• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.79-99
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• 2016

Supply network of automobile part is addressed with the e-invoices generated at real time. Automobile is composed of 8 modules. Firms which produce these modules are defined as the first outsourcing firm. Brake is the part of power control module and so, brake manufacturing firm is called the second outsourcing firm. In this paper, the third supply networks of brake manufacturing firms is analyzed with e-invoices and social network method. At the node-level, the third outsourcing firms are classified into 3 categories, interator, allocator and hub with respect to their role at the ego-network of each brake manufacturing firm. At the network level, A2, one of 3 brake manufacturing firms have more outsourcing firms and bigger centrality than the other brake manufacturing firms. Intre-firms trade patterns are, also, analyzed by using the degree of trade dedication with respect to the modes of business. It is shown that trade pattern of retail, commodity brokerage firm, rubber and plastic manufacturing firm are hierarchical trade because their degree of trade dedication is almost near to 1.

• Management & Information Systems Review
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• v.7
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• pp.21-51
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• 2001

For increasing the competitiveness and efficiency of Korea's finance industry under the new e-finance paradigm, this paper compared the practical use of finance portal site' on service parts and stage between Korea and U.S.A.. The services which can be served from site are banking, mortgage and credit loan, stock, card, retirement tax, PFM(Personal Finance Management), EBPP(Electronic Bill Presentment and Payment) and Account Aggregation and so on. The stage of site can be divided as the information provide stage which only gives information about service parts, on-line transaction stage which real-time transaction is possibile and PFM services provide stage according to development process. As a result, the beginning of finance portal service in Korea was lated about 10years and more than it of U.S.A. So the development stage of domestic portal site is still staying in the first step and the providing services and contents or business model development parts are also in the same stage than U.S.A. Resides, Korea's sites mainly focus on their first service parts even though they recently aim internet finance portal, and provide not real time transaction but finance information. On the other hand, the U.S.A. site support substantially not only various on-line transactions but also distinctive personal services like PFM(Personal Finance Management), EBPP(Electronic Bill Presentment and Payment), Account Aggregation and Trans-account, brokerage, education center, mortgage loan, mutual fund, option, pension fund and IPOs and so on. Thus, the site of Korea need to establish real type of internet finance portal which provides one-stop services on every type of finance to customers in the real time and also require the strategic integration among finance institutions. The next turn, they need to build information system and education center to give best satisfaction to customers and acquire customer information and marker environment changes and need to provide distinctive services to quality customers throughout database from this. Also the site should provide various type of banking services which refereed above like PEM, EBPP and education center etc, and the government of Korea should support the building of IT infrastructure to Physical, legal, systematic, sociocultural, technical and human resource sections. This paper provided the future movement direction of the domestic finance portal through comparison and analysis on the practical use of it between Korea and U.S.A. and also wanted to contribute for developing and reading of Korea finance portal in the new era of the finance paradigm.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.