Application Of Portfolio Theory Research Articles

Application of Portfolio Theory to Healthcare Capacity Management.

Healthcare systems worldwide are faced with continuously increasing demand for care, while simultaneously experiencing insufficient capacity and unacceptably long patient waiting times. To improve healthcare access and availability, it is thus necessary to improve capacity utilization and increase the efficiency of existing resource usage. For this, variations in healthcare systems must be managed judiciously, and one solution is to apply a capacity pooling approach. A capacity pool is a general, collaborative capacity that can be allocated to parts of the system where the existing workload and demand for capacity are unusually high. In this study, we investigate how basic mean-variance methodology from portfolio theory can be applied as a capacity pooling approach to healthcare systems. A numerical example based on fictitious data is used to illustrate the theoretical value of using a portfolio approach in a capacity pooling context. The example shows that there are opportunities to use capacity more efficiently and increase service levels, given the same capacity, and that a mean-variance analysis could be performed to theoretically dimension the most efficient pooling organization. The study concludes with a discussion regarding the practical usefulness of this methodology in the healthcare context.

ASSESSMENT OF GOLD AND/OR CRUDE OIL AS INVESTMENTS FOR PORTFOLIO DIVERSIFICATION. A WARSAW STOCK EXCHANGE CASE STUDY

The purpose of the study is to assess whether the inclusion of investments in gold and/or crude oil improves an investment portfolio consisting of shares of enterprises included in the WIG20 index (traditional investments). All possible combinations of investment portfolios with minimal risk and maximum efficiency were tested. The portfolios were determined based on Markowitz’s portfolio theory. All results were compared with a naive strategy. In total, nearly 55,000 investment portfolios consisting of three, four or five investments were constructed. The study showed that the application of portfolio theory contributes to obtaining better results than a naive strategy. The minimum risk portfolios that included gold and crude oil showed a risk reduction of 0.39 p.p. on average and a maximum cumulative loss of 7.85 p.p. on average. Portfolios with maximum efficiency achieved an average increase in the rate of return of the investment portfolio of 0.024 p.p. and an average increase in efficiency of 0.0256.

Reducing risk through crop diversification: An application of portfolio theory to diversified horticultural systems

In agricultural sciences, it is frequently stated that diversified systems are more likely to cope with risks, but rarely demonstrated in a quantitative way. On the theoretical perspective, risk reduction based on asset diversification is a well studied mechanism in economics that has been formalized in the Modern Portfolio Theory (MPT). In this paper, we thus explore the opportunities for application of MPT to quantify the benefits of diversification for risk reduction in a context of highly diversified horticultural systems. Our analysis relies on a model of the effect of crop diversification on risk and expected yield. It is based on a dataset obtained from a range of 44 species and over a period of 10 years. Results show that (i) the variability of expected crop yield can be reduced up to 77% by choosing the appropriate crop combination; (ii) most common crop classifications such as botanical classification or functional types do not make it possible to identify suitable diversification strategies. These results provide insight into how different horticultural crop combinations may shape the relationship between production and risk in diversified farming systems. Our findings also provide indications on new diversification strategies based on a quantification of risk reduction.

Portfolio theory application to prediction correction of train arrival times

The application of portfolio theory to the prediction of train arrival times is shown to improve prediction accuracy. The 'portfolio' comprises two correction methods based on a Wiener process: one uses history data for the current day and the other uses history data for previous days. These data represent several properties such as station and are used after being statistically cleaned. The error between the time predicted using a local model and the actual time is assumed to have a normal distribution. Portfolio theory is used to determine the optimal application of the two methods to the correction process. Simulation using actual dense train schedules showed that the average error in the predicted arrival time was reduced from 12 s to 3 s. This error reduction will, for example, improve the efficiency of regenerative braking systems, in which the kinetic energy of an arriving train is electrically transmitted to a departing train.

Portfolio theory application to prediction correction of train arrival times

The application of portfolio theory to the prediction of train arrival times is shown to improve prediction accuracy. The 'portfolio' comprises two correction methods based on a Wiener process: one uses history data for the current day and the other uses history data for previous days. These data represent several properties such as station and are used after being statistically cleaned. The error between the time predicted using a local model and the actual time is assumed to have a normal distribution. Portfolio theory is used to determine the optimal application of the two methods to the correction process. Simulation using actual dense train schedules showed that the average error in the predicted arrival time was reduced from 12 s to 3 s. This error reduction will, for example, improve the efficiency of regenerative braking systems, in which the kinetic energy of an arriving train is electrically transmitted to a departing train.

