• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.25-51
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• 2008

In this paper, we analyse empirically the fixed asset investment smoothing of working capital of firms listed on Korea Securities Market. The main results of this study can be summarized as follows. Firms will seek to lower long-term cost by smoothing fixed asset investment and maintaining stationary investment with working capital. Working capital is not only an important use of fund, but also a source of liquidity that should be used to smooth fixed asset investment relative to cash flow shocks if firms face financial constraints. Working capital investment is more sensitive than fixed asset investment to cash flow fluctuations. If firms face financial constraints, working capital investment will compete with fixed asset investment for the limited pool of available cash flows. So, fixed asset investment will have negative relationship with working capital investment. However, criticism that the positive correlation between cash flows and fixed asset investment could arise simply because cash flows is proxy variable for investment demand. Finally, controlling for the fixed asset investment smoothing effects of working capital results in a much larger estimate of the long run impact of financial constraints. Financial constraints is measured by dividend payout ratio and market access level. Fazzari et al. (1988), Fazzari and Petersen (1993), and Faulkender et al. (2008) emphasize that low dividend firms or market unaccessible firms are more likely to face financial constraints, and rarely make use of new equity issuing. The results from empirical analysis show that financial constraints can be better explained using 'adjustment cost' concept. Specifically, the results show that financial constraints exist and that in order to measure financial constraint effects more succinctly, fixed asset investment smoothing effects with working capital should be considered.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Korean Journal of Plant Resources
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• v.25 no.5
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• pp.624-632
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• 2012

As there have been lately many worldwide resource challenges such as potential exhaustion of fossil fuels, sudden rise of oil price and ever-rising grain pricing due to global food crisis, there have been more interests focused on recycling vegetable oils and fats into clean natural fuel and producing new resources based on waste cooking oil as a part of reusing waste resources. An Experiment was performed by using ratio of 50:50, 75:25 (w/w) mixture of based rapeseed oil, camellia oil, and olive oil. 50:50, 25:75 (w/w) mixture of based palm oil. The result was that the oleic acid ($C_{18:1}$) got the lowest percentage of 42.8%, when we combined the mixture of rapeseed oil and soybean oil. While the highest percentage of 72.1% was when the mixture of camellia oil and rapeseed oil were combined at 50:50 ratio. In 75:25 (w/w) case, mixture of rapeseed oil and soybean oil got the lowest. The highest ratio was the mixture of camellia oil and olive oil. Based on the component of palm oil, the total saturated fatty acid was decreased. It is expected that stabilizing oxidation through controlling of fatty acid after mixture and that liquidity at a low temperature. The acid value indicated that stabilizing oxidation got a range of highest to lowest. Camellia oil ranked as the highest, followed by olive oil, and the oil seeds as the lowest in rank. Controlling iodine value through mixture and improvement of stabilizing oxidation will provide a good quality. The quality of color has no significant change about mixture in ratio and maintenance. The reduction of the cost of refining process is expected by controling of mixture ratio at biodiesel production in the future.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.788-818
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• 2009

Renewable energy refers to solar energy, biomass energy, hydrogen energy, wind power, fuel cell, coal liquefaction and vaporization, marine energy, waste energy, and liquidity fuel made out of byproduct of geothermal heat, hydrogen and coal; it excludes energy based on coal, oil, nuclear energy and natural gas. Developed countries have recognized the importance of these energies and thus have set the mid to long term plans to develop and commercialize the technology and supported them with drastic political and financial measures. Considering the growing recognition to the field, it is necessary to analysis up-to-now achievement of the government's related projects, in the standards of type of renewable energy, management of sectional goals, and its commercialization. Korean government is chiefly following suit the USA and British policies of developing and distributing renewable energy. However, unlike Japan which is in the lead role in solar rays industry, it still lacks in state-directed support, participation of enterprises and social recognition. The research regarding renewable energy has mainly examinedthe state of supply of each technology and suitability of specific region for applying the technology. The evaluation shows that the research has been focused on supply and demand of renewable as well as general energy and solution for the enhancement of supply capacity in certain area. However, in-depth study for commercialization and the increase of capacity in industry followed by development of the technology is still inadequate. 'Cost-benefit model for each energy source' is used in analysis of technology development of renewable energy and quantitative and macro economical effects of its commercialization in order to foresee following expand in related industries and increase in added value. First, Investment on the renewable energy technology development is in direct proportion both to the product and growth, but product shows slightly higher index under the same amount of R&D investment than growth. It indicates that advance in technology greatly influences the final product, the energy growth. Moreover, while R&D investment on renewable energy product as well as the government funds included in the investment have proportionate influence on the renewable energy growth, private investment in the total amount invested has reciprocal influence. This statistic shows that research and development is mainly driven by government funds rather than private investment. Finally, while R&D investment on renewable energy growth affects proportionately, government funds and private investment shows no direct relations, which indicates that the effects of research and development on renewable energy do not affect government funds or private investment. All of the results signify that although it is important to have government policy in technology development and commercialization, private investment and active participation of enterprises are the key to the success in the industry.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.6
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• pp.101-110
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• 2014

This study analyzes the validity of the type classification of the type and design changes of apartment landscaping planting construction design changes that were completed in the private sector, efficiently manages the design changes that are displayed over landscaping planting work in general in the future, and performs research by placing the object underlying the presentation. The results are as follows. First, the percentage that occurred in the planting construction of design changes that have occurred in the apartment landscaping construction was carried out in the private sector and accounted for 61.8%. This indicates that part of the planting is a major design change. Second, as the cause of such a design change to be those associated with the field conditions such as lack of main construction period. In particular, due to a change in oral, appeared 7-48 times design changes of one review design change approval is complex, design changes of planting construction had shown a feature that occurs in multiple simultaneous. Third, the 7 types of Design Changes in planting design were delineated as 'design changes for consideration of the user', 'design changes for image improvement', 'design changes for ease of maintenance', 'design changes due to the mismatch of design statement', 'design changes due to the relationship with the engineering species of other', 'design changes due to lack of field study', and 'design changes due to the consideration of feasibility.' Fourth, 'design changes for consideration of the user' and 'design changes for image improvement' were found in more than half of the frequency of the overall changes. This differed from the results shown in public corporations. Fifth, if planting construction design change process, private companies, it was found that is showing the approval of the practice after the previous construction of the construction cost savings due to construction time. However, in the case of a public corporation, these exhibited a different aspect from the private sector and show a design change procedure that reflects the changes after the design change events in the field have occurred. The above results, the type of landscaping works in planting design change of public enterprises, regardless of the private sector, is the same in the seven types, the main reason of and procedures for design changes, indicating that there are other respects. In design change, it may be desirable to apply becomes liquidity rationality and efficiency of the dimension, depending on the nature of the landscape construction.