• Journal of Korea Technology Innovation Society
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• v.9 no.4
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• pp.842-864
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• 2006

This paper deals with an empirical study to statistically analyse various financial performances of the selected IPO firms using their investments on research and development(R&D) as an independent variables. The major results of statistical analyses have come up with the followings: 1) The regression analyses for change in average annual total market stock value/total assets over that of R&D expenditures showed the positive relationship, However, those of sales volume and net assets per share showed negative without statistical significances. 2) The statistical analyses in effect of the 3-year average total market stock value/total assets over the 3-year average R&D expenditures resulted in the positive coefficients what are statistically significant at 95% level. 3) Another statistical analysis showed that the financial performances of the IPO finns with deferred assets were better than those of the firms without them. In sum, the degree of investment on R&D by the IPO firms are expected to positively affect their financial performances except the Finns without having proper original technologies.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.212-222
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• 2020

The purpose of this study is to understand the current status and trends of the research studies published by the Society for Industrial and Applied Mathematics which is a leader in the field of industrial mathematics around the world. To perform this purpose, titles and abstracts were collected from 6,255 research articles between 2016 and 2019, and the R program was used to analyze the topic modeling model with LDA techniques and a regression model. As the results of analyses, first, a variety of studies have been studied in the fields of industrial mathematics, such as algebra, discrete mathematics, geometry, topological mathematics, probability and statistics. Second, it was found that the ascending research subjects were fluid mechanics, graph theory, and stochastic differential equations, and the descending research subjects were computational theory and classical geometry. The results of the study, based on the understanding of the overall flows and changes of the intellectual structure in the fields of industrial mathematics, are expected to provide researchers in the field with implications of the future direction of research and how to build an industrial mathematics curriculum that reflects the zeitgeist in the field of education.

• International Area Studies Review
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• v.13 no.3
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• pp.759-780
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• 2009

The purpose of this paper is to analyse the effect of Korean FDI(1990-2008) in China by industries on exports and imports between two countries. We use time series regression, Vector Error Correction Model and Impulse Response Function as methodologies. Our findings through empirical tests are as follows. First Korean FDI in China increases Korean exports with China but shows a tendency to decrease due to the local content of China. Second Korean FDI in China increases Korean imports in SITC 8 with China. Finally Korean trade surplus caused by Korean FDI in China shrinks due to the decreasing of exports and increasing of imports in Korea. Korean FDI in China should be oriented host country's market oriented rather than production efficiency oriented because of unfriendly foreign investment environments in China.

• Journal of Korean Society of Forest Science
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• v.77 no.3
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• pp.322-330
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• 1988

This study was analyzed the behavior of recreational use through interviewing visitors with the questionnaire (1986.11-1987.9) in Mt. Kyeryong National Park. The number of visitors have been forecasted by tune series data of the past number of visitors, population, GNP, and number of cars (1974-1986) in korea. The results of the study can be summarized as follows : 1) Visitor's subjective evaluation about recreational environment evaluated to be fair in Mt. Kyeryong National Park. 2) They preferred natural forest resources to historic remains, tourist facility, etc.. 3) Number of participation was mostly once or five times over. 4) Visitors were affirmative to re-visit to the Mt. Kyeryong National Park. 5) Most of visitors stay for one day. 6) The most suitable estimated user regression model was : Y=-5753.7350+0.1726 Pop. -0.6564 NO. of Car. According to this equation, the total number of visitors will he increased by 3% per year from 1,023 thousands people in 1987 to 1,698 thousands in 2000.

• International Area Studies Review
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• v.17 no.4
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• pp.89-106
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• 2013

In this paper, we analysed the absolute and conditional convergency hypothesis and the determinants of productivity in manufacturing industries from 2000 to 2009 with 16 provinces and metro-cities by using panel analysis. In terms of convergency hypothesis test, the results show that both of the convergency hypothesis, the absolute vs. conditional hypothesis, reject the null hypothesis(H0) implying the labor productivity of the 16 province and metro-cities converged to the steady state equilibrium. Also, the speed of the absolute and conditional convergency for the 16 province and metro-cities are average 4.4% and 0.73% respectively. In addition, the results of the determinants of the labor productivity in manufacturing industry show that human capital and manufacturing location coefficient affect to the value- added per capita significantly, but government expenditure per capita doesn't affect to the value- added per capita. As for the total factor productivity, government expenditure per capita and fixed capital per capita are important factors, but research and development doesn't. Hence the government has to revise the balanced regional development policy to develop regional manufacturing industries for the vulnerable regions. Also, it requires more study regarding income disparities and productivity.

