• The Korean Journal of Food And Nutrition
• /
• v.30 no.4
• /
• pp.681-688
• /
• 2017

This study was conducted to investigate the effect of gelatin concentration and storage on color and texture of puffer fish stock jelly. Puffer fish stock jelly was prepared by adding various amounts (2.5~10 percent) of gelatin to puffer fish stock. Color changes of puffer fish sstock jelly were also investigated during storage at $4^{\circ}C$ and $25^{\circ}C$ with varying amount of gelatin (2.5~10.0 percent). Regardless of storage temperature, the L values of puffer fish stock jelly were slightly increased, while a values of the stock jelly were negatively decreased during storage. However, L and negative b values were decreased with increasing gelatin concentration, whereas a values were increased negatively with increasing gelatin concentration. The texture profiles of puffer fish stock jelly such as hardness, springiness, cohesiveness adhesiveness, chewiness and brittleness were determined during storage at $25^{\circ}C$ and $4^{\circ}C$ at various intervals. The hardness, chewiness and brittleness were increased during storage at $25^{\circ}C$ for four days, while those of springiness and cohesiveness were decreased during storage. However, the values of all textural parameters were increased with increasing gelatin concentration in puffer fish stock jelly. Patterns of textural parameters of puffer fish stock jelly stored at $4^{\circ}C$ were practically identical to those stored at $25^{\circ}C$.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.3015-3024
• /
• 2018

This study investigates the recent asymmetric effect of news on stock return volatility in Asian five stock markets - Japan, Korea, Singapore, Taiwan, and Malaysia - since 2000. This study uses the GJR-M model which shows a different effect of a good and bad news on volatility. Empirical results show that the unexpected negative return has a more crucial effect on stock return volatility than the unexpected positive one does in all five stock markets. This implies that the bad news of the stock markets gives a more remarkable effect on volatility than good news does. This study finds that it is very important for market participants and regulation practitioners to distinguish between positive and negative return shocks in the stock markets since bad news might have a larger impacts on volatility than good news.

• Asia-Pacific Journal of Business
• /
• v.11 no.4
• /
• pp.317-332
• /
• 2020

Purpose - This study examines the information effect and trading behavior of investors for the 430 stock split data from January 2004 to June 2018 in the Korean stock market. Design/methodology/approach - The stock split samples are classified into two groups by split ratio as well as three groups by price level prior to split. We also investigate the trading behavior of investors categorized by institutional versus individual investors. Findings - First, we find a significantly positive information effect on the announcement day. In particular, the information effect is more distinct in the group of larger split ratio and higher price level of stocks. Second, we find a huge increase in turnover following the stock splits, which mainly results from the trading by individual investors. Also, the increase in turnover by individual investors is evident in the group of larger split ratio and higher price level of stocks. Third, the stock splits have a negative impact on the long-term stock performance. The negative buy-and-hold abnormal return(BHAR) makes no difference in the groups by split ratio as well as price level of stocks. Lastly, we find individual investors tend to buy splitted stocks, which exhibit the long-term under-performance. Research implications or Originality - The results in this paper suggest that the liquidity hypothesis is not supported in the Korean stock splits. In addition, we observe that individual investors are exposed to losses due to their unfavorable trading behavior following the stock split.

• International Area Studies Review
• /
• v.13 no.2
• /
• pp.255-276
• /
• 2009

This article has examined the international transmission of returns among S&P500, Nikkei225 and SENSEX stock index cash markets using the daily closing prices covered from January 4, 2002 to February 6, 2009. For this purpose we employed dynamic time series models such as the Granger causality analysis and variance decomposition analysis based on VAR model. The main empirical results are as follows; First, according to Granger causality tests we find that S&P500 stock index has a significant prediction power on the changes of SENSEX and Nikkei225 stock index market and vice versa. However, US stock market's influence is dominant to the other stock markets at a significant level statistically. Second, according to variance decomposition, SENSEX stock index is more sensitive to the movement of S&P500 than that of Nikkei225 stock index. These kinds of empirical results shows that the three stock markets are integrated over times and these results will be informative for the international investors to build the world-wide investment portfolio and risk management strategies, etc.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.12
• /
• pp.35-44
• /
• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.102-108
• /
• 2023

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

• Asia-Pacific Journal of Business
• /
• v.14 no.1
• /
• pp.265-279
• /
• 2023

Purpose - The purpose of this paper is to compare and review behavioral economics models that explain stock price changes after large-scale price shocks in the Korean stock market and to find a suitable model. In this paper, among the theories reviewed, it was confirmed that the anchoring heuristics theory has high explanatory power for stock prices after large-scale stock price fluctuations. Design/methodology/approach - This paper conducts an event study on stock price shocks in which the individual stocks that make up the KOSPI200 index show more than 10% fluctuation on a daily basis. In order to materialize the abstract predictions of heuristics theories in a varifiable form, this paper uses the daily stock price index change as a reference point for availability heuristics, and uses the 52-week highest and lowest price as reference point for anchoring heuristics. Research implications or Originality - As a result of the empirical analysis, the stock price reversals did not consistently appear for changes in the daily index. On the other hand, the stock price drifts consistently appeared around the 52-week highest and the 52-week lowest price. And in the multiple regression analysis that controlled for company-specific and event-specific variables, the results that supported the anchoring heuristics were more evident. These results suggest that it is possible to establish an investment strategy using large-scale price change in Korean stock market.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.143-156
• /
• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.1-19
• /
• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• The Korean Journal of Financial Management
• /
• v.27 no.2
• /
• pp.85-114
• /
• 2010

Using the financial institutions that have adopted performance-based stock option plan, this paper examines whether performance-based executive stock options improves effectively firm value. Over the period 2002~2005, we investigate short-term and long-term effects of the performance-based stock options on stock price. The empirical results are summarized as follows. First, the announcement of plain vanilla stock options generates no significant effects on firm value. Meanwhile, the announcement of performance-based stock options results in negative and significant abnormal returns, which is contrary to the expectation. In addition, we find that there are strong, significant and negative announcement effects when banks grant performance-based stock options. Secondly, there is no significant difference between the long-term performance of the sample granting stock options and that of the benchmarks, which is similar to the findings of the previous research. Also, we fail to get any evidence that performance-based stock option awards have improved the long-term firm value.