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This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.

Factor investment is booming in global asset management, especially environmental, social, and governance (ESG), dividend yield, and volatility factors. In this chapter, we use data from the US securities market from 2003 to 2019 to predict dividends and volatility factors through machine learning and historical data–based methods. After that, we utilize particle swarm optimization to construct the Markowitz portfolio with limits on the number of assets and weight restrictions. The empirical results show that that the prediction ability using XGBoost is superior to the historical factor investment method. Moreover, the investment performance of our portfolio with ESG, high-yield, and low-volatility factors outperforms baseline methods, especially the S&P 500 ETF.

Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.

In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model.

We show that our extended model yields a Pareto efficient outcome.

The capital asset pricing model (CAPM) model can be used for pricing long-lived assets.

Long-term modelling and sustainability can be modelled in our setting.

Our results were only known for two periods. The extension to 3 periods opens up a large scope of applicational possibilities in asset pricing, behavioural analysis and long-term efficiency.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

Every business company deals with the dilemma of how much to invest in long-term (LT) versus short-term (ST) problem (LTvST problem). LT operations increase the reputation of the company, and revenue is rewarded in the future. In contrast, ST operations result in immediate rewards. Thus, every organization faces the dilemma of how much to invest in LT versus ST activities. The former deals with the “what” or effectiveness, and the latter deals with the “how” or efficiency. The role of managers is to solve this dilemma; however, they often fail to do so, mainly because of a lack of knowledge. This study aims to propose a dynamic optimal control model that formulates and solves the LTvST problem.

This study proposes a dynamic optimal control model that formulates and solves the dilemma whether to invest in short- or LT operations.

This model is illustrated as an example of an academic institute that wants to maximize its reputation. Investing in effectiveness in the academy translates into investing in research, whereas investing in efficiency translates into investing in teaching. Universities and colleges with a good reputation attract stronger candidates and benefit from higher tuition fees. Steady-state conditions and insightful observations were obtained by studying the optimal solution and performing a sensitivity analysis.

To the best of the authors’ knowledge, this paper is the first one to explore the optimal strategy when trying to maximize the short and LT activities of a company and solve the LTvST problem. Furthermore, it is applied on universities where teaching is the ST activity and research the LT activity. The insights gleaned from the application are relevant to many different fields. The authors believe that the paper makes a significant contribution to academic literature and to business managers.

The objective of this paper is to investigate the effect of working capital efficiency (WCE) and its components on the composite financial performance of a sample of Indian firms.

Our sample includes 796 non-financial listed firms from 2015–16 to 2021–22. Sample firms’ profitability, liquidity, solvency, cash flow management, and financial and operational leverage have been used to classify them into companies with high composite financial performance (HCFP) and with low composite financial performance (LCFP) by using K-Means Clustering technique. A composite financial performance score (CFPS) of 1 has been assigned to HCFP and 0 to LCFP. We have used logistic regression models with fixed effect to estimate the effect of cash conversion cycle (CCC) and its components, i.e. inventory days, accounts receivable days and accounts payable days on CFPS in the presence of control variables such as growth, leverage, firm size, and age.

The study finds that CCC and inventory days are inversely associated with CFPS. This finding shows that the firms’ WCE leads to superior financial performance on a composite basis.

The research findings are based on samples drawn from the population of the listed Indian non-financial companies. Since the operation, financial practices, working capital policies, and management styles of firms vary greatly among nations, the results of this study should be extended to firms in other countries after taking into account the degree of resemblance to the sample firms.

The findings of this study hold significant value for industry practitioners, as they provide guidance in determining the optimal allocation of funds for working capital and devising strategies for effectively managing inventory levels, credit sales, and vendor payments in order to increase the overall value of the company. This study aims to help investors in building their investment portfolios by identifying companies with superior composite financial performance. Investors can enhance the construction of their investment portfolios by strategically selecting companies that demonstrate superior overall performance.

The results of our study will help companies improve their WCM strategies to enhance their overall value, and their significance increases manifold during economic downturns. Business firms that perform well by efficiently managing their working capital have a multiplier effect on the economy and society at large in the form of GDP contribution, labor income, taxes to the government, investment in capital assets, and payments to suppliers.

To understand the impact of WCE on firms’ performance, the extant working capital literature focuses on some specific characteristics such as profitability, valuation, solvency, and liquidity. The limitation of employing a single parameter is its inability to present the comprehensive performance evaluation of firms. This study is among the earliest studies that focus on the holistic evaluation of WCE's impact on the composite performance of a company.

This study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019.

Our study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model.

We find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms’ financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty.

Our study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets.

This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.

The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.

Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.

The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims to provide insights into enhancing revenue generation and fully leveraging the role of USPs in promoting revenue generation.

This study employs system generalized method of moments (GMM) estimation for 116 universities in China from 2008 to 2020, using hierarchical regression analysis to examine the relationships between variables.

The findings suggest that USPs play a beneficial role in fostering revenue generation. Specifically, the provision of incubation funding demonstrates a positive correlation, while USPs size exhibits an inverted U-shaped pattern, with a threshold at 3.037 and a mean value of 3.712, highlighting the prevalent issue of suboptimal personnel allocation in the majority of USPs. Moreover, the analysis underscores the critical moderating influence of regional innovation, affecting the intricate interplay between USPs size, incubation funding and revenue generation.

The single country (China) analysis relied solely on the use of secondary data. Future studies could expand the scope to include other countries and employ primary data collection. For instance, future research can further examine how regional development and USPs strategic plan impact revenue generation.

The study recommends that USPs managers and policymakers recognize the importance of incubation funding and determine the optimal quantity of USPs size to effectively foster revenue generation in USPs. Policymakers can use regional innovation as a moderating variable to reinforce the relationship between USPs size and incubation funding on revenue generation.

The study’s findings can contribute to the strategic industry growth and economic development of nations by promoting revenue generation. Leveraging the role of USPs and implementing the study’s recommendations can strengthen innovation and technology capabilities, driving strategic industry growth and economic development. This can enhance global competitiveness and promote sustainable economic growth.

This study introduces regional innovation as a moderating variable and provides empirical evidence of its influence on the relationship between USPs size and incubation funding on revenue generation. This adds value to research to the existing literature on USPs and revenue generation by showcasing the importance of examining the regional impact in research and innovation.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.