Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.
Journal of the Korean Institute of Landscape Architecture
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v.47
no.5
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pp.28-40
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2019
In order to resolve the imbalances in the supply of living SOCs according to socio-economic status, location, and population groups, the discussions on inclusive city policies are expanding. The purpose of this study is to propose an Index of Park Derivation (IPD) as an alternative indicator for the promotion of an inclusive urban park policy that can be applied in the 7 major metropolitan cities to select a region with a relatively high park needs. The main research results are as follows. First, the concept of an inclusive urban park policy is defined as "a policy to supply to manage high-quality park services with priority given to areas with low socio-economic and environmental status, such as a large amount of elderly, children, low-income families, areas vulnerable to disasters, such as heat and fine dust, and population groups." Second, we developed the index of park derivation (IPD), which is a combination of 17 variables including park service level, demographic characteristics, economic and educational level, health level, and environmental vulnerability. The variables that constitute the index of park deprivation (IPD) can be applied to SOC policies outside the parks, such as sports facilities, daycare centers, kindergartens, and public libraries. Third, applying index of park deprivation (IPD) to 1,148 Eup/Myeon/dong areas of the 7 metropolitan cities resulted in areas with relatively high park service needs. This study implies that the central and the local government suggest an alternative index to promote an inclusive urban park policy based on statistical and geographical information and data that can be easily accessed and utilized.
As the scope of supply chains expands globally, unpredictable risks continue to arise. The occurrence of these supply chain risks affects port cargo throughput and hinders port operation. In order to examine the impact of global supply chain risks on port container throughput, this study conducted an empirical analysis on the impact of variables such as the Global Supply Chain Pressure Index (GSCPI), Shanghai Container Freight Index (SCFI), Industrial Production Index, and Retail Sales Index on port traffic using the vector autoregressive(VAR) model. As a result of the analysis, the rise in GSCPI causes a short-term decrease in the throughput of Busan Port, but after a certain point, it acts as a factor increasing the throughput and affects it in the form of a wave. In addition, the industrial production index and the retail sales index were found to have no statistically significant effect on the throughput of Busan Port. In the case of SCFI, the effect was almost similar to that of GSCPI. The results of this study reveal how risks affect port cargo throughput in a situation where supply chain risks are gradually increasing, providing many implications for establishing port operation policies for future supply chain risks.
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 purpose of this study was to examine the relationship between corporate social responsibility and internationalization. The companies executed the CSR activity using the published index of economic justice (KEJI index) surveyed from 2013 to 2014. A total of 286 companies were included in the study. Results are as follows. First, the degree of internationalization groups showed significant differences. It found that the higher the proportion of international companies showed positively CSR activities. Especially, It found that environmental management and customer protection showed positively performed. Second, environmental management and customer protection showed positive effect on firm internationalization. It found that the higher the proportion of international companies thought environmental management and customer protection activity were more important. This study proposed that CSR activity are very important in firm performance. It showed CSR activities and strategy are presented for sustainable management.
Journal of the Korean Society of Hazard Mitigation
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v.5
no.4
s.19
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pp.71-78
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2005
The principal basic concepts of aseismic design minimize damage of human-life and have little probability during life of structures. For detailed understanding of the design, the best reasonable countermeasure can be possible equally the smallest damage of human-life and economic loss. As a result, it can be achieved by notion of not structure-centered but city-centered, the notion is actualized by development of a macro-level evaluation. A seismic damage between city and country is different. And the larger the city then, the greater the loss by rather collateral hazards than collapse of structures. Hence, the macro-evaluation of an earthquake disaster is suitable for an old city where is center of political and economic activity, and is concentration of population and infrastructure. This study aims to develop comprehensive earthquake desaster risk index, and assesses relative earthquake risk of six zones in Seoul metropolitan area.
