• Journal of Educational Research in Mathematics
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• v.18 no.1
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• pp.63-80
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• 2008

In this paper, we suggested generally some samples and cases of portfolio but centered on elementary mathematics in New Zealand elementary school in the aspects of the assessment for learning activity of learner and so we have found some suggestive points by comparing New Zealand portfolio with ours. Finally, we have an objects that the teachers here in Korea can use these results as a cases which are benchmarked by them. We had known through this paper that portfolio assessment in New Zealand elementary school deals with various aspects and it was accessing in the direction of creating knowledge positively through the real life, not textbookish or artificial problem and also it had a characteristics dealing with real life situation or context without filtering. Especially, it always dealt with all regions of curriculum and looked like focusing on the connections of curriculum relatively.

• Journal of Korea Technology Innovation Society
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• v.14 no.2
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• pp.279-300
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• 2011

Given the huge increase in interest in biotechnology, whose applications are being expanded as a new growth engine, investment and agency participation are also increasing. In 2008, the level of investment by the national R&D programs in future emerging technologies (6T) in the field of biotechnology was as great as that in IT, and six agencies and many relevant research institutes are now carrying out various related projects. This paper intends to review the status of investment in biotechnology by analyzing the portfolio and positioning of the national R&D biotechnology programs, which address global issues such as the quality of life, the aging society, and environment and energy, and to propose a new investment strategy and direction for the efficient implementation of the national R&D programs.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.355-366
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• 2006

Customer Satisfaction (CS) in Auto insurance market is the important factor which makes customer loyalty and retention increase. Recently On-line companies are threatening the existing Off-line companies with taking advantage of the low price through cut-offing the price by internet marketing. Therefore, the CS is becoming an indispensable survival strategy to the Off-line companies. Under these circumstances, this study finds out what the CS factors are in the auto insurance market, and produces levels of CS, customer loyalty and satisfaction Index of each category. The purpose of this study is to suggest the strategic improvement factor for elevating CS level and strategic direction for CS management by CS portfolio analysis based on the survey result.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• Journal of The Korean Association For Science Education
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• v.22 no.1
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• pp.190-203
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• 2002

The purpose of this study is to test content validity of a portfolio assessment and to analyse the information which can be obtained from student portfolios. The content validity of the portfolio was tested against the objectives of each lesson and the emphasis of curriculum. The information was identified from the analysis of student portfolios. Students' portfolios from a fourth grade class in Pyeungteak, Kyeungki-do were used for analysis. The portfolios included students' evidence of learning on (I) Strata, Unit 2 'Strata and Fossil,' and (3) Change of Object by Heat, Unit 4 'Heat and Change of Object'. Fourth-grade science textbooks were also analyzed to understand the base level information for portfolio analysis. Two science education specialists and ten elementary teachers majored in science education took part in this analysis. The results of the analysis showed $70{\sim}100%$ of agreement between the objectives of lesson and portfolio forms. Over 90% of agreement is reached between portfolio forms and the emphasis of the curriculum. Student portfolios revealed much information on comprehension, observation, will to study, and process of learning. They also revealed some information on drawing conclusion, communication. self-direction, progress of learning, self-concept, interaction, and process of learning. As a whole, the information in students' portfolios is similar with that dealt in science textbooks. However, students' portfolios have more information on anticipation, will to study, self-direction, and interaction. On the contrary, science textbook deals more with information on observation, planning inquiry, than students' portfolios. The portfolio assessment examined has very sound content validity. The results also show that much more and various information which can not be obtained from pencil and paper test could be obtained from student portfolios. The use of information, obtained from student portfolios will make it possible understand students' learning. their strength and weakness, hence improve student' science learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.1
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• pp.91-100
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• 2000

In 1997 the recession of Korean economy brought about the bankruptcy of large corporations and the large size of non-Performing financial assets which led to IMF financial crisis. One of the major reasons for IMF financial crisis was poor loan management of domestic financial institutions . During the restructuring process of financial institutions since the IMF financial crisis, the importance of the loan management has been recognized. Especially. financial institutions' credit allocation had been concentrated on a few big conglomerates and their subsidies as well as some specific business areas. Hence, risk-diversifying portfolio effects were not reflected in any loan portfolios. The IMF financial crisis in 1997 has clearly showed that credit-risk management is essential not only for individuals' loan but also for portfolios consisting of various loans The main objective of this paper is to provide some suggestions on the direction for financial institutions in Korea to improve their loan portfolio management. Particularly, for the effective management of loan portfolios, this paper introduces quantitative credit-risk management schemes such as KMV models and CreditMetrics which are commonly used in financial institutions in advanced countries. Financial institutions in Korea should make their best efforts to establish a more scientific as well as quantitative loan portfolio management.

• Convergence Security Journal
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• v.18 no.2
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• pp.123-131
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• 2018

The goal of this study is to look deeper into a reading education method for improving cognitive abilities of petty officer majoring students in community college level. Lack of the cognitive ability through the passing status of reading information processing highly can cause a problem for understanding information of context. Therefore, this study redefines the reading step to improve the cognitive ability. also, it sets up progress steps; material selection - learning - inspection - practice based on the cognitive abilities. To achieve those goals, there are two major ways. The first, setting up a proper reading assignment that is suitable for petty officer major students in community college level is a key step for this study. Second, the instructor leads the students to judge their own cognitive ability objectively by using a portfolio curriculum which contains a checking list of the portfolio, structuring a curriculum based on weekly achievements, self-checking, and setting up a direction of practice. The two presented ways are the most effective ways to develop students' cognitive ability based on continuous reading and checking. For the last, the study mentions a proposal for further tasks in this field of the study.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1305-1325
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• 2008

As index option markets grow recently, many analysts and investors become interested in forecasting the volatility of KOSPI 200 Index to achieve portfolio's goal from the point of financial risk management and asset evaluation. To serve this purpose, we introduce NN and SVM integrated with other financial series models such as GARCH, EGARCH, and EWMA. Moreover, according to the empirical test, Integrating NN with GARCH or EWMA models improves prediction power in terms of the precision and the direction of the volatility of KOSPI 200 index. However, integrating SVM with financial series models doesn't improve greatly the prediction power. In summary, SVM-EGARCH was the best in terms of predicting the direction of the volatility and NN-GARCH was the best in terms of the prediction precision. We conclude with advantages of the integration process and the need for integrating models to enhance the prediction power.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• 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.