• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.25-43
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• 2022

Purpose - This study is to investigate the direct and moderating effect of intangible variable like economic freedom to facilitating factors on FDI(foreign direct investment) inflows and the difference of facilitating factors by the stage of economic development. Design/methodology/approach - Fixed-effect panel regression analysis with 19-year macro economic data from 2000 to 2019 including economic freedom index from Fraser Institute in 13 developed and 15 developing countries was used. Research implications or Originality - In analysis of direct effect of 5 sectors in economic freedom, the influence of economic freedom was shown weaker than other macro economic factors on FDI inflows, which indicates that actual development of economic factors are more important. The effect of economic freedom on FDI inflows at the stage of economic development differed. In developed countries, human capital, GDP, export, free trade and regulation affected FDI inflows in decreasing order, as did human capital, GDP, consumption expenditure, export, investment expenditure, government expenditure, free trade and sound money in developing countries. In analysis of moderating effect of economic freedom, a domestic and international market size, a flexible labor market which can provide a cheaper good human resources and government expenditures for improving social infrastructure under free economic environment facilitated FDI inflows. However, the statistical significance of moderating effect on export was not shown, which indicates that economic freedom policy itself without actual improvement of exports could not attract FDI inflows.

• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.167-173
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• 2023

The manufacturing industry is the backbone of the Korean economy. Among them, the petrochemical industry is a strategic growth industry, which makes a profit through reexports based on eminent technology in South Korea which imports all of its crude oil. South Korea imports whole amount of crude oil, which is the raw material for many manufacturing industries, by sea transportation. Therefore, it must respond swiftly to a highly volatile tanker freight market. This study aimed to make an early warning model of crude oil shipping market using a signal approach. The crisis of crude oil shipping market is defined by BDTI. The overall leading index is made of 38 factors from macro economy, financial data, and shipping market data. Only leading correlation factors were chosen to be used for the overall leading index. The overall leading index had the highest correlation coefficient factor of 0.499 two months ago. It showed a significant correlation coefficient five months ago. The QPS value was 0.13, which was found to have high accuracy for crisis prediction. Furthermore, unlike other previous time series forecasting model studies, this study quantitatively approached the time lag between economic crisis and the crisis of the tanker ship market, providing workers and policy makers in the shipping industry with an framework for strategies that could effectively deal with the crisis.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.919-926
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• 2020

The purpose of this study is to analyze and test the effect of financial ratios and macroeconomics on Islamic stock returns listed in Jakarta Islamic Index (JII) other than to assess whether investment risk can be an intervening variable in this study. The type of research is explanatory in nature with a quantitative descriptive approach. The data used is based on secondary sources with a sample group of 29 companies listed on JII for a 5-year period ending 31 December 2018. The data obtained were analyzed by using SEM (Structural Equation Model) with AMOS (Analysis Moment of Structural) 21 program. The results of the study show that only financial ratios affect sharia stock returns and investment risk, while the mediation test found that investment risk does not act as a mediating variable between financial ratios and macroeconomics and Islamic stock return. These findings indicate that the role of the company's financial health is very important. Besides affecting the rate of return obtained, the company's financial health can also reflect the level of risk that investors will accept in the future. By improving financial performance properly, a company will have a positive impact on various interested parties and minimize the level of investor losses.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• Land and Housing Review
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• v.4 no.1
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• pp.43-54
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• 2013

