• Korean Chemical Engineering Research
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• v.44 no.6
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• pp.650-658
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• 2006

The recycling of TDA from solid waste of TDI plant(TDI-R) by near-critical hydrolysis reaction had been studied by means of a statistical design of experiment. The main and interaction effects of process variables had been defined from the experiments in a batch reactor and the correlation equation with process variables for TDA yield had been obtained from the experiments in a continuous pilot plant. It was confirmed that the effects of reaction temperature, catalyst type and concentration, and the weight ratio of water to TDI-R(WR) on TDA yield were significant. TDA yield decreased with increases in reaction temperature and catalyst concentration, and increased with an increase in WR. As a catalyst, NaOH was more effective than $Na_2CO_3$ for TDA yield. The interaction effects between catalyst concentration and temperature, WR and temperature, catalyst type and reaction time on TDA yield had been defined as significant. Although the effect of catalyst concentration on TDA yield at $300^{\circ}C$ as subcritical water was insignificant, the TDA yield decreased with increasing catalyst concentration at $400^{\circ}C$ as supercritical water. On the other hand, the yield increased with an increase in WR at $300^{\circ}C$ but showed negligible effect with WR at $400^{\circ}C$. The optimization of process variables for TDA yield has been explored with a pilot plant for scale-up. The catalyst concentration and WR were selected as process variables with respect to economic feasibility and efficiency. The effects of process variables on TDA yield had been explored by means of central composite design. The TDA yield increased with an increase in catalyst concentration. It showed maximum value at below 2.5 of WR and then decreased with an increase in WR. However, the ratio at which the TDA yield showed a maximum value increased with increasing catalyst concentration. The correlation equation of a quadratic model with catalyst concentration and WR had been obtained by the regression analysis of experimental results in a pilot plant.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Korean Journal of Food Science and Technology
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• v.32 no.4
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• pp.843-850
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• 2000

We have produced the microcapsule composed of ${\alpha}-tocopherol$ as a core material (Cm) and the gelatinized polysaccharide as a wall material (Wm). Firstly, we have developed a simple, sensitive, and quantitative analysis method of the microencapsulation product using 5% cupric acetate pyridine solution. We could then optimize all the conditions for the microencapsulation process such as the ratio of [Cm] to [Wm], the temperature of dispersion fluid, and the emulsifier concentration using response surface methodology (RSM). As for the microencapsulation of ${\alpha}-tocopherol$, the regression model equation for the yield of microencapsulation (YM, %) to the change of an independent variable could be predicted as follows : YM=99.77-1.76([Cm]:[Wm])-1.72$([Cm]\;:\;[Wm])^2$. From the ridge of maximum response, the optimum conditions for the microencapsulation of ${\alpha}-tocopherol$ were able to be determined as the ratio of [Cm] to [Wm] of 4.6:5.4(w/w), the emulsifier concentration of 0.49%, and dispersion fluid temperature of $25.5^{\circ}C$, respectively. Finally, the microcapsules produced under the optimal conditions were applied for the analysis of storage stability. The optimal conditions for the storage were found to be the values of pH 9.0 and $25{\sim}35^{\circ}C$. And the storage stability of the microcapsules containing ${\alpha}-tocopherol$ were higher than 99% for a week at pH 9.0 and $25^{\circ}C$.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.