• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.87-104
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• 2024

Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations.

• Asian Journal of Business Environment
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• v.9 no.1
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• pp.21-28
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• 2019

Purpose - The purpose of this paper is to investigate the relationship between corporate social responsibility disclosure (CSRD) and investment-cash flow sensitivity, which is a surrogate for financing constraints. Research design, data, and methodology - Taking China's A-share listed companies between 2009 and 2016 as a sample, this paper empirically tests the relationship between CSRD and investment-cash flow sensitivity by Panel VAR model. By introducing the orthogonal impulse response function, this paper distinguishes the fundamental factors and financial ones that affect corporate investment behavior. Results - Findings indicate that: (1) investment-cash flow sensitivity of firms with low level of CSRD is significantly lower than that of firms with high level of CSRD; (2) the orthogonal impulse response of corporate investment to cash flow in firms with high level of CSRD is significantly different from zero, but it is not significant in firms with low level of CSRD; (3) for firms with low level of CSRD, 0.7% of corporate investment volatility can be explained by the change in cash flow, which is lower than that of firms with high level of CSRD (1.1%). Conclusions - Corporations disclosing more and higher quality CSRD are often those faced with financing constraints. Voluntary disclosure can help them alleviate information asymmetry and financing constraints.

• Asia Pacific Journal of Business Review
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• v.1 no.2
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• pp.1-16
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• 2017

The fundamental goal of the research was to verify if the Twin Deficits Hypothesis holds for the economy of Zambia using time series data from 1980-2014. The current account and budget deficit were employed as key variables. The exchange rate was also used as a transmission mechanism to see how it contributes in the nexus. Cointegration tests confirmed a long run association of the variables. After fitting the VECM model, Granger causality tests confirmed the existence of twin deficits for Zambia. The results supported uni-directional reverse causality. The exchange rate was shown to be more significant in the long run than in the short run. The implosion of the time series as shown by the predicted cointegration equation implies that unless drastic measures are taken to cure the deficits, using the current account as the major target variable, twin deficits will persist for some time. The major policy implication of this research is that given that Zambia is a primary commodity-dependent developing country subsisting largely on copper revenues to sustain the economy, there is a need to move away from "copper addiction," given the recent volatility of earnings of primary commodities (e.g. through diversification of the economy, import substitution, and other strategies).

• Asian Journal of Business Environment
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• v.14 no.3
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• pp.37-50
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• 2024

The agricultural policy of Kim Jong-un's regime inherits the economic reform policy of the Kim Jong-Il period, which expands the autonomy of production and allows the market to dispose of products. The formation of markets represents an important factor in the business environment, as it indicates the establishment of fundamental conditions for management. However, major crops are still mainly managed by the state, and the government implements agricultural policies, such as emphasizing "Juche Farming." This study analyzed the impact of transition economic policies during the Kim Jong-un period on agricultural production using variability. Production variabilities increased for minor grain crops compared to previous years, but those of major grain (rice and maize) and horticultural crops did not change significantly. Even the production quantity of horticultural crops decreased, which is different from previous predicts that the expansion of the North Korean market would increase the consumption power of North Koreans and promote horticultural crop production. This study underscores the imperative for North Korea to develop policies aimed at stabilizing crop yields in the face of production variability. It proposes the establishment of an agricultural early warning system as a feasible solution to enhance agricultural infrastructure and promote inter-Korean cooperation.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• KDI Journal of Economic Policy
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• v.36 no.3
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• pp.1-23
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• 2014