This This paper investigates the variation of the factors to determinate housing price in Seoul metropolitan area after sub-prime financial crisis, in Korea, using a VAR model. The model includes housing price and housing rent (Jeonse) in Seoul metropolitan area from 1999 to 2011, and uses interest rate, real GDP, KOSPI, Producer Price Index and practices to impulse response and variance decomposition analysis to grasp the dynamic relation between a variable of macro economy and and a variable of housing price. Data is classified to 2 groups before and after the 3rd quater of 2008, when sub-prime crisis occurred; one is from the 1st quater of 1999 to the 3rd quater of 2008, and the other is from the 2nd quater of 1999 and the 4th quater of 2011. As a result, comparing before and after sub-prime crisis, housing price is more influenced by its own variation or Jeonse price's variation instead of interest rate and KOSPI. Both before and after sub-prime financial crisis, Jeonse price is also influenced by its own variation and housing price. While after sub-prime financial crisis, influences of Producer Price Index, KOSPI and interest rate were weakened, influence of real GDP is expanded. As housing price and housing rent are more influenced by real economy factors such as GDP, its own variation than before sub-prime financial crisis, the recent trend that the house prices is declined is difficult to be converted, considering domestic economic recession and uncertainty, continued by Europe financial crisis. In the future to activate the housing business, it ia necessary to promote purchasing power rather than relaxation of financial and supply regulation.

• International Area Studies Review
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• v.16 no.1
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• pp.147-171
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• 2012

Even though many people loosely term a period of high inflation combined with stagnation "stagflation", it has been very difficult for us to find a more detailed and theoretical definition for such a period. In addition, most economic policy makers have faced some uncertainty and difficulty in identifying stagflation periods through analyzing a lot of economic data. This paper deeply researches the literature on specific definitions of stagflation and provides an empirical method by which we can systematically identify real-time stagflation pressure. Under this method, real-time stagflation pressure can be evaluated as a complex index by using both extensive monthly economic data indicating economic conditions or inflation pressure and a logit regression model. As a result of applying this method to the first half of 2008 in Korea when there was much debate as to whether the Korean economy was experiencing a corresponding stagflation or not, this period is not now evaluated as having been a stagflation period. This paper provides some implications. Namely, we need to put more emphasis on stabilizing inflation expectations.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• KDI Journal of Economic Policy
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• v.12 no.1
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• pp.69-91
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• 1990

A major change in tariff rates was made in January 1989 in Korea. The benchmark tariff rate, which applies to about two thirds of all commodity items, was lowered to 15 percent from 20 percent. In addition, the variation in tariff rates among different types of commodities was reduced. This paper examines the economic impact of the tariff reform using a multisectoral general equilibrium model of the Korean economy which was introduced by Lee and Chang(1988), and by Lee(1988). More specifically, this paper attempts to find the changes in imports, exports, domestic production, consumption, prices, and employment in 31 different sectors of the economy induced by the reform in tariff rates. The policy simulations are made according to three different methods. First, tariff changes in industries are calculated strictly according to the change in legal tariff rates, which tend to over-estimate the size of the tariff reduction given the tariff-drawback system and tariff exemption applied to various import items. Second, tariff changes in industries are obtained by dividing the estimated tariff revenues of each industry by the estimated imports for that industry, which are often called actual tariff rates. According to the first method, the import-weighted average tariff rate is lowered from 15.2% to 10.2%, while the second method changes the average tariff rate from 6.2% to 4.2%. In the third method, the tariff-drawback system is internalized in the model. This paper reports the results of the policy simulation according to all three methods, comparing them with one another. It is argued that the second method yields the most realistic estimate of the changes in macro-economic variables, while the third method is useful in delineating the differences in impact across industries. The findings, according to the second method, show that the tariff reform induces more imports in most sectors. Garments, leather products, and wood products are those industries in which imports increase by more than 5 percent. On the other hand, imports in agricultural, mining and service sectors are least affected. Domestic production increases in all sectors except the following: leather products, non-metalic products, chemicals, paper and paper products, and wood-product industries. The increase in production and employment is largest in export industries, followed by service industries. An impact on macroeconomic variables is also simulated. The tariff reform increases nominal GNP by 0.26 percent, lowers the consumer price index by 0.49 percent, increases employment by 0.24 percent, and worsens the trade balance by 480 million US dollars, through a rise in exports of 540 million US dollars and a rise in imports of 1.02 billion US dollars.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.