• 수산경영론집
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• 제45권3호
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• pp.55-69
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• 2014

We try to test the pecking order theory of Korean fisheries firm's capital structure using debt capacity. At first, we estimate the debt capacity as the probability of assigning corporate bond rating from credit-rating agencies. We use logit regression model to estimate this probability as a proxy of debt capacity. The major results of this study are as follows. Firstly, we can confirm the fisheries firm's financing behaviour which issues new debt securities for financial deficit. Empirical test of SSM model indicates that the higher probability of assigning corporate bond rating, the higher the coefficient of financial deficit. Especially, high probability group follows this result exactly. Therefore, the pecking order theory of fisheries firm's capital structure applies well for high probability group which means high debt capacity. It also applies for medium and low probability group, but their significances are not good. Secondly, the most of fisheries firms in high probability group issue new debt securities for their financial deficit. Low probability group's fisheries firms also issue new debt securities for their financial deficit within the limit of their debt capacity, but beyond debt capacity they use equity financing for financial deficit. Therefore, the pecking order theory on debt capacity come into existence well in high probability group.

• 재무관리연구
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• 제1권1호
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• pp.133-149
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• 1985

The theory of option pricing has undergone rapid advances in recent years. Simultaneously, organized option markets have developed in the United States and Europe. The closed form solution for pricing options has only recently been developed, but its potential for application to problems in finance is tremendous. Almost all financial assets are really contingent claims. Especially, Black and Scholes(1973) suggest that the equity in a levered firm can be thought of as a call option. When shareholders issue bonds, it is equivalent to selling the assets of the firm to the bond holders in return for cash (the proceeds of the bond issues) and a call option. This paper takes the insight provided by Black and Scholes and shows how it may be applied to many of the traditional issues in corporate finance such as dividend policy, acquisitions and divestitures and capital structure. In this paper a combined capital asset pricing model (CAPM) and option pricing model (OPM) is considered and then applied to the derivation of equity value and its systematic risk. Essentially, this paper is an attempt to gain a clearer focus theoretically on the question of corporate stock risk and how the OPM adds to its understanding.

• 한국세라믹학회지
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• 제55권4호
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• pp.344-351
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• 2018

The effects of bond coat species on the growth behavior of thermally grown oxide (TGO) layer in thermal barrier coatings (TBCs) was investigated through furnace cyclic test (FCT). Two types of feedstock powder with different particle sizes and distributions, AMDRY 962 and AMDRY 386-4, were used to prepare the bond coat, and were formed using air plasma spray (APS) process. The top coat was prepared by APS process using zirconia based powder containing 8 wt% yttria. The thicknesses of the top and bond coats were designed and controlled at 800 and $200{\mu}m$, respectively. Phase analysis was conducted for TBC specimens with and without heat treatment. FCTs were performed for TBC specimens at $1121^{\circ}C$ with a dwell time of 25 h, followed by natural air cooling for 1 h at room temperature. TBC specimens with and without heat treatment showed sound conditions for the AMDRY 962 bond coat and AMDRY 386-4 bond coat in FCTs, respectively. The growth behavior of TGO layer followed a parabolic mode as the time increased in FCTs, independent of bond coat species. The influences of bond coat species and heat treatment on the microstructural evolution, interfacial stability, and TGO growth behavior in TBCs are discussed.

• 응용통계연구
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• 제31권3호
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• pp.367-382
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• 2018

회사채 신용 등급 예측 모형에 대한 연구는 신용 평가 기관이 회사채 신용 등급 평가에 사용될 것이라 예상 되는 여러 재무적 특성 변수들을 기반으로 진행되었으며 선형 회귀 모형(linear regression), 순위 로짓(ordered logit), 순위 프로빗(ordered probit), 서포트 벡터 기계(support vector machine), 랜덤 포레스트(random forest) 등 다양한 모형들을 적용하여 개발되었다. 하지만 기존 연구들에서 고려한 회사채 신용 등급은 연구에 따라 5등급에서 20등급까지 다른 등급 구간을 적용하였으며 분석에 이용된 표본 자료의 기간 및 대상도 상이하여 예측 성능의 공정한 비교에 어려움이 있다. 따라서 본 연구에서는 2013년부터 2017년까지의 회사채 신용 등급 자료와 기존 연구들에서 사용된 재무 지표들을 통합하여 기존에 발표된 예측 모형들을 동일한 자료에 적용하고 예측 성능을 비교하였다. 추가적으로 Elastic-net 벌점화 회귀 모형 및 순위 로짓, 순위 프로빗 모형을 적합하여 LASSO 벌점이 선택됨을 확인하였으며 LASSO 벌점을 고려한 예측 모형이 대응하는 기존의 예측 모형들보다 향상된 성능을 보임을 확인하였다. 본 연구의 수행 결과, 랜덤 포레스트를 이용한 예측 모형이 15등급 기준 검증 자료에서 정확한 등급 예측률이 69.6%로 다른 모형과 비교하여 높은 예측 성능을 나타내었다.

