• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.45-52
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• 2021

Stock price fluctuations affect investor returns, particularly, in this pandemic situation that has triggered stock market shocks. As a result of this situation, investors prefer to move their money into a safer portfolio. Therefore, in this study, we approach an efficient portfolio model using smart beta and combining others to obtain a fast method to predict investment stock returns. Smart beta is a method to selects stocks that will enter a portfolio quickly and concisely by considering the level of return and risk that has been set according to the ability of investors. A smart beta portfolio is efficient because it tracks with an underlying index and is optimized using the same techniques that active portfolio managers utilize. Using the logistic regression method and the data of 100 low volatility stocks listed on the Indonesia stock exchange from 2009-2019, an efficient portfolio model was made. It can be concluded that an efficient portfolio is formed by a group of stocks that are aggressive and actively traded to produce optimal returns at a certain level of risk in the long-term period. And also, the portfolio selection model generated using the smart beta, beta, alpha, and stock variants is a simple and fast model in predicting the rate of return with an adjusted risk level so that investors can anticipate risks and minimize errors in stock selection.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.48-58
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• 2013

The stock market is constantly changing and sometimes a slump or a sudden rising in stocks happens without any special reason. So the stock market is recognized as a complex system and it is hard to predict the change on stock prices. In this paper we consider the stock market to a network consisting of stocks. We analyzed the dynamics of the Korean stock market network and evaluated the changing of the correlation between shares consisting of the time series data of 137 companies belong to KOSPI200. Our analysis shows that the stock prices tend to plummet when the correlation between stocks is very high. We propose a method for recommending the stock portfolio based on the analysis of the stock market network. To show the effectiveness of the recommended portfolio, we conducted the simulated stock investment and compared the recommended portfolio with the efficient portfolio proposed Markowitz. According to the experiment results, the rate of return of the portfolio is about 10.6% which is about 3.7% and 5.6% higher than the average rate of return of the efficient portfolio and KOSPI200 respectively.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.535-545
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• 2013

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.1-21
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• 2016

The Markowitz portfolio selection model uses estimators to deduce input parameters. However, the estimation errors of input parameters negatively influence the performance of portfolios. Therefore, this model cannot be reliably applied to real-world investments. To overcome this problem, we suggest an algorithm that can exclude stocks with large estimation error from the portfolio by applying a tracking signal to the Markowitz portfolio selection model. By calculating the tracking signal of each stock, we can monitor whether unexpected departures occur on the outcomes of the forecasts on rate of returns. Thereafter, unreliable stocks are removed. By using this approach, portfolios can comprise relatively reliable stocks that have comparatively small estimation errors. To evaluate the performance of the proposed approach, a 10-year investment experiment was conducted using historical stock returns data from 6 different stock markets around the world. Performance was assessed and compared by the Markowitz portfolio selection model with additional constraints and other benchmarks such as minimum variance portfolio and the index of each stock market. Results showed that a portfolio using the proposed approach exhibited a better Sharpe ratio and rate of return than other benchmarks.

• Asia-Pacific Journal of Business
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• v.10 no.3
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• pp.37-51
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• 2019

In this paper, we investigate the performance of anomaly factors in Asia-Pacific Stock market and show the higher Sharpe ratio of the volatility managed smart beta portfolio. The smart beta portfolio combines the benefit of passive strategy and active strategy. However, the smart beta portfolios are seems to be exposed to the risk of anomaly factors from the perspective of traditional financial equilibrium model. Therefore, the smart beta strategy may generate negatively skewed returns unappealing to investors having lower risk tolerance. Our empirical investigations find that the return of the Asia-Pacific region stock market is more volatile than other regions with the lower efficiency ratio. However, the value factor and the momentum factor of Asia-Pacific region both show good performances. More interestingly, we also find that managing the volatility of the momentum factor in Asia-Pacific stock market almost doubles the efficiency ratio.

• The Journal of Asian Finance, Economics and Business
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• v.1 no.2
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• pp.31-37
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• 2014

The portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market, should make. The main purpose of this article is the determination of the optimal portfolio with regard to relations among stock returns of companies which are active in Tehran's stock market. For achieving this goal, weekly statistics of company's stocks since Farvardin 1389 until Esfand 1390, has been used. For analyzing statistics and information and examination of stocks of companies which has change in returns, factors analysis approach and clustering analysis has been used (FC approach). With using multivariate analysis and with the aim of reducing the unsystematic risk, a financial portfoliois formed. At last but not least, results of choosing the optimal portfolio rather than randomly choosing a portfolio are given.

• The Journal of Information Technology
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• v.9 no.3
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• pp.77-89
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• 2006

The event study analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Stock split announcements are generally associated with positive abnormal returns. It is important to investigate the responses of stocks to new information contained in the announcements of stock splits. So It is important to study the long term performance in the case of Stock Split. This Study forced to two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach. The event time portfolio approach exists the CAR model, BHAR model and WR model. And the calendar time portfolio approach has the 3 factor model, 4 factor model, CTAR model, and RATS model.

• Korean Management Science Review
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• v.26 no.2
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• pp.19-35
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• 2009

This paper investigated performance of the Markowitz's portfolio selection model with applications to Korean stock market. We chose Samsung-Group-Funds and KOSPI index for performance comparison with the Markowitz's portfolio selection model. For the most recent one and a half year period between March 2007 and September 2008, KOSPI index almost remained the same with only 0.1% change, Samsung-Group-Funds showed 20.54% return, and Markowitz's model, which is composed of the same 17 Samsung group stocks, achieved 52% return. We performed sensitivity analysis on the duration of financial data and the frequency of portfolio change in order to maximize the return of portfolio. In conclusion, according to our empirical research results with Samsung-Group-Funds, investment by Markowitz's model, which periodically changes portfolio by using nonlinear programming with only financial data, outperformed investment by the fund managers who possess rich experiences on stock trading and actively change portfolio by the minute-by-minute market news and business information.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.35-52
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• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.839-850
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• 2021

Risk-adjusted return is believed to be one of the optimal parameters to determine an optimum portfolio. A risk-adjusted return is a calculation of the profit or potential profit from an investment that takes into account the degree of risk that must be accepted to achieve it. This paper presents a new procedure in portfolio selection and utilizes these results to optimize the risk level of risk-adjusted Islamic stock portfolios. It deals with the weekly close price of active issuers listed on Jakarta Islamic Index Indonesia for a certain time interval. Overall, this paper highlights portfolio selection, which includes determining the number of stocks, grouping the issuers via technical analysis, and selecting the best risk-adjusted return of portfolios. The nominated portfolio is modeled using Quadratic Programming (QP). The result of this study shows that the portfolio built using the lowest Value at Risk (VaR) outperforms the market proxy on a risk-adjusted basis of M-squared and was chosen as the best portfolio that can be optimized using QP with a minimum risk of 2.86%. The portfolio with the lowest beta, on the other hand, will produce a minimum risk that is nearly 60% lower than the optimal risk-adjusted return portfolio. The results of QP are well verified by a heuristic optimizer of fmincon.