This study discuss how the meaning and role of the state in the process of globalization are defined by the media and how the regulation is related to the ideology and value inherent in the process of globalization. Specifically, we tried to examine how the meaning and role of neoliberal state, which characterizes globalization process, is justified and reinforced by media. The purpose of this study is to understand the characteristics of the dominant globalization process inherent in the discourse of national competitiveness by analyzing how the national competitiveness index report released by the World Economic Forum is reported by the media every year. In addition, we sought to understand the significance and role of the state in the globalization process by examining what areas are emphasized and excluded from the national competitiveness index composed of economic infrastructure, economic efficiency, and enterprise innovation activities.
Journal of Family Resource Management and Policy Review
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v.25
no.2
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pp.41-52
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2021
The purpose of this study is to examine how female labor force participation, family policies, and gender equality are related to fertility rate across countries. Multiple measures has been collected from various data sources(such as OECD, UNDP, and WVS) and the panel data set which includes (mostly) OECD countries range from 1990 to 2019 are analyzed. The major findings are as follows. First, based on OECD countries samples, female labor force participation is positively associated with the fertility rate, which implies that women's labor force participation does not lead to a reduction in fertility rate. Second, the length of paternity leave is positively associated with fertility rate whereas the direction is the opposite for the relationship between the length of maternity leave and fertility rate. This is attributed to the possibility that a longer period of maternity leave incurs the a higher opportunity cost of earning income, which leads to a reduced fertility rate. Third, countries with higher gender inequality index tend to have a higher fertility rate. Similarly, countries with higher gender equality value have a lower fertility rate. When the gender equality value is devideed into three sub-categories, education, politics, and employment, the gender equality value in education is the only sub-category which is negatively associated with the fertility rate. This study confirms that female labor force participation may not be a contributing factor in the lowering of fertility rate but instead can be positively associated with the fertility rate. Also, the results show that family policies or gender equality values can be significantly affect fertility rate.
All previous studies analyzing multivariate time series data of EUA (European Union Allowance) price commonly used endogenous variables within the four variables and included the period from April to June of 2006 in the analysis, when the price distortion occurred. This study uses graph theory and structural vector error correction model (SVECM) to analyze the daily time series data of the EUA (European Union Allowance) price. As endogenous variables, five variables are considered for the analysis, including prices of crude oil, natural gas, electricity and coal in addition to carbon price. Data period is Phase 2 period (April 21, 2008 to March 31, 2010) to avoid the EUA price distortion of Phase 1 period (2005~2007). Further, the monthly data including the economic variables as endogenous variables are analyzed.
We examine the information transmission between the Service Industrial Production Index and the Service Industrial Wholesale and Retail Index, based on the returns data offered by the Korea Bank. The data includes daily return data from January 2000 to September 2015. Utilizing a dynamic analytical tool-the VAR model, Granger Causality test, Impulse Response Function and Variance Decomposition have been implemented. The results of the analysis are as follows. Firstly, results of Granger Causality test suggests the existence of mutual causality the Service Industrial Production Index precede and have explanatory power the Service Industrial Wholesale and Retail Index However the results also identified a greater causality and explanatory power of the Service Industrial Wholesale and Retail Index over the Service Industrial Production Index. Secondly, the results of impulse response function suggest that the Service Industrial Production Index show immediate response to the Service Industrial Wholesale and Retail Index and are influenced by till time 5 From time 2, the impact gradually disappears. Also the Service Industrial Wholesale and Retail Index show immediate response to the Service Industrial Production Index and are influenced by till time 2.5, the impact gradually disappears. Lastly, the variance decomposition analysis shows that the changes of return of Service Industrial Production Index are dependent on those of the Service Industrial Wholesale and Retail Index. This implies that returns on the Service Industrial Production Index have a significant influence over returns on the Service Industrial Wholesale and Retail Index. It contributes to the understanding of market price formation function through analysis of detached the Service Industrial Production Index and Service Industrial Wholesale and Retail Index. Finally, our results can be used as a guide by the Korea Bank and Republic of Korea and as well as Statistics Korea.
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