• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.207-224
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• 2015

The purpose of this study is to review the various valuation techniques of intangible assets. The value of intangible asset by the income approach can be measured as the present value of the economic benefit over the intangible asset's remaining useful life. The typical methods used in intangible asset economic income projections include extrapolation method, life cycle analyses, sensitivity analyses, simulation analyses, judgment method, and tabula rasa method. There are several methods available for estimating capitalization rates and discount rates for intangible asset, in which we have discussed market extraction method, capital asset pricing model, built-up method, discounted cash flow model, and weighted average cost of capital method. As the capitalization methods for intangible asset, relief-from-royalty method, excess earnings capitalization method, profit split method, residual from business enterprise method, postulated loss of income method and so on have been reviewed.

• The Journal of Fisheries Business Administration
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• v.37 no.1 s.70
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• pp.25-44
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• 2006

Under the provision of Article 63 of the Enforcement Regulation of the Act on Acquisition and Compensation of Land and Others for Public Project that is recently enacted and implemented (hereinafter referred to as the 'Lend Compensation Act') the compensation is required to make 'When the Actual Damage Amount' is confirmed for the damage in fishery affairs that is outside of the public project area. The compensation for fishery business on the indirect damage area has been excluded from the advance compensation subject to conflict with the existing laws on fishery business compensation with the controversy in method, procedure, time and others to confirm the actual damage amount, and it lacks the standard of calculation for detailed compensation on partial damages outside of business implementation area, which caused the ceaseless conflicts and straggles between the project implementation party and the victimized fishermen regarding the calculation method of damages, standard, compensation period and others. In particular, from the numerous problems in damage compensation in fishery on the indirect damage area, the most recent problem emerged is the issue on application method of damage period in calculating the damage compensation amount that the struggle has been deepened with the differences between the project implementation party and the victimized fishermen without the stipulation on the compensation, that caused the difficulties in carrying out the public project and other serious social problems. In this study, the reasonable application method for the damage period and the calculation plan of the damage amount for calculating the damages on fishery industry outside of the public project implementation zone that is not fully specified under the Land Compensation Act, and the indirect damage area is not influenced for the notification of project recognition, and the compensation to undertake with the damage in the fishery industry in project implementation area to have the nature of damage compensation, the right to engage in fishery industry has the perpetual nature of rights, the fishery damage compensation system of Japan also recognizes the perpetual right on fishery industry to calculate the compensation amount, and the compensation for damage amount has been exercised for the period of actual damage occurrence period regardless of remaining effective period for most of fishery permit and license for fishery compensation outside of the project implementation area following the recent various public projects as well as the development process of theory on fishery loss compensation that the calculation of damage amount on the fishery industry outside of the project implementation zone would be prudent to compensate by calculating the applicable damages during the period of actual damages, and by doing so, the 'just compensation' guaranteed under the Constitution may be materialized. Therefore, the calculation of the damages from the implementation of the public project shall consider the actual period of damages and the degree of damage from the public project to calculate by the income capitalization method, however, considering the equitable consideration with the compensation following the cancellation, it shall not exceed the compensation following the termination of the applicable fishery businesses. Furthermore, the calculation method of partial damage amount on the fishery business following the project implementation shall apply, depending on the period of damage occurrence, by (1) the case of calculating the future damage amount at the present time, and (2) calculating the damage from the past to the present time as well as the damage to be incurred later, by selecting the calculation method for damages following the damage occurrence type.

• 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.