• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.203-215
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• 2022

Bayesian Networks are multivariate probabilistic factor graphs that are used to assess underlying factor relationships. From January 2005 to December 2018, the study examines how Dynamic Bayesian Networks can be utilized to estimate portfolio risk and return as well as determine inter-factor relationships among reversal profit-generating components in Pakistan's emerging market (PSX). The goal of this article is to uncover the factors that cause reversal profits in the Pakistani stock market. In visual form, Bayesian networks can generate causal and inferential probabilistic relationships. Investors might update their stock return values in the network simultaneously with fresh market information, resulting in a dynamic shift in portfolio risk distribution across the networks. The findings show that investments in low net profit margin, low investment, and high volatility-based designed portfolios yield the biggest dynamical reversal profits. The main triggering aspects related to generation reversal profits in the Pakistan market, in the long run, are net profit margin, market risk premium, investment, size, and volatility factor. Investors should invest in and build portfolios with small companies that have a low price-to-earnings ratio, small earnings per share, and minimal volatility, according to the most likely explanation.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.476-480
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• 2010

This study deals with stockout costs in the supply chain optimization model under the framework of batch-storage network. Stockout is very popular in chemical industries. Estimating stockout cost involves an understanding of customer reactions to a seller being out of stock at the time the customer wants to buy an item. This involves massively non-trivial work such as direct customer interviews and extensive mail survey. In this study, we will introduce a new interpretation of stockout costs combined with batchstorage network optimization model and thus suggest an easy way of estimating stockout costs. Optimization model suggest that optimal process and storage sizes considering stockout cost are smaller than those that do not consider stockout cost. An illustrative example support the analytical results.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.3
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• pp.131-141
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• 2013

Stock identification of Todarodes pacificus collected in the East Sea, Yellow Sea and East China Sea during the period from September to December in 2011 was analyzed by morphometric characters and mitochondrial DNA (mtDNA) cytochrome oxidase subunit I (COI) gene nucleotide variations. Frequency distributions of mantle length was analyzed by morphological method with measuring size of T. pacificus. Then each stock was estimated to confirm their maturation for mean mantle length comparing with mean mature mantle length 20-22 cm. According to morphologic stock identification, it is estimated that the northern part of East Sea is categorized as summer stock and the rest parts, including mid /southern part of the East Sea, northern part of the East China Sea and northern part of the West Sea were autumn stock. For genetic analysis, a total 49 haplotypes were defined by 33 variable nucleotide sites. From the extensive haplotype diversity, limited nucleotide diversity and star-like shape of haplotype network, T. pacificus appears to have undergone rapid population expansion from an ancestral population with a small effective population size. Although pair-wise Fst estimates which represent genetic difference among groups were low, there are relatively remarkable difference of Fst between middle and southern part of the East Sea. Although middle part of the East Sea and southern part of the East Sea were situated at the East Sea, genetically separated groups were appeared.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.61-75
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• 2020

The scope of data for understanding or predicting stock prices has been continuously widened from traditional structured format data to unstructured data. This study investigates whether commentary data collected from SNS may affect future stock prices. From "Stock Discussion Room" in Naver, we collect 20 stocks' commentary data for six months, and test whether this data have prediction power with respect to one-hour ahead price direction and price range. Deep neural network such as LSTM and CNN methods are employed to model the predictive relationship. Among the 20 stocks, we find that future price direction can be predicted with higher than the accuracy of 50% in 13 stocks. Also, the future price range can be predicted with higher than the accuracy of 50% in 16 stocks. This study validate that the investors' sentiment reflected in SNS community such as Naver's "Stock Discussion Room" may affect the demand and supply of stocks, thus driving the stock prices.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.323-335
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• 1999

In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.127-130
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• 2009

We propose a method for detecting DDoS (Distributed Denial of Service) traffic in real-time inside the backbone network. For this purpose, we borrow the concepts of MACD (Moving Average Convergence Divergence) and RoC (Rate of Change), which are used for technical analysis in the stock market Due to the fact that the method is based on a quantitative, rather than a heuristic, detection level, DDoS traffic can be detected with greater accuracy (by reducing the false alarm ratio). Through simulation results, we show how the detection level is determined and demonstrate how much the accuracy of detection is enhanced.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.3-5
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• 1999

This paper deals with the effect on the stress and deformation of the rotor bar of a cage induction motor by the thermal stress, centrifugal force and electromagnetic force. We use both the thermal network method(TNM) and the finite element method(FEM) to analyze the temperature and stress of the rotor.

• Management & Information Systems Review
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• v.7
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• pp.381-400
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• 2001

Our country has been encountered with severe economic depression since IMF loan(1997). Korean economic system should be reformed according to the globalization and the world industrial trends based on adavanced technologies and skills. Todays the venture business should be considered to be the alternative of recovering Korean economy and increasing highly estimated values. Generally speaking, the venture business is defined as a new business with high risk-high return with new technical entrepreneurship. This treatise suggests the activating alternatives of the venture business in Korea. These alternatives are suggested as follows. First, several measures to incruit excellent technicians are suggested. These measures includes incruitment of parttime technicians, application of cooperative program of education training unemployees, the stock option system, a stock-sharing plan for the employees, etc. Second, in the financial aspect, activation of the foundation investment company, activation of the COSDAQ market, strengthening of technology security are suggested. Third, in the technical development aspect, amplification of inauguration assistant center, construction of technology accumulative network, etc. These measures will contribute to the development of Korean economy and the welfare of Korean people.

• Annual Conference of KIPS
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• 2007.05a
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• pp.1349-1351
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• 2007

따라서, 현재의 국내외 정보통신 환경을 분석하고, 특히 정부 IT정책과 정보 통신업계 동향을 정밀분석하여 증권전산망이 나아가야 할 방향을 설정하는 것과, 차세대 증권전산망의 미래 모델을 제시하는 것은 무엇 보다 중요하고 본 논문을 쓰는 목적이라 할 수 있다. 사용자의 니즈를 반영한 증권전산망의 미래 발전방향 검토와 장기적인 관점에서의 추진방향 제시하였다.