• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.545-553
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• 2020

Although networking behavior is an effective job search method to students, far too little attention has been paid to mechanisms explaining the antecedents and networking behavior. The goal of this study was to demonstrate the effect of the HEXACO personality dimensions on graduated students' job search networking behavior through their network size. A survey of 773 participants was conducted to assess personality traits, network size, and networking behavior. All constructs in the study were measured by 5-point Likert scales. This study employed a structural equation model to examine the proposed conceptual model and the correlations among variables. Results showed that the personality of emotionality negatively influence students' network size, while extraversion and agreeableness are positively associated with the scope of their social network. Second, the findings confirmed that network size is directly related to the level of looking-for job behavior, particularly networking behavior. Finally, our results explored that network size played the mediating effect on how personality traits affect networking behavior. These findings suggest that network size is a dynamic mechanism that helps to understand the correlation between personality traits and job search networking behavior. The theoretical and practical implication of the study, as well as the future research direction were discussed.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• Knowledge Management Research
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• v.21 no.1
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• pp.61-78
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• 2020

Recently, in the global financial transaction field, global transactions using computer systems and server hubs between foreign exchanges beyond the one-dimensional offline transactions and two-dimensional online transactions have been actively conducted. In the previous research by Lee Hyung-wook and Lee Min-jae (2018), as the globalization has accelerated following the development of digital technology and the expansion of ubiquitous communication network, the role of companies, the attribute of economic value creation and economic structure are being reorganized. It is said that O2O (Online to Offline) transactions are increasing due to the development. As a result, a new financial transaction paradigm is emerging that solves the inconveniences of existing financial services and enhances speed and convenience. Considering the global network trend and the rapidly developing and evolving digital bank environment, the necessity of utilizing the business platform model is emerging. However, despite this necessity, there are very limited cases in which such an attempt has been applied in practice. Accordingly, this study seeks to explore the business platform of the new financial transaction system. Specifically, the case study systematically examines the actual implementation of a unique network connection model with Koscom's global investment bank, which is currently in charge of the domestic financial transaction system, and improves ICT innovation performance and process through this. I would like to suggest a solution. In particular, this study analyzed a variety of business model construction and use cases by pursuing a platform connection with digital banks, which has recently been increasingly in demand. Therefore, this study intends to pursue the original and long-term profitability of the company by utilizing ICT innovation and platform business model, and also analyzes the convenience and excellence of trading for institutional and individual investors using the platform of digital bank. The implications of this study are significant in that it explores and explores the actual cases of ICT innovation and additional digital bank platform-connected business models based on this, and suggests a unique and preemptive business strategy of the company in the future.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.770-802
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• 2022

With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.733-736
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• 1996

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bankruptcy prediciton on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediciton. Therefore, we have decided to focus on textiles and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• IE interfaces
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• v.25 no.3
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• pp.319-326
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• 2012

To effectively predict financial crisis, this paper presents an early warning system based on artificial intelligence technologies. Both Genetic Algorithms and Neural Networks are utilized for the proposed system. First, a genetic algorithm has been developed for the effective selection of economic indices, which are used for monitoring financial crisis. Then, an optimum weight of the selected indices has been determined by a neural network method. To validate the effectiveness of the proposed system, a series of experiments has been conducted by using the Korean economic indices from 2005 to 2008.

• Korean Management Science Review
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• v.14 no.2
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• pp.91-111
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• 1997

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.91-91
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• 1989

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.67-72
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• 2022

The main purpose of the study is to analyze the main aspects of state regulation of the banking business in the context of social and digital transformation. One of the key elements of the functioning of the economy of any country are banks that ensure the redistribution of financial resources and stimulate economic growth. However, the banking sector, like other activities, is dynamic and depends on the pace of development and forms of technological progress that affect the forms and types of information and digital technologies, as well as the globalization and remoteness of banking services. Accordingly, the need for effective implementation of the latest technologies becomes relevant, which will not only help increase consumer satisfaction with the banking product, but also ensure the development of the country's financial sector. As a result of the study, trends in the development of state regulation of the banking sector in the digital economy were identified.