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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.

We phrase our analysis around the connectedness effects and portfolio allocation in the “Carbon-Energy-Green economy” system.

This paper utilizes the TVP-VAR method provided by Antonakakis et al. (2020) and Chatziantoniou et al. (2021), and portfolio back-testing models, including bivariate portfolios and multivariate portfolios.

Firstly, the connectedness within the “Carbon-Energy-Green economy” system is strong, and is mainly driven by short-term (weekly) connectedness. Notably, the COVID-19 pandemic leads to a vertical increase in the connectedness of this system. Secondly, in the “Carbon-Energy-Green economy” system, most of the sectors in the green economy stocks tend to be the transmitters of shocks to other markets (particularly the energy efficiency sector), while the carbon and energy markets are always the recipients of shocks from other markets (particularly the crude oil market). Thirdly, Green economy sector stocks have satisfactory hedging effects on the market risk of carbon and energy assets. Interestingly, hedging risks in relatively “dirty” assets requires more green economy stocks than in relatively “clean” assets. Finally, the results indicate that portfolios that include green economy stocks significantly outperform portfolios that do not contain green economy stocks, further demonstrating the crucial role of green economy stocks in this system.

Understanding the interactions and portfolio allocation in the “Carbon-Energy-Green economy” system, especially identifying the role of the green economy performance in this system, is important for investors and policymakers.

The literature suggests that increasing the intensity of compensation incentives for corporate venture capital (CVC) managers can contribute to successful exits of direct CVCs. This study explores the impact of compensation incentives on the successful exits of indirect CVCs under different geographical distances between parent companies and indirect CVC managers.

The authors observed the compensation terms of CVC managers through investment announcements made by listed companies and used a probit regression model to test the hypotheses from a sample of 241 investment events with indirect CVCs in China.

The results show that if parent companies are geographically close to the managers of indirect CVCs, increasing the intensity of compensation incentives for managers will help the successful exit of indirect CVCs. However, if parent companies are not geographically close to indirect CVC managers, increasing the intensity of compensation incentives for managers will not promote the successful exit of indirect CVCs.

This study contributes significantly to the CVC literature. First, it sharpens our understanding of the differences in operational mechanisms between direct and indirect CVCs. Second, we find that the threshold returns of indirect CVC managers are non-negligible compensation incentives. Finally, the empirical evidence supports that in indirect CVC investments, the geographical distance between parent companies and managers is concerning because it affects whether compensation incentives contribute to the successful exit of indirect CVCs.

This paper studies diversification and value in the investment portfolios of (non-listed) Real Estate Investment Funds (REIFs) exploring how the value of diversification is captured by the market and by investors (beyond reported valuations).

We apply the Herfindahl-Hirschman Index (HHI) to study the level of concentration versus diversification in the investment portfolios of REIFs (both in terms of segment and geographical diversification). We use a dataset from INREV with data from 62 investment portfolios, with an average of 86 REIFs per portfolio for the period of 2008–2020 (to study segment diversification). We use a second dataset from INREV with data from 30 investment portfolios with an average of 79 REIFs per portfolio for the period of 2005–2020 (to study geographical diversification). We employ a cluster analysis approach to identify common features among the investment funds.

We conclude that (segment diversified) portfolios with higher degrees of leverage exhibit higher income yields, albeit diversification is captured indirectly through asset choices – more diversified portfolios tend to exhibit a stronger risk and return relationship. Also, geographical diversification creates value (more significantly by for the correct combination of countries carefully choosing what different geographies to group in the diversified portfolio).

One limitation of our study is that our portfolios are funds of funds, since the available data could not reach the asset detail, but we believe this does not compromise our results.

Diversification leads to higher risk-adjusted returns which suggests that properties may be undervalued (market value) in the framework of the Gordon Model, contrary to expectations (regarding investment value).

Investors capture the value of diversification differently, suggesting a gap between market value and investment value that can be explored.

This study explores entrepreneurial orientation (EO) on project portfolio success in new product development projects, with the moderating effects of digitalization capability and modularization process.

The sample data of 204 firms was used to analyze the research hypotheses. This study adopted hierarchical regression to test the theoretical conceptual model incorporating EO, digitalization capability, modularization process, and project portfolio success.

These results indicate that EO positively affects project portfolio success. More importantly, digitalization capability and modularization process positively moderate the relationship between EO and project portfolio success.

Prominent studies have focused on different antecedent and consequence factors of project portfolio success; however, the impacts of EO still need to be noticed. This study makes a pioneering effort to make up this gap and investigate the effects of EO on project portfolio success, digitalization capability, and modularization process as moderators, which can enrich the current literature on project portfolio management.

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.