• Journal of Industrial Convergence
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• v.20 no.12
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• pp.59-70
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• 2022

This study investigates the sensitivity reflected in the accounting earnings differs according to the difference in the characteristics of accounting information such as profit and loss for the same market information. For this, market information and accounting data were analyzed for 11,462 non-financial listed companies listed on the KOSPI and KOSDAQ markets from 2012 to 2020 by using Basu's measurement of conditional conservatism and Ball and Shivakumar's measurement of conservatism. Accounting earnings sensitivity was analyzed according to the combination of information. As a result of the study, it was confirmed that both earnings and losses corporates recognize losses with delay, while losses are recognized quickly by loss corporates and delayed recognition by earnings companies. It was confirmed that more strict conservatism was applied to the losses corporates compared to the earnings corporates by delaying the recognition of earnings while the early recognition of the losses. It provides empirical data on the causality between the asymmetric timeliness and the combined effect of market information and accounting information by verifying that the losses corporates responds sensitively to market information while the earnings corporates does not react sensitively to the market information.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.1-10
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• 2022

The purpose of this study is to establish a foundation for IoT-related industries to secure financial soundness and to dominate the global market after COVID-19. Through this study, the quantitative management status of IoT-related companies was checked. It also was attempted to preemptively prepare for corporate insolvency by examining the relationship between financial ratios in accordance with stock price fluctuations and designation of management items. This study selected 502 companies that were listed on the KOSPI and KOSDAQ in the stock market from 2019 to 2020. For statistical analysis, multiple regression analysis, difference analysis and logistic regression analysis were performed. The research results are as follows. First, it was found that the impact of IoT company accounting information on stock prices differs depending on before and after COVID-19. Second, it was found that there is a difference in the closing stock prices of IoT companies before and after COVID-19. Third, it was found that financial ratios according to stock price fluctuations exist differently after COVID-19. Fourth, it was found that the financial ratios according to the designation of management items after COVID-19 exist differently. Through these studies, some suggestions were made to secure the financial soundness of IoT companies and to lay the groundwork for leaping into the global market after COVID-19. Through the results of this study, it is expected that it will lead the growth of IoT companies and contribute to growth as a decacorn company of the future that can guarantee financial soundness in the changing financial market.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.149-161
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• 2017

This paper analyzed the impact of alliances on the market value of the 106 bio-pharmaceutical companies listed on the KOSPI or KOSDAQ in Korea by using the 'Event study methodology'. Although general alliances did not impact the corporate value significantly, in the analysis corresponding to the alliance type, R&D alliances created positive value, as technology acts as an important factor for the alliance. Among the R&D alliances, 'Technology Transfer alliances', in particular 'Development Technology Transfer alliances', had a positive influence on the corporate value. We interpret these differentiated results as market tends to screen for types of alliances. Meanwhile, we confirmed that the possibility of a stock price increase before the alliance announcement is high by analyzing the impact of the timing of corporate alliance announcements on the company value. It can be inferred that the possibility of information leakage is high. This paper analyzes the impact of alliances for managers and practitioners seeking to create value for domestic bio-pharmaceutical companies, and suggests the need to prevent information leakages by establishing a suitable policy.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.19-26
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• 2020

This study analyzed the status and trend of gender wage gap among listed companies in KOPSI market over the 2000-2017 period. The main results of the study are as follows. First, the gender employment gap index for KOSPI listed companies stood at 39.81 in 2017, with 40 women per 100 men being employed. Although the absolute value of the proportion of female employment remains low, it has not only been higher than 33.74 in 2000 but has also increased steadily in recent years. In terms of the number of years of service, the average number of male employees in 2017 was 9.9 years, compared with 6.9 years for female employees, and the gender tenure gap decreased over the past few years. Finally, The gender wage gap index increased from 60.57 in 2000 to 67.87 in 2017. In addition, there are slight variations in the size of the company or industry, but consistent results have shown that the gender wage gap decreases in recent years. The findings suggest that our society is developing in a way that reduces the gender employment gap and the gender wage gap.

• Industry Promotion Research
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• v.9 no.2
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• pp.29-35
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• 2024

Recently, the Korean financial market has implemented a ban on unleveraged short selling, and leveraged short selling, which involves selling borrowed securities, is called general short selling. This study sought to come up with improvement measures to revitalize short selling from the perspective of individual investors. Short selling refers to selling stocks you do not own in the stock market, predicting that the stock price of the stock will fall, and borrowing stocks to sell them. Based on the results of this study, the short selling market's growth and improvement plans are as follows. First, a plan must be developed to expand short selling opportunities for individual investors. In the domestic short selling market, including KOSPI and KOSDAQ, foreign and institutional participants account for more than 95% of the market, and individual investors are very small. Therefore, its expansion is inevitable. Second, monitoring and punishment for unfair short selling transactions must be strengthened. Representative improvement measures that can minimize the side effects of short selling include strengthening monitoring of unfair trading and short selling, and raising the level of punishment. In addition, measures must be taken to further increase the level of punishment for short selling related to unfair transactions. Third, the short selling reporting and disclosure system needs to be improved. In the case of Korea, short selling transactions are not yet as active as in developed countries, but there is a need to expand the disclosure system to strengthen market transparency in preparation for future short selling transactions becoming more active. In conclusion, it is reported that if short selling regulations are excessively strengthened, losses may occur in terms of price efficiency and market liquidity, which may ultimately have a negative impact on the market. Therefore, policies related to short selling must be made while taking into account the positive aspects of regulatory effects and the negative impact on the market.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• The Korean Journal of Financial Management
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• v.10 no.1
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• pp.249-280
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• 1993

Recent financial theory views insurance policies as financial instruments that are traded in markets and whose prices reflect the forces of supply and demand. This article analyzes individual's insurance purchasing behavior along with capital market investment activities, which will provide a more realistic look at the tradeoff between insurance and investment in the individual's budget constraint. It is shown that the financial economic concept of insurance cost should reflect the opportunity cost of insurance premium. The author demonstrates the importance of riskless and risky financial assets in reaching an equilibrium insurance premium. In addition, the paper also investigates how the investment income could affect the four established theorems on traditional insurance literature. At the present time in Korea, the price deregulation is being debated as the most important current issue in insurance industry. In view of the results of this paper, insurance companies should recognize investment income in pricing their coverage if insurance prices are deregulated. Otherwise. price competition may force insurance companies to restrict coverage or to leave the market.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.