• The korean journal of orthodontics
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• v.27 no.3 s.62
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• pp.457-471
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• 1997

The Purpose of this study was to estimate relative importance among the causative factors o( arch length discrepancy(ALD) and Possibility of prediction of the ALD in the mixed dentition. The sample consisted of the casts of the 142 young adults who had no abnormal muscle function, no skeletal abnormalities and Class I molar relationship. We classified the sample by gender and the extent of ALD, and measured mesiodistal diameters of each tooth and the dimensions of the dental arch. The computerized statistical analyses was carried out with SPSS win program. The results were as follows ; 1. Most of the variables of spacing group and some variables of dental arch dimension of crowding group were significantly different between genders. But in normal group, there were few differences. 2. In male crowding and female spacing group, mainly measurements of tooth dimension were significantly different from those of normal group. 3. In male spacing and female crowding group, measurements of dental arch dimension were significantly different from those of normal group. 4. The measurements of dimension of dental arch were highly correlated with ALD in correlation analysis and factor analysis. 5. Prediction equations for adult's ALDs by means of what can be measured in the mixed dentition(mesiodistal dimensions of incisors and first molar, intermolar width and arch length) showed R square from $63\%$ to $80\%$.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.647-658
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• 2021

Analysts' forecasts are important for providing useful guidance to investors, especially individual or small investors, and therefore it becomes critical to identify the elements which can potentially increase errors in analysts' forecasts. This study investigates potential factors which can lead to errors in forecasting by analysts, specifically in terms of the level and attributes of corporate earnings. Utilizing a sample of firms listed on the Korean stock markets, this study provides evidence that firms with more volatile and unpredictable earnings feature less accurate analyst forecasts. This study fills a void in the literature by conducting empirical tests for earnings attributes in terms of volatility and unpredictability that could potentially undermine the forecast accuracy. The negative association between the quality of earnings and forecast accuracy is more pronounced for firms with negative net income values. Additional analysis demonstrates that forecast accuracy is significantly lower for the fourth quarter than for other fiscal quarters and that fourth quarter earnings tend to be more volatile and unpredictable. This study contributes to the literature by providing new empirical evidence regarding the comprehensive effects of earnings quality and level on analysts' forecasting accuracy and further suggests potential factors contributing to the fourth quarter anomaly in analyst forecasts in terms of earnings attributes.

• Journal of Distribution Science
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• v.17 no.11
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• pp.73-79
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• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.242-246
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• 2010

In this paper, we propose a quality assessment method of biometrics for estimating an authentication result in an user authentication system. The proposed quality assessment method is designed to compute a quality score called CIMR (Confidence Interval Matching Ratio) as a result by small-sample analysis like T-test. We use the C/MR-based quality assessment method for testing how to well draw a distinction between various biometrics in a multimodal biometric system. We also test a predictability for authentication results of obtained biometrics using the mean $\bar{X}$ and the variance $s^2$ in T-test-based CIMR. As a result, we achieved the maximum 88% accuracy for estimation of user authentication results.

• Journal of Biosystems Engineering
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• v.29 no.1
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• pp.65-70
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• 2004

This study was carried out to verify and predict the soil informations such as the contents of organic matter(OM) and Mg and pH of the soil. The predictability of spacial variation in the paddy field was examined by analyzing the various soil information. The prediction models for the OM pH, and Mg, were developed using inverse distance weighted (IDW), triangulated irregular network(TIN) and Kriging model. The determination of coefficients of linear and spherical Kriging models were 0.756 and 0.578, respectively, and were very low in comparison with other soil information. For IDW and TIN model, the determination of coefficients were 1.000 and hence the performance of the models was found to be excellent. The developed models were validated using unknown soil sample obtained In 2000 and 2001. From the analysis of relationship between the measured pH and predicted 0.9353. For prediction of Mg, the determination of coefficient is more than 0.8. Since the determination of coefficients of developed models for OM were relatively low, it may be difficult to predict the content of OM using the developed models. For further study, the additional works to enhance the performance of the prediction models for soil information are required.

