Global Finance Journal最新文献

Enhancing household financial capability is critical for mitigating severe economic challenges. In the European Union (EU), financial technology (FinTech) solutions are considered critical for managing household finance, but their role in enhancing financial capability is ambiguous. Herein, we measure financial capability in the EU and investigate the effect of FinTech using three waves of panel data from Global Findex (2014, 2017, and 2021) and Eurostat Databases. According to the analyses, EU countries vary greatly in terms of FinTech and financial capability, with countries in the Union's north scoring remarkably high. Results emphasize a considerable effect of FinTech on financial capability and highlight an increase in the latter when the Human Development Index increases. Practical guidelines for measuring financial capability are also presented to assist countries that requier additional efforts to effectively address financial capability challenges.

We investigate the degree of digitalization required to gain a competitive advantage in the supply chain by developing a firm-specific metric based on firm disclosure. Using a 2011–2020 sample of Chinese listed firms, we examine whether improving corporate digitalization can bring more trade credit financing from the supply chain. Our findings indicate that firms acquire more trade credit after increasing their level of digitalization, despite the low pressure of credit rationing. Furthermore, digitalization improves operation, research and development, and governance efficiency, giving such firms a competitive advantage. Cross-sectional analyses indicate that specific characteristics, such as a large firm size, state-owned property, traditional industry type, stable macropolicy environment, high industry status, and transparent internal information environment, can bolster this competitive advantage. In general, we highlight the role of digitalization in improving firms' competitiveness in the supply chain from the perspective of trade credit financing.

Using daily data spanning from 5 January 2004 to 22 November 2022, we quantify the spillover effects between 42 global stock markets. Specifically, combining causal structure learning and Elastic-Net-VAR methods, we innovatively construct multilayer causal networks based on volume-price relationship. Then, we analyze the network characteristics of multilayer spillover networks from system and market levels. Our findings indicate that there is heterogeneity in risk spillovers of price and volume networks, highlighting how trading volume spillover network play an important role in the risk contagion. Furthermore, multilayer interconnected networks confirm the risk spillovers between volume and price, and it exhibits significant differences compared to single-layer network. In addition, at system-level, each network layer shows unique network structures and dynamic evolution characteristics. At market-level, global stock markets play different roles in emitting or receiving shocks through various transmission channels. Our study emphasizes the importance of intra- and inter-layer risk propagation in multilayer networks based on volume and price, and has significant implications for developing investment strategies and global portfolio risk management.

This study delves into the battle of factors in Canadian capital markets, employing spanning tests to evaluate 17 factors from ten multifactor models for 1991–2022. While the value factor (HML) proves redundant, its monthly updated counterpart excels. The size factor (SMB) is not improved by discounting mispriced stocks but gains potency after controlling for profitability and investment. Q-based and mispricing factors subsume the momentum factor (UMD). No single asset-pricing model emerges dominant, except in three instances. A six-factor model including market, size, monthly updated value, ROE, expected growth, and PEAD factors proves effective for asset pricing in Canadian markets.

Given the financial markets' growing preference for examining digital platforms, this study explores the dynamic relationship of oil shocks with both conventional and digital currencies. It provides a novel approach for evaluating the relationship between aggregate oil, demand, and supply shocks using three conventional currencies (Japanese Yen, Chinese Yuan, and Euro), cryptocurrencies (Bitcoin, Ethereum, Tether), and DeFi tokens (Maker, Chainlink, and Basic Attention Token). We use wavelet analysis to test the asymmetric association among variables from June 22, 2018, to July 11, 2023. The outcomes of continuous wavelet confirm the volatile behavior of oil shocks and studied currencies. In particular, a nonlinear wavelet coherence is observed between oil shocks and DeFi tokens, cryptocurrencies, and traditional currency in the short, medium, and long term. Moreover, crypto and conventional currencies are found to respond more strongly to external events than DeFi tokens. Given the empirical findings and the rapid transformation of digitalized finance with ongoing currency restructuring, this study plays a critical role by actively influencing the adaptation of regulatory frameworks to align with dynamic changes in associations, serving as a strategic guide for regulatory bodies to navigate the complexities of emerging financial paradigms.

This study examines how various forms of capital in the years leading up to the COVID-19 pandemic affected bank profitability during the crisis in emerging economies. Using data from 819 banks in 26 countries during the 2019–2020 period, we find that banks entering the crisis with a superior capital position performed better during the pandemic. High-quality capital metrics such as Tier 1 capital and total regulatory capital ratios, not the standard leverage ratio, possess the capacity to affect bank profitability. These results are robust after controlling for Basel III liquidity requirements. We also find that the capital-profitability relationship is stronger for larger banks and for those that entered the crisis with better liquidity and credit risk position. Overall, our results imply that focusing on capital quality can help reduce the adverse effect of an external shock on bank performance.

This study examines the impact of environmental, social, and corporate governance (ESG) performance metrics on executive compensation in India using a sample of top-listed firms from 2007 to 2021 and controls for various firm-level characteristics. Findings show that a higher ESG score is associated with higher executive compensation. The subsample analyses examines how business-group affiliations and environmental sensitivity affect executive compensation. Results reveal that business-group affiliated firms with higher ESG scores tend to have higher executive compensation than nonaffiliated firms. Moreover, environmentally sensitive firms with higher governance pillar scores, which represent better governance practices, show higher executive compensation. Finally, high ESG scores and executive compensation is associated with better firm performance.

CEO narcissism is an established fact in the corporate world. We argue that uncertainty moderates the relationship between CEO narcissism and corporate performance. Thus, we hypothesize that the decisiveness and craving for attention of the narcissistic CEO improve corporate performance during periods of high uncertainty, while these features of narcissism are not needed and indeed are disadvantageous during periods of low uncertainty. Our empirical results support our hypothesis. Our findings are important in understanding the context specificity of the advantages and disadvantages of CEO narcissism in an empirical corporate setting and thus important for CEO selection and CEO management.

The rapid and widespread digitalization of economies warrants a better understanding of its impact on society and economy. This study contributes to this new but important research agenda by examining how news on digitalization affects digital economy related exchange traded funds (ETFs). For this purpose, we first construct a digital economy attention index by utilizing Google Search Volume Index for several keywords. Then, using this index and two other indices that represent attention and uncertainty related to central bank digital currency (CBDC), we examine the time-varying connectedness and correlations between these three indices and digital economy ETFs. Our TVP-VAR frequency connectedness analysis shows that attention to and uncertainty around the CBDC and digital economy have strong connectedness with the ETFs in the short-term. The analysis also shows that CBDC and digital economy indices are mainly net transmitters of shocks while the majority of the ETFs are the net receivers of the shocks. The results of our wavelet local multiple correlation (WLMC) analysis show that the correlations between ETFs, digital economy and CBDC indices are time- and frequency-dependent. Moreover, both connectedness and correlations are affected by CBDC-related global events and COVID-19 pandemic. The time- and frequency-dependent relation requires active management of the portfolios containing digital economy related assets.

Extant research documents that hedge funds which bet on positive earnings surprises manage their sector risk by shorting industry exchange-traded funds (ETFs). We add to this literature by evaluating the performance of a hypothetical hedge fund that can anticipate positive earnings news. We construct return series for a naked strategy that only takes long stock positions and a hedged strategy that also holds short positions in industry ETFs around earnings announcements with positive content. Our main result is that hedging with industry ETFs improves fund performance based on various reward-to-risk ratios. This finding holds in various equity subsamples and both strategies tend to perform better among riskier stocks. Hedging with industry ETFs boosts fund performance compared to hedging with a broad market index.