• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.1-10
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• 2021

This paper compares the performance of Fama-French three-factor and five-factor models using a dataset of 20 Pakistani commercial banks for the period 2011 to 2020. We focus on an emerging economy as the findings from earlier studies on developed countries cannot be generalized in emerging markets. For empirical analysis, twelve portfolios were developed based on size, market capitalization, investment strategy, and growth. Subsequently, we constructed five Fama-French factors namely, R_M, SMB, HML, RMW, and CMA. The OLS regression technique with robust standard errors was applied to compare the predictive power of both the Fama-French models. Further, we also compared the mean-variance efficiency of the Fama-French models through the GRS test. Our empirical analysis provides three unique and interesting findings. First, both asset pricing models have similar predictive power to explain the expected portfolio returns in most cases. Second, our results from the GRS test suggest that there is no noticeable difference in the mean-variance efficiency of one asset pricing model over the other. Third, we find that all factors of both Fama-French models are statistically significant and are important for explaining the volatility of expected commercial bank returns in the context of Pakistan.

• Journal of Hydrogen and New Energy
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• v.33 no.6
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• pp.749-760
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• 2022

An efficient computational fluid dynamics model was proposed for simulating microchannel-type steam/methane reformers with thin washcoat catalyst layers. In this model, by using the effectiveness factor correlations, the overall reaction rate that occurs in the washcoat catalyst layer could be accurately estimated without performing the detailed calculation of heat transfer, mass transfer, and reforming reactions therein. The accuracy of the proposed model was validated by solving a microchannel-type reformer, once by fully considering the complex steam/methane reforming (SMR) process inside the washcoat layer and again by simplifying the SMR calculation using the effectiveness factor correlations. Finally, parametric studies were conducted to investigate the effects of operating conditions on the SMR performance.

• The Research Journal of the Costume Culture
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• v.22 no.2
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• pp.273-289
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• 2014

The purpose of this study is to identify the determinants of the susceptibility of global consumer culture. As determinants, materialism and self monitoring as psychological variables and fashion clothing product knowledge as clothing-related variable were included. It was hypothesized that both psychological variables and clothing-related variable influence susceptibility of global consumer culture. Data were gathered by surveying university students in Seoul metropolitan area, using convenience sampling, and 311 questionnaires were used in the statistical analysis. In analyzing data, exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS were conducted. Factor analysis of susceptibility of global consumer culture revealed four dimensions, 'social prestige' factor, 'quality perception' factor, 'conformity to others' factor, and 'conformity to consumption trend' factor. In addition, factor analysis of self monitoring revealed three dimensions, 'center-oriented attention' factor, 'situation-appropriate self-presentation' factor, and 'strategic displays of self-presentation' factor. The results showed that all the fit indices for the variable measures were quite acceptable. In addition, the overall fit of the model suggests that the model fits the data well. Tests of the hypothesized path show that all variables except for the one factor of self monitoring, 'center-oriented attention', and materialism influence all the factors of susceptibility of global consumer culture. The implications of these findings and suggestions for future study are also discussed.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.1-14
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• 2024

Purpose - We investigate whether a potential missing pricing factor plays a significant role in the idiosyncratic volatility puzzle. Design/methodology/approach - We theoretically show how a missing pricing factor can affect the idiosyncratic volatility puzzle, and also show how to get around the problem empirically. We adopt the Fama-French five factor model for the estimation of the idiosyncratic risk and use randomly constructed portfolios as test assets. Findings - We find that a missing factor does not drive the idiosyncratic volatility puzzle. Thus, we conclude that the idiosyncratic volatility does affect the risk premium of its stock. Research implications or Originality - The Fama-French five factor model does a pretty good job in explaining the risk premiums of stocks, and it can be used to reliably estimate idiosyncratic risk of stocks.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.233-239
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• 2010

The research which sees the fact that about model found of the duty factor whom follows in old person welfare facility worker holding office duration and organized performance researches in purpose. From theoretical background about the old person welfare facility system investigated a theory about the duty factor and organized performance of literature investigation and the old person welfare facility worker. A theory analysis of positive data led based on about the effect factor of the causality which the duty factor of the old person welfare facility worker reaches in organized performance under verifying boil the modulating effect of holding office duration. The repair which is a duty factor of the old person welfare facility worker, according to the holding office duration which is a personal quality factor job satisfaction the promotion and organized natural features, business environment and organized structure are organized performance and organization immersion and loyalty degree different assumed with the fact that will affect. From positive analysis about the research object verifies a suitable degree about demographics school register analytical and study model, structural equation model leads and construction verification and modulating effect analysis under executing boil.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.1
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• pp.27-37
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• 2001