• Korean Management Science Review
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• v.17 no.3
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• pp.61-71
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• 2000

Due to the potential growing capability that reflects future value, the market value of internet business companies (IB) are still evaluated high although major players like Amazon continuously suffer losses. Then, how do investors valuate the potential growing capabilities given that traditional financial/accounting based valuation approaches seem to be inappropriate for IB. This study attempts to provide an answer to this issue. We, therefore, analyzed the predictability of various accounting and non-accounting variables for IB value. These include book value, net income, unique visitors, page view, reach rate, public float and institutional holdings. Because of being in infant stage and difficulties in obtaining necessary web traffic data, sample of 20 pure IB were selected from Korea Stock Exchange Market, KOSDAQ, and informal market. The results of this study showed that web traffic date had the strongest relationship with IB value. In particular, unique visitors and reach rate were found to be best predictors for IB value while page view was reasonable indicator. Interestingly, net income was not found to related to IB value. This calls for an attention to the typical characteristics of IB that my hinder the usage of traditional valuation approaches for IB. Another results revealed that none of both public float and institutional holdings was significantly associated with IB value, indicating market’s supply-demand factors were less important than traffic information.

• Information Systems Review
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• v.8 no.3
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• pp.125-134
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• 2006

This article adopts a decision tree algorithm(C5.0) to predict customer churning in online auto insurance environment. Using a sample of on-line auto insurance customers contracts sold between 2003 and 2004, we test how decision tree-based model(C5.0) works on the prediction of customer churning. We compare the result of C5.0 with those of logistic regression model(LRM), multivariate discriminant analysis(MDA) model. The result shows C5.0 outperforms other models in the predictability. Based on the result, this study suggests a way of setting marketing strategy and of developing online auto insurance business.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.111-133
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• 2019

Flow through a scaled horizontal axis marine current turbine was numerically simulated after validation and the turbine design was optimized. The computational fluid dynamics (CFD) code Ansys-CFX 16.1 for numerical modeling, an in-house blade element momentum (BEM) code for analytical modeling and an in-house surrogate-based optimization (SBO) code were used to find an optimal turbine design. The blade-pitch angle (${\theta}$) and the number of rotor blades (NR) were taken as design variables. A single objective optimization approach was utilized in the present work. The defined objective function was the turbine's power coefficient ($C_P$). A $3{\times}3$ full-factorial sampling technique was used to define the sample space. This sampling technique gave different turbine designs, which were further evaluated for the objective function by solving the Reynolds-Averaged Navier-Stokes equations (RANS). Finally, the SBO technique with search algorithm produced an optimal design. It is found that the optimal design has improved the objective function by 26.5%. This article presents the solution approach, analysis of the turbine flow field and the predictability of various surrogate based techniques.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.51-62
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• 2019

In this study, a simple method to estimate the soil water content variation in a wide area was proposed using hyperspectral near-infrared images. The reflectance data of a sand, granite soils, and a kaolinite were measured by reflecting the soil samples with different wavelengths in the visible and near-infrared (VNIR) regions using hyperspectral cameras. The measured reflectances and parameters were used to build a water content prediction model using the Partial Least Square Regression (PLSR) analysis. In the water content prediction model, the Area of Reflectance (Near-infrared, NIR) parameter was the most suitable parameter to determine the water content. The parameter was applicable regardless of the soil type, as the coefficient of determination (R²) exceeded 0.9 for each soil sample. Additionally, the mean absolute percentage error (MAPE) was less than 15% when compared with the actual water content of the soil. Therefore, the predictability of water content variation for soils with water content lower than 50% was confirmed. Accordingly through this study, the predictability of water content variation in several soil types using the hyperspectral near-infrared images was confirmed. For further development, a model that incorporates soil classification would be required to improve the accuracy of the model and to predict higher range of water contents.