• Korean Journal of Mathematics
• /
• v.22 no.3
• /
• pp.529-552
• /
• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

• Journal of Family Resource Management and Policy Review
• /
• v.17 no.2
• /
• pp.219-241
• /
• 2013

The study explored the work-life balance of three dual-earning couples using the household economics approach according to the hermeneutics paradigm. Three families were analysed. The couples were interviewed individually with a non-structural interview guide about their work history and life history, and with a semi-structured interview and structured questionnaire about their work hours, childcare practice, husband-wife relation, household income and expenditure, and daily and weekly schedule. The results revealed the different paths and various strategies to adjust work-life balance among the cases. Strategies were discussed to facilitate changes in labor market policy, childcare policy, working place culture and family's daily life planning.

• IE interfaces
• /
• v.23 no.1
• /
• pp.12-23
• /
• 2010

A semiconductor market is one of the most competitive markets in the world. To survive this competition, important targets for production planning are on-time delivery and profit maximization. In our research, we modify the linear programming model for the current production planning by adding new objective functions that maximize the profit. In addition, we propose a production planning process that gives a priority to new products, reflecting daily fluctuations in demand to weekly production planning. We validate our model with real data sets obtained from a major company semiconductor manufacturer and performed the paired t-test to verify the results. The results showed that our model forecasted profit and loss with 93.2% accuracy and improved the due date satisfaction by 10%.

• Communications for Statistical Applications and Methods
• /
• v.25 no.6
• /
• pp.659-671
• /
• 2018

In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.

• Journal of the Economic Geographical Society of Korea
• /
• v.9 no.3
• /
• pp.341-357
• /
• 2006

Recently new periodic markets formed in large apartment areas where consumers live. Before, in the case of peasant periodic markets travelling merchants, consumers and producers met in specific places on decided dates. Closing time of apartment's new periodic markets is later than that of peasant periodic markets, and the number of travelling merchants is fewer than that of peasant periodic markets. The average number of apartment and household for a new periodic market is about ten and 920 respectively, and if neighboring apartment household are included, the number is larger. Apartment's new periodic markets in Cheongju city is included in Daejeon market area. The types of regional trip of travelling merchants can be divided into one round trip of two or three neighboring dongs and larger sphere of more than 4 dongs. The larger sphere round trip consists of one type combining the southeastern, southern and southwestern regions, and the other type combining southeastern and southwestern regions. About 85 percent of travelling merchants visit the periodic market 5 days in a week; about 12 percent of travelling merchants choose to visit on Saturday; only 2 percent of travelling merchants visit 4 days in a week.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.10
• /
• pp.6534-6542
• /
• 2015

This study examines the relation between mutual fund performance and fund flows in Korean market using weekly and monthly data. The results are as follows. First, the relation between the two variables varies across fund types. Even the relations in equity fund and index fund are different from each other. Second, the structural change in the mutual fund market affect significantly the relation between the two variables. Third, return chasing flow is observed constantly in bond fund and it is observed only after the structural change for equity fund and MMF. However, no return chasing flow is observed for index fund. Fourth, mutual fund flows affect subsequent fund returns only in MMF after the structural change.

• The Korean Journal of Financial Management
• /
• v.27 no.2
• /
• pp.169-204
• /
• 2010

This paper examines the dynamic relationship between fund flow and market risk at the aggregate level and explores whether sudden sharp changes in fund flow (fund run) can cause a systemic risk in the Korean financial markets. We use daily and weekly data and regression and VAR analysis. Main results of the paper are as follows: First, in the stock market, a concurrent and a lagged unexpected fund flows have a positive relationship with market volatility. A positive shock in fund flow predicts an increase in stock market volatility. In the bond market, an unexpected fund flow has a negative relationship with the default risk premium, but a positive relationship with the term premium. And an unexpected fund flow of the money market fund has a negative relationship with the liquidy risk, but the explanatory power is very low. Second, for examining whether changes in fund flow induce a systemic risk, we construct a spillover index based on the forecast error variance decomposition of VAR model. A spillover index represents that how much the shock in fund flow can explain the change of market risk in a market. In general, explanatory powers from spillover indexes are so fluctuant and low. In the stock market, the impact of shocks in fund flow on market risk is relatively high and persistent during the period from the end of 2007 to 2008, which is the subprime-mortgage crisis period. In bond market, since the end of 2008, the impact of shocks in fund flow spreads to default risk continually, while in the money market, such a systematic effect doesn't take place. The persistent patterns of spillover effect appearing around a certain period in the stock market and the bond market suggest that the shock to the unexpected fund flow may increase the market risk and can be a cause of systemic risk in the financial markets. However, summarizing the results of regression and VAR model analysis, and considering the very low explanatory power of spillover index analysis, we can conclude that changes in fund flow have a very limited power in explaining changes in market risk and it is not very likely to induce the systemic risk by a fund run in the Korean financial markets.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.81-96
• /
• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• The Korean Journal of Financial Management
• /
• v.20 no.2
• /
• pp.73-94
• /
• 2003

In general, the interest rate is forecasted by the parametric method which assumes the interest rate follows a certain distribution. However the method has a shortcoming that forecasting ability would decline when the interest rate does not follow the assumed distribution for the stochastic behavior of interest rate. Therefore, the nonparametric method which assumes no particular distribution is regarded as a superior one. This paper compares the interest rate forecasting ability between the two method for the Monetary Stabilization Bond (MSB) market in Korea. The daily and weekly data of the MSB are used during the period of August 9th 1999 to February 7th 2003. In the parametric method, the drift term of the interest rate process shows the linearity while the diffusion term presents non-linear decline. Meanwhile in the nonparametric method, both drift and diffusion terms show the radical change with nonlinearity. The parametric and nonparametric methods present a significant difference in the market price of interest rate risk. This means in forecasting the interest rate and the market price of interest rate risk, the nonparametric method is more appropriate than the parametric method.

• Journal of Technology Innovation
• /
• v.22 no.1
• /
• pp.89-117
• /
• 2014

Employing female in the field of science and engineering is becoming increasingly important with diversity and creativity emerging as key factors to build Creative Economy. Under these circumstances, it is necessary to recognize and discourage gender discrimination in the labor market by analyzing wages - the market value of labor which determines one's economic status. This study uses the Oaxaca-Ransom decomposition (1994) to analyze the gender wage gap and identify factors influencing the pay gap in science and engineering labor force. The results of this study are as follows: First, the average wage of female scientists and engineers reaches only 65% of that of male labor force, and the male scientist and engineers are superior in terms of personal attributes, for instance, education background. Second, looking at the factors that influence wages, wage premiums are associated with higher education background, older age, longer period of service, and weekly working hours for both male and female in managerial positions. Third, the wage decomposition shows that in the case of science and engineering labor force, the productivity difference by personal attributes reaches about 58%, and gender discrimination by the characteristics of the labor market stands at about 41%. This means the wage gap by productivity level in science and engineering labor force is wider, and the gender gap is smaller compared to non-science and engineering fields. However, the results of an analysis on specialties and education background of male and female scientists and engineers suggest that the discrimination against women is more serious when the percentage of the female labor force is low and the percentage of temporary workers in the labor market is high. In order to eliminate this discrimination, it is necessary to reduce the imbalance of female scientists and engineers in the labor market, among others, while female scientists and engineers, themselves, need to make continuous efforts to strengthen their capabilities.