• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.6
• /
• pp.566-576
• /
• 2016

It is known that the Real Estate Sales Market and Auction Market are closely interrelated with each other in a variety of respects and the media often mention the real estate auction market as a leading indicator of the real estate market. The purpose of this paper is to analyze the relationships between the housing market and auction market before and after macroeconomic fluctuations using VECM. The period from January 2002 to December 2008, which was before the financial crisis, was set as Model 1 and the period from January 2009 to November 2015, which was after the financial crisis, was set as Model 2. The results are as follows. First, the housing auction market is less sensitive to changes in the housing market than it is to fluctuations in the auction market. This means that changes in the auction market precede fluctuations in the housing market, which shows that the auction market as a trading market is activated. In this respect, public institutions need to realize the importance of the housing auction market and check trends in the housing contract price in the auction market. Also, investors need to ensure that they have expertise in the auction market.

• Management & Information Systems Review
• /
• v.36 no.4
• /
• pp.53-80
• /
• 2017

The purpose of this study is to examine the determinants of the number of bank relationships in Korea. Firm-specific determinants considered here include leverage, size, age, return on asset, investment grade, tangibility, liquidity, R&D expenditure. We estimate the effects of these variables, and compare the results with those from previous studies performed for other economies. Concerning the effects of business cycle, we find that the business cycle is an important factor in determining the number of bank relationships. The number of bank relationships varies over the business cycle, and we notice a counter-cyclical behavior, which means the number decreases during economic expansions and increases during contractions. This result can be interpreted as a result of firms' diversification of borrowings into multiple banks in order to reduce the liquidity risk during the recession. In the subsets, however, the number of bank relationships for large firms is stable regardless of the business cycle. Unlisted firms, non-chaebol, and low credit quality firms which have relatively limited access to alternative sources of financing show counter-cyclical behavior. Finally, such phenomena is not observed in the non-competitive credit market, while they show a counter-cyclical behavior in the competitive credit market.

• The Journal of the Korea Contents Association
• /
• v.18 no.1
• /
• pp.196-209
• /
• 2018

This study is founded on banks' profitability factors. Unlike the previous study in terms of diversification of the banks' funding structure, this research performs multiple regression analysis during the entire period and examines the comparative analysis of before and after the financial crisis. the study establishes hypotheses by using the wholesale funding ratio as a key focus variable with 8 explanatory variables and the operating profit on assets as a profitability index. The Loan-deposit rate gap, the Number of stores and the Non-performing loan ratio prove to be a significant profitability factor for all periods of time. Korean banks are also more profitable when their the Loan-deposit rate gap get bigger and the Number of stores grows. The wholesale funding ratio is analyzed to have no statistically significant effect on the profitability of banks. Rather than being influenced by macroeconomic indicators, it is indicated that the situation of individual banks and other financial environments have been affected. And banks increase profitability as banks increase their loan after the financial crisis. The empirical analysis shows that profitability factors have periodical distinctions, and in this aspect, this research has implications. The study needs to be expanded to cover the entire domestic banking sector, in consideration of the profitability of the banking industry in the future.

• KDI Journal of Economic Policy
• /
• v.14 no.1
• /
• pp.121-145
• /
• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.

