Global Finance Journal最新文献

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.

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Companies' risk preference and risk performance, which reflect their inclination to seek higher returns, significantly influence their decisions and behaviors. The current development of digital transformation is an effective strategy to improve enterprises' competitiveness. Studies have earlier examined the functions of digitalization, such as improving business operations and efficiency. Using data from 2847 listed companies in China from 2011 to 2019, this study examines the extent of digital transformation in enterprises and its impact on their risk performance behavior. The results show that digital transformation significantly improves enterprises' risk performance. Mechanism testing shows that optimized corporate governance processes and increased investment in research and innovation act as positive intermediaries through which digitalization affects the level of corporate risk performance. These findings contribute to our understanding of the role of enterprises' digital transformation behavior and recommend relevant policies to facilitate a more effective path for enterprise development and reform.

The backdrop of this research is the high global uncertainty that has amplified the demand for safe-haven assets, particularly in the East Asian market. This paper redefines the concept of a “safe-haven” currency to align with contemporary geopolitical and trade policy uncertainties, diverging from the traditional volatility index (VIX) risk measure. We investigate the risk aversion properties of East Asian currencies under these nonmarket risks using dynamic heterogeneous panel data analysis and robustness checks with double machine learning. Empirical results reveal that no East Asian currency qualifies as a safe haven under geopolitical risk and trade policy uncertainty. However, the Japanese yen (JPY) maintains its status under the VIX indicator. This study highlights the insufficiency of traditional safe havens like the JPY and underscores the importance of considering nonmarket risks, challenging the effectiveness of traditional investment strategies amid modern geopolitical and policy uncertainties. The findings suggest that investors should prioritize nonmarket risks and call for reform in the global monetary system to enhance currency resilience. The novel methodological approach to evaluating safe-haven currencies addresses the need for diversified currency portfolios to mitigate nonmarket risks.