This paper has studied the monetary policy in Korea with a time varying VAR model using four key macroeconomic variables. First, inclusion of the exchange rate was a crucial factor in evaluating Korean monetary policy since the monetary policy demonstrated sensitivity to exchange rate movements during the crisis periods of both the Asian financial crisis of 1997 and the global financial crisis of 2008. Second, a specification of the stochastic volatilities in TVP-VAR model is important in explaining excessive movements of all variables in the sample. The overall moderation of variables in 2000s was more or less due to a reduction of the stochastic volatilities but also somewhat due to the macroeconomic fundamental structures captured by impulse response functons. Third, the degree of the monetary policy effectiveness of inflation was mitigated in recent periods but with increased persistence. Lastly, the monetary policy stance towards inflation stabilization has advanced ever since the inflation targeting scheme was adopted. However, there still seems to be a room for improvement in this aspect since the degree of the monetary policy stance towards inflation stabilization was relatively weaker than to output stabilization.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.260-260
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• 2013

Multiferroic materials have attracted much attention due to their fascinating fundamental physical properties and technological applications in magnetic/ferroelectric data-storage systems, quantum electromagnets, spintronics, and sensor devices. Among single-phase multiferroic materials, $BiFeO_3 $ is a typical multiferroic material with a room temperature magnetoelectric coupling in view of high magnetic-and ferroelectric-ordering temperatures (Neel temperature $T_N$~647 K and Curie temperature $T_C$~1,103 K). Rare-earth ion substitution at the Bi sties is very interesting, which induces suppressed volatility of Bi ion and improved ferroelectric properties. At the same time, Fe-site substitution with magnetic ions is also attracting, and the enhanced ferromagnetism was reported. In this study, $Bi_{1-x}Dy_xFe_{0.95}Co_{0.05}O_3$ (x=0, 0.05 and 0.1) bulk ceramic compounds were prepared by solid-state reaction and rapid sintering. High-purity $Bi_2O_3$, $Dy_2O_3$, $Fe_2O_3$ and $Co_3O_4$ powders with the stoichiometric proportions were mixed, and calcined at $500^{\circ}C$ or 24 h to produce $Bi_{1-x}Dy_xFe_{0.95}Co_{0.05}O_3$. The samples were immediately put into an oven, which was heated up to $800^{\circ}C$ nd sintered in air for 30 min. The crystalline structure of samples was investigated at room temperature by using a Rigaku Miniflex powder diffractometer. The field-dependent magnetization measurements were performed with a vibrating-sample magnetometer. The electric polarization was measured at room temperature by using a standard ferroelectric tester (RT66B, Radiant Technologies).

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Korean small business review
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• v.33 no.4
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• pp.77-93
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• 2011

