• International Area Studies Review
• /
• v.12 no.2
• /
• pp.149-173
• /
• 2008

This paper empirically studies the relationship between R&D expenditures and firms value. First, we can conjecture that R&D expenditures are enhancing the firms value. Such findings depend on an existing research, which R&D expenditures are intangible asset rather than expenses. Although, under U.S. accounting standards, financial statements do not report intangible assets but costs. Second, we can conjecture that short-term, the rate of increase in R&D expenditures had negative influence on firms valuation, because such findings indicates that R&D spending of costs incur mis-pricing. But long-term, consistently R&D expenditures may attract investors on the stock market. Third, lately firms focus on capital efficiency management, such a firms R&D expenditures incur high ROE. Generally investors put too much confidence in capital efficiency management and high ROE may attract investors on the stock market. Finally, High-Tech through the R&D investment improve firms competitive advantage, by competitive advantage, firms have reduced cost and raised productivity in the end improve firms value.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.4
• /
• pp.26-39
• /
• 2016

Korea's main manufacturing industries, which have led its economy for the past three decades, are faced with a serious downturn and loss of competitive advantages due to the current economic depression, China's rise, and the drop of oil prices. Korean business firms must adopt the paradigm shift in their value strategies, along with a government-led industrial restructuring in order to gain sustainable competitive advantages. Business firms allocate their limited resources between value creation and value appropriation, however, what effect does strategic emphasis on value creation versus value appropriation have on a business firm's financial performance? This paper empirically addresses this issue by examining the effect of shifts in strategic emphasis on stock return. Furthermore, this study examines appropriate choices of strategic emphasis to gain differential financial performance. The data set used in this regression analysis comes from the KISLINE database of NICE Information Service. The variables that form the basis of this analysis are stock return, ROA, and Strategic Emphasis [(advertising expenditures-R&D expenditures)/assets]. The interactive effect with situational factors regarding the firm and the type of technological environment in which the firm is operating was also analyzed. Our results show that investors acknowledge a shift of strategic emphasis as a sign of stock valuation. In comparison to US, Korean business firms have weak value creation capabilities in high-technology industries, and weak value appropriation capabilities in low-technology industries. This proves Korean firms are fast followers in the global market. Our findings suggest that Korean firms have to adopt a balanced value strategy, nurturing value creation and developing value appropriation for overcoming the current economic downturn and becoming a first mover in the dawn of "Industry 4.0."

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.459-466
• /
• 2013

Risk exists in all construction projects and resides among the collection of subcontractors and their array of individual activities. Wherever risk resides, the interrelation of participants to one another becomes paramount for the way in which risk is measured. Inherent risk becomes recognizable and quantifiable within network schedules in the form of consuming float - the flexibility to absorb delays. Allocating, owning, valuing, and expending such float in network schedules has been debated since the inception of the critical path method itself. This research investigates the foundational element of a three-part approach that examines how float can be traded as a commodity, a concept whose promise remains unfulfilled for lack of a holistic approach. The Capital Asset Pricing Model (CAPM) of financial portfolio theory, which describes the relationship between risk and expected return of individual stocks, is explored as an analogy to quantify the inherent risk of the participants in construction projects. The inherent relationship between them and their impact on overall schedule performance, defined as schedule risk -the likelihood of failing to meet schedule plans and the effect of such failure, is matched with the use of CAPM's beta component - the risk correlation measure of an individual stock to that of the entire market - to determine parallels with respect to the inner workings and risks represented by each entity or activity within a schedule. This correlation is the initial theoretical extension that is required to identify where risk resides within construction projects, allocate and commoditize it, and achieve actual tradability.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.3
• /
• pp.649-664
• /
• 2015

Recently, the risk of security incidents has been increased due to change of IT environment and development of new hacking methods. Event study methodology that measures the effect of a specific security incident on the stock price is widely adopted to analyze the damage cost of security incidents on market value. However, analysis of company's temporary stock price change is limited to immediate practical implication, and reputation loss should be considered as a collateral damage caused by security incidents. We analyzed 52 security incidents of listed Korean companies in the last decade; by refining the criteria presented by Tobin's q, we quantitatively showed that the companies has significantly higher reputation loss due to security loss than the other companies. Our research findings can be used in order that the companies can efficiently allocate its resource and investment for information security.

