• The Korean Journal of Financial Studies
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• v.2 no.2
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• pp.315-344
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• 1995

The relationship of information flow and market price formation are central to the basic tenets of financial economics. Whereas information is usually treated as being either public or private(monopolistic), most empirical studies focus on the price effects of public announcements. More recent research has centered more on the role of private information, such as insider trading, in efficient pricing and whether such trading increases investor welfare. Typically, 'insider trading' refers to an officer that trades in his/her company's shares. Insider trading, however, also refers to anyone who generates private, albeit costly, information concerning a stock's fundamental value. Normally, such insider activity is more difficult to ascertain. One way in which negative information is revealed is through short-selling activity, especially the monthly short-interest positions reported by the national stock exchanges. Diamond and Verrecchia(1987) provide a theoretical paradigm that predicts a negative price adjustment upon announcement of n company's monthly short interest, if the short interest displays an unusual increase and is correlated with negative information that is not yet public. Empirical studies of the short-run, negative price effect predicted by Diamond and Verrecchia find mixed results. One explanation is that the time period studied is too short for the market to absorb the informational content of these announcements. One reason is that these announcements are an ambiguous signal that requires more individuals and time to collect and act on the same information before full revelation occurs or before the implicit information becomes publicly known. This 'long delayed reaction' also serves as a motivation for related research on the wealth effect of mergers, share repurchases, and initial equity offerings in which long-run performance differs from the initial, short-run reaction to such announcements or offerings.

• Human Ecology Research
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• v.52 no.6
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• pp.601-616
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• 2014

This study's aim was to explore the relationships among consumers' perceived risk, trust, and familiarity with products made in China. We invited 489 Korean adult consumers, who had purchased products made in China earlier, to take part in this investigation. Data analyses were conducted using a reliability test, confirmatory factor analysis, and structural equation modeling (SEM) with SPSS ver. 21.0 and AMOS ver. 21.0. We assumed that the perceived risk could be classified into four types: financial, performance, psychological, and social risk. The empirical verification through SEM indicated that all four types of the perceived risk represented a negative influence on the trust. Further, only the financial and psychological risks were significant when consumers were not familiar with products made in China (novice); in contrast, the experts (consumers familiar with products made in China) showed that the performance and social risks were effective. Finally, we found that there was a moderating effect of familiarity on the relationship between three types of perceived risk, namely financial, performance, and psychological risks, and trust, but the social risk was not affected. The results of this research should help us to understand the consumers' risk perception of imported goods and to formulate criteria on the basis of which the consumers evaluate these products. This research can help companies, particularly those in China, to formulate market strategies effectively when they enter a foreign market such as Korea by exploring the influence of the perceived risk on local consumers' purchasing behaviors as well.

• Journal of Distribution Science
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• v.14 no.10
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• pp.59-72
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• 2016

Purpose - This study expands the corporate social responsibility (CSR) model and concepts by adding to it the concepts of corporate identity and public existence responsibility. Then, this study examines the structural relationship between corporate identity and public existence responsibility. This study contributes to expanding CSR to give customers a different perspective from previous studies in that it specifically measures corporate public existence responsibility, corporate identity, and corporate value creation and investigates the structural relationship. Research design, data, and methodology - This study addresses specific research questions. First, it asks whether non-financial performance is a component of CSR; second, it asks if the improvement in the corporate image should be treated as its corporate identity; and third, it tries to expand CSR concepts from corporate citizenship and public market awareness to public existence responsibility. The research hypothesis is formulated to confirm the relationships among CSR, corporate value creation, corporate identity, and public existence responsibility. Result - This study confirms that CSR has a positive correlation with corporate value creation and that CSR has a relatively positive correlation with corporate identity and public existence responsibility. Additionally, it confirms a positive correlation between corporate identity and public existence responsibility. However, corporate identity and public existence responsibility do not have an effect on corporate value creation. However, the influence of public existence responsibility confirmed the influence of corporate value creation through corporate identity as a mediating variable. Conclusion - This study argues that CSR produces more general performance including both financial and non-financial performance. It also confirms that the goals and performance of CSR can substitute for corporate value creation from general performance. It further confirms that public existence responsibility includes market public awareness, corporate images, and corporate associations. It suggests that corporations should see themselves as having public existence responsibility. Further, they should devise strategies to build corporate identities that associate with corporate goals and visions. Finally, this study contributes to the expansion of perspectives on CSR theoretical concepts and goals of performance of the corporation throughout the corporate value creation process.

