A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

2009 ◽  
Vol 72 (16-18) ◽  
pp. 3507-3516 ◽  
Author(s):  
Tzong-Huei Lin
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Multiple Discriminant Analysis ◽  
Cross Model ◽  
Financial Distress Prediction ◽  
Model Study ◽  
Distress Prediction

scholarly journals Pemodelan Multiple Discriminant Analysis untuk Memprediksi Financial Distress Bank Umum Syariah di Indonesia

2020 ◽  
Vol 12 (1) ◽  
pp. 31-42
Author(s):  
Radia - Purbayati
Keyword(s):  
Discriminant Analysis ◽  
Prediction Model ◽  
Financial Distress ◽  
Financial Ratios ◽  
Islamic Banks ◽  
Multiple Discriminant Analysis ◽  
Islamic Bank ◽  
Financial Distress Prediction ◽  
Distress Prediction

The aims of this study is to set financial distress prediction model and to identify the best accuraction and classification from the financial distress prediction model. The objects were 9 Islamic Banks in Indonesia since 2012 to 2017 using Multiple Discriminant Analysis modelling. The variables used financial ratios, consist of ROA, BOPO, Current Assets to Current Liabilities Ratio, NPF, Equity to Total Liabilities, and FDR. The outcome shows that a variable tend to cause an Islamic bank fall into financial distress condition dominantly was NPF ratio. The accuration prediction power with 42 from 42 obervations predicted fall into health bank category were classified correctly (100%), and 2 from 12 Islamic Banks fall into financial distress category were classified incorrectly (16.7%) and were corrected into helath bank category. The classification power created by multiple discriminant analysis was 81.48%.  

scholarly journals Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Chyan-long Jan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Information Asymmetry ◽  
Error Rate ◽  
Financial Distress ◽  
Prediction Models ◽  
High Accuracy ◽  
Financial Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

Because of the financial information asymmetry, the stakeholders usually do not know a company’s real financial condition until financial distress occurs. Financial distress not only influences a company’s operational sustainability and damages the rights and interests of its stakeholders, it may also harm the national economy and society; hence, it is very important to build high-accuracy financial distress prediction models. The purpose of this study is to build high-accuracy and effective financial distress prediction models by two representative deep learning algorithms: Deep neural networks (DNN) and convolutional neural networks (CNN). In addition, important variables are selected by the chi-squared automatic interaction detector (CHAID). In this study, the data of Taiwan’s listed and OTC sample companies are taken from the Taiwan Economic Journal (TEJ) database during the period from 2000 to 2019, including 86 companies in financial distress and 258 not in financial distress, for a total of 344 companies. According to the empirical results, with the important variables selected by CHAID and modeling by CNN, the CHAID-CNN model has the highest financial distress prediction accuracy rate of 94.23%, and the lowest type I error rate and type II error rate, which are 0.96% and 4.81%, respectively.

Dynamic financial distress prediction based on class-imbalanced data batches

2021 ◽  
pp. 2150026
Author(s):  
Jie Sun ◽  
Xin Liu ◽  
Wenguo Ai ◽  
Qianyuan Tian
Keyword(s):  
Financial Distress ◽  
Time Window ◽  
Concept Drift ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Sampling Technique ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Financial Distress Prediction ◽  
Distress Prediction

This study proposes two approaches for dynamic financial distress prediction (FDP) based on class-imbalanced data batches by considering both concept drift and class imbalance. One is based on sliding time window and synthetic minority over-sampling technique (SMOTE) and the other is based on sliding time window and majority class partition. Support vector machine, multiple discriminant analysis (MDA) and logistic regression are used as base classifiers in the experiments on a real-world dataset. The results indicate that the two approaches perform better than the pure dynamic FDP (DFDP) models without class imbalance processing and the static FDP models either with or without class imbalance processing.

scholarly journals A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis

2017 ◽  
Vol 1 (3) ◽  
pp. 13-23
Author(s):  
Ehsan ul Hassan ◽  
Zaemah Zainuddin ◽  
Sabariah Nordin
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress ◽  
Prediction Models ◽  
Early Prediction ◽  
Multivariate Discriminant Analysis ◽  
Advantages And Disadvantages ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Precautionary Measures ◽  
Multivariate Discriminant

In corporate finance, the early prediction of financial distress is considered more important as another occurrence of business risks. The study presents a review of literature for early prediction of financial bankruptcy. It contributes to the formation of a systematic review of the literature regarding previous studies done in the field of bankruptcy. It addresses two most commonly used financial distress prediction models, i.e. multivariate discriminant analysis and logit. Models are discussed with their advantages and disadvantages. After methodological review, it seems that logit regression model (LRM) is more advantageous than multivariate discriminant analysis (MDA) for better prediction of financial bankruptcy. However, accurate prediction of bankruptcy is beneficial to improve the regulation of companies, to form policies for companies and to take any precautionary measures if any crisis is about to come in future.

Sign in / Sign up

Export Citation Format

Share Document