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Article
Publication date: 29 September 2020

Hari Hara Krishna Kumar Viswanathan, Punniyamoorthy Murugesan, Sundar Rengasamy and Lavanya Vilvanathan

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification…

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Abstract

Purpose

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

Design/methodology/approach

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

Findings

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

Research limitations/implications

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Practical implications

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

Originality/value

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.

Details

Benchmarking: An International Journal, vol. 28 no. 1
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 5 August 2019

Shoufeng Ma, Shixin Zhang, Geng Li and Yi Wu

Based on the literature on information security (InfoSec) education and uses and gratifications theory, the purpose of this paper is to propose and test a research model to…

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Abstract

Purpose

Based on the literature on information security (InfoSec) education and uses and gratifications theory, the purpose of this paper is to propose and test a research model to examine the impact of InfoSec education on social media usage.

Design/methodology/approach

The authors employed structural equation modeling to test the research model, with a survey data set of 293 valid subjects from a WeChat subscription about InfoSec education named secrecy view.

Findings

The results reveal the significant impacts of perceived content quality, perceived social influence and perceived entertainment on user satisfaction in the context of security education and social media. User satisfaction is significantly associated with user stickiness and security knowledge improvement. Additionally, the authors found that user’s security awareness moderated the effect of perceived entertainment on user satisfaction.

Research limitations/implications

Using a single sample might constrain the contributions of this study.

Practical implications

The authors suggest practical guidelines for InfoSec education on social media by enhancing perceived content quality. Moreover, due to diverse user attributes, the social media operators should recommend targeted content to different users.

Originality/value

This study contributes to studies on InfoSec education of social media usage and identifies factors that affect user satisfaction with social media. Furthermore, the study enriches the security education practices by uncovering differences in security awareness with regard to user satisfaction.

Details

Aslib Journal of Information Management, vol. 71 no. 5
Type: Research Article
ISSN: 2050-3806

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