Research on Financial Distress Prediction Model Based on Kalman Filtering Theory

Research of enterprises' financial distress prediction (FDP) can generate early warning signals before the outbreak of financial crisis, and how to build a relative simplicity and robust FDP model has been of concern for theorists and practitioners at home and abroad. This research introduces Kalman filtering theory into FDP modeling. It builds a process model and a measurement model to describe the dynamic financial system. It uses time update and measurement update algorithm to solve the problem of financial information filtering. And thus, an adaptive model is proposed which is proved effective by an empirical analysis. This research is expected to provide theoretical support to achieve an accurate FDP and promote the application of FDP state-space model for enterprises.