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

• Pioneer tourism default risk imbalanced prediction.
• Random oversampling, isomap and locally linear embeddings are combined.
• Random oversampling improves accuracy of tourism default risk imbalanced prediction.
• Isomap and locally linear embeddings are useful on highly screwed tourism samples.
• Sampling approaches are more helpful for long-term tourism default risk prediction.

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.