• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.119-126
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• 2021

This study is to examine the foreign direct investment (FDI) response to real effective exchange rate volatility in Vietnam by using the vector autoregression model. The research data are quarterly frequency data in the period from 2004:Q1 to 2019:Q2. The data on real effective exchange rate were collected from the statistics of Bruegel (Europe) and FDI data were collected from the International Financial Statistics. The quantitative study was conducted with two steps: (1) measuring exchange rate volatility by the GARCH(1,1) method; and (2) examining the impact of exchange rate volatility on FDI in the context of the global financial crisis. The estimation results show that FDI responded significantly to real exchange rate volatility with the lag of 3 periods at the 5% significance level. The FDI response increased after the exchange rate volatility with the lag of 3 periods, and the impact extended to the lag of 6 periods, and then gradually stabilized. The research findings indicate that FDI in Vietnam responds positively and significantly to exchange rate volatility with the lag of 3 periods. Simultaneously, the negative impact of the global financial crisis in 2008 with the lag of 2 periods leads to a slight decrease in FDI inflows into Vietnam.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.29-42
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• 2023

Amid the global movement to harness individual data and boost the data economy, MyData services that utilize personal data are being implemented in earnest in the financial sector in Korea due to the government's active encouragement policy. To this end, MyData service providers must have a service system for business operators that collects and efficiently loads personal information scattered in various financial institutions with individual consent, and comprehensively analyzes and provides it. The system must not only have strict security management capabilities, but also be built in a flexible form that takes into account future data scalability and additional services. In this paper, it has been proposed to be implemented the essential functions that MyData service system must have and the core functions that can manage the entire data life cycle from data collection, distribution to disposal in the form of a platform. In addition, the strengths of the platform structure were reviewed, and the effectiveness of the platform model was examined upon application.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Knowledge Management Research
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• v.23 no.4
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• pp.133-157
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• 2022

As data becomes a new core resource with high attention, MyData service is spreading to various fields such as finance, medical care, and the public sector. However, research on the behavior of MyData service users is insufficient. Therefore, this study aims to empirically examine the effect of MyData service traits on value perception and acceptable behavior particularly in the financial sector where MyData service is most active. To this end, this study proposed a research model based on the literature. 295 survey responses were collected from individuals and analyzed using AMOS 26.0 for hypothesis testing. As a result of the analysis, it was found that self-information control, financial convenience, and personalized service had a significant effect on perceived value, and that perceived value had a significant effect on the intention to accept MyData service. Furthermore, this study examined the role of personal innovation and technological security in the relationship between variables by suggesting them as moderators. Results show that individual innovation was found to strengthen the relationship between two variables(self-information control and personalized service) and perceived value. Also, technological security was shown to strengthen the relationship between perceived value and intention to accept financial MyData services. The findings are expected to provide useful information to understand the factors affecting the acceptance of financial MyData service users and to understand the importance of individual innovation levels and technological security.

• Journal of Families and Better Life
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• v.29 no.4
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• pp.125-145
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• 2011

The purposes of this study were to examine low-income households' financial statuses and the socio-economic characteristics of single-person and non-single person households according to the financial indexes used for evaluating financial security and growth status developed based on financial ratios. Using 2009 KLIPS(Korean Labor & Income Panel Survey) data collected by the Korean Labor Institute, the satisfaction levels from the indexes were analyzed and compared between the two household types. The results showed that 46.0% of single-person households were not satisfactory in terms of all financial growth indexes but were partially satisfactory in terms of all financial security indexes. These householders tended to be females and in the their 70s, who lived in Seoul or in a suburb of Seoul, Korea. They reported problems with a lack of financial growth possibilities. 47.0% of non-single person households were not satisfactory in terms of all financial growth indexes but were partially satisfactory in terms of all financial security indexes. These householders tended to be males in their 60s and 70s with no job who were living in Seoul or in a suburb of Seoul, Korea. They reported having low level of liquidity and high level of debt redemption. 42.6% of single-person households were not satisfactory in terms of both financial security and growth indexes partially. These householders tended to be females in their 20s or 70s who were living in rural areas. They reported problems related to an adverse balance between household income and expenditures and a large scale of debt. 43.1% of non-single households were not satisfactory in terms of both financial security and growth indexes partially. These householders tended to be males in their 60s or 70s and homeowners. They reported problem related to an adverse balance of household income and expenditures and high a level of housing expenditures and liquidity. The research findings have implication for policy makers considering financial support programs and welfare programs for low-income householders, considering the recent changes in households structures.

• Journal of Information Technology Applications and Management
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• v.22 no.3
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• pp.1-18
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• 2015

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.

• 한국디지털정책학회:학술대회논문집
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• 2004.05a
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• pp.575-592
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• 2004

In modern dynamic management environment, there is growing recognition that? information & knowledge management systems are essential for CEO's efficient/effective decision making. As a key component to cope with this current, we suggest the management performance analysis syystem based on Knowledge Discovery in Database (KDD). The system measures management performance that is considered with both VA(Value- Added), which represents stakeholder's point of view and EVA(Economic Value-Added), which represents shareholder's point of view. The relation ship between management performance and some 80 financial ratios is analyzed, and then important financial ratios are drawn out. In analyzing the relationship, we applied KDD process which includes such as multidimensional cube, OLAP(On-Line Analytic Process), data mining and AHP(Analytic Hierarchy Process). To demonstrate the performance of the system, we conducted a case study using financial data over the 16-years from 1981 to 1996 of Korean automobile industry which is taken from database of KISF AS(Korea Investors Services Financial Analysis System).