• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.717-729
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• 2021

This paper examines the efficacy of the default risk factor in an emerging market context using the Fama-French five-factor model. Our aim is to test whether the Fama-French five-factor model augmented with a default risk factor improves the predictability of returns of portfolios sorted on the firm's characteristics as well as on industry. The default risk factor is constructed by estimating the probability of default using a hybrid version of dynamic panel probit and artificial neural network (ANN) to proxy default risk. This study also provides evidence on the temporal stability of risk premiums obtained using the Fama-MacBeth approach. Using a sample of 3,806 firm-year observations on non-financial listed companies of Pakistan over 2006-2015 we found that the augmented model performed better when tested across size-investment-default sorted portfolios. The investment factor contains some default-related information, but default risk is independently priced and bears a significantly positive risk premium. The risk premiums are also found temporally stable over the full sample and more recent sample period 2010-2015 as evidence by the Fama-MacBeth regressions. The finding suggests that the default risk factor is not a useless factor and due to mispricing, default risk anomaly prevails in the Pakistani equity market.

• East Asian mathematical journal
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• v.36 no.1
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• pp.55-60
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• 2020

We study a probabilistic approach for valuing an exchange option with default risk. The structural model of Klein [6] is used for modeling default risk. Under the structural model, we derive the closed-form pricing formula of the exchange option with default risk. Specifically, we provide the pricing formula of the option with the bivariate normal cumulative function via a change of measure technique and a multidimensional Girsanov's theorem.

• 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.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.497-502
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• 2021

This study aims to investigate the determinants of default risk of rural banks in East Java, Indonesia. The method used is descriptive verification and logistic regression analysis. The data used is secondary in the form of monthly annual financial reports of rural banks in East Java during the period 2009-2018. From the results, it was shown that net interest margin (NIM) as a proxy of market risk, non-performing loan (NPL) as a proxy of credit risk, operation efficiency as a proxy of operational risk and return on assets (ROA) as a proxy of profitability have a significant influence on default risk. Meanwhile, the loan to deposit (LDR) ratio as a proxy of liquidity risk has no significant influence on default risk. Banks need to implement risk management and meet the capital adequacy requirements of regulators so that they are resistant to risk, and also, compliant with bank governance to be able to produce high returns for rural banks have an impact on sustainability and its existence. The ability to identify setbacks in bank conditions and the ability to distinguish between healthy and problematic banks will enable to anticipate default banks.

• Journal of the Korean Society of Safety
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• v.15 no.1
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• pp.161-166
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• 2000

Regulators often specify default values that are considered acceptable for use in risk analyses as input to regulatory decisions. Because both performing and validating a detailed risk analysis of a complex system are costly and time-consuming undertakings, the use of default values can greatly facilitate the process of performing a risk analysis in the first place as well as the process of reviewing and verifying the risk analysis. It may also ensure more uniform in quality of risk analyses. However, different regulatory agencies differ in their approaches to the use of default values, and the implications of these differences are not yet widely understood. Moreover, large heterogeneity among licensees makes it difficult to set suitable defaults. This paper focuses on the effect of default values on estimates of risk. Some insights on the effects of different levels of conservatism in setting defaults will be provided. The results can help decision makers evaluate the levels of safety likely to result from their regulatory policies.

• 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.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.1
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• pp.23-45
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• 2015

We consider counterparty risk in CDS rates. To do so, we use a multivariate jump diffusion process for obligors' default intensity, where jumps (i.e. magnitude of contribution of primary events to default intensities) occur simultaneously and their sizes are dependent. For these simultaneous jumps and their sizes, a homogeneous Poisson process. We apply copula-dependent default intensities of multivariate Cox process to derive the joint Laplace transform that provides us with joint survival/default probability and other relevant joint probabilities. For that purpose, the piecewise deterministic Markov process (PDMP) theory developed in [7] and the martingale methodology in [6] are used. We compute survival/default probability using three copulas, which are Farlie-Gumbel-Morgenstern (FGM), Gaussian and Student-t copulas, with exponential marginal distributions. We then apply the results to calculate CDS rates assuming deterministic rate of interest and recovery rate. We also conduct sensitivity analysis for the CDS rates by changing the relevant parameters and provide their figures.

• 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.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.715-739
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• 2009

Technology innovation activity plays an important role in increasing a sales by bringing on the improvement of product's performance and a profitability by reducing the cost of production. Thus, technology innovation activity can reduce the default risk of firms. However, in spite of these effects of technology innovation activity, this activity can make the default risk of firm because it induce a firm to much investment of resources. This study examines the effect of technology innovation activity on the sales, profitability, and default risk of firms. This study's sample consists of manufacturing firms listed on the Korea Stock Exchange from January 1, 2000 to December 31, 2008. The results show that technology innovation activity has a positive effect on the sales (profitability) but a negative effect on the default risk of firms. Also there is the significant mediation effect of sales and profitability.