• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.397-409
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• 2015

The Global Financial Crisis and introduction of International Financial Reporting Standards (IFRS) urged the banks to strengthen their asset qualities. The banks dispose their non performing loans(NPLs) consistently to maintain a sufficient BIS capital adequacy ratio. Accordingly, the interests in auctions, as a disposal method, of real estates that secured for NPLs are on the increasing. This study suggest an alternative for fairness consolidation of real estate auctions which secured for NPLs. First, the impartial entry barriers for NPL sales markets need to be eliminated for fair bidding competition in auctions for real estate that secured for NPLs. In addition, the portion of NPL disposal by real estate auctions need to be expanded. Second, the asymmetry of trade information in the retail markets of NPLs and the abuse of offset by NPL owners' also should be restricted. The Fairness improvement of NPL trading process and real estate auction process that secured for them would of great use in the protection of bidders. Futhermore, it would also contribute to the revitalization of real estate auction markets and the resolution of NPLs of banks through fair disposal of distressed assets.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.709-716
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• 2021

The main objective of this research is to find out the effect of Non-Performing Loan (NPL) of Nepalese conventional banks. The population of this study is major commercial banks in Nepal and the data obtained for this study was from the period 2015-2019. This research used secondary data and it is collected from each bank's annual report and GDP and Inflation taken from the World Bank database. The method used for data analysis in this study is multiple regression analysis. The study used NPL as a dependent variable and Return on Asset (ROA), Capital Adequacy Ratio (CAR), Bank Size, GDP growth, and Inflation as independent/explanatory variables. The result of this research shows that ROA, Bank Size, GDP, and Inflation have a significant effect on NPL but CAR does not have a significant effect on the NPL of banks. In other words, the GDP effect on NPL in this study shows a positive and significant effect while most studies show a negative effect. It demonstrates that when GDP growth increases, there is a significant increase in the growth of Nepalese banks even though there were no significant changes in income growth. Therefore, GDP growth has a positive and significant effect on the NPL of commercial banks. Thus, the bankers and policymakers need to consider GDP growth carefully while taking NPL-related decisions.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.591-599
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• 2020

This study examines how the health of the banks in ASEAN-3 countries namely Indonesia, Malaysia and Thailand respond to the change in exchange rates and foreign interest rates in four large economies. The transmissions of the two external factors through domestic factors in each ASEAN-3 countries eventually affects Non-Performing Loan (NPL) of commercial banks. This study uses the monthly time series data and the renowned Structural Vector Autoregressive (VAR) model comprising five variables, namely exchange rate, foreign interest rate, domestic interest rate, money supply, and non-performing loan (NPL). The results indicate that there are different effects between ASEAN-3 countries, which can be classified as short-run effect and long-run effect. In the long run effect, external factors have a dominant role in determining NPL in ASEAN-3 countries. Yuan has the biggest effect on Malaysia's NPL, while Indonesia is more affected by European interest rates rather than the fluctuation of the US currency and China's interest rates. Among ASEAN-3 countries, Malaysia is the one that is the most vulnerable to external factors. While Thailand's NPL is affected dominantly by domestic factors. This study shows that the Fed Funds Rate (US official interest rate) is not always the dominant factor affecting the health of domestic banks in ASEAN-3.

• Bulletin of the Korean Chemical Society
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• v.22 no.12
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• pp.1341-1344
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• 2001

Comparison of sulfur dioxide primary standard gases of the Korea Research Institute of Standards and Science (KRISS, Korea) and the National Physical Laboratory (NPL, UK) was performed. 100 ${\times}$10-6 mol/mol and 1,000 ${\times}$10-6 mol /mol primary standard gases (designated NPL S115 and S114, respectively) prepared gravimetrically and validated in NPL were used as transfer standards. Transfer standards were analyzed by NDIR sulfur dioxide analyzer and compared with KRISS PSM 112-03-624 and PSM 112-03-625 prepared gravimetrically. Adsorption corrected relative deviations of the primary standard gases were agreed to within 0.1%, and this agreement is within the expanded uncertainties (k = 2) of the primary standards at the two laboratories.

• Current Applied Physics
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• v.18 no.12
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• pp.1540-1545
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• 2018

SiGe alloy is widely used thermoelectric materials for high temperature thermoelectric generator applications. However, its high thermoelectric performance has been thus far realized only in alloys synthesized employing mechanical alloying techniques, which are time-consuming and employ several materials processing steps. In the current study, for the first time, we report an enhanced thermoelectric figure-of-merit (ZT) ~ 1.1 at $900^{\circ}C$ in ntype $Si_{80}Ge_{20}$ nano-alloys, synthesized using a facile and up-scalable methodology consisting of rapid solidification at high optimized cooling rate ${\sim}3.4{\times}10^7K/s$, employing melt spinning followed by spark plasma sintering of the resulting nano-crystalline melt-spun ribbons. This enhancement in ZT > 20% over its bulk counterpart, owes its origin to the nano-crystalline microstructure formed at high cooling rates, which results in crystallite size ~7 nm leading to high density of grain boundaries, which scatter heat-carrying phonons. This abundant scattering resulted in a very low thermal conductivity ${\sim}2.1Wm^{-1}K^{-1}$, which corresponds to ~50% reduction over its bulk counterpart and is amongst the lowest reported thus far in n-type SiGe alloys. The synthesized samples were characterized using X-ray diffraction, scanning electron microscopy and transmission electron microscopy, based on which the enhancement in their thermoelectric performance has been discussed.

