• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.87-106
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• 2022

Uncertainties in the energy market are increasing due to technology developments (shale revolution), trade wars, COVID-19, and the Russia-Ukraine war. Especially, since 2020, the risk of international trade in the energy market has increased significantly due to changes in the supply chain of transportation and due to prolonged demand reduction because of COVID-19 and the Russian-Ukraine war. Considering these points, this study analyzed connectedness between energy price, tanker index, and uncertainty to understand the connectedness between international trade in the energy market. Main results are summarized as follows. First, as a result of analyzing stable period and unstable period of the energy price model using the MS-VAR model, it was confirmed that both the crude oil market model and the natural gas market model had a higher probability of maintaining stable period than unstable period, increasing volatility by specific events. Second, looking at the results of the analysis of the connectedness between stable period and unstable period of the energy market, it was confirmed that in the case of total connectedness, connectedness between variables was increased in the unstable period compared to the stable period. In the case of the energy market stable period, considering the degree of connectedness, it was confirmed that the effect of the tanker freight index, which represents the demand-side factor, was significant. Third, unstable period of the natural gas market model increases rapidly compared to the crude oil market model, indicating that the volatility spillover effect of the natural gas market is greater when uncertainties affecting energy prices increase compared to the crude oil market.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.165-187
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• 2005

This paper tries to investigate the relationships among stock return volatility, time-varying risk premium and Korea Discount. Using Korean Composite Stock Price Index (KOSPI) return from January 4, 1980 to August 31, 2005, this study finds possible links between time-varying risk premium and Korea Discount. First of all, this study classifies Korean stock returns during the sample period by three regime-switching volatility period that is to say, low-volatile period medium-volatile period and highly-volatile period by estimating Markov-Switching ARCH model. During the highly volatile period of Korean stock return (09/01/1997-05/31/2001), the estimated time-varying unit risk premium from the jump-diffusion GARCH model was 0.3625, where as during the low volatile period (01/04/1980-l1/30/1985), the time-varying unit risk premium was estimated 0.0284 from the jump diffusion GARCH model, which was about thirteen times less than that. This study seems to find the evidence that highly volatile Korean stock market may induce large time-varying risk premium from the investors and this may lead to Korea discount.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.3
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• pp.73-85
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• 2020

The study aims to provide better understanding of sustainable earnings by a comprehensive analysis of earnings persistence of business firms in Vietnam as an example of developing economies in South-East Asia. Dataset of 1,278 publicly listed firms (excluding banking and financial services firms) on Vietnam Stock Exchange for the period from 2008 to 2017 was collected. By applying fixed effect regression model, the empirical results provided the basis to measure the persistence index (Pers index) and find low level of their earnings persistence. The literature of earnings quality analysis in developed countries suggests earnings persistence as a noteworthy determinant of future earnings forecast and stock valuation. However, research of sustainable earnings in developing countries is still highly underdeveloped. For Vietnamese listed firms, the average Pers index was estimated for the period from 2008 to 2010, indicating low level of earnings persistence. We also incorporated earnings persistence level into future earnings forecast by running the quintile regression model divided the data into four equal levels and conducted each section independently to see the difference in each percentile, thence assessed the factors' influence on the specific model. The findings provide important information on the expected returns of firms, especially helping investors make sound decisions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.143-154
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• 2021

This study aims to explore the dynamic conditional correlation (DCC) between ten Asian stock indexes, the US stock index, and Bitcoin by using the dynamic conditional correlation model. The time span of the daily data is between January 2015 to May 2021, the total observation is 1,116. DCC(1,1)-EGARCH(1,1) with multivariate t and normal distributions for the DCC and EGARCH models, respectively, outperforms other models by the goodness of fit values. Except for Bitcoin, we discovered that the majority of the securities' volatilities have a very high volatility persistence. Furthermore, the negative shocks/news have more impact on the volatilities than positive shocks/news in most of the cases, except the stock index of China and Bitcoin. Most of the correlation pairs exhibit higher correlation during the COVID-19 pandemic compared to the pre-COVID-19, except Hong Kong-The US and Malaysia-Indonesia. Moreover, the correlation between Asian stock indexes during the COVID-19 pandemic is statistically higher than the pre-COVID-19 pandemic. However, there are a few instances where the Hong Kong stock index and a few countries are identical. The result of correlation size shows the connectedness between Asian stock markets, which are well-connected within the region, especially with South Korea, Singapore, and Hong Kong.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.123-140
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• 2023

In recent times, the global economy has been subject to increasing volatility, which has made it considerably more difficult to accurately predict economic indicators compared to previous periods. In response to this challenge, the present study conducts an exploratory investigation that aims to predict the Business Survey Index (BSI) by leveraging data mining techniques on both structured and unstructured data sources. For the structured data, we have collected information regarding foreign, domestic, and industrial conditions, while the unstructured data consists of content extracted from newspaper articles. By employing an extensive set of 44 distinct data mining techniques, our research strives to enhance the BSI prediction accuracy and provide valuable insights. The results of our analysis demonstrate that the highest predictive power was attained when using data exclusively from the t-1 period. Interestingly, this suggests that previous timeframes play a vital role in forecasting the BSI effectively. The findings of this study hold significant implications for economic decision-makers, as they will not only facilitate better-informed decisions but also serve as a robust foundation for predicting a wide range of other economic indicators. By improving the prediction of crucial economic metrics, this study ultimately aims to contribute to the overall efficacy of economic policy-making and decision processes.

