• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.135-145
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• 1994

There has been increasing interest in modeling the effect of buffer stock in automatic flow lines such as transfer line, assembly line and process line. The purpose of this paper is to determine the optimal buffer stock for a two-stage process line of industry that minimize a expected cost considering line efficiency and buffer stock Analytical method for the simplified model is applied and computer simulation is conducted to real numerical example.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.189-191
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• 2009

Compatibility concerns between train signalling systems and rolling stock are a significant problems to cross-acceptance of rolling stock in urban railway system. In this paper, we measured the conducted emission flowing current through rails and analyzed results for DC rolling Stock.

• Journal of Korean Society of Forest Science
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• v.103 no.3
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• pp.446-452
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• 2014

This research aimed to provide a method to estimate growing stock and carbon stock using the characteristics of forest type map such as the age-class, DBH class and crown density class. We transformed the growing stock data of national forest inventory (mainly Kangwon-do province) onto those of time when the forest type map was established. We developed a simulation model for the growing stock using the transformed data and the characteristics of forest type map by the quantification method I. By comparing partial correlation coefficient, we found that quantification of growing stock was largely affected by age-class followed by crown density class, forest type and DBH class. The growing stock, was estimated as minimum in the broadleaved forest with age-class II, DBH class 'Small', and crown density class 'Low' as $20.0m^3/ha$, whereas showed maximum value in the coniferous forest with age-class VI, DBH class 'Large', and crown density class 'High' as $305.0m^3/ha$. The growing stock for coniferous, broadleaved, and mixed forest were estimated as $30.5{\sim}305.0m^3/ha$, $20.0{\sim}200.4m^3/ha$, and $23.8{\sim}238.1m^3/ha$, respectively. When we compared the carbon stock by forest type, the carbon stock by age class based on growing stock was maximum when DBH class was 'Large' and crown density class was 'High' regardless of forest type. This estimation of growing stock by using characteristic of forest type can be used to estimate the changes in growing stock and carbon stock resulting from deforestation or natural disaster. In addition, we hope it provide a useful advice when forest officials and policy makers have to make decisions in regard to forest management.

• Management & Information Systems Review
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• v.36 no.3
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• pp.37-49
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• 2017

This study investigates the dynamic relationship between KOSPI200 stock index and stock index futures and stock index option markets which is its derived from KOSPI200 stock index. We use 5-minutes rate of return data from 2012. 06 to 2014. 12. To empirical analysis, this study use autocorrelation and cross-correlation analysis as a preliminary analysis and then following Stoll and Whaley(1990) and Chan(1992), the multiple regression is estimated to examine the lead-lag patterns between the stock index and stock index futures and option markets by Newey and West's(1987) Empirical results of our study shows as follows. First, there exist a strong autocorrelation in the KOSPI200 stock index before 10minutes but a very weak autocorrelation in the stock index futures and option markets. Second, there is a strong evidence that stock index future and option markets lead KOSPI200 stock index in the cross-correlation analysis. Third, based on the multiple regression, the stock index futures and option markets lead the stock index prior to 10-15 minutes and weak evidence that the stock index leads the future and option markets. This results show that the market efficient of KOSPI200 stock index market is improved as compared to the early stage of stock index future and option market.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Korean journal of food and cookery science
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• v.15 no.5
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• pp.490-499
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• 1999

The purpose of this study was to investigate the changes of taste components in the boiled beef brisket soup stock and shank soup stock by varying pretreatment, boiling temperature and time. Free amino acids and nucleotides color and sensory evaluation in each samples were analyzed. The results were obtained as follows : 1. The amount of free amino acids in the brisket soup stock pretreated by soaking and blanching showed a tendency to increase in proportion to boiling time. The amount of glutamic acid in the brisket soup stock was much in order of soaking > blanching > roasting pretreatment. While the amount of glutamic acid in the boiled soup stock samples pretreated by soaking and blanching was much more at low temperature than at high temperature, the glutamic acid contents in the boiled soup stock pretreated by roasting were large at high temperature. The amount of glutamic acid in pretreated by soaked soup stock showed the highest and recorded 8.73 mg% at 6 hour-low temperature-boiling. 2. The amount of free amino acids in the shank soup stock did not show any regular tendency and had few changes in quantity by the methods of pretreatment. Each amount of glutamic acid in the shank soup stock pretreated by soaking and blanching was the highest, when boiled for 3 hours at high temperature. The samples pretreated by roasting showed the highest record 2.49 mg%, when boiled for 6 hours at high temperature, but could not recognize any regular tendency in the case of boiling at low temperature. 3. The amount of nucleotides in the brisket soup stock generally showed increase in proportion to boiling time. The amount of 5'-IMP extracted from the brisket soup stock was much in order of blanching > soaking > roaking pretreatment, but few differences between blanching and soaking soup stock samples. The amount of 5'-IMP extracted from soup stock samples pretreated by soaking and blanching was high at low-boiling and by roasting at high-boiling. Each amount of 5'-IMP extracted from soup stocks pretreated by soaking(BSL) and blanching(BBL) was the highest at 6 hour-low-boiling(37.06 mg%), and 5 hours(38.37 mg%) respectively. The amount of 5'in the soup stock pretreated by roasting(BRH) showed the highest records at 6 hour-high-boiling(10.85 mg%). 4. The amount of 5'-IMP extracted from the shank soup stock preteated by soaking and blanching showed a tendency to decrease after 3 hours boiling irrelative of boiling temperature. The amount of 5'in the shank soup stock was much in order of soaking > blanching > roasting pretreatment and showed high at the boiling of high temperature. In the sample pretreated by roasting it showed the highst records when boiled for 6 hours at high temperature(1.55 mg%). 5. The L Value of the brisket soup stock pretreared by roasting at high temperature(BRH) was the lowest and the b value of it was the highest of all the brisket samples boiled for 6 hours. No differences were found in the Value of L, a, and b in shank soup stock by the methods of pretreatment and boiling temperature. 6. The sensory scores in color and flavor of the brisket soup stock showd that BRH was higher than the other samples, and the preference in taste and overall was the highest in BSH while it was the lowest in BRH. The preference in the all sensory characteristics of SSH was higher than any other shank soup stock, but did not show any significant difference statistically.

