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The optimization of investment portfolios is a topic of major importance in financial decision making, with many relevant models available in the relevant literature. The purpose of this paper is to perform a thorough comparative assessment of different bi-objective models as well as multi-objective one, in terms of the performance and robustness of the whole set of Pareto optimal portfolios.

In this study, three bi-objective models are considered (mean-variance (MV), mean absolute deviation, conditional value-at-risk (CVaR)), as well as a multi-objective model. An extensive comparison is performed using data from the Standard and Poor’s 500 index, over the period 2005–2016, through a rolling-window testing scheme. The results are analyzed using novel performance indicators representing the deviations between historical (estimated) efficient frontiers, actual out-of-sample efficient frontiers and realized out-of-sample portfolio results.

The obtained results indicate that the well-known MV model provides quite robust results compared to other bi-objective optimization models. On the other hand, the CVaR model appears to be the least robust model. The multi-objective approach offers results which are well balanced and quite competitive against simpler bi-objective models, in terms of out-of-sample performance.

This is the first comparative study of portfolio optimization models that examines the performance of the whole set of efficient portfolios, proposing analytical ways to assess their stability and robustness over time. Moreover, an extensive out-of-sample testing of a multi-objective portfolio optimization model is performed, through a rolling-window scheme, in contrast static results in prior works. The insights derived from the obtained results could be used to design improved and more robust portfolio optimization models, focusing on a multi-objective setting.

The purpose of this paper is to present a tool to categorize companies as potentially profitable on the basis of an intellectual capital (IC) analysis.

The paper distinguishes two crucial attributions for picking shares: IC and capitalization of IC-based growth potential. Using these two attributions, the author creates a portfolio from a sample of European companies and annually rebalances it. To test its attractiveness, the author then compares the portfolio with benchmarks and random portfolios during the period from 2006 to 2013 using a Sharpe coefficient.

The comparison of the constructed portfolio with the benchmarks demonstrates the importance of IC for market investors and the validity of the proposed tool. The Sharpe ratio of the portfolio is significantly higher than the mean and median Sharpe ratios of random portfolios. In addition, the importance of IC for choosing proper investment goal increases in crisis.

This investigation can be improved by analysing other IC such as the qualification of CEOs, participation of the company in business alliances, and a company’s innovation activity. In addition, the paper considers only European companies.

The proposed tool provides a method to construct investment-attractive portfolios on the basis of IC.

The paper contributes to the literature by identifying the underestimated shares on the basis of a company’s IC and by developing an algorithm to create an IC-based investment portfolio.

The purpose of this paper is to present the development of a methodology to evaluate suppliers using portfolio analysis based on the analytical network process (ANP) and environmental factors.

The authors develop a three‐step process, first by evaluating influence/power and performance scores of suppliers using ANP. They include environmental dimensions in this analysis, then map these suppliers onto a portfolio grid. Recommendations are also made on how to manage suppliers depending on what part of the portfolio they appear based on the scores.

The technique is useful and versatile. The paper clearly discerns various characteristics of the suppliers and produced recommendations on supplier management for an exemplary case scenario.

The technique was applied for an illustrative example. Validation and application in a real world setting is required. There are many additional opportunities to further integrate other modeling tools into this process.

Managers can use this technique to help them more effectively deal with suppliers. The portfolio is a good tool for operational and strategic management of suppliers.

This tool is the first to apply ANP to supplier portfolio analysis. It is also the first tool to integrate and apply the portfolio supplier management approach to an environmentally oriented decision environment.

This study aims to establish the effect of environmental, social and governance (ESG) practices on Australian energy and utility investment performance.

Conventional and ESG-rated portfolios are constructed using monthly returns and ESG scores of S&P/ASX 300 listed energy and utility firms from 2014 to 2022. Portfolio performance is estimated using a four-factor regression model, controlling for any economic shocks associated with the COVID-19 pandemic.