• The Korean Journal of Financial Management
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• v.24 no.2
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• pp.41-79
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• 2007

This study examines the expiration-day effects of stock index futures and options in the Korean stock market. The so-called 'expiration-day effects', which are the abnormal stock price movements on derivatives expiration days, arise mainly from cash settlement. Index arbitragers have to bear the risk of their positions unless they liquidate their index stocks on the expiration day. If many arbitragers execute large buy or sell orders on the expiration day, abnormal trading volumes are likely to be observed. If a lot of arbitragers unwind positions in the same direction, temporary trading imbalances induce abnormal stock market volatility. By contrast, if some information arrives at market, the abnormal trading activity must be considered a normal process of price discovery. Stoll and Whaley(1987) investigated the aggregate price and volume effects of the S&P 500 index on the expiration day. In a related study, Stoll and Whaley(1990) found a similarity between the price behavior of stocks that are subject to program trading and of the stocks that are not. Thus far, there have been few studies about the expiration-day effects in the Korean stock market. While previous Korean studies use the KOSPI 200 index data, we analyze the price and trading volume behavior of individual stocks as well as the index. Analyzing individual stocks is important for two reasons. First, stock index is a market average. Consequently, it cannot reflect the behavior of many individual stocks. For example, if the expiration-day effects are mainly related to a specific group, it cannot be said that the expiration of derivatives itself destabilizes the stock market. Analyzing individual stocks enables us to investigate the scope of the expiration-day effects. Second, we can find the relationship between the firm characteristics and the expiration-day effects. For example, if the expiration-day effects exist in large stocks not belonging to the KOSPI 200 index, program trading may not be related to the expiration-day effects. The examination of individual stocks has led us to the cause of the expiration-day effects. Using the intraday data during the period May 3, 1996 through December 30, 2003, we first examine the price and volume effects of the KOSPI 200 and NON-KOSPI 200 index following the Stoll and Whaley(1987) methodology. We calculate the NON-KOSPI 200 index by using the returns and market capitalization of the KOSPI and KOSPI 200 index. In individual stocks, we divide KOSPI 200 stocks by size into three groups and match NON-KOSPI 200 stocks with KOSPI 200 stocks having the closest firm characteristics. We compare KOSPI 200 stocks with NON-KOSPI 200 stocks. To test whether the expiration-day effects are related to order imbalances or new information, we check price reversals on the next day. Finally, we perform a cross-sectional regression analysis to elaborate on the impact of the firm characteristics on price reversals. The main results seem to support the expiration-day effects, especially on stock index futures expiration days. The price behavior of stocks that are subject to program trading is shown to have price effects, abnormal return volatility, and large volumes during the last half hour of trading on the expiration day. Return reversals are also found in the KOSPI 200 index and stocks. However, there is no evidence of abnormal trading volume, or price reversals in the NON-KOSPI 200 index and stocks. The expiration-day effects are proportional to the size of stocks and the nearness to the settlement time. Since program trading is often said to be concentrated in high capitalization stocks, these results imply that the expiration-day effects seem to be associated with program trading and the settlement price determination procedure. In summary, the expiration-day effects in the Korean stock market do not exist in all stocks, but in large capitalization stocks belonging to the KOSPI 200 index. Additionally, the expiration-day effects in the Korean stock market are generally due, not to information, but to trading imbalances.

• 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.25 no.2
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• pp.137-170
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• 2003

Despite the great success of the derivatives market, several concerns were expressed regarding the additional volatilitystemming from program trading during the expiration of derivatives. This paper examines the impact of the expiration of the KOSPI 200 index derivatives on cash market of Korea Stock Exchange(KSE). The KOSPI 200 index derivatives market has a unique settlement price determination process. The settlement price for the expiration of derivatives is determined by call auction during the last 10 minutes after the trades for matured derivatives are finalized. We analyze typical expiration day effects such as price, volatility, and volume effects. With high frequency data, we find that there are strong expiration day effects in the KSE and try to interpret the results with the unique settlement procedures of the KOSPI 200 cash and derivatives markets.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.95-104
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• 2020

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to non-stationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea's stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market's volatility than the positive shock in the case of South Korea and Tokyo.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.1-7
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• 2021

The study examines the influence of COVID-19 on the stock market returns of Saudi Arabia. The data was analyzed through event study methodology using daily price data of Tadawul All Share Index (TASI). The study examines the behavior pattern of the Saudi Arabian stock market in different phases during the event period by selecting six-event windows with a range of 10 days. The results report a negative Abnormal Return (AR) of -0.003 on the event date, while the abnormal returns reversed the next day to 0.005 positively. The result of Cumulative Abnormal Return (CAR) is negative and significant at the 1 percent level in all the six-event windows starting from the event date to day 59 after the event for the TASI index. Even though the influence of the COVID-19 pandemic decreased after 30 days of the event date, it increased during the last ten days of the event window. The stock market volatility of Saudi Arabia increased during the post-event period compared to the pre-event period with a negative mean return of -0.326 and a greater standard deviation. In a conclusion, the study found a significant influence of the COVID-19 pandemic on the stock market returns of TASI.