• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.47-58
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• 2011

Halogen free intumescent flame retardants(IFRS), such as the mixture of melamine phosphate(MP) and char forming agents(pentaerythritol(PER), di-pentaerythritol(DiPER), tris(2-hydroxyethyl) isocyanurate(THEIC)), were prepared and characterized. Polypropylene(PP)/$IFR_S$ composites were also prepared in the presence of ethylene diamine phosphate(EDAP) as a synergist and used into flame retardant PP powder coatings. Thermoplastic PP powder coatings at 20 wt% flame retardant loading were manufactured by extruded and then mechanical cryogenic crushed to bring them in fine powder form. These intumescent flame retardant powder coatings($IFRPC_S$) were applied on mild steel surface for the purpose of protection and decorative. It is a process in which a $IFRPC_S$ particles coming in contact with the preheated mild steel surface melt and form a thin coating layer. The obtained MP flame retardant was analyzed by utilizing FTIR, solid-state $^{31}P$ NMR, ICP, EA and PSA. The mechanical properties as tensile strength, melt flow index(MFI) and the thermal property as TGA/DTA and the fire safety characteristics as limiting oxygen index(LOI), UL94 test, SEM were used to investigate the effect of $IFRPC_S$. The experimental results show that the presence of $IFR_S$ considerably enhanced the fire retardant performances as evidenced by the increase of LOI values 17.3 vol% and 32.6 vol% for original PP and $IFRPC_S$-3(PP/MP-DiPER/EDAP), respectively, and a reduction in total flaming combustion time(under 15 sec) in UL94 test of $IFRPC_S$. The prepared $IFRPC_S$-3 have good comprehensive properties with fire retardancy 3.2 mm UL94 V-0 level, LOI value 32.6%, tensile strength $247.3kg/cm^2$, surface roughness Ra $0.78{\mu}m$, showing a better application prospect. Through $IFRPC_S$-2(PP/MP-PER/EDAP) and $IFRPC_S$-3 a better flame retardancy than that of the $IFRPC_S$-1(PP/MP/EDAP) was investigated which was responsible for the formed more dense and compact char layer, improved synergy effect of MP and PER/DiPER.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.231-237
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• 2017

This study examines the relationship between voluntary information disclosure and the effective tax rate using Investor Relation (IR) as the proxy for the level of the firm's voluntary information disclosure, and effective corporate tax rate as the proxy for the level of tax avoidance. This study considers sample data from 1,396 firms listed on the Korea Composite Stock Price Index (KOSPI) from 2011-2014. The results of this study are as follows: Investor Relation (IR) had a positive correlation with effective corporate tax rate. This result got on with the result of additional analysis using extra measurement of effective corporate tax rate. According to these results, we expect that firms featuring greater voluntary information disclosure report enhanced business performance. This study contributes understanding how Investor Relation (IR) affects tax avoidance. We hope that this study can promote the development of capital markets and provide good news to investors for firms that have greater information disclosure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.699-711
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• 2011

This study is to perform several major analyses to find any differences in the leverage between the pre- and post-period of the currency crisis. Moreover, another aspect is to investigate a financial aspect which has received relatively little attention to the firms and/or industries in the emerging capital markets in comparison to those in the advanced markets. The purpose of this empirical study is to confirm whether or not, it is myth or reality that Korean business conglomerate, chaebol, firms with subsidized financing from government-owned domestic financial institutions in the pre-financial turmoil, may still maintain their higher leverage, even after the crisis. It was found that firms belonging to the chaebol in Korea maintained higher average book-value and market-value based debt ratios, relative to their counterparts not belonging to the chaebol across all of the tested models. There were positive relationships of IND3(=the chemical industry) and Ind5(=the construction industry) to the book-value leverage. This study identified that there were no differences in the explanatory variables included, between the tested models (that is, without and with including the present value of an operating lease) related to each debt ratio. Since the Korean government continue to improve the corporate governance of the domestic firms in terms of accounting transparency and corporate ownership, it would be more efficient, if utilizing this "new" ratio considering an operating lease as an effective measurement of the level of leverage. In terms of the capital structure, it may also be possible for foreign firms to utilize and benefit from the results obtained in this study when operating their new businesses in Korea, given the economic circumstances such as the ongoing progress of the Korea-America FTA or the Korea-China FTA.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.125-139
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• 2021

This study identifies the trend of Korean accounting researches on profit adjustment. We analyzed the abstract of accounting research articles published in Korean Citation Index (KCI) by using text mining technique. Among papers whose themes were profit adjustment, topics were divided into 4 parts: (i) Auditing and audit reports, (ii) corporate taxes and debt ratios, (iii) general management strategy of companies, and (iv) financial statements and accounting principles. Unlike the prediction that financial statements and accounting principles would be the main topic, auditing was analyzed as the most studied area. We analyzed topic trends based on the number of papers by topic, and could figure out the impact of K-IFRS introduction on profit adjustment research. By using Big Data method, this study enabled the division of research themes that have not been available in the past studies. This study enables the policy makers and business managers to learn about additional considerations in addition to accounting principles related to profit adjustment.

• The Korean Journal of Air & Space Law and Policy
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• v.25 no.2
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• pp.163-199
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• 2010

A frequent flyer program is a loyalty program offered by many airlines. Typically, airline customers enrolled in the program accumulate frequent flyer miles corresponding to the distance flown on that airline or its partners. There are other ways to accumulate miles. In recent years, more miles were awarded for using co-branded credit and debit cards than for air travel. Acquired miles can be redeemed for free air travel; for other goods or services, such as travel class upgrades, airport lounge access or priority bookings. The first modern frequent flyer program was created Texas International Airlines in 1979. This program was also adopted in Korean Air in 1984. Since then, the mileage programs have grown enormously. As of June 2009, the total member of two national airlines in Korea had been over thirty million. However, accumulated miles could be burden of airlines, because the korean corporations should record the annual financial report the accumulate mileage on a liability account by 'the international financial report standards(IFRS)' next year. The korean airlines need to minimize the accumulated miles, so that for instance Korean Airlines SKYPASS-miles expire 5 years after being earned. It means that miles earned on or after July 2008 will expire after five years if unredeemed. Thus, this paper attempt to analyze the unfairness of the mileage rules of korean airlines by examining a specific portion of the conditions relating to consumer protection, because many mileage users has difficulties using mileage programs and complained the amendment of the mileage rules. In conclusion, the contemporary mileage rules in Korea are rather unsatisfactory, because airlines is not only recognizing a mileage into a kind of benefit but also denying inheritance of mileage and the legal nature of mileage as a property right. It is necessary to amend relevant mileage rules in view of consumer protection, because air mileage is not simple benefit but a right of mileage user.

• 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.