• Journal of Distribution Science
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• v.20 no.10
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• pp.61-66
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• 2022

Purpose: Predicting the financial distress distribution of an enterprise is important to warn enterprises about their future. Predicting the possibility of financial distress helps companies have action plans to avoid the possibility of bankruptcy. In this study, the author conducted a forecast of the financial distress distribution of enterprises. Research design, data and methodology: The forecasting method is based on Logit and Discriminant analysis models. The data was collected from companies listed on Vietnam Stock Exchange from 2012 to 2020. In which there are both companies suffer from financial distress and non-financial distress. Results: The forecast analysis results show that the Logistic model has better predictability than the Discriminant analysis model. At the same time, the results also indicate three main factors affecting the financial distress of enterprises at all three research stages: (1) Liquidity, (2) Interest payment, and (3) firm size. In addition, at each stage, the impact of factors on financial distress differs. Conclusions: From the results of this study, the author also made several recommendations to help companies better control company operations to avoid falling into financial distress. Adjustments to current assets, debt, and company expansion considerations are the most important factors for companies.

• International Journal of Human Ecology
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• v.16 no.2
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• pp.21-31
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• 2015

This study explored determinants of family support that young renter households received to afford their housing costs. Microdata set of the 2014 Korea Housing Survey was used as secondary data for the study. Total 1,752,899 households headed by persons between 20 and 34 years of age and whose rental type was either Jeon-se or monthly rental with deposit in private rental units were selected as study subjects. For the data analysis, a series of discriminant analysis was conducted using IBM SPSS 21.0. Major findings were as follows. (1) Among the subjects, 28.2% were found to receive financial support from parents or other relatives. (2) To see the discriminant analysis results, a linear combination of seven household and housing characteristics (householder's gender, whether or not the householder worked in the previous week, whether or not the householders have a spouse, tenure type, structure type, location and deposit amount) could explain 44.6% of variance in young renter households' receipt of family support with a prediction accuracy of 77.2%. (3) To summarize the final discriminant model, Jeon-se renter households in location other than Incheon or Gyeonggi Province living in a unit in structure other than multifamily structure headed by younger householders that did not worked previous week or without spouse; with a greater deposit had the maximum tendency to receive family support to pay rental costs.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2309-2314
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• 1997

The study introduces a neural network to predict the bankruptcy of insurance companies. As a method to optimize the network, a genetic algorithm suggests optimal structure and network parameters. The neural network designed by genetic algorithm is compared with discriminant analysis, logistic regression, ID3, and CART. The robust neural network model shows the best performance among those models compared.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.1-15
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• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• Journal of Korean Society on Water Environment
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• v.23 no.2
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• pp.260-265
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• 2007

This study was carried out to complement water quality standards and to establish new concept for water quality standards reflecting current state of water quality in small streams. By this purpose, discriminant analysis was performed and Water Quality Level Model (WQLM) was developed using the data such as EC, BOD, $COD_{Mn}$, SS, T-N, T-P, $NH_3-N$ in 224 agricultural streams. To give water quality level for water quality parameters, it divided into 20% respectively in the order of excellent water quality. On the basis of the lowest water quality level, water quality level of small streams is granted. As a result of it, number of stream corresponding to Level I was no, Level II was 2 streams, Level III was 22 streams, Level IV was 70 streams, and Level V was 130 streams. Average of water quality in each level was the highest in Level V. EC, SS, and T-N of 7 parameters were selected in variance concerned water quality level. By standardized canonical discriminant function coefficient, EC of three variances was the highest in 0.625 at the discriminant power. The next was T-N (0.509), SS (0.414). By discriminant function for water quality level, Level II was equal to $-2.973+19.376{\times}(EC)+0.647{\times}(T-N)+0.009{\times}(SS)$, Level III was equal to $-3.288+19.190{\times}(EC)+0.733{\times}(T-N)+0.041{\times}(SS)$, Level IV was equal to $-4.462+27.097{\times}(EC)+0.792{\times}(T-N)+0.053{\times}(SS)$, and Level V was equal to $-9.117+40.040{\times}(EC)+1.305{\times}(T-N)+0.111{\times}(SS)$. As a result of test at real agricultural watershed of Jeongan and Euidang in Gongju city, the fitness of WQLM was high to 88.78%. But, to get accomplished water quality assessment more exactly in agricultural streams, we had to concentrate and get vast data, and WQLM was modified and complemented continually.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Journal of Korean Society of Transportation
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• v.23 no.5 s.83
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• pp.65-71
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• 2005

Area type is often used in freight demand analysis and logistics planning models. For example, in freight transportation planning. area type variable is most often commonly used in freight generation (attraction) model. Yet a reliable, forecastable and measurable definition or area type is generally not documented. In fact, there is little literature on the subject of predicting area type in the context of freight planning models. This can be troublesome when applying models to long-range logistics planning where significant changes in population and employment result in changes in the general character of an area. Through the use of Discriminant Model, GIS (Geographic Information System) analysis and Delphi methods, this paper presents the successful exploration for a quantifiable means of determining area type.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.6
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• pp.1460-1464
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• 2009

In spite of many studies on statistical model for pattern identifications (PIs), little attention has been paid to the complexity of pattern diagnosis processed by oriental physicians. The aim of this study is to develop a statistical diagnostic model which discriminates four PIs using multiple indicators in stroke. Clinical data were collected from 981 stroke patients and 516 data of which PIs were agreed by two independent physicians were included. Discriminant analysis was carried out using clinical indicators such as symptoms and signs which referred to pattern diagnosis, and applied to validation samples which contained all symptoms and signs manifested. Four Fischer's linear discriminant models were derived and their accuracy and prediction rates were 93.2% and 80.43%, respectively. It is important to consider the pattern diagnosis processed by oriental physicians in developing statistical model for PIs. The discriminant model developed in this study using multiple indicators is valid, and can be used in the clinical fields.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.198-215
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• 2004

Technology competitiveness evaluates environmental and engineered technology and process at both the scientific and market levels. There are increasing concerns to measure the effects of the technology variables on the potential market feasibility levels. However, there are very little empirical analysis studies on that issue. This study investigates the impacts of technology variables on the levels of market feasibility based on 230 data obtained from Korea Technology Transfer Center. As various statistical analysis, the canonical discriminant model, logit discriminant model and classification model were used and their results were compared. This study results showed that major technology variables had very significant relations to discriminate high and low categories of market feasibility. Finally, this study will help building management strategies to level up the potential market performance and also help financial Institutions to decide funds needed for small-sized technology firms.