The impact of Islamic portfolio on risk and return

The purpose of this study is to investigate the comparative impact of conventional and Islamic bonds over returns. It provides useful insights to investors to diversify investment by lowering the risk to the optimum level. This study examines the impact of the conventional and Islamic portfolios on returns through simple OLS regression, suggesting that Sukuk returns are positive and significant. Simultaneously, conventional bonds show a negative trend, but in the long run, the returns are significant. It indicates that the market is volatile due to macroeconomic factors that can reduce risks through portfolio diversification. Thus, this research suggests that investment can be secured by taking a rational portfolio decision that confirms robustness. Therefore, it is a good opportunity for the investors to get high margins over the investment tenure.

Bounds in Tree-Based Approaches to Generate Project Portfolios in the Presence of Interactions

Portfolio decision models have become an important branch of decision analysis. Portfolio problems are inherently complex, because of the combinatorial explosion in the number of portfolios that can be constructed even from a small number of items. To efficiently construct a set of portfolios that provide good performance in multiple criteria, methods that guide the search process are needed. Such methods require the calculation of bounds to estimate the performance of portfolios that can be obtained from a given partial portfolio. The calculation of such bounds is particularly difficult if interactions between items in the portfolio are possible. In the paper, the authors introduce a method to represent such interactions and develop various bounds that can be used in the presence of interactions. These methods are then tested in a computational study, where they show that the bounds they propose frequently provide a good approximation of actual outcomes, and also analyze specific properties of the problem that influence the approximation quality of the proposed bounds.

Decision Support for Portfolio Management by Information System with Black–Litterman Model

An algorithm is derived for the development of portfolio decision-support information service. The algorithm allows being automated evaluations for the definition and solution of portfolio problems. Small set of historical data of asset returns with limited set of assets are used for the portfolio, which is the case for no institutional portfolio manager. The algorithm applies analytical relations for decreasing the computational workload for the estimation of the market parameters due to the limited number of assets. The subjective expert views in the Black–Litterman (BL) model are defined from additional assessment of historical data of the asset returns. The algorithm makes comparisons of the results for active portfolio management from the mean variance (MV) model, the BL one and the equal-weighted investment strategy. Benefits of the algorithm are the usage of small set of historical data and limited number of assets, which are proved in investment rolling horizon.

Liquidity, Trading, and Price Determination in Equity Markets: A Finance Course Application

Trading is the implementation of an investment decision. After a portfolio decision has been made by a portfolio manager, it must be implemented, and especially for handling large orders and navigating stressful markets, specific skills and responsibilities are needed that require the expertise of a professional trader. However, the efficiency with which orders are handled and turned into trades depends, not just on traders’ abilities, but also on a market’s liquidity, on the design of the marketplace where shares are traded, and on the regulatory environment. In this chapter, we cover trading costs, liquidity, volatility, price discovery, market structure, and market structure regulation.

Friction and Decision Rules in Portfolio Decision Analysis

In portfolio decision analysis, features comprise the objectives, alternatives, physics, and information that define a decision context. By modeling features, decision analysts forecast the expected utilities of the alternatives. A model is complete if it contains all the features. A model is well-calibrated if it correctly predicts the probability distributions of each alternative’s utility, whereas ill-calibrated models, like those that suffer the optimizer’s curse, do not. Friction identifies qualities of a situation that prevent decision analysts from creating complete, well-calibrated models. When friction is significant, can maximizing expected utility be a suboptimal decision rule? Is satisfying decision theory’s axioms a necessary or sufficient condition for good decision making? Can rules that violate the axioms outperform rules that satisfy them? A simulation study of how unbiased, imprecise forecasts of payoffs affect project selection finds that, for the example tested, the answers are yes, no, and yes, which suggests that further studies of friction may be worthwhile. Discussions of friction bookend the study, starting the paper by defining friction and concluding by presenting three frameworks, each one from a different field of study, that provide mathematical tools for studying friction.

Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis

This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions of a non-profit funder of vaccine technology platform development for rapid response to emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability of Success (PoS) data was obtained for 16 platform projects through expert assessments and stakeholder portfolio preferences via a Discrete Choice Experiment (DCE). The structure of preferences and the uncertainties in project PoS suggested a non-linear, stochastic value maximization problem. A simulation-optimization algorithm was employed, identifying optimal portfolios under different budget constraints. Stochastic dominance of the optimization solution was tested via mean-variance and mean-Gini statistics, and its robustness via rank probability analysis in a Monte Carlo simulation. Project PoS estimates were low and substantially overlapping. The DCE identified decreasing rates of return to investing in single platform types. Optimal portfolio solutions reflected this non-linearity of platform preferences along an efficiency frontier and diverged from a model simply ranking projects by PoS-to-Cost, despite significant revisions to project PoS estimates during the review process in relation to the conduct of the DCE. Large confidence intervals associated with optimization solutions suggested significant uncertainty in portfolio valuations. Mean-variance and Mean-Gini tests suggested optimal portfolios with higher expected values were also accompanied by higher risks of not achieving those values despite stochastic dominance of the optimal portfolio solution under the decision maker’s budget constraint. This portfolio was also the highest ranked portfolio in the simulation; though having only a 54% probability of being preferred to the second-ranked portfolio. The analysis illustrates how optimization modelling can help health R&D decision makers identify optimal portfolios in the face of significant decision uncertainty involving portfolio trade-offs. However, in light of such extreme uncertainty, further due diligence and ongoing updating of performance is needed on highly risky projects as well as data on decision makers’ portfolio risk attitude before PDA can conclude about optimal and robust solutions.

Nonadditive Multiattribute Utility Functions for Portfolio Decision Analysis

Axiomatic Foundations for Nonadditive Multiattribute Portfolio Utility Functions