• Journal of the Korea Society of Computer and Information
• /
• v.4 no.4
• /
• pp.244-250
• /
• 1999

This study aimed to analyze the effect of the change in CP class of a firm on the abnormal yield of its stock price. As a result, it was found that the change in CP class of a firm had an effect on the abnormal yield. That is. the abnormal yield rose when the class of CP rose while it dropped when the class of CP dropped. And it was analyzed that the class of CP in the firm in which its current net gain was great while it dropped in the firm in which the current net gain was small. And it was found that the CP class of the firm with the high debt to equity ratio rose when the CP class of the firm changed, whereas it rose in the firm with the low debt to equity ratio. But it was found that the size of majority shareholders equity rate in a firm, the size of corporate value of the firm, the size of cash flow of the firm and the size of the burden of financial costs of the firm were not related to the abnormal yield of its stock price. This study has its significance in analyzing the effect of the information on the change in CP class of the firm on the capital market. But it has its limitations in the sample firm and the selection of the point in time of disclosure.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.3
• /
• pp.603-613
• /
• 2015

Smart-phone Applications are consists of UI(User Interface). During using applications, UI events such as button click and scroll down are transmitted to Smart-phone system with many changes of UI. In these UI events, various information including user-input data are also involved. While Keylogging, which is a well-known user-input data acquisition technique, is needed a restrictive condition like rooting to obtain the user-input data in android environment, UI events have advantage which can be easily accessible to user-input data on user privileges. Although security solutions based keypad in several applications are applied, we demonstrate that these were exposed to vulnerability of application security and could be obtained user-input data using UI events regardless of presence of any security system. In this paper, we show the security threats related information disclosure using UI events and suggest the alternative countermeasures by showing the replay-attack example based scenarios.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.1
• /
• pp.417-425
• /
• 2020

In this paper, value relevance of the level of dependence on separate accounts in non-life-insurance companies is studied. As noted by Shim et al. (2015), the separate accounts of insurance companies consist of contracts with different attributes from the general accounts, so it is likely that firm value will vary depending on the insurer's dependence on the separate accounts. Thus, in this paper, an empirical analysis has been conducted using quarterly financial data and stock price data from domestic listed non-life-insurance companies from 2011 to 2018. The analysis shows that variables representing the level of dependence on separate accounts have a significant negative relevance to firm value. These results may suggest that changes in the proportion of a non-life-insurer's separate accounts may result in a change to its firm value under the same net assets and net income scales in aggregate accounts. This study provides management implications for the operation of separate accounts from the perspective of maximizing firm value. In addition, this study suggests that disclosure system improvement would be necessary to more directly report the operational performance of the separate accounts.

• The Journal of Industrial Distribution & Business
• /
• v.11 no.12
• /
• pp.39-45
• /
• 2020

Purpose: The stock price delay phenomenon refers to a phenomenon in which stock prices do not immediately reflect corporate information and the reflection is delayed. A prior study reported that the stock price delay phenomenon appears strongly when the quality of corporate information is low (Callen, Khan, & Lu, 2013). The purpose of the internal accounting control system is to improve the reliability of accounting information. Specifically, the more professionals such as certified public accountants are placed in the internal accounting control system, the more information is prevented from being distorted, so the occurrence of stock price delay will decrease. Research design, data and methodology: In this study, companies listed on the securities market from 2012 to 2016 were selected as a sample to analyze whether the stock price delay phenomenon is alleviated as accounting experts are assigned to the internal accounting control system. The internal control personnel data were collected in the "Internal Accounting Control System Operation Report" attached to the business report of each company of the Financial Supervisory Service's Electronic Disclosure System(DART). The measurement method of the stock price delay phenomenon was referred to the study of Hou and Moskowitz (2005). The final sample used in the study is 2,641 firm-years. Results: It was found that companies with certified accountants in the internal accounting control system alleviate the stock price delay phenomenon. This result can be interpreted as increasing the speed at which corporate information is reflected in the stock price by improving the reliability of information disclosed in the market by the placement of experts in the system. Conclusions: The results of this study suggest that accounting professionals assigned to the internal accounting control system are playing a positive role in providing high-quality information to the market. In this study, focusing on the fact that the speed at which corporate information is reflected in the stock price is very important for the stakeholders in the capital market, we find that having a certified public accountant in the internal accounting control system alleviates the stock price delay phenomenon.

• Asia-Pacific Journal of Business
• /
• v.12 no.3
• /
• pp.329-342
• /
• 2021

Purpose - The purpose of this study is to analyze the effect of investment by the National Pension Service, which has a high share as a single fund, on the dividend payout ratio. Design/methodology/approach - This study secured a share through DART of the Financial Supervisory Service and disclosure of the National Pension Service. We also used a fixed-effects model and 2SLS to analyze the data. Findings - First, it was found that there was a possibility of conflicting interests among shareholders concerning the company's dividend payment policy. Second, in the range of 3% to 4.9% of the National Pension Service shareholding, an additional increase in the holding ratio was found to have a positive (+) effect on the dividend rate. Third, after the introduction of the Stewardship Code, it was found that the increase in ownership of the fund had a positive (+) effect on the company's dividend payout ratio, regardless of the share ratio range. Moreover, the relationship between the fund ownership and the dividend payout ratio showed a clear positive relationship when free cash flow was high along investment opportunities were low. Research implications or Originality - First, This study included less than 5% of the share in the analysis. Second, We used the recent changes in fund shareholder activities. Third, We tested an instrumental variable to confirm the relationship between the National Pension Service share and the dividend ratio.

