Global Finance Journal最新文献

This study investigates whether and how local executives' hometown identities affect enterprises' environmental decoupling. Environmental decoupling refers to the discrepancy between a company's environmental sustainability promises and its actual environmental sustainability performance. Based on the place attachment theory, we expect local executives to engage in fewer corporate environmental decoupling actions than non-local executives. Using the annual data for Chinese companies listed on the A-share market between 2009 and 2021, we tested this hypothesis. Our findings reveal that local executives' sense of identification and belonging to their hometowns lessens the likelihood of businesses engaging in environmental decoupling. This result is more pronounced for companies located in regions with low population mobility, a slow marketization process, strict environmental regulations, and companies with high financing constraints and low corporate governance levels. Furthermore, we find that local executives decrease underreporting of the environmental performance of firms, thereby reducing firms' environmental decoupling. Following several robustness tests, the primary conclusions remain valid. This study offers new evidence that executive characteristics affect company sustainability from an environmental performance decoupling standpoint. It shows that identifying the executives' hometowns motivates them to uphold a sincere commitment to corporate social responsibility.

In recent years, a series of new digital financial technologies (fintech) such as open banking and distributed finance have emerged; however, the advancement of financial regulatory technology has been relatively sluggish. This study conducts a thorough analysis and simulation of a proposed innovative financial regulation solution inspired by central bank digital currency that combines homomorphic encryption algorithms with blockchain technology. Our findings demonstrate that the solution can assist regulatory authorities in validating input data through a process of obtaining accurate regulatory indicators from concrete financial scenarios without revealing confidential information from financial institutions. Moreover, the integration of homomorphic encryption algorithms with blockchain technology has promise for the automated and intelligent oversight of financial systems. Finally, our research findings offer vital insights to promote the implementation and expansion of financial regulation in the digital economy era.

Advances in digital technologies, platforms, and infrastructures drive digital transformation, which significantly influences firm strategies, processes, and innovation efforts. This study investigates the impact of digital transformation and audit quality on the innovation efficiency of Chinese firms, focusing on firms' risk-taking behavior. Analyzing data from 2007 to 2021, we find that digital transformation substantially enhances innovation efficiency, especially in smaller firms and those experiencing slow growth during economic downturns. Furthermore, our research underscores the importance of audit quality in promoting innovation efficiency, going beyond traditional financial reporting. However, the study also reveals that although digital transformation and high audit quality are essential for boosting efficiency, excessive risk-taking negatively affects this relationship. The impact of digital transformation and audit quality on innovation efficiency is further moderated by several factors such as firm size, business cycle, and growth. These findings hold significant implications for investors and policymakers, emphasizing the importance of integrated governance and digital strategies in optimizing innovation outcomes.

Climate disasters are increasing in frequency and severity. While a large body of research has shown that extreme climate affects various economic decisions, how climate events influence investment decisions remains largely under-investigated. This paper examines whether, and to what extent, climate disasters influence insider transactions, which are important investment decisions that attract increasing attention from both corporate management and policymakers. We find that the monthly value of insider trades increases markedly in firms headquartered in counties with a climate disaster. Climate-induced insider trading holds in general but is stronger when investors are distracted and less prevalent when insiders face higher litigation risk. Climate disasters trigger uncertainty about short-term firm fundamentals, and insiders benefit by selling prior to this being priced. Insiders living in disaster counties do not trade more than those in unaffected counties, which does not support a personal liquidity motivation. Our paper documents a new way through which climate impacts investor behavior and financial markets.

This study investigates the impact of emerging economies' trade levels with the US and exchange rate systems on their interdependency with the US market. We employ a comprehensive approach, analyzing both local factors (such as illiquidity and dividend yield) and US risk factors (including the S&P500 Index, US effective exchange rate, and term spread) to discern various market phases and capture equity returns. Utilizing a State-dependent International CAPM framework, we reveal a common trend among market returns: the reduced informativeness of both US and local variables during transitions from low to high volatility states. Notably, the majority of emerging markets respond to signals from the US equity market during bullish periods. We also highlight the critical role of exchange rate regimes in explaining the sensitivity of emerging markets to US risk factors. While the illiquidity ratio emerges as a significant local risk factor, its informativeness wanes during bear markets. These findings offer valuable insights for asset allocation, diversification, and risk management strategies tailored to the dynamic nature of emerging markets.