Since 2000, the employment rate of small and medium enterprises (SMEs) has dwindled while the creation of new jobs and the emergence of healthy SMEs have been stagnant. The fundamental reason for these symptoms is that the economic structure is disadvantageous to SMEs. In particular, the greater gap between SMEs and large enterprises has resulted in polarization, and the resulting imbalance has become the largest obstacle to improving SMEs' competitiveness. For example, the total productivity has continued to drop, and the average productivity of SMEs is now merely 30% of that of large enterprises, and the average wage of SMEs' employees is only 53% of that of large enterprises. Along with polarization, rapid industrialization has also caused anti-enterprise consensus, the collapse of the middle class, hostility towards establishments, and other aftereffects. The general consensus is that unless these problems are solved, South Korea will not become an advanced country. Especially, South Korea is now facing issues that need urgent measures, such as the decline of its economic growth, the worsening distribution of profits, and the increased external volatility. Recognizing such negative trends, the MB administration proposed a win-win growth policy and recently introduced a new national value called "ecosystemic development." As the terms in such policy agenda are similar, however, the conceptual differences among such terms must first be fully understood. Therefore, in this study, the concepts of win-win growth policy and ecosystemic development, and the need for them, were surveyed, and their differences from and similarities with other policy concepts like win-win cooperation and symbiotic development were examined. Based on the results of the survey and examination, the study introduced a South Korean model of win-win growth, targeting the promotion of a sound balance between large enterprises and SMEs and an innovative ecosystem, and finally, proposing future policy tasks. Win-win growth is not an academic term but a policy term. Thus, it is less advisable to give a theoretical definition of it than to understand its concept based on its objective and method as a policy. The core of the MB administration's win-win growth policy is the creation of a partnership between key economic subjects such as large enterprises and SMEs based on each subject's differentiated capacity, and such economic subjects' joint promotion of growth opportunities. Its objective is to contribute to the establishment of an advanced capitalistic system by securing the sustainability of the South Korean economy. Such win-win growth policy includes three core concepts. The first concept, ecosystem, is that win-win growth should be understood from the viewpoint of an industrial ecosystem and should be pursued by overcoming the issues of specific enterprises. An enterprise is not an independent entity but a social entity, meaning it exists in relationship with the society (Drucker, 2011). The second concept, balance, points to the fact that an effort should be made to establish a systemic and social infrastructure for a healthy balance in the industry. The social system and infrastructure should be established in such a way as to create a balance between short- term needs and long-term sustainability, between freedom and responsibility, and between profitability and social obligations. Finally, the third concept is the behavioral change of economic entities. The win-win growth policy is not merely about simple transactional relationships or determining reasonable prices but more about the need for a behavior change on the part of economic entities, without which the objectives of the policy cannot be achieved. Various advanced countries have developed different win-win growth models based on their respective cultures and economic-development stages. Japan, whose culture is characterized by a relatively high level of group-centered trust, has developed a productivity improvement model based on such culture, whereas the U.S., which has a highly developed system of market capitalism, has developed a system that instigates or promotes market-oriented technological innovation. Unlike Japan or the U.S., Europe, a late starter, has not fully developed a trust-based culture or market capitalism and thus often uses a policy-led model based on which the government leads the improvement of productivity and promotes technological innovation. By modeling successful cases from these advanced countries, South Korea can establish its unique win-win growth system. For this, it needs to determine the method and tasks that suit its circumstances by examining the prerequisites for its success as well as the strengths and weaknesses of each advanced country. This paper proposes a South Korean model of win-win growth, whose objective is to upgrade the country's low-trust-level-based industrial structure, in which large enterprises and SMEs depend only on independent survival strategies, to a high-trust-level-based social ecosystem, in which large enterprises and SMEs develop a cooperative relationship as partners. Based on this objective, the model proposes the establishment of a sound balance of systems and infrastructure between large enterprises and SMEs, and to form a crenovative social ecosystem. The South Korean model of win-win growth consists of three axes: utilization of the South Koreans' potential, which creates community-oriented energy; fusion-style improvement of various control and self-regulated systems for establishing a high-trust-level-oriented social infrastructure; and behavioral change on the part of enterprises in terms of putting an end to their unfair business activities and promoting future-oriented cooperative relationships. This system will establish a dynamic industrial ecosystem that will generate creative energy and will thus contribute to the realization of a sustainable economy in the 21st century. The South Korean model of win-win growth should pursue community-based self-regulation, which promotes the power of efficiency and competition that is fundamentally being pursued by capitalism while at the same time seeking the value of society and community. Already existing in Korea's traditional roots, such objectives have become the bases of the Shinbaram culture, characterized by the South Koreans' spontaneity, creativity, and optimism. In the process of a community's gradual improvement of its rules and procedures, the trust among the community members increases, and the "social capital" that guarantees the successful control of shared resources can be established (Ostrom, 2010). This basic ideal can help reduce the gap between large enterprises and SMEs, alleviating the South Koreans' victim mentality in the face of competition and the open-door policy, and creating crenovative corporate competitiveness. The win-win growth policy emerged for the purpose of addressing the polarization and imbalance structure resulting from the evolution of 21st-century capitalism. It simultaneously pursues efficiency and fairness on one hand and economic and community values on the other, and aims to foster efficient interaction between the market and the government. This policy, however, is also evolving. The win-win growth policy can be considered an extension of the win-win cooperation that the past 'Participatory Government' promoted at the enterprise management level to the level of systems and culture. Also, the ecosystemic development agendum that has recently emerged is a further extension that has been presented as a national ideal of "a new development model that promotes the co-advancement of environmental conservation, growth, economic development, social integration, and national and individual development."