Soil erosion by outflow of water or rainfall has caused many environmental problems as declining agricultural productivity, damaging pasture and preventing flow of water. As the interest in environment is increasing lately, soil erosion is considered as a serious problem, whereas the systematic regulation and analysis for that have not established yet. This research shows the method of extracting factor entered model which expects soil erosion by GSIS. There are several erosion model such as ANSWER, WEPP, RUSLE. The research used RUSLE erosion model which could expect general soil erosion connected easily with GSIS data. RUSLE's input factors are composed of rainfall runoff factor(R). soil erodibility factor(K), slope length factor(L), slope steepness factor(S), cover management factor(C) and support practice factor(P). The general equation used to extract L, S factor on the RUSLE to be oriented for agricultural area has some limitation to apply whole watershed. So, on this study we used a revised empirical equation applicable to the watershed by grid on the GSIS. Also, we analyzed RUSLE factors by watershed being analyzed with watershed extraction algorithm. Then we could calculate the minimum, maximum. mean and standard deviation of RUSLE factors by watershed.

• New & Renewable Energy
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• v.7 no.1
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• pp.22-28
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• 2011

The surface solar radiations were calculated and analyzed with spatial resolutions (4 km and 1 km) using by GWNU (Gangneung-Wonju National University) solar radiation model. The GWNU solar radiation model is used various data such as aerosol optical thickness, ozone amount, total precipitable water and cloud factor are retrieved from Moderate Resolution Imaging Spectrometer (MODIS), Ozone Monitoring Instrument (OMI), MTSAT-1R satellite data and output of the Regional Data Assimilation Prediction System(RDAPS) model by Korea Meteorological Administration (KMA), respectively. The differences of spatial resolutions were analyzed with input data (especially, cloud factor from MTSAT-1R satellite). And the Maximum solar radiation by GWNU model were found in Andong, Daegu and Jinju regions and these results were corresponded with the MTSAT-1R cloud factor.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.141-157
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• 1997

The structure and dimensionality of the User Information Satisfaction (UIS) construct is an important theoretical issue that received considerable attentions. The acceptance of UIS as a standardized instrument requires confirmation that it explains and measures the user information satisfaction construct and its component. Based on a simple of 670 respondents who participated in dealing with the Public Management Information System (PMIS), this research used a confirmatory factor analysis to test the alternavtive models of underlying factor structure and assessed the reliability and validity of these factors and items in the PMIS. The result provided a support for a revised UIS model with four first-order factors and one PMIS The result provided a support for a revised UIS model with four first-order factors and one second-order (higher-order) factor in PMIS. To cross-validata these results, the author reexamined two prior data sets. The results showed that the revised model provides better model-data fit in all three data sets.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.141-150
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• 2003

This paper presents the multiple testing method of an autoregressive parameter in stationary AR(1) model using the usual Bayes factor. As prior distributions of parameters in each model, uniform prior and noninformative improper priors are assumed. Posterior probabilities through the usual Bayes factors are used for the model selection. Finally, to check whether these theoretical results are correct, simulated data and real data are analyzed.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.3-20
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• 1990

The multisector model is designed to analyze and forecast structural change in industrial output, employment, capital and relative price as well as macroeconomic change in aggregate income, interest rate, etc. This model has 25 industrial sectors, containing about 1,300 equations. Therefore, this model is characterized by detailed structural disaggregation at the sectoral level. Individual industries are based on many of the economic relationships in the model. This is what distinguishes a multisector model from a macroeconomic model. Each industry is a behavioral agent in the model for industrial investment, employment, prices, wages, and intermediate demand. The strength of the model lies in the simulating the interactions between different industries. The result of its simulation will be introduced in the next paper. In this paper, we only introduce the structure of the multisector model and the coefficients of the equations. The multisector model is a dynamic model-that is, it solves year by year into the future using its own solutions for earlier years. The development of a dynamic, year-by-year solution allows us to combine the change in structure with a consideration of the dynamic adjustment required. These dynamics have obvious advantages in the use of the multisector model for industrial planning. The multisector model is a medium-term and long-term model. Whereas a short-term model can taken the labor supply and capital stock as given, a long-term model must acknowledge that these are determined endogenously. Changes in the medium-term can be analyzed in the context of long-term structural changes. The structure of this model can be summarized as follow. The difference in domestic and world prices affects industrial structure and the pattern of international trade; domestic output and factor price affect factor demand; factor demand and factor price affect industrial income; industrial income and relative price affect industrial consumption. Technical progress, as measured in terms of total factor productivity and relative price affect input-output coefficients; input-output coefficients and relative price determine the industrial input cost; input cost and import price determine domestic price. The differences in productivity and wage growth among different industries affect the relative price.