• Title/Summary/Keyword: Merton's Model

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Henry James's The Wings of the Dove: Free Self and Identity (헨리 제임스의 『비둘기의 날개』 : 자유와 정체성의 문제)

  • Kim, Kyung-ah
    • English & American cultural studies
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    • v.9 no.1
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    • pp.27-50
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    • 2009
  • Henry James tries to describe minutely in The Wings of the Dove the process in which a bad faith grows, is practiced in one's self, and spreads to a society. Through this fictional specificity, he embodies an analogy between a bad faith and social role-playing. That is, he shows, through the main characters such as Milly Theale and Merton Densher, how self interacts with the other and a society. In this interaction, there is some essential element, namely, an organic relationship between a self identity and a social role-model, which James describes very meticulously. Therefore, the characters are depicted as seeking to define self identity and eventually distorting it. Thus, The Wings of the Dove can be seen as a tragedy in which the characters who have this wrongly distorted self identity come to experience its effects. The distorted self identity appears to function as a social role. Milly distorts her true self identity by internalizing a dove-image for it. This results in a bad faith. Moreover, the American girl Milly utilizes it as a convenient social role-model which makes it easy for her to interact and engage with the others in the European society. Merton also evades adventurous and painful self-reflection and self-criticism by sticking to the mannerisms of gentlemanship and imitates the sublimity which Milly shows him. Thus, Milly and Merton clearly omit self-inspection and self-inquiry for the contact between a free self and a society, which is essential to obtain social objectivity, namely, intersubjectivity.

Capital Structure and Default Risk: Evidence from Korean Stock Market

  • GUL, Sehrish;CHO, Hyun-Rae
    • The Journal of Asian Finance, Economics and Business
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    • v.6 no.2
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    • pp.15-24
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    • 2019
  • This study analyzes the effect of the capital structure of Korean manufacturing firms on default risk based on Moody's KMV option pricing model where the probability of default is obtained by measuring the distance to default as a covariant in logit model developed by Merton (1974). Based on the panel data of manufacturing firms, this study achieves its primary objective, using a fixed effect regression model and examines the effect of a firm's capital structure on default risk amongst publicly listed firms on Korea exchange during 2005-2016. Empirical results obtained suggest that the rise in short-term debt to assets leads to increase the risk of default whereas the increase in long-term debt to assets leads to decrease the default risk. The benefits of short-term debt financing over a short-term period fade out in the presence of information asymmetry. However, long-term debt financing overcomes the information asymmetry and enjoys the paybacks of tax advantage associated with long-term debt. Additionally, size, tangibility and interest coverage ratio are also the important determinants of default risk. Findings support the trade-off theory of capital structure and recommend the optimal use of long-term debt in a firm's capital structure.

The Default Risk of the Research Funding with Uncertain Variable in South Korea, Along with the Greeks (옵션민감도를 고려한 기술자금의 경제적 가치와 실패확률)

  • Sim, Jaehun
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.44 no.1
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    • pp.1-8
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    • 2021
  • As a nation experiencing rapid economic growth, South Korea and its government have made a continuous effort toward efficient research investments to achieve transformation of the Korean industry for the fourth industrial revolution. To achieve the maximum effectiveness of the research investments, it is necessary to evaluate its funding's worth and default risk. Thus, incorporating the concepts of the Black-Scholes-Merton model and the Greeks, this study develops a default-risk evaluation model in the foundation of a system dynamics methodology. By utilizing the proposed model, this study estimates the monetary worth and the default risks of research funding in the public and private sectors of Information and Communication technologies, along with the sensitivity of the R&D economic worth of research funding to changes in a given parameter. This study finds that the public sector has more potential than the private sector in terms of monetary worth and that the default risks of three types of research funding are relatively high. Through a sensitivity analysis, the results indicate that uncertainty in volatility, operation period, and a risk-free interest rate has trivial impacts on the monetary worth of research funding, while volatility has large impacts on the default risk among the uncertain factors.

A FAST AND ROBUST NUMERICAL METHOD FOR OPTION PRICES AND GREEKS IN A JUMP-DIFFUSION MODEL

  • JEONG, DARAE;KIM, YOUNG ROCK;LEE, SEUNGGYU;CHOI, YONGHO;LEE, WOONG-KI;SHIN, JAE-MAN;AN, HYO-RIM;HWANG, HYEONGSEOK;KIM, HJUNSEOK
    • The Pure and Applied Mathematics
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    • v.22 no.2
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    • pp.159-168
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    • 2015
  • Abstract. We propose a fast and robust finite difference method for Merton's jump diffusion model, which is a partial integro-differential equation. To speed up a computational time, we compute a matrix so that we can calculate the non-local integral term fast by a simple matrix-vector operation. Also, we use non-uniform grids to increase efficiency. We present numerical experiments such as evaluation of the option prices and Greeks to demonstrate a performance of the proposed numerical method. The computational results are in good agreements with the exact solutions of the jump-diffusion model.

