Portfolio Decision Model Research Articles

A resource portfolio model for equipment investment and allocation of semiconductor testing industry

Profitable but risky semiconductor testing market has led companies in the industry to carefully seek to maximize their profits by developing a proper resource portfolio plan for simultaneously deploying resources and selecting the most profitable orders. Various important factors, such as resource investment alternatives, trade-offs between the price and speed of equipment and capital time value, further increase the complexity of the simultaneous resource portfolio problem. This study develops a simultaneous resource portfolio decision model as a non-linear integer programming, and proposes a genetic algorithm to solve it efficiently. The proposed method is employed in the context of semiconductor testing industry to support decisions regarding equipment investment alternatives (including new equipment procurement, rent and transfer by outsourcing, and phasing outing) for simultaneous resources (such as testers and handlers) and task allocation. Experiments have showed that our approach, in contrast to an optimal solution tool, obtains a near-optimal solution in a relatively short computing time.

Is the International Diversification Potential Diminishing? Foreign Equity In and Out of the US

Over the past two decades international markets have become more open, leading to a common perception that global capital markets have become more integrated. In this paper, I will ask what this integration and its resulting higher correlation would imply about the diversification potential across countries. Moreover, this question relates to the related questions of whether the optimal foreign portfolio allocation has changed over time. For this purpose of answering these questions, I intend to examine two basic groups of international returns: (1) foreign market indices and (2) foreign stocks that are listed and traded in the US. I will examine the first group since this is the standard approach in the international diversification literature. I intend to study the second group since some have argued that US-listed foreign stocks are the more natural diversification vehicle (Errunza et al (1999)). In order to consider the possibility of shifts in the covariance of returns over time, I develop in the proposal an extension of the break-date estimator of Bai and Perron (1998,2003) to test for and estimate possible break dates along with their standard errors. In the preliminary results, I find that the covariances among country stock markets have indeed shifted over time for a majority of the countries. However, in contrast to the common perception that markets have become significantly more integrated over time, the covariance between foreign markets and the US market have increased only slightly from the beginning to the end of the last twenty years. At the same time, the foreign stocks in the US markets have become significantly more correlated with the US market. To consider the economic significance of these parameter changes, I calibrate a simple portfolio decision model in which a US investor could choose between US and foreign portfolios. I find that the optimal allocation in foreign market indices actually increases over time while the optimal allocation into foreign stocks in the US has decreased. These results suggest that while diversification potentials have declined, they remain important for foreign stocks that are not listed in the US market. A number of features are missing from my preliminary analysis detailed below. First, I have so far focused upon American Depositary Receipts (ADRs) on the NYSE as the group of US-listed foreign stocks. However, Canada is the foreign country that has the greatest number of cross-listed companies, yet these are not included as ADRs. Therefore, I intend to include the Canadian stock market and the cross-listed stocks from Canadian companies in the analysis. Second, the analysis described in the proposal below conditions foreign stocks in the US on the residual of the foreign stock market on the US. The model based upon this assumption was the more straightforward version to work out initially. However, it has the undesirable feature that it implies that the betas of foreign stocks listed in the US shift if the foreign market as a whole shifts. I am currently working on an extension that relaxes this assumption and allows independent breaks between individual foreign stocks listed in the US and foreign stock markets as a whole. Third, while I focus upon the NYSE stocks in order to avoid spurious effects from the “tech bubble” in the late 1990s, it may be useful to consider the foreign stocks listed on NASDAQ as well. My projected time line for finishing this project is as follows: • By December 2006 – Collect Canadian data; rerun analysis with Canadian effects included. • By April 2007 – Finish joint estimation allowing for differential changes between foreign market and foreign stocks listed in the US. • By August 2007 – Consider whether to include NASDAQ; Write up draft; Submit to journal • By August 2008 – Finalize comments and suggestions from referees; Final paper delivered Of course, the time line can be earlier or later depending upon unforeseen obstacles or breakthroughs.

Research Portfolio Analysis Using Extensions of Quality Function Deployment

Research portfolio analysis can effectively promote participation in strategic and complex decision processes. This develops a consensus for and an understanding of organizational goals and the associated performance metrics. To achieve this goal, a portfolio decision model should provide analysis of current program information without the distraction of excessive complexity. Specifically, the model's benefits should exceed the difficulty and effort required for model development, use, and maintenance.This paper describes a research portfolio model that achieves these goals by applying quality function deployment (QFD) methods. The model identifies the most important research activities and quantifies their impact on research program objectives. Important characteristics of research planning are integrated into the QFD framework including interaction of research activities, probability of success of research programs, and progress adjusted importance of strategic objectives.The model was developed at the NASA Langley Research Center to support the needs of program managers to coordinate and prioritize research activities. It provides a practical methodology to develop analytical information and supports the consensus building required to select targeted research programs with a strategic focus. The model provides a flexible platform for future development.