Search results

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.

This study aims to examine the impact of economic globalization on bank profitability in Sub-Saharan Africa.

The empirical analysis is based on panel data of banks spanning 2008–2016. Relying on the KOF Globalization Index, the study uses financial globalization and trade globalization as measures of economic globalization. The authors employ the system generalized method of moments technique to establish the relationship between economic globalization and bank profitability while controlling for the effect of bank-specific and macroeconomic factors.

The results show a negative significant effect of financial and trade globalization on bank profitability, signifying the intense competition of banks in Sub-Saharan Africa accelerated by globalization. The negative effect of economic globalization holds irrespective of the indicator of bank profitability. Bank size exerts a significant effect on profitability though the impact is negative for return on equity measure. The findings further reveal a positive significant impact of GDP growth and inflation on profitability.

This paper presents a pioneering work on the impact of economic globalization on bank profitability in the Sub-Saharan African context per the researchers' knowledge.

This real estate insight provides a comprehensive analysis of the current state and future potential of tokenization in the real estate industry mentioning several challenges to overcome to take advantage of this technology. We highlight potential benefits, including enhanced liquidity, increased security and improved accessibility. Additionally, the real estate insight critically discusses potential drawbacks, such as regulatory challenges and technological risks, and explores the impact of tokenization on real estate prices.

This real estate insight employs a comprehensive literature review alongside a qualitative analysis of various case studies to explore current implementations of tokenization within the real estate industry. Multiple applications of tokenization in the real estate industry are examined, including fractional ownership, property management and transaction processes. The study investigates the optimization potential of tokenization for asset liquidity in the real estate area, transaction transparency and security. It also critically discusses potential challenges, such as regulatory compliance, security vulnerabilities and market adoption.

The future of real estate tokenization, driven by blockchain technology and smart contracts, offers significant potential for growth, enhancing liquidity and accessibility through fractional ownership. Smart contracts automate and secure transactions, while evolving standards and regulatory frameworks in regions like North America, Europe and Asia support market expansion. Since its initial implementation with the St. Regis Aspen Resort STO, a stream of successful projects has highlighted the viability of tokenization. However, challenges remain, including the need for regulatory clarity, industry and customer education, displacements of market participants and jobs and environmental impacts. Integrating advanced technologies like AI and IoT can further streamline property management and investment decisions.

The real estate insight’s practical implications extend to industry professionals, policymakers and technology developers. Professionals gain insights into how tokenization can enhance liquidity and security in the real estate sector, guiding strategic decision-making. For policymakers, understanding potential challenges like regulatory compliance and technological risks informs the development of supportive regulations. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Highlighting the need for robust security measures and regulatory compliance in tokenization systems may foster better design practices. Therefore, the real estate insight’s findings could significantly shape the future development of tokenization integration in the real estate industry.

This real estate insight offers original value through a comprehensive analysis of the current and future impacts of tokenization in the real estate industry. It examines various applications of tokenization and critically discusses the potential challenges. The focus on informing strategic decisions for professionals and policymakers enhances its utility as a resource. Additionally, by addressing both the benefits and drawbacks, this study contributes to the broader discourse on the societal implications of tokenization. In the context of rapid technological advancement, such thorough studies are rare, further underscoring the real estate insight’s originality.

The authors evaluate the Sell in May effect in the Canadian context to comprehensively explore the Sell in May effect as well as its interactions with the size effect and risk and with multiple indices.

The authors use ordinary least squares (OLS) regressions to examine the Sell in May effect and Huber M-estimation to handle potential outliers. They also use the generalized autoregressive conditional heteroskedasticity (GARCH) models to explore the role of risk in the Sell in May effect.

The results demonstrate that the Sell in May effect is present in all three main Canadian stock market indices. More telling, the anomaly is strongest in small cap indices and in indices that give equal weighting to small and large cap stocks. They do not find that the effect is driven by risk.

While several papers have explored the Sell in May phenomenon in several countries, little scholarly attention has been paid to this effect in Canada and to its interaction with the size effect. The authors contribute to the literature by examining of the interactions between Sell in May and the size effect in Canada. They examine the Sell in May effect using CFMRC value-weighted and equally weighted indices of all Canadian companies. They also incorporate in their analysis the role of risk.

By combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity.

We used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms.

Our findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions.

The insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm’s adoption of corporate actions (ESG practices).

To the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms.

The authors investigate the impact of the US stock market on the economies of the BRICS and major industrialized economies (G7).

The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. Global vector autoregressive (GVAR) empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

The authors summarize the results in four points: (1) financial integration variables increase the effect of the US stock market on the BRICS and G7, (2) the US shock produces similar responses in these groups regarding industrial production, stock markets and confidence but different responses regarding domestic currencies: in the BRICS, the authors detect appreciation of the currencies, while in the G7, the authors find depreciation, (3) G7 stock markets and policy rates are more sensitive to the US shock than the BRICS and (4) the estimates point out to heterogeneities such as the importance of industrial production to the transmission shock in Japan and China, the exchange rate to India, Japan and the UK, the interest rates to the Eurozone and the UK and confidence to Brazil, South Africa and Canada.

The results reinforce the importance of taking into account different levels of economic development.

