• 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 Agricultural Science
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• v.9 no.1
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• pp.357-370
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• 1982

Recent concern regarding price and availability of fossil fuels has spurred the interest in alternative sources for farm crop drying. Among the available options such as biomass energy, wind power, nuclear energy and solar energy etc., the increasing attention is being directed to the utilization of heat from solar energy especially for farm crop drying. Even though solar energy is dispersed over a large land area and only a relatively small amount of energy can be simply collected, the advantages of solar energy is that the energy is free, non-polluting. The study reported here was designed to help supply the informations for the development of simple and relatively inexpensive solar warehouse for farm crop drying and storage. Specifically, the objectives of this study were to determine the performance of the solar collector fabricated, to compare solar supplemented heat drying with natural air drying and to develop a simulation model of temperature in stored grain, which can be used to study the effects due to changes in ambient air temperature. For those above objectives, solar collector was fabricated from available materials. Corrugated steel galvanized sheet, painted flat black, was used as absorbers and clear 0.2mm polyethylene sheet was the cover material. The warehouse for rough rice drying and storage was constructed with concrete block, and the solar collector was used as the roof of warehouse instead of original roofing system of it. The results obtained in this study were as follows: 1. The thermal efficiency of the solar collector was average 26 percent and the overall heat transfer coefficient of the collector was approximately $25kJ/hr.m^2\;^{\circ}K$. 2. Solar heated air was sufficient to dry one cubic meter of rough rice from 23.5 to 15.0 percent in 7 days and natural air was able to dry the same amount of rough rice from 20.0 to 5 percent in l2 days. 3. Drying with solar heat reduced the required drying time to dry the same amount of rough rice into a half compared to natural air drying, but overdrying problems of the bottom layer were so severe that these problems should be thoroughly analyzed. 4. Simulation model of temperature in stored grain was developed and the results of predicted temperature agreed well with test results. 5. Based on those simulated temperature, changes in the grain-temperature were a large at the points of the wallside and the damage of the grain would be severe at the contact area of wall.