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

Hassan, Ehsan ul; Zainuddin, Zaemah; Nordin, Sabariah

doi:10.52962/ipjaf.2017.1.3.15

Cited by 27 publications

(15 citation statements)

References 38 publications

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“…As for the research methods, the CCR assessment used to go with traditional statistical methods such as linear discriminant analysis and multivariate discriminant analysis, and the like (Chen et al, 2016;Mahmoudi and Duman, 2015;Ul Hassan et al, 2017), meaning to find the best linear correlation of input indicators. However, there are assumptions that linear separability, variable independence and multivariate normality cannot handle complex relationships between multiple variables (Chen et al, 2016), therefore many recent studies have shifted to intelligent methods which can mine complex relationships between variables without relying on the restrictive assumptions, and support vector machine (SVM), in particular, has been verified as a very powerful intelligent algorithm in that it allows for complex decision boundaries and performs well on the non-linear datasets with high dimensional variables (Cervantes et al, 2020;Ghaddar and Naoum-Sawaya, 2018).…”

Section: Number Of Indicatorsmentioning

confidence: 99%

Monitoring corporate credit risk with multiple data sources

Lim

et al. 2022

IMDS

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PurposeMonitoring corporate credit risk (CCR) has traditionally relied on such indicators as income, debt and inventory at a company level. These data are usually released on a quarterly or annual basis by the target company and include, exclusively, the financial data of the target company. As a result of this exclusiveness, the models for monitoring credit risk usually fail to account for some significant information from different sources or channels, like the data of its supply chain partner companies and other closely relevant data yet available from public networks, and it is these seldom used data that can help unveil the immediate CCR changes and how the risk is being propagated along the supply chain. This study aims to discuss the a forementioned issues.Design/methodology/approachGoing beyond the existing CCR prediction data, this study intends to address the impact of supply chain data and network activity data on CCR prediction, by integrating machine learning technology into the prediction to verify whether adding new data can improve the predictability.FindingsThe results show that the predictive errors of the datasets after adding supply chain data and network activity data to them are made the ever least. Moreover, intelligent algorithms like support vector machine (SVM), compared to traditionally used methods, are better at processing nonlinear datasets and mining complex relationships between multi-variable indicators for CCR evaluation.Originality/valueThis study indicates that bringing in more information of multiple data sources combined with intelligent algorithms can help companies prevent risk spillovers in the supply chain from causing harm to the company, and, as well, help customers evaluate the creditworthiness of the entity to lessen the risk of their investment.

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Section: Number Of Indicatorsmentioning

confidence: 99%

Monitoring corporate credit risk with multiple data sources

Lim

et al. 2022

IMDS

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“…Literature source [13] used an LSSVM for corporate financial crisis prediction, and empirically illustrated that the prediction effect on a considerable improvement compared with the SVM. Literature source [14] analyzed logistic regression and multivariate discriminant methods, while pointing out the scope of the application of the two methods in the financial crisis with the actual situation. Literature source [15] proposed that the logistic regression model can effectively reduce the financial sub-risk of enterprises.…”

Section: Related Knowledgementioning

confidence: 99%

Research on Financial Risk Crisis Prediction of Listed Companies Based on IWOA-BP Neural Network

Li¹,

Li²

2022

Journal of Internet Technology

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<p>To avoid the risk brought by the financial crisis, the Improved Whale Optimization Algorithm-Back Propagation neural network (IWOA-BP) financial crisis early warning model is proposed. This paper selects the data from financial statements of some of the listed Chinese manufacturing companies from 2015-2019 as the research sample. First, the financial data of enterprises are screened by principal component analysis, and the early warning model is constructed from the financial and nonfinancial factors of six indicators: solvency, operating capacity, profitability, development capacity, cash flow and risk level factors. Second, the Whale Optimization Algorithm is optimized by the chaos strategy, as well as by the dynamic weight and sine cosine algorithm. Finally, the improved Whale Algorithm is optimized for BP neural network parameters. In the simulation experiments, the performance of the improved whale optimization algorithm is substantially improved. In addition, in the empirical analysis, compared to the prediction model with other algorithms, the prediction model of this paper has better results in terms of prediction accuracy.</p> <p> </p>

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“…A very common approach developing new spectroscopic techniques for medical diagnosis is the statistical classification problem. This kind of problems are widely modeled by logistic regression in many research fields such as sociology [117]- [119], finance [120]- [122] and medicine [123]- [126], since allows to model the probability of occurrence of an event from a set of predictors, and the predictors contribution can be quantitatively studied in terms of the odds ratio [127], as will be shown later.…”

Section: Functional Logit Modelmentioning

confidence: 99%