The authors construct the world economy and the vulnerability between economies using three economic integration variables: bilateral trade, bilateral direct investment and bilateral equity positions. GVAR empirical studies usually adopt trade integration to estimate models. The authors complement these studies by using bilateral financial flows.

This study aims to test the contagion effect of the Tunisian revolution on the Egyptian stock market. Thus, the purpose of this research is to distinguish the contagion effect from the simple interdependence between these markets.

This paper examines the contagion hypothesis between Tunisia and Egypt during the Arab Spring, using a DCC-MGARCH model to capture time-varying contagion effects and dynamic linkages in stock markets. Therefore, to identify the contagion effect from the simple interdependence, the authors apply the pure contagion test developed by Forbes and Rigobon (2002).

The findings indicate a contagion effect, as the EGX 30 index exhibited similar changes, positive or negative, as the Tunindex index during the period of the Tunisian revolution. Moreover, the analysis demonstrates the presence of an interdependence between the Tunisian revolution and the Egyptian market, emphasizing the interconnections between these two economies.

The findings provide investors with a better understanding of financial market dynamics in times of major political unrest, notably on the Tunisian and Egyptian markets. By understanding the contagion effect of the Tunisian revolution on the Egyptian stock market, investors can further explore the complexities of these markets in times of financial crises, which can help mitigate losses and identify strategic investment opportunities.

This study makes two significant contributions to the field. First, it addresses the scarcity of research specifically focused on the contagion effect during the Arab Spring, aiming to fill this gap by testing the contagion effect of the Tunisian revolution on a nearby market. Second, it extends the contagion test of Forbes and Rigobon (2002), which associates “pure” contagion with a significantly higher correlation between markets during a crisis.

This research aims to examine the macro determinants that significantly affect financial development in the Middle East and North Africa (MENA) region, which could be used furtherly to play a major role in economic sustainability since one of the major driving forces for economic development is the financial development.

The significant determinants of financial development should be efficiently used by the MENA region countries for creating huge financial sector development and innovation, stimulating economic development in turn and leading to the completion of the cycle of development and sustainability. To achieve this study's objective, the researcher employed a quantitative method to develop an econometric model.

This model consisted of two Panel EGLS Cross-Section Random Effects Models (REMs) in which Domestic credit to the private sector as a percentage of GDP (?PCGDP?_it) and stock market capitalization ratio (?SMC?_it) were taken as the dependent variables. In addition, the independent variables included the corruption perception index, financial freedom (FF), political stability (PS) and trade openness (TO). The researcher extracted the data for the analysis from different databases including the World Bank, the Organization for Economic Cooperation and Development and the International Monetary Fund. Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

The reform and opening-up of capital market is valued for promoting sustainable development, while its impact presented as the form of deregulation of short-selling on the green innovation of enterprises in developing countries remains unclear. The purpose of this study is to outline the significance of gradual reform of financial markets in developing countries for low-carbon transformation and provide implications for achieving carbon peaking and carbon neutrality goals.

Based on the green subdivided patent data and financial data of China’s A-share listed companies, this paper takes the implementation of securities margin trading program as a quasi-natural experiment and applies the difference-in-differences (DID) model to examine the impact of deregulation of short-selling constraints on the enterprises’ green transformation.

The findings reveal that the initiating securities margin trading program significantly enhances the green innovation performance of enterprises. These findings are valid after performing a series of robustness tests such as the parallel trend test, the placebo test and the methods to exclude other policy interference. Mechanism analyses demonstrate a two-faceted effect of the securities margin trading program on the green innovation of enterprises, in which short-selling policy increases the pressure on capital market deregulation and meanwhile induces the environmental protection investment. The heterogeneity results demonstrate that the impulsive effect imposed by securities margin trading program is more significant in experimental group samples with characteristics of lower financing constraints, belonging to heavy polluting industries and possessing better environmental supervision capability.

First, previous studies have focused on the impact of financial policies implemented by banking institutions on the green innovation of enterprises, but few literatures have explored the validity of relaxing short-selling restrictions or opening the capital market in the field of enterprise’s green transformation in developing country. From the view of securities market reform, this paper broadens the incentive and supervision effects of the relaxation of short-selling control on enterprise’s green innovation performance after the implementation of securities financing and securities lending policy in China’s capital market. Second, previous studies have explored the impact of command-and-control environmental regulations, as well as market-incentivized environmental regulations such as green finance, low-carbon pilots and environmental tax reform, on the green transition of enterprises. Recently the role of the securities market in the green development of enterprises has received more attention in academia. The pilot of margin financing and securities lending is essentially a market-incentivized regulatory tool, but there is few in-depth research on how it affects the green innovation of enterprises. This paper enriches the research on whether the market incentive financial regulation policy can contribute to the green transformation of enterprises under the Porter hypothesis. Third, some previous studies used the ordinary panel regression model to explore the impact of financial policy on enterprise’s innovation performance. However, due to the potential endogenous problems of the estimated model, it might get biased conclusions. Therefore, based on the method of quasi-natural experiment, this paper selects the margin trading pilot policy as an exogenous shock to solve the endogenous or reverse causality problem in traditional measurement model and applies the DID model to study the relationship between core indicator variables.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.