This study investigates the integration of cutting-edge financial technologies, such as distributed ledger diversity, next-generation payment trackers, democratized banking, and cryptocurrency, and their impact on global sustainability and ethical ratings in Islamic markets. Our analysis spans the period from May 31, 2018, to May 10, 2023, and uses a novel analytical framework based on quantile time–frequency connectedness and causality analysis. Results showed that democratized banking, future payment opportunities, and sustainability-screened markets had stronger associations than distributed ledger technologies and crypto-based tokenization. Second, Islamic sustainable markets have slightly closer ties to the digital financial ecosystem than traditional responsible investments. Third, causality analysis revealed bidirectional relationships between sustainable markets and democratized banking and potential payment opportunities in the short to mid-term. Fourth, in the short term, there was unidirectional causality from conscious markets to investments in digitized currencies and blockchains; however, no long-term causality between the digital financial ecosystem and sustainability investments. Fifth, the diversity of distributed ledgers had a limited relationship with green investments, whereas advanced transaction monitoring platforms and inclusive financial infrastructure could serve as catalysts for nature-based portfolios. These findings inform policymakers in green finance and provide insights for digital financial network strategists, investors, and regulators, enabling sustainable interdisciplinary investment practices. This comprehensive examination sheds light on the evolving landscape of sustainable finance in the context of rapidly advancing financial technologies, paving the way for informed decision-making and strategic planning in the field of green investments.

This paper provides empirical evidence on the finance-growth nexus, making key contributions by focusing on previously understudied Emerging Markets and Developing Economies (EMDEs) and employing mixed-frequency data. Utilizing panel forecasting models for 50 countries from 1990 to 2019, we examine the empirical link between macroeconomic indicators (e.g., aggregate production) and financial indicators (e.g., stock market indexes). Our results support the notion that financial indicators can indeed serve as robust predictors of macroeconomic indicators. Further, the use of mixed data sampling (MIDAS) models enhances the results, confirming the presence of valuable predictive information in higher-frequency data, even for lower-income countries. These findings bear particular significance for policymakers and investors, given the persistent challenge of accessing timely and reliable data on real indicators in EMDEs.

This study applies the panel smooth transition regression model to a 13-year sample of 16 Taiwanese non-life insurance companies to examine market competition's impact on Asset risk.

Underwriting risk Investment risk and differentiate between financial holding companies (FHCs) and non-FHCs (NFHCs). For NFHCs, increased competition reduces asset risk in high-leverage firms, supporting the modified moral hazard hypothesis. For FHCs, greater competition lowers asset risk only above a leverage threshold, indicating superior risk management and affirming the competition stability hypothesis. The effect on underwriting and investment risks depends on operational tenure; below a certain threshold, competition increases underwriting and investment risk, whereas competition above the threshold decreases risk, showing that experience improves risk management. This study offers key insights into how competition influences risk across different types of insurance companies in Taiwan.

We evaluate the performance of expected return proxies during extreme credit market conditions and extreme phases of business cycles when realized returns on banks stocks are large in absolute value. We construct three sets of expected return proxies for individual bank stocks: (i) characteristic-based proxies; (ii) standard risk-factor-based proxies; and (iii) risk-factor-based proxies in which betas depend on firm characteristics. Based on the newly developed minimum error variance (MEV) criterion (Lee et al., 2020), the best performing expected return proxy is the risk-factor-based model that allows betas to vary with firm characteristics. We also examine whether these three expected return proxies can capture actual returns during either extreme credit market or extreme business-cycle conditions. We find that both risk-factor-based proxies explain returns better than characteristic-based proxies during these periods.

This study investigates the contribution of the limit order book to the price discovery process of blue-chip stocks traded on Borsa Istanbul. Using various price series, including the last trade price, best prices of the order book, and price steps beyond the best price levels, we measure the contribution of orders beyond the best prices to price discovery. This contribution is evaluated through information shares. Our findings highlight the significant informational role of the order book in price discovery, emphasizing its importance alongside trading activity for a comprehensive understanding of the market. Additionally, this analysis is conducted across distinct stock characteristics, specifically return, size, volume, and illiquidity, revealing notable variations in the information share of the limit order book.