• Journal of Environmental Policy
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• v.10 no.4
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• pp.3-37
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• 2011

This paper investigates the existence of the environmental Kuznets curve (EKC) for carbon dioxide $CO_2$ emissions and its causal relationships with economic growth and openness by using time series data (1971-2006) from China (an emerging market), Korea (a newly industrialized country), and Japan (a developed country). The sample countries span a whole range of development stages from industrialized to newly industrialized and emerging market economies. The environmental consequences according to openness and economic growth do not show uniform results across the countries. Depending on the national characteristics, the estimated EKC show different temporal patterns. China shows an N-shaped curve while Japan has a U-shaped curve. Such dissimilarities are also found in the relationship between $CO_2$ emissions and openness. In the case of Korea, and Japan it represents an inverted U-shaped curve while China shows a U-shaped curve. We also analyze the dynamic relationships between the variables by adopting a vector auto regression or vector error correction model. These models through the impulse response functions allow for analysis of the causal variable's influence on the dynamic response of emission variables, and it adopts a variance decomposition to explain the magnitude of the forecast error variance determined by the shocks to each of the causal variables over time. Results show evidence of large heterogeneity among the countries and variables impacts.

• Journal of Korean Society of Forest Science
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• v.50 no.1
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• pp.29-35
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• 1980

The purpose of this study is to analyze and to forecast the long-term domestic demand and export demand for timber in Korea by regression models with time series data during 1962~1978. The method applied in this study was econometric analysis using Time Series Processor. The most important explanatory variables of timber demand were found to be the production activities of wood products industries to the prices of substitute goods. On the basis of the long-term forecast made according to the guidelines of the Fifth Five-Year Plan. According to the projection, domestic timber demand is projected at 8 million cubic meters in 1987 and 10.6 million cubic meters in 1991. On the other hand, the total demand (domestic demand plus export demand) for timber is projected 21.4 million cubic meters in 1987 and 27.2 million cubic meters in 1991.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.735-744
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• 2009

In this paper, we introduce a fast approach for Bayesian detection of change points in long-memory processes. Since a heavy computation is needed to evaluate the likelihood function of long-memory processes, a method for simplifying the computational process is required to efficiently implement a Bayesian inference. Instead of estimating the parameter, we consider selecting a element from the set of possible parameters obtained by categorizing the parameter space. This approach simplifies the detection algorithm and reduces the computational time to detect change points. Since the parameter space is (0, 0.5), there is no big difference between the result of parameter estimation and selection under a proper fractionation of the parameter space. The analysis of Nile river data showed the validation of the proposed method.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.723-737
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• 2017

In order to decide the national technical standards policy for national policy/market economy activities, the people in charge commonly make policy decisions based on the current technology level/concentration/utilization by means of major common technology field analysis using patent data. One possible source of such patent data is the domestic mobile carriers through the Korea Intellectual Property Rights Information System (KIPRIS) of the Korean Intellectual Property Office (KIPO). Using this system, we collected 20,294 patents and 152 International Patent Classification (IPC) types and confirmed KTs (9,738 cases / 47.98%), which perform relatively high technology retention activities compared to other mobile carriers through the KIPRIS of KIPO. Based on these data, we performed three analyses (SNA, PCA, ARIMA) and extracted 30 IPC types from the SNA and 4 IPC types from the PCA. Based on the above analysis results, we confirmed that 4 IPC (H04W, H04B, G06Q, H04L) types are the major common technology field of the domestic mobile carriers. Finally, the number of 4 IPC (H04W, H04B, G06Q, H04L) forecast averages of the ARIMA forecast result is lower than the number of existing time series patent data averages.