• Communications for Statistical Applications and Methods
• /
• v.21 no.4
• /
• pp.335-347
• /
• 2014

This paper employs dynamic conditional correlation (DCC) model to examine time-varying comovement in the Korean stock market with a focus on the financial industry. Analyzing the daily returns of KOSPI 200 eight sector indices from January 2008 to December 2013, we find that stock market correlations significantly increased during the GFC period. The Financial Sector had the highest correlation between the Constructions-Machinery Sector; however, the Consumer Discretionary and Consumer Staples sectors indicated a relatively lower correlation between the Financial Sector. In terms of model fitting, the DCC with t distribution model concludes as the best among the four alternatives based on BIC, and the estimated shape parameter of t distribution is less than 10, implicating a strong tail dependence between the sectors. We report little asymmetric effect in correlation dynamics between sectors; however, we find strong asymmetric effect in volatility dynamics for each sector return.

• Journal of Digital Convergence
• /
• v.12 no.2
• /
• pp.45-59
• /
• 2014

Real estate has been the most preferable investment asset since 1980's has begun. Especially the ups and downs of housing price influence significantly on the household and national economy for a digital economy. In this analysis, monthly movement of apartment price of Seoul and its correlation with KOSPI, construction concerned shares, securities concerned shares, interest rate and exchange rate for 320 months(from January, 1987 to August, 2013) are shown. From the analysis, correlation coefficient of the price of apartment in Seoul and KOSPI is 0.8566 which is highly positive while the price of apartment in Seoul and interest rate are shown strong negative correlation which is -0.7846. The rise of stock market does affect the rise of the price of apartments in Seoul, on the contrary, the price goes down when the interest rate goes up.

• East Asian Economic Review
• /
• v.21 no.2
• /
• pp.147-165
• /
• 2017

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.

• Management & Information Systems Review
• /
• v.28 no.4
• /
• pp.93-108
• /
• 2009

This study examines the asymmetric volatility in the Korean bond market and stock market by using the KTB Prime Index and KOSPI. Because accurate estimation and forecasting of volatility is essential before investing assets, it is important to understand the asymmetric response of volatility in bond market. Therefore I investigate the existence of asymmetric volatility in Korean bond market unlike the previous studies which mainly focused on stock returns. The main results of the empirical analysis with GARCH and GJR-GARCH model are as follow. At first, it exists the asymmetric volatility on KOSPI returns like the previous studies. Also, I find that the GJR-GARCH is more suitable one than GARCH model for forecasting volatility. Second, it does not exist the asymmetric volatility on KTB Prime Index returns. This result is showed by that using the GARCH model for forecasting volatility in bond market is sufficient.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.4
• /
• pp.446-453
• /
• 2018

The study examines corporate research & development (R&D) expenditure in modern finance. Firms may face one of the essential issues to maintain their optimal levels of R&D expenditures in order to increase corporate profit. Accordingly, financial determinants that may influence R&D spending are statistically tested for firms listed on the KOSPI stock market during the period from 2010 to 2015. Financial determinants which may discriminate between firms in high-growth and low-growth industries are examined on a relative basis. Explanatory variables including one-period lagged R&D expenses (Lag_RD), cross-product term between the Lag_RD and type of industry (as a dummy variable), and advertising expenses (ADVERTISE) significantly influenced corporate R&D intensity. Moreover, high-growth firms in domestic capital markets showed higher Lag_RD, profitability (PROF) and foreign equity ownership (FOS) than their counterparts in low-growth sectors, whereas low-growth firms had higher market-value based leverage (MLEVER) and ADVERTISE. Overall, these results are expected to influence decision-making of firms concerning the optimal level of R&D expenditure, which may in turn enhance shareholder wealth.

• The Korean Journal of Financial Management
• /
• v.25 no.3
• /
• pp.79-109
• /
• 2008

According to asymmetric information hypothesis (for example, Ross (1977), Myers and Majluf (1984)), the impact of seasoned equity offering (SEO) announcement on the stock price depends mainly on the informational market efficiency. Despite of the importance of this fact, most of the previous SEO-related studies have done under the assumption of equal informational market efficiency among sample firms. This study intends to solve this problematic assumption and explores the real impact of SEO announcement on the stock prices. For this purpose, we divide 122 SEO firms into two subgroups; one with firms from KOSPI200 and the other including firms from the rest of KOSPI, assuming the former is more informationally efficient than the latter. Different from the US market-based study demonstrating short-and long-term negative price impacts of SEO announcement, most of the Korean market-based ones show price increases up until the announcement and decreases just after the announcement and in the long run. These previous studies attribute this difference to the different market system and regulation between them. Our results indicate that this discrepancy can be attributed to the different degree of market efficiency as well as the different market system and regulation.

• Communications for Statistical Applications and Methods
• /
• v.23 no.3
• /
• pp.203-213
• /
• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.1
• /
• pp.7-12
• /
• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2008.10a
• /
• pp.76-94
• /
• 2008

This paper investigates performance of the Markowitz's portfolio selection model with applications to Korean stock market. We choose Samsung-Group-Funds and KOSPI index for performance comparison with the Markowitz's portfolio selection model. For the most recent one and a half year period between March 2007 and September 2008, KOSPI index almost remains the same with only 0.1% change, Samsung-Group-Funds shows 20.54% return, and Markowitz's model, which is composed of the same 17 Samsung group stocks, reaches 52% return. We perform sensitivity analysis on the duration of financial data and the period of portfolio change in order to maximize the return of portfolio. In conclusion, according to our empirical research results with Samsung-Group-Funds, investment by Markowitz's model, which periodically changes portfolio by using nonlinear programming with only financial data, outperforms investment by the fund manager who possesses rich experiences on stock trading and actively changes portfolio based on minute-by-minute market news and business information.

• Management & Information Systems Review
• /
• v.35 no.2
• /
• pp.1-12
• /
• 2016

In this study, price discovery between the KOSPI200 spot, and leveraged ETFs(Leveraged KODEX, Leveraged TIGER, Leveraged KStar) is investigated using the vector error correction model(VECM). The main findings are as follows. Leveraged KODEX(Leveraged TIGER, Leveraged KStar) and KOSPI200 spot are cointegrated in most cases. There is no interrelations between the movement of Leveraged KODEX(Leveraged TIGER, Leveraged KStar) and KOSPI200 spot markets in case of daily data. Namely, in daily data, Leveraged KODEX(Leveraged TIGER, Leveraged KStar) doesn't plays more dominant role in price discovery than the KOSPI200 spot.