Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Distressed Chinese firm prediction with discretized data

Distressed Chinese firm prediction with discretized data <jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.</jats:p> </jats:sec> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Decision CrossRef

Distressed Chinese firm prediction with discretized data

Management Decision , Volume 55 (5): 786-807 – Jun 19, 2017

Distressed Chinese firm prediction with discretized data


Abstract

<jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.</jats:p>
</jats:sec>

Loading next page...
 
/lp/crossref/distressed-chinese-firm-prediction-with-discretized-data-umr4JKL5cW

References (86)

Publisher
CrossRef
ISSN
0025-1747
DOI
10.1108/md-08-2016-0546
Publisher site
See Article on Publisher Site

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.</jats:p> </jats:sec>

Journal

Management DecisionCrossRef

Published: Jun 19, 2017

There are no references for this article.