• 재무관리연구
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• 제21권2호
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• pp.125-151
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• 2004

본 연구는 1998년 7월 1일부터 2003년 12월 31일까지 국내 주요 채권현물(spot market)시장(콜금리, 3년물 및 5년물 국채, 3년물 회사채)에 대한 미국 장단기 국채 현물시장(3개월물 T-bill, 5년물 T-note, 10년물 T-bond)의 가격발견(price discover)기능에 대한 분석을 실시하였다. 전체분석기간을 채권시가평가제도가 도입된 2(in년 7월 1일 전후로 나누어 변동성이전효과 여부를 시간변동 일변량(univariate) AR(1)-GARCH(1,1)-M모형을 이용하여 추정하였으며, 주요 분석결과는 다음과 같다. 첫째, 전체분석 기간동안 국내 콜금리, 3년물 및 5년물 국채, 3년만기 회사채에 대한 미국 3개월물 T-bill, 5년물 T-note 및 10년물 T-bond의 변동성이전효과(volatility spillover effect)가 1%수준에서 통계적으로 유의하게 존재하는 것으로 나타났다. 둘째, 채권시가평가제도 도입이전보다는 도입이후에 조건부 변동성이전효과가 더 강하고 지속적인 것으로 나타났으며, 특히 미국의 3개월물 T-bill 및 5년물 T-note보다는 대표적인 장기금리인 10년물 T-bond 금리는 국내 주요금리에 대한 조건부평균 및 변동성이전효과가 통계적으로 유의한 수준에서 모두 존재하는 것으로 나타났다. 이러한 분석결과는 주식시장을 이용한 변동성이전효과와 마찬가지로 IMF 외환위기 이후 국내자본시장개방 및 정보통신발달에 따른 국제자본시장통합(int'1 capital market integration)에서 기인하는 것으로 보인다. 또한 이러한 채권시장의 변동성이전효과에 대한 이해는 국내채권 투자자들의 자본자산가격결정(valuation), 위험관리(risk management) 및 국제포트폴리오관리 (int'1 portfolio management) 측면에 다소 시사점이 있을 것으로 여겨진다.다중회귀분석에서 각각 일관되게 관찰할 수 있었다. 또한 이러한 결과는 IMF 이후에도 여전히 유지되는 것으로 나타났다.과와는 별개의 PER효과가 여전히 존재하며, 다만 이 PER 효과는 전통적 의미의 일반적으로 낮은 PER종목이 초과수익률을 내는 것이 아니라, 기업규모가 크더라도 그 기업의 개별특성을 고려했을 때 이와 비교해 상대적으로 PER가 낮은 종목에 투자하면 초과수익을 낼 수 있음을 의미한다. 발견하였다.적 일정하게 하는 소비행동을 목표로 삼고 소비와 투자에 대한 의사결정을 내리고 있음이 실증분석을 통하여 밝혀졌다. 투자자들은 무위험 자산과 위험성 자산을 동시에 고려하여 포트폴리오를 구성하는 투자활동을 행동에 옮기고 있다.서, Loser포트폴리오를 매수보유하는 반전거래전략이 Winner포트폴리오를 매수보유하는 계속거래전략보다 적합한 전략임을 알 수 있었다. 다섯째, Loser포트폴리오와 Winner포트폴리오를 각각 투자대상종목으로써 매수보유한 반전거래전략과 계속거래 전략에 대한 유용성을 비교검증한 Loser포트폴리오와 Winner포트폴리오 각각의 1개월 평균초과수익률에 의하면, 반전거래전략의 Loser포트폴리오가 계속거래전략의 Winner포트폴리오보다 약 5배정도의 높은 1개월 평균초과수익률을 실현하였고, 반전거래전략의 유용성을 충분히 발휘하기 위하여 장단기의 투자기간을 설정할 경우에 6개월에서 36개월로 이동함에 따라 6개월부터 24개월까지는 초과수익률이 상승하지만, 이후로는 감소하므로, 반전거래전략을 활용하는 경우 주식투자기간은 24개월이하의 중단기가 적합함을 발견하였다. 이상의 행태적 측면과 투자성과측면의 실증결과를 통하여 한국주식시장에 있어서 시장수익률을 평균적으로 초과할 수 있는 거래전략은 존재하므로 이러한 전략을 개발 및 활용할 수 있으며, 특히, 한국주식시장에 적합한 거래전략은 반전거래전략이고, 이 전략의 유용성은 투자자가 설정한 투자기간보다

• Asia pacific journal of information systems
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• 제19권2호
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.741-744
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• 1996

Artifical Neural Network(ANN) models were used for forecasting interest rate as a new methodology, which has proven itself successful in financial domain. This research intended to construct ANN models which can maximize the performance of prediction, regarding Corporate Bond Yield (CBY) as interest rate. Synergistic Market Analysis (SMA) was applied to the construction of models [Freedman et al.]. In this aspect, while the models which consist of only time series data for corporate bond yield were devloped, the other models generated through conjunction and reorganization of fundamental variables and market variables were developed. Every model was constructed to predict 1,6, and 12 months after and we obtained 9 ANN models for interest rate forecasting. Multi-layer perceptron networks using backpropagation algorithm showed good performance in the prediction for 1 and 6 months after.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.19-22
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• 1996

A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.

• Asia pacific journal of information systems
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• 제20권2호
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• pp.23-37
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• 2010

Conventional Support Vector Machines (SVMs) have been utilized as classifiers for binary classification problems. However, certain real world problems, including corporate bond rating, cannot be addressed by binary classifiers because these are multi-class problems. For this reason, numerous studies have attempted to transform the original SVM into a multiclass classifier. These studies, however, have only considered nominal classification problems. Thus, these approaches have been limited by the existence of multiclass classification problems where classes are not nominal but ordinal in real world, such as corporate bond rating and multiclass customer classification. In this study, we adopt a novel multiclass SVM which can address ordinal classification problems using ordinal pairwise partitioning (OPP). The proposed model in our study may use fewer classifiers, but it classifies more accurately because it considers the characteristics of the order of the classes. Although it can be applied to all kinds of ordinal multiclass classification problems, most prior studies have applied it to finance area like bond rating. Thus, this study applies it to a real world customer level classification case for implementing customer relationship management. The result shows that the ordinal multiclass SVM model may also be effective for customer level classification.