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This chapter presents the portfolio optimization problem formulated as a multi-criteria mixed integer program. Weighting and lexicographic approach are proposed. The portfolio selection problem considered is based on a single-period model of investment. An extension of the Markowitz portfolio optimization model is considered, in which the variance has been replaced with the Value-at-Risk (VaR). The VaR is a quantile of the return distribution function. In the classical Markowitz approach, future returns are random variables controlled by such parameters as the portfolio efficiency, which is measured by the expectation, whereas risk is calculated by the standard deviation. As a result, the classical problem is formulated as a quadratic program with continuous variables and some side constraints. The objective of the problem considered in this chapter is to allocate wealth on different securities to maximize the weighted difference of the portfolio expected return and the threshold of the probability that the return is less than a required level. The auxiliary objectives are minimization of risk probability of portfolio loss and minimization of the number of security types in portfolio. The four types of decision variables are introduced in the model: a continuous wealth allocation variable that represents the percentage of wealth allocated to each asset, a continuous variable that prevents the probability that return of investment is not less than required level, a binary selection variable that prevents the choice of portfolios whose VaR is below the minimized threshold, and a binary selection variable that represents choice of stocks in which capital should be invested. The results of some computational experiments with the mixed integer programming approach modeled on a real data from the Warsaw Stock Exchange are reported.

This study aims to develop a framework for the optimal selection of construction project portfolios for a construction holding company. The objective is to minimize risks, align the portfolio with the organization’s strategic objectives and maximize portfolio returns and net present value (NPV).

The study develops a multi-objective genetic algorithm approach to optimize the portfolio selection process. The construction company’s portfolio is categorized into four main classes: water projects, building projects, road projects and healthcare projects. A mathematical model is developed, and a genetic algorithm is implemented using MATLAB software. Data from a construction holding company in Iraq, including budget and candidate projects, are used as a case study.

The case study results show that out of the 34 candidate projects, 13 have been recommended for execution. These selected projects span different portfolio classes, such as water, building, road and healthcare projects. The total budget required for executing the selected projects is $64.55m, within the organization’s budget limit. The convergence diagram of the genetic algorithm indicates that the best solutions were achieved around generation 20 and further improved from generation 60 onwards.

The study introduces a specialized framework for project portfolio management in the construction industry, focusing on risk management and strategic alignment. It uses a multi-objective genetic algorithm and risk analysis to minimize risks, increase returns and improve portfolio performance. The case study validates its practical applicability.

This study contributes to project portfolio management by developing a framework specifically tailored for construction holding companies. Integrating a multi-objective genetic algorithm allows for a comprehensive optimization process, taking into account various objectives, including portfolio returns, NPV, risk reduction and strategic alignment. The case study application provides practical insights and validates the effectiveness of the proposed framework in a real-world setting.

This study aims to specify whether heterogeneous reference-point-based aspirations are related to the cooperation levels of R&D alliance portfolios in a positive or negative (or nonlinear) way, and to unveil how cooperative behaviors evolve in recurrent project cooperation.

This study establishes a network containing a cooperation subnetwork and a project subnetwork based on patent data in the “deep learning” field to investigate how cooperative behaviors evolve in R&D alliance portfolios. A model of evolutionary games on complex networks is constructed to gain insight into the dynamic evolution of DMs’ strategies.

First, the heterogeneous aspirations of DMs can improve the cooperation level in R&D alliance portfolios. Second, compared to prudent DMs, aggressive DMs are more likely to choose the cooperation strategy, implying that an appropriate aspiration level nurtures cooperative R&D endeavors with partners. Third, the effects of effort complementarity, knowledge reorganization capabilities and cooperation supervision on cooperation are contingent on the distribution of DMs’ aspiration types.

Policymakers should identify aspiration types of DMs when screening partners. They can encourage partners to focus more on historical payoffs and establish relatively higher aspiration levels to improve the cooperation level. Developing highly detailed contracts becomes crucial when cooperating with firms that possess extensive knowledge reorganization capabilities.

This work contributes a theoretical framework for investigating cooperation in R&D alliance portfolios through the lens of evolutionary games on complex networks, thus revealing the effects of heterogeneous reference-point-based aspirations of DMs on R&D cooperation.

Though of fairly recent origin, the capital‐asset pricing model (CAPM) is becoming a dominant influence in the analysis of financial and investment decisions. While continuing to undergo stringent theoretical and empirical examination, the demonstrable explanatory and predictive ability of the CAPM have led to its widespread recognition as the foundation of modern financial management. Though usually attributed to Sharpe, Lintner and Mossin, the origins of the CAPM can be traced back to the celebrated work of Harry Markowitz on portfolio selection.

The purpose of this paper is to investigate relative portfolio performance between sin stock returns and faith‐based returns.

Similar to Hong and Kacperczyk, Jensen's alpha was utilized to conduct tests along with three asset‐pricing models and rolling regression technique to reveal that faith‐based and sin betas move in opposite directions during most of the sample period.

