• 한국콘텐츠학회논문지
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• 제16권6호
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• pp.305-322
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• 2016

한국 영화의 배급은 CJ E & M, 롯데, 쇼박스, NEW의 4대 메이저 배급사를 통해서 이뤄지고, CJ CGV, 롯데 시네마, 메가 박스의 3대 멀티플렉스를 통해서 관객에게 선보인다. 2009년부터 2014년까지 배급된 한국영화 538편수 중에서, 이들 4대 배급사는 290편을 배급하여 배급점유율은 53.9% 이나 관객점유율은 85.74%를 차지한다. 나머지 248편을 배급하여 배급편수의 46.09%를 차지하는 4대 배급사 이외의 기타배급의 형태는 14.26%의 관객점유율을 가지고 있는 현실이다. 4대 배급사 중에 CJ와 롯데는 배급과 극장 사업을 함께 가지고 있다. 멀티플렉스와 와이드 릴리스 전략, 4대 메이저 배급 회사와 3대 멀티플렉스 체인의 독과점의 영향으로 한국 영화 흥행의 양극화 현상은 심화되고 있다. 작품수, 동원 관객 수, 상영 스크린 수, 상영 횟수, 좌석 확보수 등의 척도에서 기타 배급사의 영화들은 각 흥행순위 구간에서, 모든 척도의 평균값보다 낮은 수치를 보였다. 유일한 예외는 작품수로 기타배급은 흥행순위 중간 40%구간에 61편, 하위50%구간에 186편이 위치하고, 상위2-9%구간에는 1편, 상위 1%구간에는 작품이 없다. 하위50%구간의 기타배급 영화의 좌석점유율은 동일구간의 4대 배급사 영화의 좌석점유율이나 평균점유율보다 높은 최고로 16.83%를 기록한다. 기타배급의 이러한 열악한 상황은 해가 갈수록 점점 악화되어서, 우리나라 흥행 양극화의 심화를 가져오고 있다. 이러한 상황을 벗어나고자 하는 몸부림과 VOD 시장의 활성화는 1980년대 VCR 의 등장과 함께 활성화된 에로영화의 유행을 다시 가져왔다. 스크린점유율과 더불어서, 상영점유율을 통해서 우리나라 영화시장 경쟁의 공정성을 주시하고, 새로운 배급채널과 플랫폼에 대한 연구가 필요하다.

• 디지털콘텐츠학회 논문지
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• 제18권3호
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• pp.585-592
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• 2017

개인용 디바이스의 발달로 개인들이 손쉽게 인터넷에 접속할 수 있게 되었으며, 소셜미디어를 통한 정보의 공유와 습득이 일반화 되고 있다. 특히 분야별 전문 커뮤니티가 발달하며 사회적 영향력을 행사하고 있어 기업과 정부는 이들의 의견을 반영하여 전략을 수립하는 일에 관심을 기울이고 있다. 온라인상의 다양한 텍스트로부터 대중의 의견을 읽어내는 것을 오피니언마이닝이라고 한다. 그 중 하나인 감성사전은 방대한 비정형데이터를 빠르게 파악하는 도구로 여러 분야에서 활용되고 있다. 주식시장은 사회의 여러 요인을 반영하여 변동한다. 최근에는 버즈량 분석 등 빅데이터를 기반으로 오피니언마이닝을 활용한 주식시장 연구가 시도되고 있다. 대표적인 예로 뉴스와 같은 텍스트 데이터 분석을 활용한 연구들이 발표되고 있다. 본 논문에서는 뉴스의 정제된 형식과 한정된 어휘를 사용한 기존연구를 보완하고자 증권전문 사이트 'Paxnet'의 게시 글을 분석대상으로 삼아 주식시장 맞춤형 감성사전을 구축하여 투자자들의 감성을 분석하는 데 기여했다.

• Asia pacific journal of information systems
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• 제24권4호
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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.

• 한국의상디자인학회지
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• 제9권1호
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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.

• 통상정보연구
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• 제13권2호
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• pp.205-234
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• 2011

본 연구에서는 우리나라와 주요 FTA 상대국과의 디지털 제품에 대한 국제경쟁력의 변화를 분석하였다. UN의 무역자료를 이용하여 FTA 상대국과의 무역량 변화와 경쟁력 변화에 대해 분석하였다. 분석대상국가로는 최근 우리나라와 FTA 협약을 체결한 미국과 EU를 선정하였으며, 분석기간은 2000년부터 2009년까지 최근 10년간을 대상으로 하였다. 분석방법은 UN의 무역자료를 이용하여 각 국가별디지털 제품의 무역구조와 세계시장점유율, 무역특화지수, 세계시장 및 미국과 EU 시장을 대상으로 현시비교우위지수의 연도별 변화에 대해 분석하였다. 분석결과 우리나라의 디지털 제품의 국제경쟁력은세계시장점유율, 무역특화지수, 현시비교우위지수 모든 부문에서 세계시장은 물론 미국과 EU 시장에서도 비교열위에 있는 것으로 나타났으며, 품목별의 차이는 있지만 미국, EU는 국제경쟁력에서 비교우위에 있음을 알 수 있었다. 따라서 우리나라의 디지털 제품 산업은 성장 속도가 빨라지고 있는 디지털시대에 국제경쟁력을 갖추고 살아남기 위해서는 기업들의 경쟁력 강화 노력 외에도 정부의 정책적 뒷받침이 필요할 것으로 판단되었다. 본 연구는 우리나라와 FTA 상대국을 대상으로 진화하고 있는 디지털 제품 및 콘텐츠 산업의 국제경쟁력과 향후 전개될 산업구조를 이해하는데 도움을 줄 것이다.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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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.

