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By using higher moments, this paper extends the quadratic local risk-minimizing approach in a general discrete incomplete financial market. The local optimization subproblems are convex or nonconvex, depending on the moment variants used in the modeling. Inspired by Lai et al. (2006), the authors propose a new multiobjective approach for the combination of moments that is transformed into a multigoal programming problem.

The authors evaluate financial derivatives with American features using local risk-minimizing strategies. The financial structure is in line with Schweizer (1988): the market is discrete, self-financing is not guaranteed, but deviations are controlled and reduced by minimizing the second moment. As for the quadratic approach, the algorithm proceeds backwardly.

In the context of evaluating American option, a transposition of this multigoal programming leads not only to nonconvex optimization subproblems but also to the undesirable fact that local zero deviations from self-financing are penalized. The analysis shows that issuers should consider some higher moments when evaluating contingent claims because they help reshape the distribution of global cumulative deviations from self-financing.

A detailed numerical analysis that compares all the moments or some combinations of them is performed.

The quadratic approach is extended by exploring other higher moments, positive combinations of moments and variants to enforce asymmetry. This study also investigates the impact of two types of exercise decisions and multiple assets.

In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

The purpose of this study is twofold. On the one hand, it studies the impact of IFRS 9 adoption on the firm value; and on the other hand, it investigates the impact of the classification of financial assets on the firm value.

The study covers a sample of 55 listed banks in the Middle Eastern and North African (MENA) region. Data is collected for three years (2017–2019).

The findings show that banks’ value is not impacted by IFRS 9 adoption but by financial assets’ classification. Firm value is positively affected by fair value through other comprehensive income assets, while it is negatively affected by amortized cost and fair value through profit or loss assets. The results of the additional analysis show consistent outcomes.

This research reveals important managerial implications. Priority should be given to the financial assets’ classification strategy following the adoption of IFRS 9 to boost the market valuation of banks. It may be useful for investors, managers and regulators in their decision-making.

This study enriches previous research as IFRS 9 is a new standard, and its adoption consequences need to be investigated. A few recent studies have focused on IFRS 9 as a whole or on other parts of IFRS 9, namely, the impairment regime and hedge accounting and concern developed contexts. However, this research adds to the knowledge of capital market studies by investigating the application of IFRS 9 in terms of classification in the MENA region.

This study aims to investigate limited partners’ (LPs) influence on venture capital (VC) fund returns.

We merge data from Preqin and SDC’s VentureXpert spanning from 1993 to 2014 and conduct multiple regression analysis to examine the influence of LPs on VC fund performance. Additionally, we conduct three distinct robustness tests to verify the credibility of our findings.

Our empirical analysis demonstrates that newbie LPs consistently exert a significant positive influence on VC fund returns.

VC and LP data is self-reported, and there is no comprehensive dataset as some LPs prefer to maintain anonymity.

Extant literature on LPs’ contribution to VC fund performance is limited. The general assumption is that the role of LPs in VC fund performance is confined to funding. We introduce a new variable, LP track record, as a proxy for LP experience to examine if this variable influences VC performance.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Healthcare property has become an important alternate property sector in recent years for many international institutional investors. The purpose of this paper is to assess the risk-adjusted performance, portfolio diversification benefits and performance dynamics of French healthcare property in a French property portfolio and mixed-asset portfolio over 1999–2020. French healthcare property is seen to have different performance dynamics to the traditional French property sectors of office, retail and industrial property. Drivers and risk factors for the ongoing development of the direct healthcare property sector in France are also identified, as well as the strategic property investment implications for institutional investors.

Using annual total returns, the risk-adjusted performance, portfolio diversification benefits and performance dynamics of French direct healthcare property over 1999–2020 are assessed. Asset allocation diagrams are used to assess the role of direct healthcare property in a French property portfolio and in a French mixed-asset portfolio. The role of specific drivers for French healthcare property performance is also assessed. Robustness checks are also done to assess the potential impact of COVID-19 on the performance of French healthcare property.