Norm‐neglect was found, in that Jensen's alpha is positive and significant for the sin portfolio. Further, evidence in favor of norm‐conforming investor behavior was found, where Jensen's alpha is negative and significant for the faith‐based portfolio. These findings provide evidence that the sin portfolio outperforms the faith‐based portfolio relative to the market. A rolling regression technique reveals that faith‐based and sin betas tend to move in opposite directions during most of the sample period. The evidence suggests that faith‐based beta has an average estimated beta of one, mimicking the market. The sin portfolio, however, has an average estimated beta of one‐half. Finally, the reward‐to‐risk measure, Sharpe ratio, is statistically higher for the sin portfolio relative to the faith‐based portfolio.

This paper contributes to the literature in the following distinct ways. First, three asset‐pricing models are estimated to examine Jensen's alpha for sin and faith‐based portfolios. Second, a rolling regression procedure is used to examine the dynamic behavior relative to the market of the sin and faith‐based portfolios. Third, use is made of the Jobson and Korkie test, which allows for statistical comparisons of Sharpe ratios. Lastly, daily instead of monthly data and a different sample period are used to examine the research questions posed in this study.

Though stock portfolio return autocorrelation is well documented in the literature, its cause is still not clearly understood. Presently, evidence of private information induced stock return autocorrelation is still very limited. The difficulty in obtaining foreign country information by small investors makes the private information of institutional investors in the ADR (American Depository Receipt) market more significant and influential. As such, the ADR market provides a favorable environment for testing the effect of private information on return autocorrelation. The purpose of this paper is to address this issue.

In this paper, ADRs are sorted annually into three groups based on market equity capitalization. Within each capitalization group, ADRs are further sorted into three groups based on the fraction of shares held by institutional investors. Each ADR is assigned to one of the nine groups and group membership is rebalanced each year. The return autocorrelation of individual ADR securities and ADR portfolios for each group are then calculated.

The results demonstrate that ADR individual stock and portfolio daily return autocorrelations are positively related to institutional ownership. It is also found that other explanations, such as non‐synchronous trading, bid‐ask spread and volatility of ADR, cannot explain the positive relation between daily return autocorrelations and institutional ownership of ADR.

Since ADR market is more suitable than other markets for testing the role of private information, stronger and clearer results are got accordingly. This paper suggests that trading strategy based on private information of institutional investors can lead to stock return autocorrelation in ADR daily returns.

The purpose of this paper is to provide a quantitative methodology based on information‐gap decision theory for dealing with (true) Knightian uncertainty in the management of portfolios of assets with uncertain returns.

Portfolio managers aim to maximize returns for given levels of risk. Since future returns on assets are uncertain the expected return on a portfolio of assets can be subject to significant uncertainty. Information‐gap decision theory is used to construct portfolios that are robust against uncertainty.

Using the added dimensions of aspirational parameters and performance requirements in information‐gap theory, the paper shows that one cannot simultaneously have two robust‐optimal portfolios that outperform a specified return and a benchmark portfolio unless one of the portfolios has arbitrarily large long and short positions.

The paper has considered only one uncertainty model and two performance requirements in an information‐gap analysis over a particular time frame. Alternative uncertainty models could be introduced and benchmarking against proxy portfolios and competitors are examples of additional performance requirements that could be incorporated in an information‐gap analysis.

An additional methodology for applying information‐gap modeling to portfolio management has been provided.

This paper provides a new and novel approach for managing portfolios in the face of uncertainties in future asset returns.

The purpose of this paper is to add to the literature on the impact of the Morningstar ratings by examining the impact of individual stock ratings in the Hare and Tortoise portfolios.

This study uses an event study approach, where the effect of the information release on stock prices was examined.

The release of the Hare and the Tortoise portfolios does have a significant impact on the stock prices on the day before the release of the reports. The significant impact of the Hare portfolio appears to be related to risk and growth factors and the new listing of a stock in the portfolio. The significant impact of the Tortoise portfolio appears to be related to growth and a downgrading of the estimated value.

There have been numerous studies on the impact of the Morningstar ratings on mutual funds. This paper adds to that literature by examining the impact of individual stock ratings in the Hare and Tortoise portfolios.

The paper identifies the need for a portfolio approach to the management of real estate assets, and sets out its key components as a ‘macro’ level process. Portfolio management is positioned within an overall model of the corporate real estate function, from which a definition is developed. The main generic components of real estate portfolio management are described, and the most significant findings from a survey of current practices among a group of corporate organisations are presented. The paper concludes that in overall terms a more robust approach to the portfolio management of real estate assets is required to maximise the portfolio’s functional and financial value to the business.

Introduction The principal objective of a portfolio manager's work is the construction and maintenance of successful, efficient portfolios of investment assets. It is necessary, therefore, that methods are designed and made available through which the success and efficiency of portfolios may be assessed. Only through the continuous monitoring of the achieved results can long term investment strategies succeed.