• Journal of Distribution Research
• /
• v.17 no.2
• /
• pp.93-115
• /
• 2012

This study empirically examines the impact of changes in market shares among retailing types on the price index. The retailing type is classified into 6 groups: department store, big mart, super market, convenient store, specialty merchant, and on-line store. The market shares of retailing types are calculated by the ratio of each retailing type monthly sales to total monthly retailing sales in which total retailing sales is the sum of each retailing type sales. We employed several price indices: consumer price index (CPI), CPI for living necessaries, and fresh food price index. In addition, this study used fundamental price indices based on 25 product families as well as 42 representative products. The empirical model also included several variables in order to control for the macroeconomic effects and those variables are the exchange rate, M1, an oil price, and the industrial production index. The data is monthly time-series data spanning over the period from January 2000 to December 2010. In order to test for the stability of data series, we conducted ADF test and PP test in which the model and length of lag were determined by the relevant previous literature and based on the AIC. The empirical results indicate that changes in market shares among retailing types have impacts on the price index. Table A shows that impacts differ as to which price index to use and which product families and products to use. For department store, it lowers the price of food and non-alcoholic beverages, home appliances, fresh food, fresh and vegetables, but it keeps the price high for fresh fruit. The big mart retailing type has a positive impact on the price of food, nut has a negative effect on clothing and foot wear, non-food, and fresh fruit. For super market, it has a positive impact on food and non-alcoholic beverages, fresh food, fresh shellfishes, but increases the price of CPI for living necessaries and non-food. The specialty merchant retailing type increases the price level of CPI for living necessaries and fresh fruit. For on-line store type, it keeps the price high for CPI for living necessaries and non-food as well as fresh fruit. For the analysis based on 25 product families shows that changes in market shares among retailing types also have different effects on the price index. Table B summarizes the different results. The 42 representative product level analysis is summerized in Table C and it indicates that changes in market shares among retailing types have different effects on the price index. The study offers the theoretical and practical implication to these findings and also suggests the direction for the further analysis.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.69-88
• /
• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

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

• Journal of Navigation and Port Research
• /
• v.38 no.6
• /
• pp.701-708
• /
• 2014

This study is aimed to obtain the influence of port hinterlands on container cargo volumes of Incheon port using System Dynamics(SD). Also, macro economic index such as exchange rates(US dollar), balance of current account, capital balance, Japan trade, China trade, export unit value index, import unit value index, total turnover of Incheon port were used as the factors that influence container cargo volumes of Incheon port. Moreover micro index regarding port hinterlands' operating companies such as total sales, rental fee, number of employees were introduced in the simulation model. In order to measure accuracy of the simulation, this study implemented MAPE analysis. And after the implementation, the simulation was decided as a much more accurate model because MAPE value was calculated to be within 10%. This study respectively examined factors using the sensitivity analysis. As a result, in terms of the effects on cargo volume in Incheon Port, the factor named 'cargo volumes of port hinterlands' operating companies' is most significant. And increasing the rental fee of hinterland was resulted in decreasing the cargo volumes of Incheon port.

• Information Systems Review
• /
• v.18 no.2
• /
• pp.173-198
• /
• 2016

Financial distress can damage stakeholders and even lead to significant social costs. Thus, financial distress prediction is an important issue in macroeconomics. However, most existing studies on building a financial distress prediction model have only considered idiosyncratic risk factors without considering systematic risk factors. In this study, we propose a prediction model that considers both the idiosyncratic risk based on a financial ratio and the systematic risk based on a business cycle. Ultimately, we build several IT artifacts associated with financial ratio and add them to the idiosyncratic risk factors as well as address the imbalanced data problem by using an oversampling technique and synthetic minority oversampling technique (SMOTE) to ensure good performance. When considering systematic risk, our study ensures that each data set consists of both financially distressed companies and financially sound companies in each business cycle phase. We conducted several experiments that change the initial imbalanced sample ratio between the two company groups into a 1:1 sample ratio using SMOTE and compared the prediction results from the individual data set. We also predicted data sets from the subsequent business cycle phase as a test set through a built prediction model that used business contraction phase data sets, and then we compared previous prediction performance and subsequent prediction performance. Thus, our findings can provide insights into making rational decisions for stakeholders that are experiencing an economic crisis.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.4
• /
• pp.480-488
• /
• 2016

As the subprime mortgage crisis spread globally, it depressed not only the financial market, but also the construction business in Korea. In fact, according to CERIK, the BSI of the construction businesses plunged from 80 points in December 2006 to 14.6 points in November 2008, and the extent of the depression in the housing sector was particularly serious. In this respect, this paper analyzes the influence of the financial market fluctuation on the housing market before and after the Global Financial Crisis using VECM. The periods from January 2000 to December 2007 and January 2008 to October 2015, before and after the financial crisis, were set as Models 1 and 2, respectively. The results are as follows. First, when the economy is good, the Gangnam housing market is an attractive one for investment. However, when it is depressed, the Gangnam housing market changes in response to the macroeconomic fluctuations. Second, the Gangbuk and Gangnam housing markets showed different responses to fluctuations in the financial market. Third, when the economy is bad, the effect of low interest rates is limited, due to the housing market risk.