• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1061-1073
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• 2009

Stock price index option investing is a scientific investment method and various index and investment strategies have been developed. The purpose of this study is to apply the variety of option investment strategies that have been introduced in the market and validate them using past option trading data. Option data was based on an actual stock exchange market tick data ranging from September 2001 to January 2007. Visual Basic is used to propose an option back-testing model. Validation process was carried out by transferring the tick data into ten-minute intervals and empirically analyzed. Furthermore, most option-related strategies have been applied to the model, and the usefulness of each strategies can be easily evaluated. As option investment has high leverage followed by high risks and profit, the optimal option investment strategy should be used according to the market condition at the time to make stable profit with minimum risk.

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

• The Journal of Asian Finance, Economics and Business
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• v.4 no.3
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• pp.5-17
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• 2017

This study extends research into whether disclosure of corporate and financial information is associated with firms' costs of equity capital. This study sets out to examine empirically the determinants of corporate disclosure in the annual reports of 37 largest and most liquid firms listed on Kazakhstan Stock Exchange (KASE) in Kazakhstan. It also reports the results of the association between company-specific characteristics and disclosure of the sample companies. Based on the analysis of existing empirical research, the disclosure index has been constructed and regression analysis of the influence of the disclosure index on the cost of equity capital has been conducted. The obtained results show that the received findings correlate with foreign empirical studies, and the disclosure index in this sample has a negative impact on the cost of equity capital. Using cost of equity capital estimates derived from capital asset pricing model, we find that firms with higher levels of financial transparency are associated with significantly lower costs of equity capital. Economic theory assumes that by increasing the level of corporate reporting, firms not only increase their stock market liquidity, but also decrease the investors' estimation risk, arising from uncertainty about future returns and payout distributions. The results show that firms on the Kazakhstan market can reduce their cost of equity capital by increasing the level of their voluntary corporate disclosures.

• Convergence Security Journal
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• v.8 no.3
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• pp.83-92
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• 2008

In respect complication of group and period, the sales of retail trade is composed of various factors. This paper studies focus on estimating the determinants of the sales of retail trade. The volume of analysis consist of 7 groups. Analyzing period be formed over a 36 point(2005. 1$\sim$2007. 12). In this paper dependent variable setting up sales of retail trade, explanatory(independent) variables composed of composite stock price index, the number of the consumer's online buying behavior company, the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry), the consumer price index. The result of estimating the determinants of sales of retail trade provides empirical evidences of significance positive relationships between the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry). However this study provides empirical evidences of significance negative relationships between the consumer price index. The explanatory variables, that is, composite stock price and the number of the consumer's online buying behavior company, are non-significance variables. Implication of these findings are discussed for content research and practices.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Information Systems Review
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• v.26 no.1
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• pp.289-313
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• 2024

Korean pharmaceutical and bio-health companies began applying for FDA approval in 2000. However, drug companies in South Korea are not required to obtain FDA approval to market their products on the South Korean market, and the approval process is highly resource-intensive. This study utilizes event study methodology to examine the information effect of US FDA approval announcements on the stock prices of pharmaceutical and bio-health companies listed on South Korean stock markets. The study's results show that FDA approval announcements caused abnormal increases in corporate stock prices, indicating that these announcements have a transnational information effect on South Korean companies' value. Furthermore, the results show that the impact of FDA approval announcements on stock prices is greater for small companies than mid-sized and large companies and in bio and healthcare industries than in the traditional pharmaceutical industry. This impact is also more significant on the KOSDAQ (Korea Securities Dealers Automated Quotation) companies than the KOSPI (Korean Composite Stock Price Index) companies and after the expansion of stock price limits. These findings signal that the information effect is more significant when regulatory controls are weaker. The results also indicate that obtaining FDA approval brings above-normal returns for companies and that FDA application is a high-risk, high-return investment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1630-1648
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• 2021

Owing to the unprecedented COVID-19 pandemic, the pharmaceutical industry has attracted considerable attention, spurred by the widespread expectation of vaccine development. In this study, we collect relevant topics from news articles related to COVID-19 and explore their links with two South Korean pharmaceutical indices, the Drug and Medicine index of the Korea Composite Stock Price Index (KOSPI) and the Korean Securities Dealers Automated Quotations (KOSDAQ) Pharmaceutical index. We use generalized Dirichlet-multinomial regression (g-DMR) to reveal the dynamic topic distributions over metadata of index values. The results of our analysis, obtained using g-DMR, reveal that a greater focus on specific news topics has a significant relationship with fluctuations in the indices. We also provide practical and theoretical implications based on this analysis.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.585-592
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• 2017

As development of personal devices have made everyday use of internet much easier than before, it is getting generalized to find information and share it through the social media. In particular, communities specialized in each field have become so powerful that they can significantly influence our society. Finally, businesses and governments pay attentions to reflecting their opinions in their strategies. The stock market fluctuates with various factors of society. In order to consider social trends, many studies have tried making use of bigdata analysis on stock market researches as well as traditional approaches using buzz amount. In the example at the top, the studies using text data such as newspaper articles are being published. In this paper, we analyzed the post of 'Paxnet', a securities specialists' site, to supplement the limitation of the news. Based on this, we help researchers analyze the sentiment of investors by generating a domain-specific sentiment lexicon for the stock market.

• New Physics: Sae Mulli
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• v.68 no.12
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• pp.1364-1373
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• 2018

A recognizable form having meaning is called a sign in semiotics. The sign is transformed into a physical counter form in this work. Its internal structure is restricted on the linguistic concept structure. We borrow the concept of a mathematical function from the utility function of a rational personal in the economy. Universalizing the utility function by introducing the consistency of independency on the manner of construction, we construct the probability. We introduce a random variable for the probability and join it to a position variable. Thus, we propose a physical sign and its serial changes in the forms of stochastic equations. The equations estimate three patterns (jumping, drifting, diffusing) of possible solutions, and we find them in the one-day stock-price flow. The periods of jumping, drifting and diffusing were about 2, 3.5, and 6 minutes for the Kia stock on 11/05/2014. Also, the semiotic sign (icon, index, symbol) can be expected from the equations.