• 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.

• 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.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• The Journal of Information Technology
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• v.9 no.4
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• pp.75-85
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• 2006

This study is to test the influence of stock option granting information on the firm's performance. The important issue in stock option is that agent cost is the important determinant factor for the long term performance. The agent cost arises between the manager and shareholders. So many study are concentrated in diminishing the agent cost, and develop some substitute tools to measure the agent cost. The event study about stock option analyzes returns around event date at a time. Event study provides estimation periods and cumulative returns. Announcements about stock option are generally associated with positive abnormal returns in short term period, but not showing positive effect in long term period. It is important to investigate the responses of stocks to new information contained in the announcements of stock option. Therefore it is important to study the long term performance in the case of stock option. 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. This study is forced to develop and arrange two approach method in evaluating the performance, the event time portfolio approach and calendar time portfolio approach.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.135-145
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• 2020

This paper provides the practical application of a linear shrinkage framework on Vietnam stock market. The cumulative data points observed in this analysis are 468 weeks from January 2011 to December 2019. All the companies listed on Ho Chi Minh City Stock Exchange (HOSE), except the companies under two years period from Initial Public Offering (IPO), are considered. The cumulative number of stocks picked is therefore 350 companies. The VNINDEX, which is the Vietnam Stock Index, is used as a reference index for shrinking to a single-index model. The empirical results show that the shrinkage of covariance matrix for portfolio optimization gives the promising results for the investors on Vietnam stock market. The shrinkage method helps the investors to produce the optimal portfolio in the sense of having higher profit with lower levels of risk compared to the portfolio of the traditional SCM method. Moreover, the portfolio turnover of shrinkage method is always kept at low magnitudes, and this makes the shrinkage portfolios save much transaction costs and reduce the liquidity risks in the trading process. In addition, the ability of shrinkage method in making profit is once again confirmed by the Alpha coefficient that achieves a high positive value.

• IE interfaces
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• v.20 no.2
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• pp.94-102
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• 2007

Portfolio management deals with decision making on 'when' and 'how' to revise an existing portfolio. In this paper, we show that a classical statistical process control (SPC) chart for normal data, a wellestablished tool in quality engineering, can effectively be used for signaling times for revising a portfolio. Noting that the day-to-day performance of a portfolio may be auto-correlated, we use the exponentially weighted moving average center-line chart to develop an automatic portfolio management procedure. The portfolio management procedure is extensively tested on historical data of equities traded in the Korea Exchange (KRX), the American Stock Exchange (AMEX), and the New York Stock Exchange (NYSE). In comparison with the performances of the KOSPI, XAX, and NYA indices during the same time periods, results from these experiments show that SPC chart-based portfolio revision presents itself a convenient and reliable method for optimally managing portfolios.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.23-30
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• 2021

The purpose of this study is to compare the level of risk and return of Islamic stocks in the Jakarta Islamic Index (JII) with conventional stocks on the IDX30 in the period from January 2017 to July 2019. The Sharpe ratio method is used to calculate risk and stock returns. The performance of the stock portfolio is measured by comparing the risk premium portfolio with the portfolio risk that is expressed as a standard deviation of the total risk. This study uses secondary data collected by the Indonesia Stock Exchange (IDX), which provides the names of stock issuers included in the JII and IDX30 indices along with their montly closing price. The results of the descriptive analysis show that the JII Sharpe ratio index from January 2017 to July 2019 is from the minimum range of -0.28820 to a maximum range of 0.05622, while the IDX30 Sharpe ratio index from January 2017 to July 2019 is from the minimum range of -0.09290 to the maximum range of 0.17436. The results of inferential analysis using a different test show that there is a significant difference between the Sharpe ratio JII and IDX30 in measuring the performance of the stock portfolio.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.

• IE interfaces
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• v.25 no.2
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• pp.163-169
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• 2012

This paper proposes an efficient portfolio management methodology named sSPPM with consideration of risk and required return. sSPPM employs Markowitz's portfolio model to select securities and adopts ($s$, $S$) policy that is a well-known technique in the inventory control area to revise the current portfolio. Computational experiments using virtual stock prices generated by monte carlo simulation method as well as real stock ones of KOSPI for recent 4 years are conducted to show the excellence of the portfolio management under ($s$, $S$) policy framework. The result shows that sSPPM is remarkably superior to both 6 or 12 months based periodic portfolio revision method and market (KOSPI index).

• Journal of Distribution Science
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• v.13 no.9
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• pp.37-45
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• 2015

Purpose - This paper's aim is to investigate whether or not gross profitability explains the cross-sectional variation of the stock returns in the Korean stock market. Gross profitability is an alternative profitability measure proposed by Novy-Marx in 2013 to predict cross-sectional variation of stock returns in the US. He shows that the gross profitability adds explanatory power to the Fama-French 3 factor model. Interestingly, gross profitability is negatively correlated with the book-to-market ratio. By confirming the gross profitability premium in the Korean stock market, we may provide some implications regarding the well-known value premium. In addition, our empirical results may provide opportunities for the fund distribution industry to promote brand new styles of funds. Research design, data, and methodology - For our empirical analysis, we collect monthly market prices of all the companies listed on the Korea Composite Stock Price Index (KOSPI) of the Korea Exchanges (KRX). Our sample period covers July1994 to December2014. The data from the company financial statementsare provided by the financial information company WISEfn. First, using Fama-Macbeth cross-sectional regression, we investigate the relation between gross profitability and stock return performance. For robustness in analyzing the performance of the gross profitability strategy, we consider value weighted portfolio returns as well as equally weighted portfolio returns. Next, using Fama-French 3 factor models, we examine whether or not the gross profitability strategy generates excess returns when firmsize and the book-to-market ratio are controlled. Finally, we analyze the effect of firm size and the book-to-market ratio on the gross profitability strategy. Results - First, through the Fama-MacBeth cross-sectional regression, we show that gross profitability has almost the same explanatory power as the book-to-market ratio in explaining the cross-sectional variation of the Korean stock market. Second, we find evidence that gross profitability is a statistically significant variable for explaining cross-sectional stock returns when the size and the value effect are controlled. Third, we show that gross profitability, which is positively correlated with stock returns and firm size, is negatively correlated with the book-to-market ratio. From the perspective of portfolio management, our results imply that since the gross profitability strategy is a distinctive growth strategy, value strategies can be improved by hedging with the gross profitability strategy. Conclusions - Our empirical results confirm the existence of a gross profitability premium in the Korean stock market. From the perspective of the fund distribution industry, the gross profitability portfolio is worthy of attention. Since the value strategy portfolio returns are negatively correlated with the gross profitability strategy portfolio returns, by mixing both portfolios, investors could be better off without additional risk. However, the profitable firms are dissimilar from the value firms (high book-to-market ratio firms); therefore, an alternative factor model including gross profitability may help us understand the economic implications of the well-known anomalies such as value premium, momentum, and low volatility. We reserve these topics for future research.