文献类型：期刊文献

英文题名：Dynamic financial distress prediction based on Kalman filtering

作者：Bao, Xinzhong[1];Tao, Qiuyan[1];Fu, Hongyu[1]

第一作者：鲍新中

通讯作者：Fu, HY[1]

机构：[1]Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2015

卷号：42

期号：2

起止页码：292-308

外文期刊名：JOURNAL OF APPLIED STATISTICS

收录：;Scopus(收录号：2-s2.0-85027932611);WOS:【SSCI(收录号:WOS:000344560700005)，SCI-EXPANDED(收录号:WOS:000344560700005)】；

语种：英文

外文关键词：state space equations; financial distress; Kalman filtering; dynamic prediction

摘要：In models for predicting financial distress, ranging from traditional statistical models to artificial intelligence models, scholars have primarily paid attention to improving predictive accuracy as well as the progressivism and intellectualization of the prognostic methods. However, the extant models use static or short-term data rather than time-series data to draw inferences on future financial distress. If financial distress occurs at the end of a progressive process, then omitting time series of historical financial ratios from the analysis ignores the cumulative effect of previous financial ratios on the current consequences. This study incorporated the cumulative characteristics of financial distress by using the characteristics of a state space model that is able to perform long-term forecasts to dynamically predict an enterprise's financial distress. Kalman filtering is used to estimate the model parameters. Thus, the model constructed in this paper is a dynamic financial prediction model that has the benefit of forecasting over the long term. Additionally, current data are used to forecast the future annual financial position and to judge whether the establishment will be in financial distress.