Portfolio theory as a management tool to guide conservation and restoration of multi‐stock fish populations

Habitat degradation and harvest have upset the natural buffering mechanism (i.e., portfolio effects) of many large‐scale multi‐stock fisheries by reducing spawning stock diversity that is vital for generating population stability and resilience. The application of portfolio theory offers a means to guide management activities by quantifying the importance of multi‐stock dynamics and suggesting conservation and restoration strategies to improve naturally occurring portfolio effects. Our application of portfolio theory to Lake ErieSander vitreus(walleye), a large population that is supported by riverine and open‐lake reef spawning stocks, has shown that portfolio effects generated by annual inter‐stock larval fish production are currently suboptimal when compared to potential buffering capacity. Reduced production from riverine stocks has resulted in a single open‐lake reef stock dominating larval production, and in turn, high inter‐annual recruitment variability during recent years. Our analyses have shown (1) a weak average correlation between annual river and reef larval production (ρ̄ = 0.24), suggesting that a natural buffering capacity exists in the population, and (2) expanded annual production of larvae (potential recruits) from riverine stocks could stabilize the fishery by dampening inter‐annual recruitment variation. Ultimately, our results demonstrate how portfolio theory can be used to quantify the importance of spawning stock diversity and guide management on ecologically relevant scales (i.e., spawning stocks) leading to greater stability and resilience of multi‐stock populations and fisheries.

Application of portfolio theory to asset-based biosecurity decision analysis

A key challenge for biosecurity decision-making is how best to allocate scarce resources across multiple environmental assets. The allocation of funds for the best return from investment requires a careful assessment of expected return and uncertainty. In this paper, we applied a portfolio theory-based decision support tool that helps determine resource allocation in a way that maximizes expected return and minimizes uncertainty. Our framework offers three advancements to the literature. First, it helps in making resource allocation decisions across multiple pests that affect multiple environmental assets. Second, it incorporates multiple sources of uncertainty in the decision analysis including economic value uncertainty. Finally, it demonstrates a generic approach to design a choice experiment study to estimate monetary values of a broad group of environmental assets. We find that a portfolio-based framework applied in conjunction with a choice experiment study can be a useful tool to guide biosecurity resource allocation decisions. Our results show that disregarding value uncertainty may cause bias by underestimating true uncertainty in the opportunity set. The choice experiment study revealed substantial positive non-market values generated by environmental biosecurity in Australia. However, significant preference heterogeneity across respondents with regards to different biosecurity outcomes was observed.

Application of Portfolio Theory Based on CVaR in Determining Optimal Spinning Reserve with Consideration of Load and Wind Power Uncertainties

With the increased installed capacity of wind power in power system, determining optimal spinning reserve capacity is one of the most important problems in operation of electricity power system. CVaR (conditional value at risk) is introduced to calculate the risk of the cost associated with load shed and abandoning wind power with the consideration of load and wind power prediction uncertainties. Portfolio theory based on CVaR is used to build the Cost-CVaR model. Efficient frontier, which can support the system operators (SO) with the decision of optimal spinning reserve, can be obtained by solving the Cost-CVaR model. The analysis of RTS example can demonstrate the usefulness and efficiency of the model.

CISS — A Portfolio-Theoretic Framework for the Construction of Composite Financial Stress Indices

This paper introduces a novel indicator of current stress in the …nancial system as a whole named Composite Indicator of Systemic Stress (CISS). Its speci…c statistical design is shaped in accordance with standard de…nitions of systemic risk. The main innovative feature of the CISS is the application of portfolio theory to the aggregation of individual stress indicators into the composite index. Along the lines of how portfolio risk is computed from the risks of individual assets, we propose to compute the level of stress in the system as a whole by aggregating …ve market-speci…c subindices of stress - comprising a total of 15 individual stress indicators - on the basis of a time-varying measure of the cross-correlations � We thank Philipp Hartmann for inspiring and supporting this project throughout all stages. Philipp also invented the indicator’s name and its abbreviation CISS (pronounced like “kiss”). We thank Tommy Kostka for outstanding research assistance and for several good ideas which helped improving the CISS. Very helpful comments from Geert Bekaert, Hans Degreyse, Wolfgang Lemke, Simone Manganelli, Seth Pruitt and an anonymous referee are gratefully acknowledged. We …nally thank seminar participants at the Euro Area Business Cycle Network conference “Econometric Modelling of Macro-Financial Linkages”in Florence, the 5th CSDA International Conference on Computational and Financial Econometrics in London, the Board of Governors of the Federal Reserve System, the Sveriges Riksbank, the AEA 2013 Annual Meeting in San Diego and the joint Federal Reserve Bank of Cleveland and O¢ ce of Financial Research conference “Financial Stability Analysis: Using the Tools, Finding the Data”for fruitful discussions and comments. However, the views expressed in this paper are those of the authors and do not necessarily re‡ect those of the European Central Bank, the Eurosystem or the Magyar Nemzeti Bank.

