Gated recurrent unit network: A promising approach to corporate default prediction

This paper presents a promising approach using gated recurrent unit (GRU) network to predict bankruptcy based on the whole sequence of financial statements of the companies listed on an unregulated market. This approach contrasts with the traditional literature where default prediction is usually tackled with methods that do not fully account for a company's history. The GRU network can be used to model long‐term dependencies thanks to its update and reset gates, which prevent the vanishing gradient problem and decide how much of the past information is relevant for predicting a default. This aspect may be of utmost importance in alternative markets where the signal detection problem is particularly strong. The performance of the GRU network is compared against the performance of other standard machine learning methods including Cox proportional hazards, gradient‐boosted Cox proportional hazards model, extreme gradient boosting, random survival forest, and standard recurrent neural networks on the basis of a broad selection of performance metrics. The GRU network not only outperforms standard machine learning methods in out‐of‐sample forecasts but also seems to be more robust in terms of in‐ versus out‐of‐sample performance.