• Management & Information Systems Review
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• v.34 no.5
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• pp.23-47
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• 2015

In the Korea stock market, individual investors have perceived stock as short arbitrage investment, not long-term investment strategy. In order to reinforce stock market transparency and soundness, it is important to enforce the measures for stock market management. Especially, stock market event caused by financial policy can be given individual investors negative information regarding a stock trading. Thus, it is a need for investigating whether comprehensive review of listing eligibility is influenced on individual investors' responses and stock behaviors in respect of effectiveness. The purpose of this study to examine the relations between such stock market management and transitional aspect of individual investors' trading types and response on the based of pre- and post-event occurrence. Using an dataset of user's text messages on 9 firms posted on the firm-based social media (i.e., Naver, Daum, Paxnet) over the period 2009 to 2014. And we performed text-clustering and topic modeling according to keywords for classifying into investors group and non-investors groups and two types of investors were categorized depending on main topic transition by event windows in Comprehensive review of listing eligibility. The results indicated that a variety of stockholders existed in the stock. And the ratio of non-investors group was on the decrease, on the other hand, the proportion of investors group veer onto the side of pre-pattern after comprehensive review of listing eligibility. A distinctive feature of our study is to explain the influence of stock market management on response changes of individual investors as well as to categorize in accordance with time progression. Implications an suggestions for future research were also discussed.

• Journal of Distribution Science
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• v.13 no.9
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• pp.37-45
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• 2015

Purpose - This paper's aim is to investigate whether or not gross profitability explains the cross-sectional variation of the stock returns in the Korean stock market. Gross profitability is an alternative profitability measure proposed by Novy-Marx in 2013 to predict cross-sectional variation of stock returns in the US. He shows that the gross profitability adds explanatory power to the Fama-French 3 factor model. Interestingly, gross profitability is negatively correlated with the book-to-market ratio. By confirming the gross profitability premium in the Korean stock market, we may provide some implications regarding the well-known value premium. In addition, our empirical results may provide opportunities for the fund distribution industry to promote brand new styles of funds. Research design, data, and methodology - For our empirical analysis, we collect monthly market prices of all the companies listed on the Korea Composite Stock Price Index (KOSPI) of the Korea Exchanges (KRX). Our sample period covers July1994 to December2014. The data from the company financial statementsare provided by the financial information company WISEfn. First, using Fama-Macbeth cross-sectional regression, we investigate the relation between gross profitability and stock return performance. For robustness in analyzing the performance of the gross profitability strategy, we consider value weighted portfolio returns as well as equally weighted portfolio returns. Next, using Fama-French 3 factor models, we examine whether or not the gross profitability strategy generates excess returns when firmsize and the book-to-market ratio are controlled. Finally, we analyze the effect of firm size and the book-to-market ratio on the gross profitability strategy. Results - First, through the Fama-MacBeth cross-sectional regression, we show that gross profitability has almost the same explanatory power as the book-to-market ratio in explaining the cross-sectional variation of the Korean stock market. Second, we find evidence that gross profitability is a statistically significant variable for explaining cross-sectional stock returns when the size and the value effect are controlled. Third, we show that gross profitability, which is positively correlated with stock returns and firm size, is negatively correlated with the book-to-market ratio. From the perspective of portfolio management, our results imply that since the gross profitability strategy is a distinctive growth strategy, value strategies can be improved by hedging with the gross profitability strategy. Conclusions - Our empirical results confirm the existence of a gross profitability premium in the Korean stock market. From the perspective of the fund distribution industry, the gross profitability portfolio is worthy of attention. Since the value strategy portfolio returns are negatively correlated with the gross profitability strategy portfolio returns, by mixing both portfolios, investors could be better off without additional risk. However, the profitable firms are dissimilar from the value firms (high book-to-market ratio firms); therefore, an alternative factor model including gross profitability may help us understand the economic implications of the well-known anomalies such as value premium, momentum, and low volatility. We reserve these topics for future research.

• Korean journal of food and cookery science
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• v.15 no.5
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• pp.483-489
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• 1999

In order to study effects of herbs on the changes of the taste compounds, color and sensory evaluation of soup stock. The crude protein, free amino acids and nucleotide contents in brisket soup stock were investigated by use of semimicro-kjeldahl method and HPLC. In addition, color measurement and sensory evaluation were investigated. Generally, The crude protein, free amino acids and nucleotides contents in various soup stocks increased by heating time. The crude protein contents in the Go, Ca and A soup stocks increased much more than control soup stock. The free amino acids were the highest content in the Go$\_$5/ soup stock specially, arginine, alanine, glycine, threonine and glutamic acid. The free amino acid contents were lower in C$\_$5/ and O$\_$5/ soup stock than B$\_$5/ soup stock. 5'-IMP, inosine and hypoxanthine concentration in Go$\_$5/(33.4 mg%) soup stock showed highest value. But those in the C$\_$5/(5.8 mg%) and O/sun 5/(5.7 mg%) soup stocks were lower than that in the B$\_$5/ soup stock. From a sensory evaluation, the all of sensory score of samples was not significantly difference.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.