Dynamic Prediction of Corporate Financial Crisis Based on N-Step Ahead Kalman Filter

The occurrence of financial crisis in a corporation is a gradual and cumulative process. The use of short-duration data does not allow visualization of the development process of financial crises. This paper aim to propose a financial trajectory dynamic tracking algorithm for quantifying the financial status process of a corporation. Including clustering module for calculating financial signal scores and signal cluster probabilities, feature module for attention assignment of financial indicators, and trajectory tracking module for dynamic prediction of financial crises, which deriving an N-step ahead algorithm based on the Kalman filter to achieve dynamic prediction of corporate financial crises. The finding shows that the N-step ahead Kalman filter can perform real-time dynamic prediction of financial trajectories. A prediction accuracy of 93.8% was achieved in the first 4 years of the financial crisis. N-step ahead filter does not need to store a large amount of historical data, which can realize real-time update of financial signals. The empirical demonstration shows the accuracy and sophistication of the algorithm, which provides a new idea in the field of financial crisis dynamics prediction.