• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.220-225
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• 2022

The purpose of this study is to develop an e-teaching portfolio to perform a teaching portfolio of an instructor on the web. I order to carry out this study, an initial model of the e-teaching portfolio was developed through systematic literature review, and the final e-teaching portfolio was developed by selecting and applying five students, then modifying and supplementing them. The study period was from May 1 to May 20, 2022. As a result of the study, the components of the finally developed e-teaching portfolio are Step 1: Understanding oneself, Step 2: Goal setting, Step 3: Learning strategy, Step 4: Self-check. In conclusion, the program developed through this study is a convenient function that can process everything in one place by connecting the fragmented teaching results, and the developed e-teaching portfolio can promote interaction between individuals by building a community. It has possible characteristics. In order to systematically activate the e-teaching portfolio developed through this study, it is necessary to establish an online management system for systematic operation. Furthermore, an institutional device is needed to guarantee the result of the developed e-teaching portfolio. In order to continuously manage the quality of the teaching portfolio, extrinsic rewards that stimulate the instructor's intrinsic motivation should be provided.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• The Mathematical Education
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• v.52 no.2
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• pp.175-190
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• 2013

The purpose of this article is to suggest using portfolio for professional development of secondary pre-service mathematics teachers based on actual application case. To achieve this goal, 28 pre-service mathematics teachers developed their own portfolios in the regular study course for one semester under the pre-planned components of portfolio. Then they participated in the survey of their beliefs in mathematics and mathematics education and in the structured interview for drawing implications of using a pre-service mathematics teacher portfolio. According to the collected data, developing a pre-service mathematics teacher portfolio made a significant difference in beliefs of mathematics teachers' roles and showed the potential to improve the professional development of pre-service mathematics teachers as well as their learning. Continued investigation for more effective components of a pre-service mathematics teacher portfolio would be needed.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.88-93
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• 2009

e-Portfolio in e-Learning environment is defined as the results which either teachers or learners systematically manage global contents related to their teaching and learning, respectively. When the e-Portfolio is applied to their work including teaching and learning, it is very useful for both teachers and learners to prepare their teaching plan, and to get the opportunity of their reflection by checking out their learning output, respectively. To generate e-Portfolios, there are two ways; one for collecting it directly from teachers and learners and another for collecting it automatically as a result of learning activities through e-learning system. In this paper, for efficient management of e-Portfolio, the data is layered, classified, restructured, and provided for users' purposes and various situations. Users can construct their own e-Portfolios by adding their own logic with their purposes in different situations.

• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.19-28
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• 1999

The major purposes of this study are to examine the effects of portfolio assessment on elementary school student's science knowledge, inquiry ability, science attitudes and to investigate students' perceptions on portfolio assessment. Control group consists of 45 fourth-graders at M-Elementary school located at Miwon, Chongwon-gun, Chung-buk and experimental group 36 fourth-graders of G-Elementary school located in Daejeon-si. The inventories of scientific knowledge I, inquiry ability, and science attitudes were administered to both groups as a pre-test. The experimental group was given portfolio assessment instruction and control group traditional instruction for about six weeks. Inventories about scientific knowledge 2, inquiry ability, and science attitudes were administered to both groups as a post-test. A questionnaire on the perception on portfolio assessment was given to experimental group after the treatment. The results were statistically analyzed with SPSS. Control group showed higher score on scientific knowledge than that of experimental group (p<0.5). No statistically meaningful difference was identified in inquiry ability and scientific attitude. More in-depth analysis revealed that scientific attitudes were improved statistically meaningfully by portfolio assessment. The students' perceptions on portfolio assessment is very positive. Students have positive responses on interests in portfolio assessment, feelings of involvement in learning, self-regulated learning, higher levels of thinking, intentions of participation in portfolio assessment.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.552-564
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• 2017

This study is aimed to overcome the limitation of traditional learning competence and career management system, and conducted to design the function of integrated ePortfolio and the elements of the system for administrative control of curricular and extracurricular program of the university as well as to develop a printout-based prototype in the context of S-university. Researchers deducted the main menus and functions of the integrated ePortfolio by two experts validification procedures, searched for the subfunctions, and secured their validity. Mainly 6 elements of integrated ePortfolio system are designed as follows: basic information, learning and competence management, course and career management, portfolio management, and community. Among these, the three elements of learning and competence, course and career, and portfolio management are assessed as excellent and differentiated from traditional ePortfolios. The study also developed a printout-based prototype of ePortfolio system and provided authentic guide for the ePortfolio system. At the same time, the result of the study contributed to increasing the sense of the developmental direction of the ePortfolio in the institute.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.573-588
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• 2023

Purpose: This study presents a research approach that utilizes deep reinforcement learning to construct optimal portfolios based on the business cycle for stocks and other assets. The objective is to develop effective investment strategies that adapt to the varying returns of assets in accordance with the business cycle. Methods: In this study, a diverse set of time series data, including stocks, is collected and utilized to train a deep reinforcement learning model. The proposed approach optimizes asset allocation based on the business cycle, particularly by gathering data for different states such as prosperity, recession, depression, and recovery and constructing portfolios optimized for each phase. Results: Experimental results confirm the effectiveness of the proposed deep reinforcement learning-based approach in constructing optimal portfolios tailored to the business cycle. The utility of optimizing portfolio investment strategies for each phase of the business cycle is demonstrated. Conclusion: This paper contributes to the construction of optimal portfolios based on the business cycle using a deep reinforcement learning approach, providing investors with effective investment strategies that simultaneously seek stability and profitability. As a result, investors can adopt stable and profitable investment strategies that adapt to business cycle volatility.

• 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.

• Smart Media Journal
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• v.10 no.3
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• pp.39-47
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• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.23-30
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• 2019

As deep learning with the network-based algorithms evolve, artificial intelligence is rapidly growing around the world. Among them, finance is expected to be the field where artificial intelligence is most used, and many studies have been done recently. The existing financial strategy using deep-run is vulnerable to volatility because it focuses on stock price forecasts for a single stock. Therefore, this study proposes to construct ETF products constructed through portfolio methods by calculating the stocks constituting funds by using deep learning. We analyze the performance of the proposed model in the KOSPI 100 index. Experimental results showed that the proposed model showed improved results in terms of returns or volatility.