French healthcare property is shown to have different performance dynamics to the traditional French property sectors of office, retail and industrial property. French direct healthcare property delivered strong risk-adjusted returns compared to French stocks, listed healthcare and listed property over 1999–2020, only exceeded by direct property. Portfolio diversification benefits in the fuller mixed-asset portfolio context were also evident, but to a much lesser extent in a narrower property portfolio context. Importantly, this sees French direct healthcare property as strongly contributing to the French property and mixed-asset portfolios across the entire portfolio risk spectrum and validating the property industry perspective of healthcare property being low risk and providing diversification benefits in a mixed-asset portfolio. However, this was to some degree to the loss or substitution of traditional direct property exposure via this replacement effect. French direct healthcare property and listed healthcare are clearly shown to be different channels in delivering different aspects of French healthcare performance to investors. Drivers of French healthcare property performance are also shown to be both economic and healthcare-specific factors. The performance of French healthcare property is also shown to be different to that seen for healthcare property in the UK and Australia. During COVID-19, French healthcare property was able to show more resilience than French office and retail property.

Healthcare property is an alternate property sector that has become increasingly important in recent years. The results highlight the important role of direct healthcare property in a French property portfolio and in a French mixed-asset portfolio, with French healthcare property having different investment dynamics to the other traditional French property sectors. The strong risk-adjusted performance of French direct healthcare property compared to French stocks, listed healthcare and listed property sees French direct healthcare property contributing to the mixed-asset portfolio across the entire portfolio risk spectrum. French healthcare property’s resilience during COVID-19 was also an attractive investment feature. This is particularly important, as many institutional investors now see healthcare property as an important property sector in their overall portfolio; particularly with the ageing population dynamics in most countries and the need for effective social infrastructure. The importance of French direct healthcare property sees direct healthcare property exposure accessible to investors as an important alternate real estate sector for their portfolios going forward via both non-listed healthcare property funds and the further future establishment of more healthcare REITs to accommodate both large and small institutional investors respectively. The resilience of French healthcare property during COVID-19 is also an attractive feature for future-proofing an investor’s portfolio.

This paper is the first published empirical research analysis of the risk-adjusted performance, diversification benefits and performance dynamics of French direct healthcare property, and the role of direct healthcare property in a French property portfolio and in a French mixed-asset portfolio. This research enables empirically validated, more informed and practical property investment decision-making regarding the strategic role of French direct healthcare property in a portfolio; particularly where the strategic role of direct healthcare property in France is seen to be different to that in the UK and Australia via portfolio replacement effects. Clear evidence is also seen of the drivers of French healthcare property performance being strongly influenced by healthcare-specific factors, as well as economic factors.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

Renewable energy infrastructure is an important asset class in the context of reducing global carbon emissions going forward. This includes solar power, wind farms, hydro, battery storage and hydrogen. This paper examines the risk-adjusted performance and diversification benefits of listed renewable energy infrastructure globally over Q1:2009–Q4:2022 to examine the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio. The performance of renewable energy infrastructure is compared with the other major infrastructure sectors and other major asset classes. The strategic investment implications for institutional investors and renewable energy infrastructure in their portfolios going forward are also highlighted. This includes identifying effective pathways for renewable energy infrastructure exposure by institutional investors.

Using quarterly total returns, the risk-adjusted performance and portfolio diversification benefits of global listed renewable energy infrastructure over Q1:2009–Q4:2022 is assessed. Asset allocation diagrams are used to assess the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio.

Listed renewable energy infrastructure was seen to underperform the other infrastructure sectors and other major asset classes over 2009–2022. While delivering portfolio diversification benefits, no renewable energy infrastructure was seen in the optimal infrastructure portfolio or mixed-asset portfolio. More impressive performance characteristics were seen by nonlisted infrastructure funds over this period. Practical reasons for these results are provided as well as effective pathways going forward are identified for the fuller inclusion of renewable energy infrastructure in institutional investor portfolios.

