imbalanced samples to relax the requirement on sample availability. Four specific approaches were employed: standardization; standardization random oversampling; standardization -> isomap + locally linear embeddings; and standardization -> random oversampling -> isomap + locally linear embeddings. Empirical results indicate that: random oversampling successfully improved the tourism default risk prediction; the integration of isomap and locally linear embeddings is beneficial in default risk prediction using highly skewed tourism data with absolute minority samples; and after the use of random oversampling on initial data, the integrated approach improved in forecasting tourism default risk prior to two years versus one year. (C) 2013 Elsevier Ltd. All rights reserved.