• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.247-262
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• 2022

Purpose - The purpose of this study is to examine the impact of the Pandamic Declaration on 'untact firms' listed in KOSPI and KOSDAQ market in order to verify Investor Mania Hypothesis. Design/methodology/approach - This study collected financial data for 44 untact firms in KOSPI and KOSDAQ market. Then, we employed ESM(Event Study Methodology), EGARCH model and DID(Difference-In-Difference) for analysis. Findings - First, in contrast with the benchmarking index, KOSPI 200 which shows a negative (-) abnormal return trend, the untact firms have positive abnormal return trend consistently. Second, after the Pandemic Declaration, the variability of abnormal return for the untact firms is found to be significantly positive. Third, we find that the cumulative abnormal return and volatility of the untact firms significantly increase after the Pandemic Declaration. Research implications or Originality - Based on the Investor Mania Hypothesis, we confirm that the market potential of untact firms after the Pandemic Declaration is observed when compared with the KOSPI 200.

• Environmental and Resource Economics Review
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• v.29 no.2
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• pp.219-246
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• 2020

From the Phase II (2018~2020) of K-ETS, the offset credit from 'CDM projects that domestic companies and others have carried out in foreign countries' can be used in the K-ETS. As a result, stakeholders in the K-ETS market are actively developing overseas CDM projects, such as the 'high-efficiency cook stove project'. which can secure a large amount of credits while marginal cost is relatively low. This paper develops the investment decision-making model of offset project for the 'high-efficiency cook stove project' using the real option approach. Under the uncertainty of the emission allowance price, the optimal investment threshold (p^⁎) is derived and sensitivity analysis is conducted. As a result, in the standard scenario (PoA-S), the optimal investment threshold is 29,054won/ton, which is lower than the stock price (p^⁎_spot). However, allocation entities are not only economics in the CDM project, but also CDM risk factors such as non-renewable biomass ratio, cook stove replacement ratio, equity ratio with host country, investment period and submission limitation of emission allowance. In addition, offset project developers will be able to derive the optimal investment threshold for each business stage and use it for economic feasibility checks.

• Management & Information Systems Review
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• v.37 no.2
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• pp.259-281
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• 2018

Advocates of mandatory IFRS adoption claim that IFRS increase financial statement comparability, which in turn leads to greater cross-border investment(Securities and Exchange Commision, 2008). The notion is that improved financial statement comparability reduces the information acquisition costs of global investors and thereby increase their investment in foreign firms. The purpose of this study is to examine this assertion by examining whether the K-IFRS adoption rusults in improved comparability that leads to increased investment by foreign investment. We also examined whether the relation between comparability and foreign investment has strengthen after adoption of K-IFRS. To achieve the purpose of our study, we measure Korean firms comparability using stock price model, stock return model and cash flow from operation model by Barth et al.(2012). We use both foreign ownership in the end of year and average during the year for dependent variables were to reduce bias. We test our hypothesis using 1,817 firm-year observation of KOSPI firms during the period of our analysis, 2011-2015. Consistent with our hypothesis, we find K-IFRS adoption results in a greater increase in foreign investment in firms with high comparability firms. This result indicate that the adoption of K-IFRS intends to achieve the international accounting convergence as stated in the roadmap and to reduce the Korea Discount.

• Environmental and Resource Economics Review
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• v.27 no.1
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• pp.67-104
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• 2018

In this paper, we compare the spread effects of the carbon tax imposition method using the real business cycle model considering the productivity and energy price shocks. Scenario 1 sets the carbon tax rate that encourages the representative firm to maintain a constant $CO_2$ reduction ratio in accordance with its green house gas reduction targets for each period. Scenario 2 sets the method of imposing the steady state value of the carbon tax rate of Scenario 1 during the analysis period. The impulse response analysis shows that the responses of $CO_2$ emissions to external shocks are relatively sensitive in scenario 2. And simulation results show that the cost of $CO_2$ abatement is more volatile in scenario 1, and $CO_2$ emissions and $CO_2$ stock are more volatile in scenario 2. In particular, the percentage changes in volatility between the two scenarios of $CO_2$ emissions and $CO_2$ stock increase as the green house gas reduction target is harder. When the green house gas reduction target is 60% and over, the percentage changes(absolute value) between the two scenarios exceed the percentage change(absolute value) of the $CO_2$ reduction cost between them.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.269-283
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• 2023

Based on daily data from January 4, 2016 to September 27, 2022, the impact of extreme movements of BDI on shipping companies' network connectivity was analyzed using CoVaR network connectivity. The main results and policy implications are as follows. First, according to the copula model results, the Student-t copula was selected as the most suitable model for COSCO, HMM, HRAG, MAERSK, and WAN. EVER was selected as a time-varying Gumbel copula, and YANG was selected as a time-varying rotated-Gumbel copula. Second, as a result of analysis using the TVP-VAR model, the linkage between shipping companies tended to increase when the BDI turned into an extreme risk state. In the comparison of net connectivity, the roles of COSCO and EVER changed. In addition, in the analysis of net pairwise connectivity, it was found that the change in the extreme risk state of BDI also affected the connectivity of shipping companies. In particular, EVER, WAN, and COSCO showed large changes. Taken together, the extreme fluctuations in BDI changed the role of Asian shipping companies, intensifying competition among shipping companies and strengthening risk delivery. It was confirmed that BDI has a great influence on the network connectivity of shipping companies and has an important influence on the stability of the stock market network. Therefore, the results of this study should consider not only the connectivity of shipping companies according to market conditions, but also the connectivity in extreme situations.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1049-1060
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• 2009