• Asia-Pacific Journal of Business
• /
• v.14 no.4
• /
• pp.233-254
• /
• 2023

Purpose - This study aims to verify whether the effect of tax avoidance on corporate value is non-linear in the Korean financial markets. Design/methodology/approach - This study believes that the cause of the inconsistent empirical analysis results of previous studies that verified the relationship between tax avoidance and firm value may be an error in assuming linearity, and verifies whether a nonlinear relationship exists. The sample company in this study is a December settlement corporation listed on the Korean stock market, and the analysis period is from 2000 to 2021. In the empirical analysis model, Tobin's Q is used as a proxy for corporate value, tax avoidance is used as the main independent variable, and a regression model is designed with corporate size, growth rate, and debt ratio set as control variables. Findings - As a result of the empirical analysis, it can be confirmed that there is an inverted U-shaped nonlinear relationship between tax avoidance and corporate value. In the additional analysis using Ohlson (1995) firm valuation model for the robustness of the results of the empirical analysis, the same nonlinear value relationship between tax avoidance can be confirmed. Research implications or Originality - This study is considered to be meaningful in that it verifies the non-linear relationship of tax avoidance, which has not been attempted in previous studies. The meaning of the inverted U-shaped nonlinear relationship presented in this study is that corporate tax avoidance acts as a factor that increases corporate value up to a certain level, but rather becomes a factor that decreases corporate value when it exceeds a critical point. These results are expected to provide new perspectives and perspectives on tax avoidance to companies belonging to the Korean capital market.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.2
• /
• pp.463-474
• /
• 2016

As IT environment has changed, paths of information security in financial environment which is based on IT have become more diverse and damage caused by information leakage has been more serious. Among security incidents, personal information leakage incident is liable to give the greatest damage. Personal information leakage incident is more serious than any other types of information leakage incidents in that it may lead to secondary damage. The purpose of this study is to find how much personal information leakage incident influences corporate value by analyzing 21 cases of personal information leakage incident for the last 15 years 1,899 listing firm through case research method and inferring investors' response of to personal information leakage incident surveying a change in transaction before and after personal information leakage incident. This study made a quantitative analysis of what influence personal information leakage incident has on outcome of investment by types of investors by classifying types of investors into foreign investors, private investors and institutional investors. This study is significant in that it helps improve awareness of importance of personal information security by providing data that personal information leakage incident can have a significant influence on outcome of investment as well as corporate value in Korea stock market.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.243-249
• /
• 2020

This study analyzes the earnings management that can occur in the process of public offering in the process of SMEs reducing cost of capital, risks and seeking opportunities for direct financing. Since a company is subject to strict supervision during the IPO process, it is possible to prevent the phenomenon that the company value evaluated in the market is underestimated, or to perform earnings management in consideration of overestimation. This study attempted to verify the degree of earnings management through discretionary accruals and actual earnings management values that can affect the earnings ratio of the IPO of a company. For this study, total accruals were calculated and analyzed through discretionary accruals, sales, costs, and actual earnings management adjustments from production activities. As a result of the analysis, discretionary accruals, which are the countermeasures for earnings management during the listing process, have a positive(+) relationship in both the stock price return and the sales adjustment value, which can be viewed as a factor that induces high valuation. As a result of this, there may be a risk of adverse selection for the benefit amount, and information asymmetry may exist for public offering stocks. This study can provide useful guidelines for evaluating corporate value to domestic SMEs and investors that do business with Chinese companies as well as China through the current and type of earnings management of Chinese listed companies.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.105-129
• /
• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.