• The Journal of Asian Finance, Economics and Business
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• v.5 no.1
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• pp.11-16
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• 2018

This research examines the alternative ways of estimating the coefficient of non-diversifiable risk, namely beta coefficient, in Capital Asset Pricing Model (CAPM) introduced by Sharpe (1964) that is an essential element of assessing the value of diverse assets. The non-parametric methods used in this research are the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator). The Jackknife, the resampling technique, is also employed to validate the results. According to finance literature and common practices, these coecients have often been estimated using Ordinary Least Square (LS) regression method and monthly return data set. The empirical results of this research pointed out that the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) performed much better than Ordinary Least Square (LS) in terms of eciency for large-cap stocks trading actively in the United States markets. Interestingly, the empirical results also showed that daily return data would give more accurate estimation than monthly return data in both Ordinary Least Square (LS) and robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) regressions.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3417-3426
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• 2012

This study aims to determine how ownership structure (share-holding ratio of insiders, foreigners) affects agent costs (the portion of asset efficiency or non-operating expenses) through empirical analysis. However, as existing studies on correlations between ownership structure and agent costs adopted Pooled OLS Model, this study focused on additionally formulating Fixed Effect Model and Random Effect Model aimed to reflect the time of data formation and corporate effects as study models based on verification results on the suitability of Pooled-OLS Model before comparative analysis for the purpose of improvement of credibility and statistical validity of the results of empirical analysis based on the premise that the Pooled OLS Model is not reliable enough to verify massive panel data. The data has been accumulated over 10 years from 1998 to 2007 after the IMF crisis hit the nation, from a subject 331 companies except for financial institutions. As a result of the empirical analysis, verification of the suitability of model has determined that the Random Effect Model is appropriate in terms of asset efficiency among agent costs items. On the other hand, the Fixed Effect Model is appropriate in terms of non-operating costs. As a result of the empirical analysis according to the appropriate model, no hypothesis adopted in the Pooled OLS Model has been accepted. This suggests that developing an appropriate model is more important than other factors for the purpose of generating statistically significant empirical results by showing that different empirical results are produced according to the type of empirical analysis.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Journal of the Korean Solar Energy Society
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• v.30 no.4
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• pp.15-21
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• 2010

Radiation data are the best source of information for estimating average incident radiation. Lacking this or data from nearby locations of similar climate, it is possible to use empirical relation ships to estimate radiation from days of cloudiness. It is necessary to estimate the regression coefficients in order to predict the daily global radiation on a horizontal surface. There fore many different equations have proposed to evaluate them for certain areas. In this work a new correlation has been made to predict the solar radiation for 16 different areas over Korea by estimating the regression coefficients taking into account cloud cover. Particularly, the straight line regression model proposed shows reliable results for estimating the global radiation on a horizontal surface with monthly average deviation of -0.26 to +0.53% and each station annual average deviation of -1.61 to +1.7% from measured values.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.97-105
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• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.21-29
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• 2018

In many companies handling flammable liquids, explosion-proof electrical equipment have been installed according to the Korean Industrial Standards (KS C IEC 60079-10-1). In these standards, hazardous area for explosive gas atmospheres has to be classified by the evaluation of the evaporation rate of flammable liquid leakage. The evaporation rate is an important factor to determine the zones classification and hazardous area distance. However, there is no systematic method or rule for the estimation of evaporation rate in these standards and the first principle equations of a evaporation rate are very difficult. Thus, it is really hard for industrial workplaces to employ these equations. Thus, this problem can trigger inaccurate results for evaluating evaporation range. In this study, empirical models for estimating an evaporation rate of flammable liquid have been developed to tackle this problem. Throughout the sensitivity analysis of the first principle equations, it can be found that main factors for the evaporation rate are wind speed and temperature and empirical models have to be nonlinear. Polynomial regression is employed to build empirical models. Methanol, benzene, para-xylene and toluene are selected as case studies to verify the accuracy of empirical models.

• Journal of the Korean Society of Propulsion Engineers
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• v.11 no.2
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• pp.71-78
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• 2007

An experimental investigation was conducted to clarify the combustion characteristics of Polyethylene-GOX(PE-GOX) hybrid motor using a single-port fuel grain configuration. Data from the experiments were analyzed to evaluate the length-averaged regression rate of PE-GOX propellants. Based on the existing theories, the empirical regression rate formulas provided from Marxman[3,4] and Altman[14] showed good concordance with the PE-GOX experiments. The accuracy of the regression rate was then evaluated and compared with the measured one. As a result, Marxman's model was somewhat more precise than Altman's model in these experiments. Moreover, the consideration of the empirical regression rate showed that O/F ratio has minor variation due to the quasi constant inflow of the fuel during motor firing.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1199-1215
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• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.95-105
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• 2000

The Purpose of this study is to present a new Trip Generation Model using Backpropagation Neural Networks. For this purpose, it is compared the performance between existing linear/nonlinear Regression models and a new TriP Generation model using Neural Networks. The study was performed according to the below. First, it is analyzed the limits of conventional Regression models, next Proved the superiority of Neural Networks model in theoretical and empirical aspects, and lastly Presented a new approach of Trip Generation methodology. The results show that Backpropagation Neural Networks model is predominant in estimation and Prediction comparable to Regression analysis. Such results mean the possibility of Neural Networks\` application in Trip Generation modeling. Specially under the circumstances of the chancing transportation situations and unstable transportation on vironments, its application in transportation fields will be extended.