• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1044-1056
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• 2021

Globalization has integrated the world through interaction among countries and people with the help of information and telecommunication technology (ICT). The rapid mode of globalization has put a new life in ICT and economic sector. The key focus of this study is to examine the nexus among the globalization, ICT and economic growth. This study uses autoregressive distributed lag model (ARDL), vector error correction model (VECM) and econometric method spanning from 1990 to 2015. The empirical result highlights that the globalization stimulates economic growth of a country. In addition, both the internet penetration and the mobile phone usage contribute to the economic growth. Lastly, this article contributes important policy lessons on strengthening the economy by utilizing ICT with the rapid globalization.

• 중국학논총
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• no.71
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• pp.41-59
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• 2021

With the progress of social economy and science and technology, network media technology has developed rapidly, China has ushered in the network information age, and the network buzzwords emerged to reflect the interaction and influence between language and society. The network buzzwords of "X+ ren "indirectly show the social psychology and value orientation of modern people with their unique structural characteristics, semantic connotation and cultural deposits, and so on. Based on this, we have conducted a multi-angle investigation on the network buzzwords "X+ ren". This paper first analyzes the structure types and syntactic functions of the lexical model of "X+ ren ", then makes a semantic analysis of the lexical model of "X+ Ren ", and finally investigates the causes and influences of the popularity of "X+ ren ". Through the investigation, we believe that "X+ ren "will continue to grow, and "X+ ren" will continue to attract the attention of the academic community.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.366-375
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• 2007

Our nation has a problem with discrimination of income distribution and inefficient of resources distribution caused by real estate price rising from a sudden economy growth and industrialization. Specially, in recent years, there is a great disparity of condominium price between the north and south of the Han river. Because the housing price is deciede by the immanent value of a house and neighborhood effects of the regional where the house is situated, the housing price is occurred difference. In this study, I analyzed the differences of housing price determinants about condominium developments in the old and new residential areas, and found the important factors that affect the condominium price using Structural Equation Modeling(SEM) The purpose of study is to analyze the influence of various factors of housing price. Also, this study tried to predict real estate market and to establish previous effective real estate policy.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.445-449
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• 2015

Construction industry is one of the most significant sector in national economic, but the portion of construction has been falling regularly with the regional development. In spite of decrease in economic portion, role of construction industry does not changed irrespective of development, as the foundation of development. To distinguish each state of the maturity, countries are grouped on GDP per capita, than compared with variance of GVA in construction and GFCF per GDP as level of construction industry. GVAc% and GFCF% shows corn-shaped plotting in increase of GDP per capita, and each value converge to around 20% and 5% as GDP per capita increase. The definition of maturity is consist of 4 stages; pre-developing, ascending, stabilization, and maturement. Maturity of construction industry is a term of broad sense of construction industry that is easily to figure current state of regional construction and shows what normal condition of construction is in regional economy.

• Korea Trade Review
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• v.47 no.4
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• pp.1-14
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• 2022

This study examines the impact of trade facilitation on China's trade for the period 2010-2017 using a gravity model with a measurement of APEC trade facilitation through principal component analysis. The empirical results confirmed that trade facilitation was a key factor to have a positive effect on Chinese exports and that the higher the level of trade facilitation in APEC countries, the more positive the increase in exports and quantities with China. Further, the size of the economy, the total population, and the border between the trading partner had a positive effect on Chinese trade volume. To promote economic growth through increase in trade volume, countries should actively improve trade facilitation and participate in global trade facilitation reform through continuous cooperation with trading partners.

• Korea Trade Review
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• v.46 no.2
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• pp.281-299
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• 2021

This paper analyzes the feasibility of using machine learning methods to forecast the employment. The machine learning methods, such as decision tree, artificial neural network, and ensemble models such as random forest and gradient boosting regression tree were used to forecast the employment in Busan regional economy. The following were the main findings of the comparison of their predictive abilities. First, the forecasting power of machine learning methods can predict the employment well. Second, the forecasting values for the employment by decision tree models appeared somewhat differently according to the depth of decision trees. Third, the predictive power of artificial neural network model, however, does not show the high predictive power. Fourth, the ensemble models such as random forest and gradient boosting regression tree model show the higher predictive power. Thus, since the machine learning method can accurately predict the employment, we need to improve the accuracy of forecasting employment with the use of machine learning methods.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.1-14
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• 2024

Purpose - We investigate whether a potential missing pricing factor plays a significant role in the idiosyncratic volatility puzzle. Design/methodology/approach - We theoretically show how a missing pricing factor can affect the idiosyncratic volatility puzzle, and also show how to get around the problem empirically. We adopt the Fama-French five factor model for the estimation of the idiosyncratic risk and use randomly constructed portfolios as test assets. Findings - We find that a missing factor does not drive the idiosyncratic volatility puzzle. Thus, we conclude that the idiosyncratic volatility does affect the risk premium of its stock. Research implications or Originality - The Fama-French five factor model does a pretty good job in explaining the risk premiums of stocks, and it can be used to reliably estimate idiosyncratic risk of stocks.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.145-157
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• 2024

Purpose - In this study, we propose an empirical model for predicting seasoned equity offering (SEO here after) using machine learning methods. Design/methodology/approach - The models utilize the random forest method based on decision trees that considers non-linear relationships, as well as the gradient boosting tree model. SEOs incur significant direct and indirect costs. Therefore, CEOs' decisions of seasoned equity issuances are made only when the benefits outweigh the costs, which leads to a non-linear relationship between SEOs and a determinant of them. Particularly, a variable related to market timing effectively exhibit such non-linear relations. Findings - To account for these non-linear relationships, we hypothesize that decision tree-based random forest and gradient boosting tree models are more suitable than the linear methodologies due to the non-linear relations. The results of this study support this hypothesis. Research implications or Originality - We expect that our findings can provide meaningful information to investors and policy makers by classifying companies to undergo SEOs.

• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• International Area Studies Review
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• v.21 no.2
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• pp.21-44
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• 2017

Under the new normal, the China's economy growth has changed rapid growth to moderate growth since 2007. With new paradigm, China is facing an abnormally severe employment situation. Also the financial expenditure is an important macro adjustment method. The research analyzes both implications of financial expenditures to employment in China, and the trend of implication in different regions. The research was conducted by 2SLS method using the panel data of 31 Chinese local governments(provinces, cities, and autonomous districts) during 1998 to 2015. The main findings are as follows. In the new normal model(2008-2015), the financial expenditure to urban employment have higher effect than total employment. Also, higher income region have more positive effect than lower income region. Medical, technology expenditure have positive effect to total employment, social security, education expenditure have positive effect to urban employment. In the total model(1998-2015) have similar results with new normal model, but the elasticity is more higher than total model. Ultimately, it can be seen that the efficiency of financial expenditure is lower than new normal model. The government should increase the proportion of expenditure in fields of social security, education, medical, technology, and improve the expenditure structure. So as to promote the effect of financial expenditure to employment in new normal economy.