The findings show that the lower the ESG score associated with the overall ESG and environmental portfolios, the greater the performance compared to the market (but not the conventional and other ESG portfolios). High ESG scores do not appear to influence the performance of the energy and utility portfolios, which contrasts expectations that the uptake of ESG should deliver superior risk-return outcomes for investors. The findings also indicate that a contrarian investment approach may be a reasonable performance indicator for high-rated ESG portfolios. ESG practices did not impact portfolio performance during the COVID-19 pandemic.

This research has contributed to the literature by offering ESG investment insights for policymakers, regulators, fund managers and investors. Consistent with the agency perspective on ESG practices and efficient market hypothesis, the evidence implies that, regardless of ESG scores (either high or low), investors should consider investing passively in diversified energy and utility portfolios or low-cost index fund equivalents.

The purpose of this paper is to compare the risk and return characteristics as well as the allocation of mean‐variance (MV) and downside risk (DR) optimized portfolios of international real estate stock markets and to discuss implications for portfolio management.

The analysis focuses on real estate markets only and examines the appropriateness of the Markowitz approach based on MV optimization in comparison to the DR framework suggested by Estrada. Therefore, the two frameworks are presented before the properties of the return distributions are analyzed. Afterwards, the risk and return characteristics as well as the allocation of the efficient portfolios in both frameworks and the divergences are analyzed.

Because of non‐normally distributed returns, negative skewness, and probably non‐quadratic utility functions of investors, MV optimization is not appropriate and the alternative approach by Estrada has its merit compared with other DR frameworks. Furthermore, MV‐efficient and DR‐efficient portfolio allocation differ, as shown by a similarity index. Summarizing, MV optimization is inherent with misleading results and DR optimization shows stronger out‐of‐sample performance – at least during time periods characterized by high market volatility and financial market turmoil.

This study provides some interesting and valuable insights into the DR of international securitized real estate portfolios and the limitations for portfolio management based on MV optimization.

This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19.

This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures.

The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off.

To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.

A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.

The aim of this paper is to present a purchasing portfolio model for determining purchasing strategy at the component level of a product to support business strategy, addressing weaknesses in the often cited Kraljic‐type models. The work draws on Fisher's model to match supply strategy to product nature. However, Fisher's model was criticised very recently by Lo and Power in this journal because it is unclear how the “leagile” option should fit into it. This paper addresses this issue.

The new portfolio model is based on the literature, particularly Fisher's seminal work. It is then applied to two case studies; an electric boiler manufacturer and an elevator manufacturer, both in South Korea. The analytic hierarchy process (AHP) is used to position purchased components in the model.

Different purchasing strategies should be assigned to different components according to their impact on the competitive priorities. As the electric boiler is a functional product, while the elevator is an innovative product, the case studies show how this can vary across the two product‐types identified by Fisher.

The new model has been tested on only two case studies, which limits the ability to generalise the findings. Future work will use the lean and agile purchasing portfolio model in research and knowledge exchange activities with other industrial partners to further develop and test its efficacy.

The new model captures the finding of Fisher and others that products should be classified as functional or innovation to determine their suitability for lean or agile supply respectively. However, this classification is extended here to the component level and with the addition of the leagile and non‐strategic supply options, and it depends on the impact a component has on the four competitive priorities; cost, quality, time and flexibility.

The traditional approach and methodology can no longer cope effectively with the complexities and problems associated with large scale property investment. The level of sophistication of the analysis of property investments is still much lower than the analysis of investments in other media. There is a need to establish an analytical framework which could facilitate the management of the complex decision making and management problems associated with large property investment portfolios. The principal aim of this paper is to identify and rationalise the property portfolio problem in order to pave the way for the applications of recent developments in investment and portfolio theory. The definition of the general portfolio problem is followed by a comparison of the nature and characteristics of property portfolios and stock market security portfolios. The property portfolio problem is defined as a complex decision making problem requiring effective decision making in three stages: investment policy, selection and portfolio assembly, and finally management and portfolio rationalisation.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.