• 산경연구논집
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• 제9권1호
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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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• 제24권2호
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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.

• 지능정보연구
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• 제26권1호
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• pp.1-21
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• 2020

인공지능 기술의 급속한 발전과 함께 빅데이터의 상당 부분을 차지하는 비정형 텍스트 데이터로부터 의미있는 정보를 추출하기 위한 다양한 연구들이 활발히 진행되고 있다. 비즈니스 인텔리전스 분야에서도 새로운 시장기회를 발굴하거나 기술사업화 주체의 합리적 의사결정을 돕기 위한 많은 연구들이 이뤄져 왔다. 본 연구에서는 기업의 성공적인 사업 추진을 위해 핵심적인 정보 중의 하나인 시장규모 정보를 도출함에 있어 기존에 제공되던 범위보다 세부적인 수준의 제품군별 시장규모 추정이 가능하고 자동화된 방법론을 제안하고자 한다. 이를 위해 신경망 기반의 시멘틱 단어 임베딩 모델인 Word2Vec 알고리즘을 적용하여 개별 기업의 생산제품에 대한 텍스트 데이터를 벡터 공간으로 임베딩하고, 제품명 간 코사인 거리(유사도)를 계산함으로써 특정한 제품명과 유사한 제품들을 추출한 뒤, 이들의 매출액 정보를 연산하여 자동으로 해당 제품군의 시장규모를 산출하는 알고리즘을 구현하였다. 실험 데이터로서 통계청의 경제총조사 마이크로데이터(약 34만 5천 건)를 이용하여 제품명 텍스트 데이터를 벡터화 하고, 한국표준산업분류 해설서의 산업분류 색인어를 기준으로 활용하여 코사인 거리 기반으로 유사한 제품명을 추출하였다. 이후 개별 기업의 제품 데이터에 연결된 매출액 정보를 기초로 추출된 제품들의 매출액을 합산함으로써 11,654개의 상세한 제품군별 시장규모를 추정하였다. 성능 검증을 위해 실제 집계된 통계청의 품목별 시장규모 수치와 비교한 결과 피어슨 상관계수가 0.513 수준으로 나타났다. 본 연구에서 제시한 모형은 의미 기반 임베딩 모델의 정확성 향상 및 제품군 추출 방식의 개선이 필요하나, 표본조사 또는 다수의 가정을 기반으로 하는 전통적인 시장규모 추정 방법의 한계를 뛰어넘어 텍스트 마이닝 및 기계학습 기법을 최초로 적용하여 시장규모 추정 방식을 지능화하였다는 점, 시장규모 산출범위를 사용 목적에 따라 쉽고 빠르게 조절할 수 있다는 점, 이를 통해 다양한 분야에서 수요가 높은 세부적인 제품군별 시장정보 도출이 가능하여 실무적인 활용성이 높다는 점에서 의의가 있다.

• 노동경제논집
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• 제44권2호
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• pp.33-69
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• 2021

본 연구는 2010~2018년 기간 동안의 16개 시도 지역 패널데이터를 구축하여 내외국인근로자의 고용관계를 추정한다. 산업별(제조업과 건설업), 규모별(5~29인, 5~99인, 5~299인 그리고 5인 이상) 분석을 시도하고 외국인근로자의 내생성을 통제하기 위하여 지역 내 외국인근로자의 출신 국가 점유율(Foreigner's Concentration Index: FCI)을 도구변수로 사용한다. 최종적으로는 외국인근로자의 내생성을 고려하지 않는 패널 GLS와 이를 고려한 도구변수법의 결과를 비교한다. 분석 결과, 패널 GLS 분석에서는 내외국인근로자 사이에 보완관계가 확인되었다. 그러나 도구변수법에서는 내외국인근로자의 고용관계가 통계적으로 유의하지 않았지만, 정(+)에서 부(-)로 바뀌었으며, 제조업과 건설업을 결합한 분석에서는 통계적으로 유의한 대체관계가 나타났다. 본 연구는 내외국인근로자의 고용관계를 추정하는 데 있어서 내생성 통제 여부의 중요성을 확인하고 이를 통제하기 위해 도구변수를 국내 최초로 고안했다는 점에서 높은 평가를 받을 수 있다.