Research on Financial Distress Prediction Model Based on Kalman Filtering Theory

DOI

10.2991/icetms.2013.304How to use a DOI?

Keywords

financial distress prediction, Kalman filter, state-space model

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qian Zhuang
AU  - Liang-hua Chen
PY  - 2013/06
DA  - 2013/06
TI  - Research on Financial Distress Prediction Model Based on Kalman Filtering Theory
BT  - Proceedings of the 2013 Conference on Education Technology and Management Science (ICETMS 2013)
PB  - Atlantis Press
SP  - 1123
EP  - 1125
SN  - 1951-6851
UR  - https://doi.org/10.2991/icetms.2013.304
DO  - 10.2991/icetms.2013.304
ID  - Zhuang2013/06
ER  -