• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.123-138
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• 2022

Purpose - This study compares the performances of dynamic asset allocation strategies using Korean stocks and U.S. dollar, which have been negatively correlated for a long time, to examine the diversification effects in the portfolios of them. Design/methodology/approach - In the current study, we use KOSPI200 index, as a proxy of the aggregated portfolio of Korean stocks, and USDKRW foreign exchange rate to implement various portfolio management strategies. We consider the equally-weighted, risk-parity, minimum variance, most diversified, and growth optimal portfolios for comparison. Findings - We first find the enhancement of risk adjusted returns due to risk reduction rather than return increasement for all the portfolios of consideration. Second, the enhancement is more pronounced for the trading strategies using correlations as well as volatilities compared to those using volatilities only. Third, the diversification effect has become stronger after the global financial crisis in 2008. Lastly, we find that the performance of the growth optimal portfolio can be improved by utilizing the well-known momentum phenomenon in stock markets to select the length of the sample period to estimate the expected return. Research implications or Originality - This study shows the potential benefits of adding the U.S. dollar to the portfolios of Korean stocks. The current study is the first to investigate the portfolio of Korean stocks and U.S. dollar from investment perspective.

• 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 Data and Information Science Society
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• v.28 no.6
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• pp.1481-1500
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• 2017

This study examines the stability and excellence of portfolio investment strategies based on the accounting information of the Korean stock market. In the process of constructing the portfolio, various combinations of financial ratios are used to select the stocks with high expected return and to measure their performance. We also tried to improve our investment performance by using genetic algorithm optimization. The results of this study show that portfolio strategies using accounting information are effective for investment decision making and can achieve high investment performance. We also verify that portfolio strategy using genetic algorithms can be effective for investment decision making.

• 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.

• Korean Journal of General Education
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• v.6 no.1
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• pp.241-267
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• 2012

The purpose of this study was focused on finding out the effect of "Learning Strategies and Career Development" classes for first year student. To this end, I developed the learning portfolio based instructional method, textbook, learning portfolio, teaching guidebook and teaching materials. For the study, I conducted literature reviews, analysis of domestic and international best practices, expert advices and survey researches. This study implemented on 3,200 freshman and 72 professors of A university that is located at Gyungbuk. The results of applying research findings show that learning portfolio based instruction in "Learning Strategies and Career Development" classes is useful to improve first year students' competency of learning strategies and career search. And instructors can use the learning portfolio, teaching guidebook and teaching materials, therefore they are satisfied with the materials.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.2
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• pp.375-383
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• 2016

This paper investigates an optimal consumption, portfolio, and life insurance strategies of a family when there is a borrowing constraint and risk aversion change at the time of death of the breadwinner. A CRRA utility is employed and by using the dynamic programming method, we obtain analytic expressions for the optimal strategies.

• IE interfaces
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• v.23 no.4
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• pp.300-310
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• 2010

This paper is about an optimal investment portfolio strategy. Financial data of stocks, bonds, and savings from January 2. 2001 through October 30. 2009 were utilized in order to suggest the optimal portfolio strategies. Fundamental analysis and technical analysis were used in stocks-related strategy, whereas passive investment strategy and active investment strategy were used in bond-related strategy. The score is assigned to each stock index according to the suggested strategies and set trading rules are based on the scores. The simulation has been executed about each 29,400-portfolios and we figured out with the simulation result that 26.75% of 7,864 portfolios are more profitable than average stock market profit (22.6%, Annualized). The outcome of this research is summarized in two parts. First, it's the rebalancing strategy of portfolio. The result shows that value-oriented investment(long-term investment) strategy yields much higher than short-term investment strategies of stocks or active investment of bonds. Second, it's about the rebalancing cycle forming the portfolios. The result shows that the rate of return for the portfolio is the best when rebalancing cycle is 12 or 18 months.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.109-126
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• 2004

This paper deals with two problems of optimal portfolio strategies in continuous time. The first one studies the optimal behavior of a firm who is forced to withdraw funds continuously at a fixed rate per unit time. The second one considers a firm that is faced with an uncontrollable stochastic cash flow, or random risk process. We assume the firm's income can be obtained only from the investment in two assets: a risky asset (e.g., stock) and a riskless asset (e.g., bond). Therefore, the firm's wealth follows a stochastic process. When the wealth is lower than certain legal level, the firm goes bankrupt. Thus how to invest is the fundamental problem of the firm in order to avoid bankruptcy. Under the case of different lending and borrowing rates, we obtain the optimal portfolio strategies for some reasonable objective functions that are the piecewise linear functions of the firm's current wealth and present some interesting proofs for the conclusions. The optimal policies are easy to be operated for any relevant investor.

• Korean Journal of Human Ecology
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• v.16 no.1
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• pp.75-87
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• 2007

The purpose of this study was to classify the financial strategies in elderly households. The data of 4,577 households with all ages and 1255 elderly households is from the Korean Labor and Income Panel Study(2000, 2003). The data were analyzed by various statistical methods such as frequency, mean-test, Duncan's multiple range test, k-mean cluster analysis and logistic regression. Findings were as follows; First, the classified household financial strategy types were Residual(44.3%), Financial Assets(24.0%), Informal Institutional(19.7%), Diversified Portfolio(7.6%), Real Estate(4.5%). Second, the criteria of classification of the financial strategies were relative, not absolute. Third, households(both elderly households and all households) that employed a diversified portfolio strategy had the greatest net wealth.

• Journal of Families and Better Life
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• v.22 no.6 s.72
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• pp.165-175
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• 2004

The purpose of this study was to classify the household financial strategies and investigate major determinants of the household financial strategies and financial performance. The data of 3,994 households is from the Korean Labor and Income Panel Stud?. The major findings were as follows. (1) The classified household financial strategies types were Residual ($44.6\%$), Informal Institutional ($13.3\%$), Financial Assets ($16.7\%$), Real Estate ($13.4\%$), and Diversified Portfolio ($12.0\%$). (2) The criteria of classification of the household financial strategies were relative, not absolute. (3) The household financial strategy types changed largely during a short period(1999-2000). (4) In all households, the variables that affected changes in household financial strategies were education, occupation, number of children, residential location and home ownership. (5) Households that employed a diversified portfolio strategy had the greatest financial performance (2,316,000 won net gain). (6) In all households, the variables that had the greatest influence on financial performance were the number of children, assets and debts. 1'he financial performance was significantly different according to changes in the household financial strategy.