• 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 Distribution Science
• /
• v.14 no.6
• /
• pp.75-80
• /
• 2016

Purpose - A growing demand for sustainability reporting has placed pressure on firms with non-financial information that affects firm valuation, growth, and development. In particular, a number of researchers have investigated various topics in Corporate Social Responsibility (CSR), non-financial information. Prior studies suggest that CSR may affect corporate outcomes like corporate reporting, financial performance, and disclosures. However, the results from prior studies are not clear whether CSR affects corporate outcomes. This is partially due to the measurement issues with CSR. In this study, we examine whether CSR affects the quality of corporate reporting, one of the popular measures in corporate outcomes. We find an evidence that CSR positively affects the quality of corporate reporting. Research design, data, and methodology - In this study, we collected a unique dataset of CSR from MSCI. Total 169 firms listed in the Korean Stock Exchange from 2011 to 2014 were collected and analysed with the detailed CSR reports. Using a correlation test, we found a weak association between CSR and the quality of corporate reporting. However, the regression tests provided a strong relationship between CSR and the quality of corporate reporting after controlling for other variables that may affect the quality of corporate reporting. Additionally, we calculated the t-statistics based on heteroskedaticity-consistent standard errors (White, 1980). Results - Before we run the regression test, we sort the measures of the two dependent variables into each rating of CSR (from AAA to CCC). The results indicate that the quality of corporate reporting measured by discretionary accruals and performance-matched discretionary accruals monotonically decrease as the CSR ratings increase. This supports our hypothesis. In the regression tests, the coefficient on MJDA (PMDA) is -0.183 (-0.173) and significant at the 5% level. We can interpret the results as CSR affecting the quality of corporate reporting in positive ways. Other coefficients on control variables are consistent with prior studies. For example, the coefficients on both LOSS and LEV are positive and significant at conventional level, meaning that firms with financial difficulty may harm their quality of corporate reporting. Conclusion - We found an evidence that CSR is positively associated with the quality of corporate reporting. This study contributes to the literature in various ways. First, this study extends the line of CSR research by providing additional evidence in the setting of ethical behaviors by managements. This is consistent with the hypothesis and supports the results of prior studies. Second, to the best of my knowledge, this is the first study using the MSCI CSR ratings. In contrast with prior studies using different measures of CSR, the MSCI CSR ratings allow us to provide in-depth analysis. Third, the additional measure of dependent variable (PMDA) allows us to improve the robustness of our results. Overall, the results provided this study to extend the findings in prior studies by providing incremental evidence.

• 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.