Technology Innovation Activity and Default Risk of Firms : Focusing on a Mediation Effect of Profitability (기술혁신활동이 부도위험에 미치는 영향 : 수익성 매개효과를 중심으로)

  • Kim, Jinsu;Lee, HyunChul
    • Knowledge Management Research
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    • v.11 no.1
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    • pp.19-35
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    • 2010
  • This study explores the effects of technology innovation activity on a profitability and the default risk of firms. Sample for this study consists of manufacturing firms listed on the Korea Stock Exchange from 1st January 2000 to 31st December 2007. We use of R&D ratio as a proxy of technology innovation activity. The default probability proxied for the default risk of firms is measured by the Merton's (1974) model where accounts for a market value of firms and a volatility of it. This study provides evidence that technology innovation activity has a positive effect on a profitability, but a negative effect on the default risk of firms. Our study also finds the significant mediation effect of profitability that the enhancement in profitability resulting from technology innovation activity lowers the default risk of firms.

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An Empirical Study on the Variable Rate Deposit Insurance Premium in Korea (변동예금보험료율의 부과에 관한 실증연구)

  • Kim, Dae-Ho
    • The Korean Journal of Financial Management
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    • v.20 no.1
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    • pp.279-304
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    • 2003
  • This study presents some empirical results on variable rate deposit insurance premium in Korea. The study estimates deposit insurance premium for all insured financial institutions in Korea using Ronn and Verma(1986) model which is based on Merton(1977)'s option pricing model. The sample period is 1995-2001 and the study includes trend analysis and cross-sectional analysis for premium estimation. The study also includes the correlation analysis between the estimates and profitability and capitalvariables such as BIS capital ratios, ROE and ROA. The results show that the estimates differ across financial institutions and sample periods. Thus it supports that each deposit premium should reflect its own risks. It also supports the necessity for the system of variable rate deposit insurance premium.

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Machine learning-based corporate default risk prediction model verification and policy recommendation: Focusing on improvement through stacking ensemble model (머신러닝 기반 기업부도위험 예측모델 검증 및 정책적 제언: 스태킹 앙상블 모델을 통한 개선을 중심으로)

  • Eom, Haneul;Kim, Jaeseong;Choi, Sangok
    • 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.

Technology Innovation Activity and Default Risk (기술혁신활동이 부도위험에 미치는 영향 : 한국 유가증권시장 및 코스닥시장 상장기업을 중심으로)

  • Kim, Jin-Su
    • Journal of Technology Innovation
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    • v.17 no.2
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    • pp.55-80
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    • 2009
  • Technology innovation activity plays a pivotal role in constructing the entrance barrier for other firms and making process improvement and new product. and these activities give a profit increase and growth to firms. Thus, technology innovation activity can reduce the default risk of firms. However, technology innovation activity can also increase the firm's default risk because technology innovation activity requires too much investment of the firm's resources and has the uncertainty on success. The purpose of this study is to examine the effect of technology innovation activity on the default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Securities Market and The Kosdaq Market from January 1,2000 to December 31, 2008. This study makes use of R&D intensity as an proxy variable of technology innovation activity. The default probability which proxies the default risk of firms is measured by the Merton's(l974) debt pricing model. The main empirical results are as follows. First, from the empirical results, it is found that technology innovation activity has a negative and significant effect on the default risk of firms independent of the Korea Securities Market and Kosdaq Market. In other words, technology innovation activity reduces the default risk of firms. Second, technology innovation activity reduces the default risk of firms independent of firm size, firm age, and credit score. Third, the results of robust analysis also show that technology innovation activity is the important factor which decreases the default risk of firms. These results imply that a manager must show continuous interest and investment in technology innovation activity of one's firm. And a policymaker also need design an economic policy to promote the technology innovation activity of firms.

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A Study on Industries's Leading at the Stock Market in Korea - Gradual Diffusion of Information and Cross-Asset Return Predictability- (산업의 주식시장 선행성에 관한 실증분석 - 자산간 수익률 예측 가능성 -)

  • Kim Jong-Kwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.355-380
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    • 2004
  • I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

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