The expansion of volatility in Korean Stock Market made it more difficult for the individual to invest directly and increased the weight of indirect investment through a fund. The purpose of this study is to construct the EIF(enhanced index fund) model achieves an excessive return among several types of fund. For this purpose, this paper propose portfolio optimization model to manage an index fund by using GA(genetic algorithm), and apply the trading amount and the closing price of standard index to earn an excessive return add to index fund return. The result of the empirical analysis of this study suggested that the proposed model is well represented the trend of KOSPI 200 and the new investment strategies using this can make higher returns than Buy-and-Hold strategy by an index fund, if an appropriate number of stocks included.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4354-4358
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• 2011

Many Korean construction companies have fallen on hard times because the construction business continues to stagnate. Therefore it is necessary to measure the management efficiency for efficient operation and strengthening competitiveness of them in order to survive a difficult situation. This paper proposes a combined ANP and DEA model to analyze the efficiency of the listed construction firms. In order to determine the input and output variables of DEA, the ANP model is applied to evaluate the importance of input and output variables. The benchmarking companies and efficiency value for the construction firms with inefficiency are also provided to improve the their efficiency. The 57 listed construction companies consisted of 36 listed on KOSPI and 21 listed on KOSDAQ are analyzed in this study. The analysis results show that 11 companies whose values of CCR are 1, and 14 firms whose values of BCC efficiency are 1. In additions, the 19 firms have the scalability efficiency. Finally, we test the correlation between efficiency and the stock price.

• KDI Journal of Economic Policy
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• v.32 no.4
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• pp.35-73
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• 2010

This paper examines consumption dynamics in relation to asset prices in Korea. Empirical analysis based on the error correction model shows that personal consumption is affected by changes in asset prices but the consumption converges to the long-run level of consumption corresponding to the total income flow in two years. This adjustment in consumption implies that the consumption error, reflected in the error correction term, should have predictability for the future consumption growth during the adjustment period. It is found that the error correction term has a long-run predictability for consumption over up to about 3 years; thus, confirming the error correction model. It is also found that housing prices have larger effects on consumption compared with stock prices in Korea. In addition, the effects of income and asset prices on consumption show bigger effects during contractionary period than expansionary period in business cycles. This paper also analyzes effects of asset wealth that reflects changes in both price and quantity. It is found that asset wealth has a long-run effect on consumption in addition to total income as determinants of consumption. Since wealth effects usually indicate the long-run effect of changes in asset wealth on consumption that is not explained by labor income, which is the proxy for human source of wealth, it is estimated with labor income used as a control variable. According to the estimation, the marginal propencity to consume out of asset wealth is approximately 2%. It means that 1,000won increase in asset wealth may lead to 20 won increase in consumption.

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

• Journal of Distribution Science
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• v.16 no.2
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• pp.83-88
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• 2018

Purpose - For distribution corporate, the method of recognizing sales revenue may be different depending on the type of distribution transaction. Until the change in accounting standards for revenue recognition was made in 2002, the distribution corporate recognized the full amount of sales of goods regardless of the type of transaction. However, in accordance with accounting standards for revenue recognition, which began to be applied in 2003, distribution corporate differ in sales revenue recognition by transaction type. The Purpose of this study is to analyze the impact of sales revenue on the corporate value after the change of the revenue recognition accounting standards. Research design, data, and methodology - We selected a comprehensive wholesale and retail corporate listed on Korea Exchange. The research model extends the Ohlson(1995) model and regresses whether sales revenue affecting the corporate value is discriminatory value relevance between the corporate affected by changes in accounting standards for revenue recognition and those not. Results - The results of the analysis are as follows. First, The average value of stock price, net asset per share, and earnings per share are all higher than those before the change of accounting standards for revenue recognition. However, the average value of sales per share is lower than that before the change of accounting standards for revenue recognition. Second, the relationship between corporate value and net asset per share, earnings per share and sales per share, the coefficient of net asset per share, earnings per share and sales per share are all statistically significant positive value. Therefore, in explaining corporate value, besides net asset per share and earnings per share, sales per share provides additional information. And the coefficient of interaction variable between accounting standard change and sales per share is a statistically significant positive value. This result indicating that after the change of the revenue recognition accounting standards the usefulness of sales revenue has increased. Conclusions - The change in accounting standards for revenue recognition led to a decrease in distribution corporate sales revenue but the higher the relevance of the corporate value of the sales revenue information. These results shows that the change of accounting standards that reflects the transaction type of retailers was a revision to increase the value relevance of sales revenue in valuation of corporate value.