Distressed Chinese firm prediction with discretized data

Huang, Jun; Wang, Haibo; Kochenberger, Gary A.

doi:10.1108/md-08-2016-0546

Cited by 33 publications

(17 citation statements)

References 81 publications

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“…Return on equity (ROE) measures the performance of shareholders during the year. It is the ratio of net income to average net equity (Geng et al , 2015; Huang et al , 2017). Higher the ROE for the company, the more income for shareholders, as it reflects the bottom-line measure of performance (Lieu et al , 2008; Ross et al , 2013).…”

Section: Methodsmentioning

confidence: 99%

Modeling corporate financial distress using financial and non-financial variables

Balasubramanian

Radhakrishna

Sridevi

et al. 2019

IJLMA

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Purpose This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique. Design/methodology/approach This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error. Findings The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress. Originality/value This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

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Section: Methodsmentioning

confidence: 99%

Modeling corporate financial distress using financial and non-financial variables

Balasubramanian

Radhakrishna

Sridevi

et al. 2019

IJLMA

View full text Add to dashboard Cite

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“…Sermpinis et al [35] found that LASSO can significantly improve prediction performance. Huang et al [36] employed LASSO to select financial ratios and build parsimonious models based on these ratios to increase interpretability of a model. Chang et al [32] used LASSO to discover the un-revisit intension factors of hotels.…”

Section: Feature Selection With Lassomentioning

confidence: 99%

“…Many feature selection methods have been well-developed. Among them, least absolute shrinkage and selection operator (LASSO) has been widely and successfully applied in many domains, such as unrevisit intension factors discovery [32], fraud risk detection [33], bankruptcy prediction [34], power prediction [35], and financial ratio selection [36]. Moreover, LASSO can improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…LASSO is powerful and helpful to this study, as it selects a few keywords that are more representative to the model, so we can easily label the words, and it is also facilitated by five-fold cross validation experiments to rank those words. In previous studies, Huang et al [36] found that better results could be accomplished by including fewer but relatively more crucial features in a model. LASSO is useful for selecting the most fitted coefficients in the linear regression [37].…”

Section: Introductionmentioning

confidence: 99%

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Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry

2020

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Due to the COVID-19 pandemic, the sales of fast-food businesses have dropped sharply. Customer satisfaction has always been one of the key factors for the sustainable development of enterprises. However, in the fast-food restaurant business, gaining the knowledge of customer satisfaction is one of the critical tasks. Moreover, text reviews in social media have become one of important reference sources for customers’ decisions in buying services and products. Therefore, the main purpose of this study is to explore whether customer voices from social media reviews are different during the COVID-19 outbreak and to propose a new method to reduce interpersonal contact when collecting data. A text mining scheme which includes least absolute shrinkage and selection operator (LASSO) and decision trees (DT) are presented to discover the essential factors for customers to increase their satisfaction from unstructured online customer reviews. Finally, three real world review sets were employed to validate the effectiveness of the presented text mining scheme. Experimental results can help companies to properly adapt to similar epidemic situations in the future and facilitate their sustainable development.

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“…Tian et al [29] adopted LASSO method to screen model variables and found that accounting characteristics had stronger predictive ability than market characteristics. Huang et al [30] also use LASSO technology to sort the information content of each financial indicator, so as to improve the interpretability of the financial distress model.…”

Section: Related Workmentioning

confidence: 99%

A Financial Distress Prediction Model Based on Sparse Algorithm and Support Vector Machine

Zeng

Yang

2020

Mathematical Problems in Engineering

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Classification learning is a very important issue in machine learning, which has been widely used in the field of financial distress warning. Some researches show that the prediction model framework based on sparse algorithm has better performance than the traditional model. In this paper, we explore the financial distress prediction based on grouping sparsity. Feature selection of sparse algorithm plays an important role in classification learning, because many redundant and irrelevant features will degrade performance. A good feature selection algorithm would reduce computational complexity and improve classification accuracy. In this study, we propose an algorithm for feature selection classification prediction based on feature attributes and data source grouping. The existing financial distress prediction model usually only uses the data from financial statement and ignores the timeliness of company sample in practice. Therefore, we propose a corporate financial distress prediction model that is better in line with the practice and combines the grouping sparse principal component analysis of financial data, corporate governance characteristics, and market transaction data with support vector machine. Experimental results show that this method can improve the prediction efficiency of financial distress with fewer characteristic variables.

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Distressed Chinese firm prediction with discretized data

Cited by 33 publications

References 81 publications

Modeling corporate financial distress using financial and non-financial variables

Modeling corporate financial distress using financial and non-financial variables

Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry

A Financial Distress Prediction Model Based on Sparse Algorithm and Support Vector Machine

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