• Animal Bioscience
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• v.37 no.8
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• pp.1333-1344
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• 2024

Objective: Litter size and piglet loss at birth significantly impact piglet production and are closely associated with sow parity. Understanding how these traits vary across different parities is crucial for effective herd management. This study investigates the patterns of the number of born alive piglets (NBA), number of piglet losses (NPL), and the proportion of piglet losses (PPL) at birth in Landrace sows under tropical conditions. Additionally, it aims to identify the most suitable model for describing these patterns. Methods: A dataset comprising 2,322 consecutive reproductive records from 258 Landrace sows, spanning parities from 1 to 9, was analyzed. Modeling approaches including 2nd and 3rd degree polynomial models, the Wood gamma function, and a longitudinal model were applied at the individual level to predict NBA, NPL, and PPL. The choice of the best-fitting model was determined based on the lowest mean and standard deviation of the difference between predicted and actual values, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results: Sow parity significantly influenced NBA, NPL, and PPL (p<0.0001). NBA increased until the 4th parity and then declined. In contrast, NPL and PPL decreased until the 2nd parity and then steadily increased until the 8th parity. The 2nd and 3rd degree polynomials, and longitudinal models showed no significant differences in predicting NBA, NPL, and PPL (p>0.05). The 3rd degree polynomial model had the lowest prediction standard deviation and yielded the smallest AIC and BIC. Conclusion: The 3rd degree polynomial model offers the most suitable description of NBA, NPL, and PPL patterns. It holds promise for applications in genetic evaluations to enhance litter size and reduce piglet loss at birth in sows. These findings highlight the importance of accounting for sow parity effects in swine breeding programs, particularly in tropical conditions, to optimize piglet production and sow performance.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.4
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• pp.61-68
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• 2008

Of the seven base units of the international system of units, only the kilogram is still defined in terms of a material artifact. One of the experimental approaches opening the way to a new definition of the kilogram is the watt balance To improve the performance of the NPL watt balance, we need to quantify and reduce hysteresis effects in the balance knives. In this paper, we discuss the mechanical characteristics of the knife edges used in the NPL watt balance. The hysteresis mechanism is analyzed using the finite element method. It is found that the cause of hysteresis is not normal stress but shear, and the deformation of the flat, rather than the knife, is an important factor in the hysteresis mechanism. The study presented here, using finite element analysis, suggests that parameters such as material property, tip radius and knife straightness can be more important than others, such as friction coefficient, tip angle, etc.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.95-105
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• 2021

A prerequisite of a sound financial system is effective channeling of financial resources to efficient users; hence maximizing economic and societal welfare. To that end, the prevalence of bad loans in banks in emerging economies is a major policy concern. In an attempt to add to the growing body of literature explaining the interrelationship between macroeconomic and firm-specific factors, and non-performing loans (NPL), this paper examines data from 24 scheduled commercial banks in Bangladesh from 2008 to 2019. Macroeconomic factors as well as firm-specific factors related to profitability, capital strength, and efficiency are considered. Panel data regression analysis is performed to estimate pooled OLS, fixed effects, and random effects models. Following the necessary testing, it was found that the fixed effects model with robust standard error is appropriate. Results show that return on assets and inflation have a negative influence on NPL, but GDP growth has a favorable impact. The paper concludes by asserting that the evidence supports similar findings from studies both in Bangladesh and elsewhere and it is noted that a combination of these macroeconomic and firm-specific factors explains only a small portion of the total variation in NPL.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.145-155
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• 2022

Since 2014, the IFRS 9 has been the focus of the attention of many scholars across disciplines. The futuristic prediction of bank loan provision via a flexible ECL model has been observed as a game changer from the prior models offered in IAS 39. This study has two objectives; the first is to examine the impact on loan loss provisions (LLP), nonperforming loans (NPL), and the impairment loan losses (ILL) after the IFRS 9 in gulf banks. The second is to capture any variation in LLP, NPL, and ILL before and after IFRS9. The study used the two-way fixed effect model (TWFE) estimation and the DiD approach to attain its objectives. 54 gulf banks were selected from the periods between 2012 and 2020. The results indicate that LLP has significantly increased after the transition to IFRS 9, while the NPL has significantly decreased. The results did not capture a significant change in ILL after IFRS9 implementation. The results also indicate more consistency in LLP and NPL reporting after implementing the ECL model adopted in IFRS9. The study concluded that ECL model outcomes are in tandem with prior observation worldwide and pointed out some improvement opportunities for the future.