Spectrum management in cognitive radio: Applications of portfolio theory in wireless communications

Portfolio theory (PT) has been widely used in finance as a tool to aid capital allocation and investment analysis, and to measure economic welfare. Recently, PT has been applied as a spectrum management tool for cognitive radio for QoS management in CR and the pricing of CR access rights. PT is characterized by the analysis of risk and return, concepts that feature widely in wireless communications due to the random nature of traffic and radio channels. Compared with traditional CR resource management techniques (e.g., game theory), where the aim has been the maximization of QoS or another utility measure, the key advantage of PTbased resource management stems from the focus on both the mean utility achieved by a communication system, as well as utility variance. Ignoring QoS (e.g., throughput) variance in communication system design may lead to systems that are unusable by some applications, even though mean QoS remains high. In applications such as pricing-based spectrum management, PT analysis incorporates the variance of investment returns, a key measure of economic welfare, into pricing and trading strategies. In this article we examine the key concepts behind PT, risk and return, as well as their importance in CR and wireless communications. As examples, we analyze the application of PT in solving CR spectrum management problems through QoS management and pricing of spectrum access rights.

Modeling and design of future multi‐energy generation and transmission systems

AbstractOngoing changes in energy systems offer the opportunity to reconsider the design of our current energy infrastructures. As many new grid participants like combined heat and power plants involve other forms of energy in addition to electricity, the consideration of multi‐energy networks represents a useful planning approach. This approach consists in an integrated analysis of combined infrastructures for conversion, transmission and storage of electricity, heat and chemical energy carriers. The integrated operation of systems with different energy carriers offers a new degree of freedom to system planners: energy can be generated, stored, and transmitted in various forms before being distributed to consumers in the adequate form. Different possible solutions for the design of multi‐energy systems will exhibit differences in costs and risks. In order to adequately assess these two quantities, mean‐variance portfolio theory is applied. Portfolio theory has been used in several cases of electricity generation planning. This paper shows the required modeling adaptations for the analysis of portfolios with multiple energy outputs and illustrates the application of portfolio theory in the context of multi‐energy infrastructures. The presented method can be used for the integrated planning of energy systems including the generation, the transmission and the possibility of additional conversion of multiple energy carriers. Copyright © 2009 John Wiley & Sons, Ltd.

Wheat Variety Selection to Maximize Returns and Minimize Risk: An Application of Portfolio Theory

This research shows that a portfolio of wheat varieties could enhance profitability and reduce risk over the selection of a single variety for Kansas wheat producers. Many Kansas wheat farmers select varieties solely based on published average yields. This study uses portfolio theory from business investment analysis to find the optimal, yield-maximizing and risk-minimizing combination of wheat varieties in Kansas.

Application of portfolio theory to risk-based allocation of surveillance resources in animal populations

Distribution of finite levels of resources between multiple competing tasks can be a challenging problem. Resources need to be distributed across time periods and geographic locations to increase the probability of detection of a disease incursion or significant change in disease pattern. Efforts should focus primarily on areas and populations where risk factors for a given disease reach relatively high levels. In order to target resources into these areas, the overall risk level can be evaluated periodically across locations to create a dynamic national risk landscape. Methods are described to integrate the levels of various risk factors into an overall risk score for each area, to account for the certainty or variability around those measures and then to allocate surveillance resources across this risk landscape. In addition to targeting resources into high risk areas, surveillance continues in lower risk areas where there is a small yet positive chance of disease occurrence. In this paper we describe the application of portfolio theory concepts, routinely used in finance, to design surveillance portfolios for a series of examples. The appropriate level of resource investment is chosen for each disease or geographical area and time period given the degree of disease risk and uncertainty present.

