• Korean Management Science Review
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• v.29 no.2
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• pp.167-184
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• 2012

Business diversification is inevitable to survive under the current competitive business environments. The advent of new businesses makes corporate governance more complicated through corporate combinations. Recent introduction of new accounting standard, International Financial Reporting Standards(IFRS), accelerates the need for corporate governance analysis. This study analyses the complex corporate governance system and its relation to the business performance using social network analysis. Corporate inter-governance networks can be visualized easily in a social network diagram. 552 corporate governance data are empirically analysed in the Korean stock market. The changes in In-Degree between networks are positively related with the changes in corporate sales volume. We can find the same results using operating profits as corporate performance proxy. The results show that social network analysis technique can be applied to investments in the stock markets.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.313-317
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• 2001

The approach to predict time series without neglecting the fluctuation in a short period is tried by using a digital FIR filter and a neural network. The differential waveform of the Nikkei average closing price is filtered by the FIR band-pass filter of 101 length. It is filtered into the five frequency bands of 0-1Hz, 1-2Hz, 2-3Hz, 3-4Hz and 4-5Hz by setting the sampling frequency 10Hz. The each filtered waveform is learned and forecasted by the neural network. The neural network of the back propagation method is adopted in the learning the waveform. By inputting the data of 20 days in the past, the prediction of 10 days ahead is carried out. After learning the time series of each frequency band by the neural network, the predicted data far each frequency band are obtained. The predicted waveforms of each frequency band are synthesized to obtain a final forecast. The waveform can be forecasted well as a whole.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.77-85
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• 2019

In this paper, we propose a "Safe Web Using Scrapable Headless Browse" Because in a network separation environment for security, It does not allow the Internet. The reason is to physically block malicious code. Many accidents occurred, including the 3.20 hacking incident, personal information leakage at credit card companies, and the leakage of personal information at "Interpark"(Internet shopping mall). As a result, the separation of the network separate the Internet network from the internal network, that was made mandatory for public institutions, and the policy-introduction institution for network separation was expanded to the government, local governments and the financial sector. In terms of information security, network separation is an effective defense system. Because building a network that is not attacked from the outside, internal information can be kept safe. therefore, "the separation of the network" is inefficient. because it is important to use the Internet's information to search for it and to use it as data directly inside. Using a capture method using a Headless Web browser can solve these conflicting problems. We would like to suggest a way to protect both safety and efficiency.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.335-351
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• 2011

In order to resolve data scarcity problem related to crisis, Oh and Kim (2007) proposed to use stability oriented approach which focuses a base period of financial market, fits asymptotic stationary autoregressive model to the base period and then compares the fitted model with the current market situation. Based on such approach, they developed financial market instability index. However, since neural network, their major tool, depends on the base period too heavily, their instability index tends to suffer from inaccuracy. In this study, we consider linear asymptotic stationary autoregressive model and neural network to fit the base period and produce two instability indexes independently. Then the two indexes are combined into one integrated instability index via newly proposed combining method. It turns out that the combined instability performs reliably well.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1189-1198
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• 2015

For the effects of the global financial crisis cause hedge funds to have a strong influence on financial markets, it is needed to study new approach method to predict hedge fund survival. This paper proposes to organize fuzzy neural network using hedge fund data as input to predict hedge fund survival. The variables of hedge fund data are ambiguous to analyze and have internal uncertainty and these characteristics make it challenging to predict their survival from the past records. The object of this study is to evaluate the predictability of fuzzy neural network which uses grades of membership to predict survival. The results of this study show that proposed system is effective to predict the hedge funds survival and can be a desirable solution which helps investors to support decision-making.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.5
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• pp.67-74
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• 2002

Technology Management and Research & Development(R&D) have been one of the most difficult divisions for measurement and evaluation. In spite of these difficulties, the importance of R&D has been dramatically increased. It is very difficult to manage more efficiently and effectively than any other departments of production, finance, marketing and so on. As criticizing the shortcomings of the traditional evaluation system in making decisions for corporate management which has only been focused on financial indices, so Kaplan & Norton has suggested the Balanced Scorecard(BSC) which can be managed Critical Success Factors(CSF) in accordance with corporate's strategy. The Analytic Network Process(ANP), based on the Analytic Hierarchy Process, allows the decision makers to leap beyond the traditional hierarchy to the interdependent environment of network modeling. Based on BSC, this study has developed the evaluation system for R&D which has used ANP transforming quantitative and qualitative indices to the quantifying scales in evaluating R&D.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.219-222
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• 2006

In this paper an intrusion detection system technique of wireless Ad Hoc network is explained and the advantage of making them work in IEEE 802.15.4/ZigBee wireless standard is also discussed. The methodology that is mentioned here is intrusion detection architecture based on a local intrusion database [1]. An ad hoc network is a collection of nodes that is connected through a wireless medium forming rapidly changing topologies. Due to increased connectivity (especially on the Internet), and the vast spectrum of financial possibilities that are opening up, more and more systems are subject to attack by intruders. An ideal IDS should able to detect an anomaly caused by the intruders quickly so that the misbehaving node/nodes can be identified and appropriate actions (e.g. punish or avoid misbehaving nodes) can be taken so that further damage to the network is minimized