• 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.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.13 no.2
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• pp.65-80
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• 1980

Processing conditions of the krill products such as paste food, krill protein concentrate, seasoned dried krill, powdered seasoning, meat ball, and snack have been examined and the quality was evaluated chemically and organoleptically. In the processing of paste food, krill juice was yielded $71\%$ and krill scrap $29\%$. The yields of paste and broth from the krill juice showed $53\%$ and $43\%$, respectively. In amino acid composition of the krill paste, proline, glutamic acid, aspartic acid, lysine, and leucine were abundant, while histidine, methionine, tyrosine, serine and threonine were poor. The optimum condition for solvent extraction in the processing of krill protein concentrate was the 5 times repetitive extraction using isopropyl alcohol at $80^{\circ}C$ for 5 mins. The yield of krill protein concentrate when used fresh frozen materials was $10.2\%$ in isopropyl alcohol solvent and $8.8\% in ethyl alcohol, and when used preboiled frozen materials, the yield was $13.0\%$ in isopropyl alcohol and $11.8\%$ in ethyl alcohol. Amino acid composition of krill protein concentrate showed a resemblance to that of fresh frozen krill meat. In quality comparison of the seasoned dried krill, hot air dried krill was excellent as raw materials and sun dried krill was slightly inferior to hot air dried krill, but preboiled frozen krill showed the poorest quality. The result of quality evaluation for seasoning made by combination of dried powdered krill, parched powdered sesame, salt, powdered beef extract, monosodium glutamate, powdered red pepper and ground pepper showed that the hot air dried krill was good in color and sundried krill was favorable in flavor. When krill meat ball was prepared using wheat flour, monosodium glutamate and salt as side materials, the quality of the products added up to $52\%$ of krill meat was good and the difference in quality upon the results of the organoleptic test for raw materials was not recognizable between fresh frozen and preboiled frozen krill. In the experiment for determining the proper amount of materials such as dried Powdered krill, $\alpha-starch$, sweet potato starch, sugar, salt, monosodium glutamate, glycine, potassium tartarate, ammonium bicarbonate, and sodium bicarbonate in processing krill snack, sample B(containing $7.7\%$ of dried powdered krill) and sampleC (containing $10.8\%$ of dried powdered krill) showed the most palatable taste from the view point of organoleptic test. Sweet potato starch in testing side materials was good in the comparison of suitability for processing krill snack. Corn starch and kudzu starch were slightly inferior to sweet potato starch, while wheat flour was not proper for processing the snack. In the experiment on frying method, oil frying showed better effect than salt frying and the suitable range of frying temperature was $210-215^{\circ}C$.