• East Asian Journal of Business Economics (EAJBE)
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• v.12 no.2
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• pp.1-7
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• 2024

Purpose: This study delves into the application of the Ohlson 1995 valuation model, particularly addressing the intricacies of the "Other information" variable. Our goal is to pinpoint the most suitable variables for substitution within this category, focusing specifically on the Mongolian Stock Exchange (MSE) context. Research design, data, and methodology: Employing data spanning from 2012 to 2022 from 60 MSE-listed companies, we conduct a comprehensive analysis encompassing both financial and non-financial indicators. Through meticulous examination, we aim to identify which variables effectively substitute for the "Other information" component of the Ohlson model. Results: Our findings reveal significant outcomes. While all financial variables within the model exhibit importance, certain non-financial indicators, notably the company's level and state ownership participation, emerge as particularly influential in determining stock prices on the MSE. Conclusions: This study not only contributes to a deeper understanding of valuation dynamics within the MSE but also provides actionable insights for future research endeavors. By refining key variables within the Ohlson model, this research enhances the accuracy and efficacy of financial analysis practices. Moreover, the implications extend to practitioners, offering valuable insights into the determinants of stock prices in the MSE and guiding strategic decision-making processes.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.686-689
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• 2002

This paper proposes a weighted least-squares(WLS) method for designing variable one-dimensional (1-D) FIR digital filters with simultaneously variable magnitude and variable non-integer phase-delay responses. First, the coefficients of a variable FIR filter are represented as the two-dimensional (2-D) polynomials of a pair of spectral parameters: one is for tuning the magnitude response, and the other is for varying its non-integer phase-delay response. Then the optimal coefficients of the 2-D polynomials are found by minimizing the total weighted squared error of the variable frequency response. Finally, we show that the resulting variable FIR filter can be implemented in a parallel form, which is suitable for high-speed signal processing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.518-522
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• 2002

This paper proposes a new application of Fuzzy logic to Variable Step Size Least Mean Square (VS-LMS) adaptive beamforming algorithm in CDMA systems. The proposed algorithm adjusts the step size of the Least Mean Square (LMS) by using the application of Fuzzy logic in which the increase or decrease of step size depends on the fuzzy inference results of the Mean Square Error (MSE). Computer simulation results show that the proposed algorithm has a better capacity of tracking compared with the conventional LMS algorithms and other variable step size LMS algorithms.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.429-440
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• 2003

Variable sampling rate (VSR) control charts vary the sampling interval and/or the sample size according to value of the control statistic. It is known that $\bar{X}$ charts with VSR scheme lead to large improvements in performance over those with fixed sampling rate (FSR) scheme. In this paper, we studied $\bar{X}$ charts with several VSR schemes, and compared their statistical performance each other.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.1-7
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• 2020

This paper introduces two different techniques for dealing with incomplete data and algorithms for learning this data. The first method is to process the incomplete data by assigning the missing value with equal probability that the missing variable can have, and learn this data with the SVM. This technique ensures that the higher the frequency of missing for any variable, the higher the entropy so that it is not selected in the decision tree. This method is characterized by ignoring all remaining information in the missing variable and assigning a new value. On the other hand, the new method is to calculate the entropy probability from the remaining information except the missing value and use it as an estimate of the missing variable. In other words, using a lot of information that is not lost from incomplete learning data to recover some missing information and learn using deep learning. These two methods measure performance by selecting one variable in turn from the training data and iteratively comparing the results of different measurements with varying proportions of data lost in the variable.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.3
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• pp.262-268
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• 1988

In this paper conditions are derived, which ensure the existence of a quasi-sliding mode on the control switching hyperplane in discrete variable structure control systems and also remove the reaching phase problem observed in continuous-time variable structure systems. In addition, a discrete variable structure tracking controller which has adaptive properties is devised based on these results. This controller has useful properties, such as small sensitivity to the variation of plant parameters and to disturbances and its performing speed is fast compared to that of other adaptive controller.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.65-70
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• 2018

This paper describes a effective information compressor algorithm for the fourth industrial technology. One of the difficult problems for outdoor is to obtain effective updating process of background images. Because input images generally contain the shadows of buildings, trees, moving clouds and other objects, they are changed by lapse of time and variation of illumination. They provide the lowering of performance for surveillance system under outdoor. In this paper, a effective information algorithm for variable environment variable under outdoor is proposed, which apply the Kalman Estimation Modeling and adaptive threshold on pixel level to separate foreground and background images from current input image. In results, the better SNR of about 3dB~5dB and about 10%~25% noise distribution rate in the proposed method. Furthermore, it was showed that the moving objects can be detected on various shadows under outdoor and better result Information.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.251-261
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• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.17-20
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• 2000

This paper addresses a novel algorithm for variable rate channel coding with interleaver punctured convolutional code for wireless communication. In other to increase the coding performance and achieve the variable channel coding rate, serially concatenated convolutional coding scheme will be applied. In this paper, we characterize the effect of interleaver puncturing on the effectiveness of the proposed scheme some simulation results are presented, in which the channel model of additive Gaussian noise is assumed.

• Communications for Statistical Applications and Methods
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• v.20 no.5
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• pp.357-366
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• 2013

This paper defines an improvement for estimating the population mean of a study variable using auxiliary information and known values of certain population parameter(s), when there is a non-response in a study as well as on auxiliary variables. Under a simple random sampling without a replacement (SRSWOR) scheme, the mean square error (MSE) of all proposed estimators are obtained and compared with each other. Numerical illustration is also given.