• Management & Information Systems Review
• /
• v.33 no.3
• /
• pp.167-187
• /
• 2014

While Korean GAAP had detailed regulations for the measurement and disclosure of operating income in the past, K-IFRS did not provide specific rules for operating income until 2011. Some firms that adopted K-IFRS before 2011 did not disclose or calculated operating income in an inconsistent manner although operating income is usually considered as one of the core information items to assess firm valuation. Inconsistency in firms' treatment of operating income invoked much criticism from diverse users of financial statement. The Korean Accounting Institute (KAI hereafter) revised the K-IFRS rules relevant to operating income in September 2010 in response to the voices raised by the business community, whereby the operating income number is allowed to be calculated in conformity with the previous K-GAAP. This study was motivated by the revision of K-IFRS and aims to provide a clue on the validity of such policy decision. To achieve the research objective, we test the relative value relevance of the alternative operating income numbers under K-IFRS versus K-GAAP. Our main findings are as follows. The value relevance of operating income reported before K-IFRS is proved to be higher than after K-IFRS. K-IFRS operating income adjusted to the previous K-GAAP has greater explanatory power for market values relative to one calculated under the K-IFRS regime. In an additional analysis, the sample was decomposed according to whether the operating income under K-IFRS is greater than under K-GAAP. The difference in the value relevance of K-IFRS versus K-GAAP operating income is significant only in the subsample consisting of firms which reports higher operating income under K-IFRS compared to K-GAAP. Also, the firms which would have reported negative operating income on a consecutive basis are more likely to have chosen K-IFRS, resulting in higher numbers than otherwise. It is likely that firms facing the threat of delisting due to consecutive operating loss reporting are more likely to have adopted K-IFRS disclosure rules by which they could report higher operating income numbers. To sum up, these results corroborate the limitation inherent in the K-IFRS regarding operating income disclosures. This paper suggests that the recent revision of K-IFRS implemented by KAI is likely to mitigate some of afore-mentioned limitations effectively.

• The Korean Journal of Financial Management
• /
• v.20 no.1
• /
• pp.261-277
• /
• 2003

The Korean Stock Market Surveillance System is desinged to control the volatility of stocks by drawing investor's attention and suppressing disguised demand, when stocks run up so rapidly in short period of time. Yet the Surveillance System has not been under empirical examination about its role and evolved in line with the Price Limit System. This study looks at the security returns under surveillance designation for 1995 -2001 period. The results indicate that the volatility of stocks has not been affected after surveillance designation. The constraints against the disguised demand, however, seems to limit the security returns rather than volatilities. These findings raises a question about the role of The Korean Stock Market Surveillance System for the control of volatility. The Surveillance System needs to be examined thoroughly about its role, function, and its conditions. Otherwise, the shareholders with less information could be placed at a disadvantage. This paper suggests that the system should be amended in an effort to make the volatility of stocks under control.

• The Journal of Society for e-Business Studies
• /
• v.23 no.3
• /
• pp.13-32
• /
• 2018

Btcoin has made a big progress through anonymity, decentralized authority, sharing economy, multi-ledger book-keeping, block-technology and the convenient financial vehicle. Bitcoin has the characteristics of mining and supply by decentralized suppliers, limited supply quantity and the partial money-like function as well as gold. The paper studies the hedge and safe-haven of Bitcoin and gold on daily frequency data over the period of July 20, 2010-Dec. 27, 2017 employing Asymmetric Vector GARCH. It finds that gold has the hedge and safe-haven against inflation and capital markets while Bitcoin has the weak hedge and the weak safe-haven. It shows insignificant effects of inflations of US and Korea on the volatilities of Bitcoin and gold. It also suggests the necessity of clearing of vagueness behind the anonymity for fair and transparent trade through the law application in the absence or fault in law (Lucken im Recht). following the spirit of the living constitution (lebendige gutes Recht oder Vorschrift). The relevant institutions are hoped to be given some of obligations such as registration, minimum required capital. report, disclosure, explanation, compliance and governance with autonomous corresponding rights. The study also suggests the reestablishment of the relevant financial law and taxation law. The hedge would not be successfully accomplished without the vigilant cautions of investors.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.75-79
• /
• 2023

ESG management means to thoroughly consider the investor's perspective when evaluating corporate value, and environmental, social, and governance issues are continuous and strategic monitoring issues in identifying risk and opportunity factors related to corporate management activities. In other words, the perspective of value creation is reflected in business relationships. The fundamental purpose of ESG management is continuous business value creation and thorough management of investment risks and business transactions in contractual relationships. It is also a requirement of linked investors. The field that Korean companies are currently experiencing the most is the recognition that 'ESG information collection is necessary and maintenance must be prioritized' in investor IR and global sales and marketing departments, and the primary need for this is emerging. In addition, as the legal affairs office, environmental safety department, and human resources department, which conduct compliance management, carry out related tasks, clarity at the organizational level must precede in order to properly establish an information integration and management system. It covers the scope of securing new market opportunities such as management, disclosure and communication. Therefore, in regard to the newly emerging ESG management and response methods, it is necessary to review and implement it repeatedly so that sustainable exchange profits can be created by simultaneously managing non-financial risks as well as efforts to enhance corporate value for financial returns.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.83-102
• /
• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.