• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.305-322
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• 2016

With the multiplex and wide release strategy, the monopolies of four major distribution companies and three multiplex chain, the polarization of Korean movie's Box Office performance is deepening. The four major distributor of NEW, CJ CGV, Lotte Cinema distributed 290 movies of 538 movies produced from 2009 to 2014 in Korea. The audience market share of these four distributors is 85.74%, while other 248 movies covers only 14.26%, which are distributed by outsides of the four major distribution system. The concentration of film admission has been deepened in Gini Index from 0.53 in 2004 to 0.85 in 2014. The movies distributed by others rather than four major companies suffers inequality in numbers of secured screens, screening times, and secured seats of movie theaters. In the highest 10% of box-office ranking, there is only one movie distributed by others. The lowest 50% of box-office ranking, there are 186 movies by others, while four companies have 81 movies. However, Occupancy rate of seat of major companies is lower than 16.83% of that of the others in the lowest 50% section. Workers of Korean movie industry are suffered from this polarization and they seek their breakthrough by producing erotic movies for VOD in recent years.

• 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.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.559-575
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• 2014

Individual investors are increasingly flocking to message boards to seek, clarify, and exchange information. Businesses like Seekingalpha.com and business magazines like Fortune are evaluating, synthesizing, and reporting the comments made on message boards or blogs. In March of 2012, Yahoo! Finance Message Boards recorded 45 million unique visitors per month followed by AOL Money and Finance (19.8 million), and Google Finance (1.6 million) [McIntyre, 2012]. Previous studies in the finance literature suggest that online communities often provide more accurate information than analyst forecasts [Bagnoli et al., 1999; Clarkson et al., 2006]. Some studies empirically show that the volume of posts in online communities have a positive relationship with market activities (e.g., trading volumes) [Antweiler and Frank, 2004; Bagnoli et al., 1999; Das and Chen, 2007; Tumarkin and Whitelaw, 2001]. The findings indicate that information in online communities does impact investors' investment decisions and trading behaviors. However, research explicating the correlation between information on online communities and stock market activities (e.g., trading volume) is still evolving. Thus, it is important to ask whether a volume of posts on online communities influences trading volumes and whether trading volumes also influence these communities. Online stock message boards offer two different types of information, which can be explained using an economic and a psychological perspective. From a purely economic perspective, one would expect that stock message boards would have a beneficial effect, since they provide timely information at a much lower cost [Bagnoli et al., 1999; Clarkson et al., 2006; Birchler and Butler, 2007]. This indicates that information in stock message boards may provide valuable information investors can use to predict stock market activities and thus may use to make better investment decisions. On the other hand, psychological studies have shown that stock message boards may not necessarily make investors more informed. The related literature argues that confirmation bias causes investors to seek other investors with the same opinions on these stock message boards [Chen and Gu, 2009; Park et al., 2013]. For example, investors may want to share their painful investment experiences with others on stock message boards and are relieved to find they are not alone. In this case, the information on these stock message boards mainly reflects past experience or past information and not valuable and predictable information for market activities. This study thus investigates the two roles of stock message boards-providing valuable information to make future investment decisions or sharing past experiences that reflect mainly investors' painful or boastful stories. If stock message boards do provide valuable information for stock investment decisions, then investors will use this information and thereby influence stock market activities (e.g., trading volume). On the contrary, if investors made investment decisions and visit stock message boards later, they will mainly share their past experiences with others. In this case, past activities in the stock market will influence the stock message boards. These arguments indicate that there is a correlation between information posted on stock message boards and stock market activities. The previous literature has examined the impact of stock sentiments or the number of posts on stock market activities (e.g., trading volume, volatility, stock prices). However, the studies related to stock sentiments found it difficult to obtain significant results. It is not easy to identify useful information among the millions of posts, many of which can be just noise. As a result, the overall sentiments of stock message boards often carry little information for future stock movements [Das and Chen, 2001; Antweiler and Frank, 2004]. This study notes that as a dependent variable, trading volume is more reliable for capturing the effect of stock message board activities. The finance literature argues that trading volume is an indicator of stock price movements [Das et al., 2005; Das and Chen, 2007]. In this regard, this study investigates the correlation between a number of posts (information on stock message boards) and trading volume (stock market activity). We collected about 100,000 messages of 40 companies at KOSPI (Korea Composite Stock Price Index) from Paxnet, the most popular Korean online stock message board. The messages we collected were divided into in-trading and after-trading hours to examine the correlation between the numbers of posts and trading volumes in detail. Also we collected the volume of the stock of the 40 companies. The vector regression analysis and the granger causality test, 3SLS analysis were performed on our panel data sets. We found that the number of posts on online stock message boards is positively related to prior stock trade volume. Also, we found that the impact of the number of posts on stock trading volumes is not statistically significant. Also, we empirically showed the correlation between stock trading volumes and the number of posts on stock message boards. The results of this study contribute to the IS and finance literature in that we identified online stock message board's two roles. Also, this study suggests that stock trading managers should carefully monitor information on stock message boards to understand stock market activities in advance.

