• The Journal of Economics, Marketing and Management
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• v.9 no.1
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• pp.25-40
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• 2021

Purpose: The forecasting is the likelihood scientifically proved judgment about the prospects, the possible conditions of this or that phenomenon in the future and (or) about the alternative ways and the means of their realization. To adapt the instruments of budgeting for the analysis cash flow of company. Research design, data and methodology: The creates the budget of cash flow were carried out on the basis of data of the report for the 2017 of corporations POSCO and in the first half of the 2018 Daewoo Shipbuilding & Marine Engineering of South Korea. Results: The simultaneous use of budgeting techniques and the simple financial analysis allows to systematize the transactions, to identify the main problem areas in the movement cash flows. Therefore, working capital analysis is to determine the limits of their fluctuations in view of the changes in the business processes. Conclusions: In the pedagogical context solved the features of budgeting in the part evaluation current assets, its financing, its elements: the cash, the debtor. In the process of budgeting of cash flow, in credit budget, in financial budget we can see the main indicators: the current assets, the functioning capital, the optimum number of debtors, the optimum amount of cash and another.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.323-336
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• 2021

The present study aims to examine the impact of real earnings management on the future value of the firm and its persistence. The study also tests suspect firm effects on the relationship between real earnings management and the future value of the firm. The sample of the present study consists of all listed non-financial firms from the year 2011 to 2018. Real earnings management has been measured in three alternative ways viz., abnormal operating cash flows, abnormal discretionary spending, and abnormal production cost. Tobin's Q is used as a measure of firm value. The interaction term of real earnings management and Tobin's Q is used to test firm value persistence. The results of the analysis disclose that out of three measures of real earnings management, abnormal reduction in discretionary spending only has a significant negative impact on the persistence of firm value. Moreover, the suspect firm analysis reveals that when the underlying motive of real earnings management is to meet zero earnings, both abnormal increases in operating cash flows and abnormal reduction in discretionary spending have a significant negative impact on firm value persistence.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.67-72
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• 1989

This paper concerns with the decision maker has the job of forecasting capital investments and operating expenses to aid the decision making in choosing and evaluating present annual and future alternatives. The cost estimating function eventually analysis, evaluates and choose the alternatives. And also, the marginal analysis performed originally from a preliminary design of some sort, and eventually plans are made to investigate investment possibilities. This paper provide the discounted net cash flows and the present, annual and future worth methods. In despite of any choice for an analytical methods, there remains the problems of predicting and assessments certain future events. Therefore, these models dealing with the optimal plant sizing, equipments replacement, and lease or buy will be discussed.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.563-572
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• 2007

Transportation infrastructure is critical to economic growth of a country such as China. Careful evaluation of investments in traffic infrastructure projects is therefore pertinent. As traditional evaluation methods do not consider the uncertainty of future cash flows and mobility during project execution, the real option approach is gradually gaining recognition in the context of valuing construction and infrastructure projects. However, many of the cases only evaluate individual options separately although multiple options often exist in a typical large infrastructure project. Using a highway project in China as a case study, this paper first evaluates a deferment option and a growth option embedded in the project. Subsequently, the values are combined using the fuzzy analytical hierarchy process. It is found that the combined value is less than the sum of the two option values. This finding is consistent with the theoretical observations given in past real option literature despite the use of a different approach.

• Journal of Korean Society of Transportation
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• v.26 no.6
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• pp.17-27
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• 2008

Logistic activities of shippers contains only origin-destin commodity flow information, but also contract relationships, cash flows and distribution channels. Under the lack of understanding of physical distribution channels, most studies have focused on the social and psychological aspects between manufacturers and retailers (e.g., mutual trust, power, conflict, reciprocal commitment, and so on). This study reports empirical results of distribution channel choice drawn from 2001 Korean Commodity Flow Survey(CFS) conducted by Korean government. Based on the CFS data, four distribution channels are classified. This study scrutinized how various factors including mode, commodity and firms characteristics affect distribution channel choice, and reported the problems of 2001 CFS survey questionaires and future directions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.199-210
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• 1999

The number of car accident is Recently on the increase in Korea because of the explosive increase of cars, the poor road condition, the lack of safety facility, and others. The insurant with a accident has to decide whether receiving a insurance or not. In this paper, we represent a reasonable decision support material by calculating the approximate insurance fee based on the discount rate and premium additive rate, which is changed by the accident type and the accident expenditure. Practically, there is difference in the standard insurance rate and premium additive rate according to the accident type and the accident expenditure in Korea. The premium additive rate is assessed considering the number of accident, the pattern of accident, and the reason of accident for 3 years. In this paper, we represent a decision making method considering not only the first-time car accident but also the future car accident. For considering the repeated accident, we analyzed the real data accumulated until the year of 1996 from S Insurance Company, and estimated the probability density function between the first and the second-time accident, and executed the goodness of fit test using ARENA and STATISTICA software. Using this conditional PDF, we can calculate the insurance fee next 3 years and compare the insurance fee with the equivalent present value of cash flows. The program performing this analysis is represented, and written in VISUAL BASIC Language. We tried to suggest an accurate guideline for the insurant to decide the insurance coverage rationally, and tried to correct a wrong idea of dependence on the car insurance only by the amount of the accident expenditure. And we expect this study can generally be applied to many different accident types under the uncertain circumstances in our daily life.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.46
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• pp.265-313
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• 2010

Kyoto Protocol is an international convention on concrete performance program for UNFCCC(United Nations Framework Convention on Climate Change), which regulate and prevent to global warming and officially came into effect on February 16, 2005. Kyoto flexible mechanisms, the agreed environmental system in March 1997 in the Third Conference of Parties in UNFCCC General Assembly, Emission Trading System(ETS), Clean Development Mechanism(CDM) and Joint Implementation(JI), are key policies related to environment. In advanced countries, greenhouse gas emissions should be reduced average 5.2% level compared to 1990 in total emissions during 2008-2012. World leading carbon market finished the trial on the EU ETS I greenhouse gas emissions trading system, EU ETS II is operated regularly after 2008. World Bank leads to make 'Prototype Carbon Fund(PCF)' in April 2004, which is the world first carbon fund and a representative public carbon fund type, World Bank operate various funds including present PCF. Thus, I would like to propose as follows in relation to this study: First, in the validity analysis of carbon funds, it would be needed to analyze the Emission Reduction Cost Efficiency(ERCE) of carbon. The ERCE is a break-even value which brings the Net Present Value(NPV) to zero. NPV approach is used among projects and it enables potential projects to be compared and evaluated the ERCE on the basis of the net present value of net future cash flows. Therefore, according to results of analysis, carbon funds should be developed and invested. Second, it would be necessary to allow of issuing bonds together with carbon funds, carbon finance etc. Third, carbon funds, it would be reasonable to have a relatively enough maturity in project and as a financial derivatives in the international financial markets, it is needed various types of transactions. Fourth, it would be needed to standardize the carbon emissions trading for more efficiently. Fifth, it would be necessary to establish and invest in various kinds of domestic and overseas global carbon funds, including governments, privates, governments and privates sectors. And it is also needed to establish the medium and long term plans for carbon funds. Sixth, it would be needed to foster the advanced trade mechanisms for carbon funds in the most effective ways. Finally, carbon funds should be used in harmony with international societies to reduce global warming as the social responsible investing funds and it should be contribute to sustainable development. In addition, it would seem that carbon funds should be studied on establishing the contributable standard of sustainable development in the future assignment.

• 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.