A Method of Corporate Credit Rating Classification Based on Support Vector Machine and Its Validation in Comparison of Sequential Logit Model

Tanaka, Katsuhiro; Nakagawa, Hidetoshi

doi:10.15807/torsj.57.92

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…We formulated it as a mixed integer linear optimization (MILO) problem by applying a piecewiselinear approximation to the logistic loss functions. The computational results confirmed that our formulation has a clear advantage over the mixed integer quadratic optimization (MIQO) formulation proposed in the previous study [32].…”

Section: Discussionsupporting

confidence: 84%

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mentioning

confidence: 99%

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Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

Sato

Takano

Miyashiro

2017

JORSJ

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This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between the logistic loss function and its quadratic approximation, their formulation may fail to find a good subset of features. To overcome this drawback, we apply a piecewise-linear approximation to the logistic loss function. Accordingly, we frame the feature subset selection problem of minimizing an information criterion as a mixed integer linear optimization problem. The computational results demonstrate that our piecewise-linear approximation approach found a better subset of features than the quadratic approximation approach.

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Section: Discussionsupporting

confidence: 84%

“…

…”

mentioning

confidence: 99%

Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

Sato

Takano

Miyashiro

2017

JORSJ

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“…The aim is to check whether a financial condition has changed significantly and to investigate what kind of event has a big influence on business management. In the previous research in financial analysis, discriminant analysis on corporate rating (Tanaka and Nakagawa (2014)), regression analysis on stock prices (Wen et al (2014)) have been studied, but we could not find research using statistical methods to calculate change in financial data.…”

Section: Introductionmentioning

confidence: 91%

<b>Using change point identification in financial data to</b><b> detect turning points in companies</b>

Kawamata

Nagata

2018

TQS

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Change point search methods are often used in quality engineering. These methods detect the points in time when important changes can occur, and are applied to various types of time series data. In this study, we search for change points in financial data from Japanese companies across various industries. By using these methods, we can easily and quantitatively identify companies turning points, helping financial institutions analyze financial indicators over a certain period of time. To obtain sufficient data for the analysis, we use quarterly financial data over the period 2006 to 2015 to apply methods focusing on average values. For the analysis, we select financial indicators such as return on assets, capital adequacy ratio, and rate of sales growth. For each financial indicator, we use the t test to identify change points in data. Then, we run the analysis by grouping data for multiple financial indicators and employing the Hotelling Tsquare test. In this way, we are able to detect turning points that could not be found from a single financial indicator. Finally, we survey the major economic events that occurred around each turning point.

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“…The former include linear regression [1][2][3], probit regression [4,5], logistic regression [6][7][8]. The latter consists of neural networks [9][10][11], random forests [12] and support vector machines (SVM) [13][14][15][16][17]. In particular, logistic regression, random forests, and SVM are the method most commonly used in financial practice.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of least-squares probabilistic classifier for corporate credit rating classification problem

Saito

Yamanaka

2021

JSIAM Letters

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The corporate credit rating classification problem has attracted lots of research interests in the literature of financial risk management. This article introduces the least-squares probabilistic classifier to the problem in an attempt to provide a model with better explanatory power. Empirical results show that the least-squares probabilistic classifier outperforms the logistic regression model, random forest, and the support vector machine in prediction accuracy ratios and F1 scores, for the samples of bond issuer firms in Japan.

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A Method of Corporate Credit Rating Classification Based on Support Vector Machine and Its Validation in Comparison of Sequential Logit Model

Cited by 9 publications

References 5 publications

Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

<b>Using change point identification in financial data to</b><b> detect turning points in companies</b>

Performance evaluation of least-squares probabilistic classifier for corporate credit rating classification problem

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