• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of the Korean Society of Food Culture
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• v.18 no.6
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• pp.551-561
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• 2003

To determine the conditions of the fermentation and storage for Chonggak kimchi in kimchi refrigerator, prepared Chonggak kimchi took into kimchi refrigerators which were controlled at four different modes of the fermented temperature and time, and fermented and kept for 16 weeks. The pH in Chonggak kimchi fermented at $20^{\circ}C$ for 24 hours/stored at $-1^{\circ}C$ dropped greater than all of kimchi fermented at other combinations, and the changes of pH at any combinations were not greater than those in Baechu kimchi, because pH in Chonggak kimchi did not dropped below 4.5. Acidities in Chonggak kimchi were greatly increased at higher temperature. The acidity in Chonggak kimchi during the first week of fermentation was lower than that in Baechu kimchi and then it was rather higher because of the addition of waxy rice paste. In texture, puncture force of Chonggak kimchi was decreased slowly until 8 weeks of fermentation and then did not changed much and the highest values showed in Chonggak kimchi stored directly at $-1^{\circ}C$ without any fermentation. In sensory evaluation, the scores for the carbonated flavor and the sourness were the highest in Chonggak kimchi fermented at $20^{\circ}C$ for 24 hours/stored at $-1^{\circ}C$, but the lowest in Chonggak kimchi stored directly at $-1^{\circ}C$ without any fermentation because of some undesirable flavors. The lowest hardness showed in Chonggak kimchi fermented at highest temperature and the best hardness was in Chonggak kimchi fermented at $5^{\circ}C$ for 3 days or 6 days/stored at $-1^{\circ}C$. The appearance was the best in Chonggak kimchi fermented at $20^{\circ}C$ for 24 hours/stored at $-1^{\circ}C$ and the worst was in Chonggak kimchi stored directly at $-1^{\circ}C$ without any fermentation. The overall acceptability of Chonggak kimchi fermented at $20^{\circ}C$ for 24 hours/stored at $-1^{\circ}C$ was good after 4 weeks of fermentation, but in Chonggak kimchi fermented at $5^{\circ}C$ for 3 days or 6 days/stored at $-1^{\circ}C$ it was good after 6 weeks. Total microbial counts in most of Chonggak kimchi were reached to a maximum number within 7 days, and then decreased similarly at all modes. Leuconostoc spp. and Lactobacillus spp. increased to maximum number of $1.48{\times}10^9\;and\;5.62{\times}10^9$, respectively, in Chonggak kimchi fermented for 7 days. Yeast counts showed a increasing trend not depends on fermenting temperature and they were lower counts than those in Baechu kimchi. Waxy rice paste which added to Chonggak kimchi resulted in increasement of glucose as a carbon source and stimulated to reproduce the microbes in Chonggak kimchi.