• Journal of the Korea Fashion and Costume Design Association
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• v.9 no.1
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• pp.23-33
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• 2007

Clothing exports of Korea has grown rapidly till the latter half of the 1980's, contributing Korean economic development. However from the 1990's, the amount, the world market share and the international competitiveness of clothing exports have declined. Based on these phenomena, the purpose of this study was to identify the characteristics of export articles in Korean Clothing Trade focused on the 1990's. Statistical data of clothing articles(SITC 84 : Articles of apparel & clothing accessories) were used. The relative importance, trade orientation tendency and unit price of each export clothing articles were analyzed. The results of the study were as follows. On the relative importance, trade orientation tendency and unit price of each export clothing articles, outer garments or products that required complicated production process(e.g., coats, suits, ensembles, jackets, dress) had been decreased in the portion and weakened in the export orientation tendency. But one item in a set or casual wear like trousers, skirts, blouses, shirts, Jerseys, pullovers, T-shirts has been increased in the portion and risen in the unit price. These trends means that clothing exports of Korea were more focused on those category and the international competitiveness on those articles were advanced. From these results, this study can be contributed to establish the concrete clothing export articles strategies of Korean firms.

• International Commerce and Information Review
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• v.13 no.2
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• pp.205-234
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• 2011

The study analyzes the shifts of international competition in the digital products market between South Korea and major FTA partners. The analysis utilizes trade statistics to calculate changes in the volume of trade, and in competitiveness between FTA partners. The target countries for this analysis include USA and the European Union with whom Korea has made agreements recently, and the period is set for the decade from 2000 to 2009. The trade records of the UN are employed to investigate the indexes of each country: trade structure and market share of digital products, the trade specialization index (TSI), and annual change of revealed comparative advantage index (RCA) against global market and both the American and EU markets. This analysis shows clearly the status quo of the development and growth of the international competitiveness of South Korea. The study will improve the understanding of international competitiveness in digital products and contents industry, which is rapidly evolving, and of the resulting industrial structure.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.349-360
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• 2020

The study aims to empirically examine the determinants of bank margins from Pakistan, an emerging South Asian economy. To elucidate the importance of the Pakistani banking sector, secondary data has been used, which was extracted from the annual accounts of twenty-four Pakistani scheduled commercial banks (20 conventional, four full-fledged Islamic) over a sample period of 2006 to 2017. The factors identified in the dealership model and the subsequent empirical developments in the dealership model categorized as bank-specific, diversification, regulatory, and industry concentration are analyzed by applying the most-common linear dynamic panel-data estimator, the Generalized Method of Moments (GMM) estimator, developed by Arellano and Bond (1991). The findings reveal that, among the bank-specific variables, funding cost, credit risk, managerial efficiency, market share, and operating cost are significant predictors of bank margins. For diversification variables employed in the study, both variables including net non-interest income and asset diversity are as well significant predictors of bank margins. It is also found that the market concentration variable proxied by the Herfindahl-Hirschman Index (HHI) is significantly predicting bank margins. Subsequently, one of the regulatory variables, the opportunity cost of holding reserves, and one bank-specific variable, the degree of risk aversion, are insignificant in the model.

• The Journal of Industrial Distribution & Business
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• v.9 no.1
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• pp.89-97
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• 2018

Purpose - The Regional Comprehensive Economic Partnership (RCEP) among 16 countries including South Korea, the largest free trade agreement in the Asia-Pacific region, will be concluded next year. The participating countries decided to pursue a comprehensive and high -quality agreement, while ensuring flexibility considering development level of each country. In this study, trade structures between nations from 2005 through 2016 were examined to see the impact that this agreement will have on Korea and to come up with effective countermeasures. Research design, data, and methodology - The method of analysis includes the analysis of the trade matrix, which is useful for identifying the dependency of the individual countries on the market in the region and the reciprocal dependency of the member countries on the market, and the index of intensity of trade, which is useful for figuring out the share of trade between the parties in total trade. Results - The results showed that first, the international trade coefficients of Vietnam and Philippines are higher than those of China and Japan. Secondly, the international inducement coefficients between China and Japan were high, and that between Indonesia and Burma were low, indicating that Korea's exports did not have much effect on export increase of these countries. Third, as a result of analyzing Korea's trade intensity, it was found that export intensity and import intensity were greater than 1 in Vietnam and Philippines, which shows that there is a high degree of relational bond with these countries. India and Laos countries still have a low level of relational bond, which indicates that there is room for improvement in economic relations when the agreement is concluded. After the signing of the agreement in the future, more diverse industrial structures should be continuously studied. Conclusions - The analysis of trade matrix, trade structure, trade inducement coefficient and trade intensity between Korea and RCEP participating countries shows that the majority of the countries have the high level of economic relationship with Korea. Korea should drive a harder bargain when negotiating the terms of the RCEP, in comparison with the level of the existing FTA agreement excluding Japan.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of Labour Economics
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• v.44 no.2
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• pp.33-69
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• 2021

This study estimates the employment relationship of domestic and foreign workers by establishing 16 municipal and provincial panel data for the period 2010-2018. It attempts to analyze industry-specific (manufacturing and construction), scale-specific (5-29, 5-29, 5-299 and 5 above) and uses the foreign worker's national share (foreigner's concentration index) as an instrumental variable to control the endogeneity of foreign workers. Finally, it compares the results of panel GLS, which does not consider the endogeneity of foreign workers, with the results using instrumental variable method that considers it. As a result of the analysis, the complementary relationship between domestic and foreign workers was confirmed in the panel GLS analysis. However, although the employment relationship between domestic and foreign workers was not statistically significant in the instrumental variable method, the analysis of the combination of manufacturing and construction industry showed a statistically significant substitute relationship. This study is highly regarded for the first time in Korea that an instrumental variable method was created to identify and control the endogeneity of foreign workers in estimating employment relationships between domestic and foreign workers.