NDLI: Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market

Content Provider	Springer Nature Link
Author	Hwang, Ruey Ching Chung, Huimin Ku, Jiun Yi
Copyright Year	2012
Abstract	The dynamic logit model (DLM) with autocorrelation structure (Liang and Zeger Biometrika 73:13–22, 1986) is proposed as a model for predicting recurrent financial distresses. This model has been applied in many examples to analyze repeated binary data due to its simplicity in computation and formulation. We illustrate the proposed model using three different panel datasets of Taiwan industrial firms. These datasets are based on the well-known predictors in Altman (J Financ 23:589–609, 1968), Campbell et al. (J Financ 62:2899–2939, 2008), and Shumway (J Bus 74:101–124, 2001). To account for the correlations among the observations from the same firm, we consider two different autocorrelation structures: exchangeable and first-order autoregressive (AR1). The prediction models including the DLM with independent structure, the DLM with exchangeable structure, and the DLM with AR1 structure are separately applied to each of these datasets. Using an expanding rolling window approach, the empirical results show that for each of the three datasets, the DLM with AR1 structure yields the most accurate firm-by-firm financial-distress probabilities in out-of-sample analysis among the three models. Thus, it is a useful alternative for studying credit losses in portfolios.
Starting Page	321
Ending Page	341
Page Count	21
File Format	PDF
ISSN	09208550
Journal	Journal of Financial Services Research
Volume Number	43
Issue Number	3
e-ISSN	15730735
Language	English
Publisher	Springer US
Publisher Date	2012-05-05
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Autocorrelation structure Dynamic logit model Expanding rolling window approach Predictive interval Predicted number of financial distresses Recurrent financial distresses Finance/Investment/Banking Macroeconomics/Monetary Economics
Content Type	Text
Resource Type	Article
Subject	Finance Accounting Economics and Econometrics

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