Institutional investors have an important role in supporting reduced global carbon emissions via their investment mandates and asset allocations. Renewable energy infrastructure will be a key asset to assist in the delivery of this important agenda for a greener economy and addressing global warming. Based on this performance analysis, effective pathways are identified for institutional investors of different size assets under management (AUM) to access renewable energy infrastructure. This will see institutional investors embracing critical investment issues as well as environmental and social issues in their investment strategies going forward.

This paper is the first published empirical research analysis on the performance of renewable energy infrastructure at a global level. This research enables empirically validated, more informed and practical decision-making by institutional investors in the renewable energy infrastructure space. The ultimate aim of this paper is to articulate the potential strategic role of renewable energy infrastructure as an important infrastructure sector in the institutional real asset investment space and to identify effective pathways to achieve this renewable energy infrastructure exposure, as institutional investors focus on the strategic issues in reducing global carbon emissions in the context of increased global warming.

This study examines the determinants of idiosyncratic risk (IR) or unsystematic risk. The study also examines the determinants of IR by dividing the firms into different categories: beta-based firms, liquid and illiquid firms and financially constrained (FC) and unconstrained (FUC) firms.

The fixed effects static panel data model specifications are formulated based on Hausman (1978) test for BRICS (Brazil, Russia, India, China, and South Africa) member countries over the period 2000–2019. Moreover, the t-test is applied to see whether the returns of different types of portfolios are significantly different.

The portfolio analysis results show that, on average, high IR firms tend to be small in size, highly leveraged, have low competitiveness, low profitability, less dividend yield and low returns for all the sampled countries. The sample paired t-test also confirms that a significant difference exists between extreme portfolios: small and large size and low IR and high IR portfolios. The panel regression results show that firm size, market power, price-to-earnings ratio, return on equity (ROE) and dividend yield negatively relates to IR. Yet, both leverage and liquidity are positively related to IR. However, the sign of momentum returns is mostly positive for the entire sample. The coefficient values for high-beta, FC and illiquid firms are more significant and large than the firms' counterparts for all BRICS member countries. These results support the hypothesis of an under-diversified portfolio and suggest that the above-mentioned firm-specific variables are the significant determinants of unsystematic risk.

The securities exchange commission, as the supervisor of the public limited companies, needs to increase its role in investor protection related to the uncertainty of investment in the capital market. Accordingly, in making investment decisions in a stock exchange, investors can use the information that captures unsystematic risk for investment decision-making.

This study is the first to explore the determinants of IR in top emerging countries. Second, none of the existing studies has focused on the determinants of the IR based on different categories of firms.

The purpose of this study is to investigate if a firm's exchange complexity profile (that is, the linkages between the firm and its environment) influences investor behavior at the negotiation table where a firm valuation is derived.

The authors utilize Qualitative Comparative Analysis (QCA). Specifically, the authors utilize fuzzy-set Qualitative Comparative Analysis (fsQCA), a QCA variant that allows the researcher to assign graduated membership in sets.

When the authors dichotomize their positions as either higher stakes that favor the seller (high capital, low equity, high valuation) or lower stakes that favor the buyer (low capital, high equity, low valuation), and when the authors focus primarily on the equity outcome, the authors find that investors adopt a reductionist stance that adheres to a transaction cost economics logic under conditions of lower stakes and higher complexity as well as higher stakes and lower complexity conditions. The authors interpret this to mean that equity serves as a counter-balancing lever for a firm's exchange complexity configuration.

On a theoretical level, the authors showcase the exchange complexity framework and differentiate its position within the extant frameworks that address a firm's competitive advantage. More generally, the authors note that this framework brings the discipline of micro-economics and the field of strategic management closer together, providing scholars with a new tool enabling research across industries for the portfolio level of analysis.