• Journal of Energy Engineering
• /
• v.29 no.3
• /
• pp.1-6
• /
• 2020

A portfolio matrix analysis was conducted to improve R&D productivity of the government-funded R&D projects in the energy sector. 27 projects (42 detailed technologies) in 2018 were evaluated on a 5-point scale in terms of availability and technology competitiveness, and portfolio matrix analysis was conducted twice. The results of the portfolio matrix analysis could provide the landscape of on-going R&D projects at a time and could be utilized as feedback data to establish development strategies for individual projects, while establishing differentiated management directions to improve R&D productivity in each of the four areas of the portfolio matrix.

• Journal of Korea Spatial Information System Society
• /
• v.9 no.3
• /
• pp.81-89
• /
• 2007

We analyzed the technology portfolio matrix for the car navigation technology using QFD method and accordingly suggested the navigation technology development direction. QFD is a useful tool to analyze the customer demands and the technologies. Depending on the survey results from the latent customers and the technology capabilities from the study of the national institutions, we suggested technology portfolio matrix. The visual HMI technology, safe driving support technology, and the navigator information management technology are the most prospective area for R&D investment according to the portfolio matrix.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.4
• /
• pp.260-270
• /
• 2013

This paper proposes a strategic portfolio model for managing performance of online games. The portfolio matrix is composed of two dimensions: financial performance and non-financial performance. Financial performance is measured by the conventional measure, average revenue per user (ARPU). In terms of non-financial performance, five non-financial key performance indicators (KPIs) that have been widely used in the online game industry are utilized: RU (Register User), VU (Visiting User), TS (Time Spent), ACU (Average Current User), MCU (Maximum Current User). Data envelopment analysis (DEA) is then employed to produce a single performance measure aggregating the five KPIs. DEA is a linear programming model for measuring the relative efficiency of decision making unit (DMUs) with multiple inputs and outputs. This study employs DEA as a tool for multiple criteria decision making (MCDM), in particular, the pure output model without inputs. Combining the two types of performance produces the online game portfolio matrix with four quadrants: Dark Horse, Stop Loss, Jack Pot, Luxury Goods. A case study of 39 online games provided by company 'N' is provided. The proposed portfolio model is expected to be fruitfully used for strategic decision making of online game companies.

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

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.210-212
• /
• 2015

The growth-share matrix is a portfolio planning tool developed by the Boston Consulting Group (BCG) to assist competitive positioning in the international market including those in the construction industry. This matrix is helpful in balancing the firm's cash-flow, and it can suggest strategic directions for each business unit. However, its effectiveness and applicability have long been debated in the academic field due to the complex and unique industrial context of construction. To solve the dispute, this research clarifies the applicability of theories underlying the growth-share matrix to the construction industry. Empirical research based on actual financial data of Korean construction firms is adopted for the statistical analysis including one-way analysis of variance and correlation analysis. The results of this research show that empirical findings on the relationship between performance variables. In this context, this research can provide important insights on the concept of portfolio management in the construction industry.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.6
• /
• pp.677-682
• /
• 2018

The relationship-type research and development (R&D) portfolio is a method for selecting core technologies based on their unique purposes and characteristics when the criteria for selecting them are independent. This study presents a relationship-type R&D portfolio method as a way to derive core technologies, and describes the methodology by dividing it into three steps: 1) analyze the relationships between selection criteria and analytical indicators, 2) form a portfolio matrix that best matches each selection criteria, and 3) derive the core technologies. In this study, the relationships between four selection criteria for selecting core technologies and the analytical indicators for identifying the technology level, economics, and the technology itself, are written in a table with HoQ. Based on the relationship table, analytical indicators to be considered were derived to satisfy each selection criterion, and the derived analytical indicators and the selected technologies were constructed with two axes in the portfolio matrix. The satisfied portfolio, P0, that satisfies all four criteria, and the portfolio, P1~P4, that satisfies selection criteria based on the unique characteristics of the four criteria, were constructed, and core technologies derived. The selected core technologies can be utilized in selecting a core area against the future security environment through a process like key word analysis based on the specifications.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.4
• /
• pp.137-152
• /
• 2014

In this paper, I suggest several techniques to estimate covariance matrix and compare the performance of the global minimum variance portfolio (GMVP) in terms of out of sample mean standard deviation and return. As a result, the return differences among the GMVPs are insignificant. The mean standard deviation of the GMVP using historical covariance is sensitive to the estimation window and the number of assets in the portfolio. Among the model covariance, the GMVP using constant systematic risk ratio model or using short sale restriction shows the best performance. The performance difference between the GMVPs using historical covariance and model covariance becomes insignificant as the historical covariance is estimated with longer estimation window. Lastly, the implied volatilities from ELW prices do not lead to superior performance to the historical variance.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.39-55
• /
• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of KIBIM
• /
• v.8 no.4
• /
• pp.1-12
• /
• 2018

Nowadays, BIM(Building Information Modeling) technology has been slowly accepted and developed around the world, making smart construction possible. Many countries are also actively promoting the comprehensive application of BIM and changing the traditional construction methods of the construction industry. This study reviews foreign and domestic literature reviews on BIM application barriers and smart construction applications, providing a theoretical basis for Chinese construction enterprises to reduce or eliminate BIM application barriers. Based on the common feature of policies or strategies that promote the development of smart construction in developed countries, such as the United States, the United Kingdom, and Singapore, the deficiencies of China's smart construction policies for construction enterprises are analyzed. Moreover, according to the literature review of the development status of China's construction industry, the SWOT analysis matrix of China's smart construction strategy is obtained. Finally, based on the SWOT matrix analysis results, combined with the development status of China's construction industry and the obstacles faced by smart construction, the portfolio strategies and recommendations for the development of smart construction are proposed in this work. These portfolio strategies and recommendations can provide a reference value for construction enterprises.

• Asia-Pacific Journal of Business
• /
• v.11 no.4
• /
• pp.37-48
• /
• 2020

Purpose - This study empirically investigates whether the risk property included in fat-tails of return distributions is systematic or unsystematic based on the devised statistical methods. Design/methodology/approach - This study devised empirical designs based on two traditional methods: principal component analysis (PCA) and the testing method of portfolio diversification effect. The fatness of the tails in return distributions is quantitatively measured by statistical probability. Findings - According to the results, the risk property in the fat-tails of return distributions has the economic meanings of eigenvalues having a value greater than 1 through PCA, and also systematic risk that cannot be removed through portfolio diversification. In other words, the fat-tails of return distributions have the properties of the common factors, which may explain the changes of stock returns. Meanwhile, the fatness of the tails in the portfolio return distributions shows the asymmetric relationship of common factors on the tails of return distributions. The negative tail in the portfolio return distribution has a much closer relation with the property of common factors, compared to the positive tail. Research implications or Originality - This empirical evidence may complement the existing studies related to tail risk which is utilized in pricing models as a common factor.