• 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.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.189-203
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• 2020

Purpose - This study aims in analyzing the dynamic relationship between household loans and housing prices according to the characteristics of depository institutions after the financial crisis, identifying the recent trends between them, and making policy suggestions for stabilizing house prices. Design/methodology/approach - The monthly data used in this study are household loans, household loan interest rates, and housing prices ranging from January 2012 to May 2020, and came from ECOS of the Bank of Korea and Liiv-on of Kookmin Bank. This study used vector auto-regression, generalized impulse response function, and forecast error variance decomposition with the data so as to yield analysis results. Findings - The analysis of this study no more shows that the household loan interest rates in both deposit banks and non-bank deposit institutions had statistically significant effects on housing prices. Also, unlike the previous studies, there was statistically significant bi-directional causality between housing prices and household loans in neither deposit banks nor non-bank deposit institutions. Rather, it was found that there is a unidirectional causality from housing prices to household loans in deposit banks, which is considered that housing prices have one-sided effects on household loans due to the overheated housing market after the financial crisis. Research implications or Originality - As a result, Korea's housing market is closely related to deposit banks, and housing prices are acting as more dominant information variables than interest rates or loans under the long-term low interest rate trend. Therefore, in order to stabilize housing prices, the housing supply must be continuously made so that everyone can enjoy housing services equally. In addition, the expansion and reinforcement of the social security net should be realized systematically so as to stop households from being troubled with the housing price decline.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Knowledge Management Research
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• v.22 no.4
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• pp.87-101
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• 2021

ESG Investment is emerging as a trend and common sense in the financial market. ESG Investment is an investment method that simultaneously pursue social sustainability and investment returns from a long-term perspective by reflecting non-financial factors such as environment, society and governance in addition to corporate financial performance in investment decisions. This study checked how the characteristics of ESG investment have been changed after Covid-19. Afterwards, it was confirmed that Covid-19 actually acted as a negative factor in the securities market by applying VAR model. At the same time, it was demonstrated that ESG indices of the US and Korea outperformed their benchmark in terms of return and risk during the pandemic regime. The result of this study hints that the importance of ESG investment will be unchanged after Covid-19. At the same time, it suggests that managers should avoid passive ESG management and engage in strategic ESG management based on knowledge management.

• Journal of the Economic Geographical Society of Korea
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• v.8 no.2
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• pp.247-266
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• 2005

The purpose of this paper is to analyse the effects of a firm's financial and non-financial factors on the relationship formation with its main bank in the industry of parts and material in Pusan-Kyungnam region. The results, out of accordance with the relation-banking or regional financial market perspective, do not support the hypothesis that regional financial institutions would be useful for decreasing the financial difficulties of the small and medium firms in the region. The analyses about the effects of non-financial factors on the formation of main bank relations show that while Kookmin Bank and Industrial Bank play important roles as main banks of small businesses other national banks put emphasis on the transaction lending. And the analyses about the effects of financial factors show that firms having main bank relations with non-bank financial institutions and Kookmin bank are more profitable and stable than firms having main bank relations with other banks including local banks. On the whole it seems that local banks are not making a commitment to the regional economy and their operational grounds are not strong enough.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Korea Technology Innovation Society
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• v.16 no.3
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• pp.724-753
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• 2013

This paper empirically examines the effects of green technology patent on the financial performance of SMEs and venture specialized green enterprises. In particular, this paper is focused on analysing the financial performance difference by comparing the financial condition of 1st year before and 1st year after the application of green technology patent, and the one of 1st year before and 2nd year after it using sales, operating profit, net income, ratio of operating profit to net sales, and ratio of net profit to net sales. The statistical significances were accepted on sales after 1st and 2nd year, operating profit and ratio of net profit to net sales after 1st year, and ratio of operating profit to net sales after 2nd year. This paper proposes the vitalization of green consumption market, the reinforcement of green financial policy, the installation of financing windows, the improvement of unfair business conducts of large enterprises, and the reinforcement of win-win partnership between large enterpsises and SMEs as policy issues of Korean government in order to promote SMEs and venture specialized green enterprises.