پروژه های مخلوط "تحقیق و توسعه" و مدل انتخاب پرتفوی اوراق بهادار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|17267||2008||16 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||15 روز بعد از پرداخت||946,620 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||8 روز بعد از پرداخت||1,893,240 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : European Journal of Operational Research, Volume 185, Issue 2, 1 March 2008, Pages 700–715
The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.
During the past decade there has been a dramatic increase in the institutional investment. Although most of those investments remain focused on the traditional securities investment, there is growing attention to various forms of alternative investment classes, e.g., venture capital, private equity, private debt and real estate. With the extension of investment asset classes, the overall portfolio risk can be lowered while the potential for more benefit can be increased over the long term. The mean variance methodology for portfolio selection proposed by Markowitz  and  has been central to research activities in the traditional securities investment field. Following Sharpe , some researchers proposed a series of linear risk functions ,  and , e.g., Konno and Yamazaki’s  mean absolute deviation risk function, Mansini and Speranza’s  mean semi-absolute deviation risk function, Gini’s mean difference risk function , Young’s  minimax risk function. Recently, Lai et al.  formulated portfolio selection models with interval numbers. In their models, the semi-absolute deviation risk function is extended to the interval case. Fang et al.  studied the portfolio selection problem based on the fuzzy decision theory.
نتیجه گیری انگلیسی
Currently, investors invest in various asset classes to keep their competitive advantage. Some securities and R&D projects can be integrated into a mixed asset portfolio. The mixed asset portfolio increases the investors’ benefit opportunities. Considering the different characteristics of securities investment and R&D projects investment, we have proposed a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, a bi-objective mixed-integer stochastic programming model with the constraints on the initial capital budget, the risk control in each intermediate stage and no selling short of securities is presented. Moreover, the model uses the semi-absolute deviation risk function to measure the risk of mixed asset portfolio. Based on the idea of moments approximation, we give a scenario generation procedure for the stochastic returns of single-stage R&D projects and multi-stage securities. Given a scenario tree, efficient solutions of the bi-objective mixed-integer stochastic programming model can be obtained by transforming it into a single objective mixed-integer linear programming model. Thus the proposed model enables the investors to construct and manage their mixed securities and R&D projects portfolio effectively.