Terrorism: A Mean-Variance Analysis: an Application of Portfolio Theory to the Analysis of Terrorism

Terrorism: A Mean-Variance Analysis: an Application of Portfolio Theory to the Analysis of Terrorism

Geographic diversification of carbon risk—A methodology for assessing carbon investments using eddy correlation measurements

In the context of the international market for greenhouse gas emissions, I examine applications of portfolio theory for investment decisions regarding biological carbon sequestration projects. Using ecosystem-scale eddy correlation carbon flux measurements, I show how to determine how much financial risk of carbon is diversifiable. This method allows a quantitative assessment of the potential for geographical diversification of carbon sink investments. In a case study of six ecosystems in the temperate Northern hemisphere, a significant benefit from diversification is demonstrated even among sites that seem to have broadly similar characteristics. This benefit derives in part from differences in ecosystem response to varying weather conditions and differences in ecosystem type, both of which affect the sites’ covariances. In providing a quantitative common language for scientific and corporate uncertainties, the concept of carbon financial risk provides an opportunity for expanding communication between these elements essential to successful climate policy.

Portfolio evaluation of health programs: a reply to Sendi et al.

Sendi et al. (Soc. Sci. Med. 57 (2003) 2207) extend previous research on cost-effectiveness analysis to the evaluation of a portfolio of interventions with risky outcomes using a “second best” approach that can identify improvements in efficiency in the allocation of resources. This method, however, cannot be used to directly identify the optimal solution to the resource allocation problem. Theoretically, a stricter adherence to the foundations of portfolio theory would permit direct optimization in portfolio selection, however, when we include uncertainty in our analysis in addition to the traditional concept of risk (which is often mislabelled uncertainty) complexities are introduced that create significant hurdles in the development of practical applications of portfolio theory for health care policy decision making.

Conglomerate Mergers as Defense Against the Risk of Relative Price Variability

This paper studies the effects of the risk of relative price variability on the optimal structure of a firm's cash flows with an application of portfolio theory. It is suggested that, when relative price risk is greater, it is optimal for the management to diversify, at the margin, and emphasize economies of scale or scope when the risk is smaller. Conglomerate mergers should therefore be positively associated with relative price risk, given the variables affecting mergers as a whole. Empirical evidence is found to lend support to the theory. The inflation rate explains conglomerate mergers even better, which suggests that relative price risk may constitute another real resource cost of inflation.

Application of portfolio theory in decision-tree analysis

A problem of subset selection when actions are interdependent is formulated within a multiple criteria framework. More specifically, a novel definition and characterization of interdependence of actions applicable to Multiple Criteria Decision Making (MCDM) are presented. The effects of interdependence of actions on the modeling and resolution of a subset choice problem are shown, and the importance of taking interdependence of actions into account is discussed. Most of the discussion is generalized to independence and interdependence of sets of actions, which are then compared to the case of individual actions. A general approach to evaluate a combination of interdependent actions is proposed and the use of the multiple criteria structure to eliminate some difficulties in evaluating a set of interdependent actions is explained.

Application of Portfolio Theory in Decision Tree Analysis

A general application of portfolio analysis for herd decision tree analysis is described. In the herd environment, this methodology offers a means of employing population-based decision strategies that can help the producer control economic variation in expected return from a given set of decision options. An economic decision tree model regarding the use of prostaglandin in dairy cows with undetected estrus was used to determine the expected return of the decisions to use prostaglandin and breed on a timed basis, use prostaglandin and then breed on sign of estrus, or breed on signs of estrus. The risk attributes of these decision alternatives were calculated from the decision tree, and portfolio theory was used to find the efficient decision combinations (portfolios with the highest return for a given variance). The resulting combinations of decisions could be used to control return variation.

Investment Choices in Industry.

Investment Choices in Industry lays the groundwork for increasingly broader and more varied application of portfolio theory to the making of operating decisions in firms. Portfolio theory until now has been used mainly to allocate investments in financial assets such as stocks and bonds; here Constance Helfat extends the theory to evaluate the risk of nonfinancial investment decisions in firms. Analyzing the investment and R & D decisions of large oil companies, she uses portfolio theory as a tool to predict firm behavior and proposes that, when making internal resource allocations, firms seek not only to earn profits but also to minimize total firm investment risk as well.Helfat's study differs from existing work in a number of respects. The theoretical model it develops (an adaptation of the Tobin-Markowitz portfolio selection model of stock market investment) is highly comprehensive. It focuses on the composition of a firm's investment portfolio as well as on the risk and return characteristics of each individual investment. The model also incorporates the dependence of certain asset prices - such as those determined by auction - on internal firm covariance risk. Unlike many empirical studies, the method used in implementing the model relies on micro-level, projectspecific data to forecast ex ante risks and returns to firm investments.Finally, Helfat uses a unique data set - the Financial Reporting System of the U.S. Department of Energy - to analyze the composition of actual investment expenditures in the petroleum industry.Constance E. Helfat is Assistant Professor at the J. L. Kellogg Graduate School of Management at Northwestern University.