• The Journal of Asian Finance, Economics and Business
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• 제8권6호
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• pp.543-552
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• 2021

The study examines how foreign capital inflows affect stock market development in Pakistan for the period from July 2008 to June 2018. Several components of foreign capital inflows were used for empirical analysis, namely, foreign direct investment, foreign portfolio investment, and remittances. Further, market capitalization was used as a proxy for stock market development. The study uses an ARDL model for examining the long-run and short-run relationships between variables. We also analyze the bi-directional causality between the variables through the Granger causality test. Further, the presence of structural breaks was analyzed through the CUSUM and CUSUM Square test. The results suggest that in the long run, remittances have a positive and significant relationship with stock market development. However, foreign direct investment, foreign portfolio investment, and USD-PKR exchange rate do not have a significant impact on stock market development. The results also suggest that in the short run there is a negative relationship between FDI, USD-PKR exchange rate and market capitalization. Contrarily, we found a positive relationship between FPI and market capitalization. The results of Granger causality test suggest that remittances and USD-PKR exchange rate have a causal relationship with stock market development. Finally, we found no evidence of structural breaks in the dataset.

• 대한산업공학회지
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• 제39권6호
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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.

• Management Science and Financial Engineering
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• 제10권1호
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• pp.25-41
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• 2004

There have been many studies to build a model that can help investors construct optimal portfolio. Most of the previous models, however, are based upon the path-breaking Markowitz model (1959) which is a quantitative model. One of the most important problems with that kind of quantitative model is that, in reality, most of the investors use not only quantitative, but also qualitative information when they select their optimal portfolio. Since collecting both types of information from the markets are time consuming and expensive, making a set of target assets smaller, without suffering heavy loss in the rate of return, would attract investors. To extract only desired assets among all available assets, we need knowledge that identifies investors' preference for the risk of the assets. This study suggests two-layer decision-making rules capable of identifying an investor's risk preference and an architecture applying them to a quantitative portfolio model based on risk and expected return. Our knowledge-based portfolio system is to build an investor's preference-oriented portfolio. The empirical tests using the data from Korean capital markets show the results that our model contributes significantly to the construction of a better portfolio in the perspective of an investor's benefit/cost ratio than that produced by the existing portfolio models.

• 한국경영과학회지
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• 제41권3호
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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 information systems
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• 제30권1호
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• 통상정보연구
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• 제10권1호
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• pp.375-393
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• 2008

The purpose of this study is to investigate the Chinese investment environment and analyze the actual investment condition of Korean enterprises in China and examine the points at issue. In general, the investment environment in China shows satisfactory progress. China has a multiple and regional extension policy in investment. And the environment for investment changes to insufficiency of company profit, extension of service market, maintenance of legislative system, and insufficiency of preference about foreign company. There are situations of inclining to manufacturing, inclining of region, preference of independence investment, small-sized investment by small and medium enterprises, difficulty of financial assistance, excess of logistic cost, delay of logistic term, difficulty of settlement of legal dispute and difficulty of taking a relative information in investment of Korean enterprise in China. The results of the study indicate mostly that the investment of Korean enterprise into China needs turnover of service trade-tertiary industry, portfolio of investment territory, cooperation with Chinese enterprise through joint venture investment and a large-scale investment for extension of Chinese domestic market.

• 충청수학회지
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• 제27권2호
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• pp.219-225
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• 2014

I use the dynamic programming approach to study the optimal consumption and investment problem with regime-switching and constant labor income. I derive the optimal solutions in closed-form with constant absolute risk aversion (CARA) utility and constant disutility.

• 충청수학회지
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• 제26권1호
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• pp.85-90
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• 2013

We use the dynamic programming method to investigate the optimal consumption and investment problem with regime-switching. We derive the optimal solutions in closed-form with constant absolute risk aversion (CARA) utility.

• 아태비즈니스연구
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• 제13권2호
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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.

• 한국IT서비스학회지
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• 제20권1호
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• pp.11-27
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• 2021

The defense IT master plan is the plan for promoting defense informatization and is established by the Ministry of National Defense every five years. In addition, an implementation plan has been established in accordance with the master plan every year to promote the informatization project. However, since there is no systematic methodology to check and analyze the master plan comprehensively, it is difficult to judge whether the goals suggested in the master plan are achieved, and continuous monitoring of the project is limited. This study proposes a concept and framework for defense IT investment management and proposes a methodology for examining and analyzing the progress of the defense IT plan by referring to the U.S. Department of Defense's IT Plan and the performance management structure of the national IT master plan. As a methodology for managing defense IT investment, we propose an IT investment management code system from the perspective of an IT portfolio and verify its applicability through case studies. The results of this study are expected to improve the defense IT performance management system and improve the efficiency and effectiveness of the defense IT project.