• Journal of Technology Innovation
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• v.25 no.4
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• pp.239-261
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• 2017

This paper analyzes how corporate product innovation affects firms' revenue and financial stability, and thereby draws the implications for the corporate strategy for sustainable growth. Corporate product innovation is defined as the development of new products within the firm, including bought-in products. Corporate revenue is measured by per capita sales and its growth rate, while financial stability is measured by debt-to-equity ratio and liquidity ratio. In the empirical analysis, the two-stage estimation method was used to control for the endogeneity of new product development. The data are drawn from the first (2005) to the sixth (2015) wave of the Human Capital Corporate Panel (HCCP) Survey, which are matched to the data from the Korea Investors Service (KIS). The results of the first-stage estimation indicate that product innovation of the firm is promoted by the firm's knowledge capital stock, human resources investment, and market-leading strategy. The second-stage estimation results indicate a positive relationship between the firm's level of activity in product innovation and short-term revenue (per capita sales and its growth), and financial stability (lower debt-to-equity ratio and higher liquidity ratio). These findings confirm that the firm's investment in technology innovation and subsequent product innovation are important strategies to enhance both short-term corporate revenue and long-term financial stability.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.338-357
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• 2012

This study investigated any possible financial attributes of the CDS spreads of a firm belonging to financial industries headquartered in the Republic of Korea. There were few studies on this issue, especially for the firms located in emerging capital markets. Coupled with the models such as a multiple regression and a principal component analysis(PCA), this research has identified that only two explanatory variables such as SLOPE and INTER3 (i.e. interaction effect between the BETA and the SLOPE) consistently showed their statistically significant influence on the CDS spreads through the 'selected' model without and with applying a stepwise regression procedure for the robustness. Given the rapid developments of sophisticated financial derivatives, this study may suggest a valuable insight to foreign and domestic investors to identify the possible determinants of CDS spreads at the firm- and/or the industry-level.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.9
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• pp.251-257
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• 2017

Private banking, which is a part of the financial services industry, is an investment advisory business targeting high-net-worth individuals (HNWIs). The demand for asset management services, which isexpanding in the world market as well as in the Asia-Pacific region, is rapidly increasing in Korea as the low-growth, low-interest environment has stabilized. In Korea, the private banking business is ahead of growth, and the evaluation and compensation system of playing institutions remains a challenge. While the aging of the population increases the demand for services, the increasing competition and regulations have decreased the profit margins in the industry. In this paper, a business model is derived from a professional service quality model. Modular advisory services, value-proposition through sophisticated services, and investment expertise with increased clarity would help wealth management firms pursue their opportunities. By interviewing the experts of wealth management banks, this study constructsa business model with elements derived from a relevant literature review. The contribution of this research is to enable these institutions to understand the key factors affecting their financial performances, in order to improve them. This study is limited by one of the research models, and it will be necessary to conduct an empirical test in the future.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.309-321
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• 2014

Existing research indicates that job design influences not only a worker's psychological well-being but also the individual and organizational performance. This study has been motivated by the need to provide useful implications for increasing the efficiency of business operations. The purpose of this research is to investigate the relationships between job characteristics and performance factors in the domestic financial industry. The conceptual framework has been developed based on Hackman & Oldham's job characteristics model and the related literature. We conducted a survey of employees in the domestic financial firms to test the conceptual model and hypotheses, and analyzed the data using the structured equation modeling method. The results of the empirical analysis indicate that the variety and autonomy of a job positively affect corporate innovation, confidence, and job satisfaction and that corporate innovation and job satisfaction positively influence job commitment. This research designed to examine the relationship between job characteristics and organizational performance will make a contribution to the improvement of business efficiency in our society.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.375-389
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• 2018

A successful succession of family businesses is a very important issue that greatly affects the sustainability of the business. Among the factors affecting successful succession, the successors of the family business were identified on the entrepreneurship held by small business owners and on the management performance of the business. This study was analyzed empirically by the managers who currently run the actual business through completion of or process of succession. Successive factors were established as a place for education and succession of heirs, and entrepreneurship was divided into innovativeness, risk-taking and pro-activeness, and management performance was divided into financial and non-financial achievements. The analysis shows that the education and training of successors among succession factors have significant implications for both managerial performance and entrepreneurship, and that the success of successors has no significant impact on non-financial performance, innovativeness and pro-activeness. In addition, while the risk-taking nature of entrepreneurship was concerned with the performance of management, innovativeness and pro-activeness were not all significant. According to this study, the education and training of successors and the determination of successors in succession family businesses have a significant impact on management performance and entrepreneurship.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.1
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• pp.15-21
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• 2015

In this paper our main focus is to provide insight into the history of M&A's for this purpose we have analysed the different waves of M&A. We have analysed these waves in context of available literature and fact and figures. During the study we realised that almost all of the waves of M&A's ended because of financial crises, although impact and severity of that crises may differ. We analysed the impact of current crises on M&A in global context and in order to establish how companies have and in post crises era i.e. after crises of 2007 onwards how the companies have changed their corporate strategies to accommodate M&A's. We have also analysed which factors fuelled M&A's in past and were these factors present in post crises era M&A activities. By first quarter of 2011 the many firms saw new growth opportunities in M&A activities seemed to rebound as large companies used M&A's as part of their corporate strategy but this was cut short by events like US debt ceiling, down grade of USA's credit ratings along with fears about Eurozone's financial health and their impact on future prospects of M&A's would they continue to prosper or would they be weighed down by these events.

• Information Systems Review
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• v.9 no.2
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• pp.129-147
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• 2007

At the time the local credit-card companies plunged into a liquidity crisis in 2002, S-Card was urged to take into account the estimated customer income (ECI) to enhance its customer credit evaluation function for the first time in Korean financial industry. Before this new attempt by S-Card, most credit-card companies including S-Card had performed a customer's credit evaluation based on the customer's behavioral factors such as the amount of purchase on credit, debt payment, and financial history that is provided from the Credit Bureau. However, this approach failed to measure customer's potential value which is one of the major factors in judging the customer's ability to pay, and hence, led to difficulties in risk management. The purpose of this case study is to present the better approach to sophisticated risk management for financial firms in Korea by reviewing S-Card's process of customer income estimation and its application to risk management.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.67-79
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• 2019

This research is to examine the factors affect technological innovation, which in turn is likely to related to financial performance in SMVs (Small-Medium Ventures). Empirical analyses showed that overseas market shares, R&D investment, firm size, firm capability, and centralization in decision-making are statistically related to technological innovation measured with patents and intellectual property. Additionally, technological innovation was found to have a positive relationship with financial performances measured with firms' assets and sale revenues. In conclusion, this research assist to devise a strategic plan for motivating technological innovation and realizing its performance in the SMVs.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.427-436
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• 2020

Prior literature shows that audit market concentration which is measured as the Herfindahl index is negatively associated with audit quality in Korea. This study analyzes whether characteristics of an audit committee have effect on the relation between audit market concentration and audit quality. This is because it is expected that effective audit committees restrict the tendency of dominant auditors to neglect their duties. The empirical results of this study using the sample firms having an audit committee listed in the Korea Composite Stock Price Index market for 2006-2015 are summarized as follows. First, consistent with prior research, audit quality decreases as audit market concentration increases. However, audit quality is not lowered as audit market concentration rises only when the audit committee has financial expertise among representative characteristics of an audit committee - independence, financial expertise, and activity. This research finds contributions in that it explores the effect of audit committee characteristics on audit quality in consideration of audit market concentration and utilizes the differential types of the characteristics of an audit committee at the same time.