International Journal of Finance & Economics最新文献

Digital economy (DE), driven by digital technology, enhances digitalisation, networking and intelligence of the economy and society by integrating it with the real economy. Based on network effects theory, this study examines how DE empowers regional green development efficiency (GDE) and explores its mediating role in addressing the contradiction between environmental protection and economic development. Using data from 30 provinces in China from 2008 to 2021, this study empirically examines the impact of DE on regional GDE from multiple perspectives. This study finds that (1) DE directly promotes the regional GDE and produces positive spatial spillover effects. All four dimensions of the DE can enhance the regional GDE. The driving effect size is in the order of digital technology (DT) > digital foundation (DF) > digital platform (DP) > digital inclusive finance (DIF); (2) A ‘U’-shaped relationship exists between the DE and regional GDE, initially inhibiting efficiency but actively promoting it once a threshold of approximately 51% is exceeded; (3) The mechanism test shows that the DE effectively improves regional GDE by advancing regional green technology; and (4) The test based on regional heterogeneity shows that the impact of the DE on regional GDE differs significantly based on regional income disparities. These results provide strong theoretical support for DE in sustainably empowering regional green development in China. To enhance DE's impact on GDE, the central government should promote spatial policy interconnection, while local governments tailor strategies to their development needs for effective structural and efficiency transformation.

In this paper, we examine the conditions under which the capital asset pricing model (CAPM) holds with heterogeneous probability weighting. Using the generalised Wang transform within rank-dependent expected utility, we show that CAPM holds for heterogeneous risk-averse investors, while the security market line theorem (SMLT) applies to heterogeneous loss-averse investors. However, CAPM under loss aversion requires homogeneous investors. Revisiting skewness pricing, we find that probability weighting, rather than the S-shaped value function, drives skewness overpricing. The preference for skewed assets stems from the high distorted mean under probability weighting.

In the context of frequent trade frictions and geopolitical conflicts, the increase in economic policy uncertainty highlights the application value of digital technologies. Digital finance business development originates from applying achievements in financial technology innovation and is also rooted in specific economic policy environments. This work explores the relationship between regional economic policy uncertainty and digital finance. Using data from 2011 to 2021 across 30 Chinese regions, we discover that economic policy uncertainty positively impacts digital finance and that there is regional heterogeneity. Economic policy uncertainty effectively drives government investment. Meanwhile, government investment plays a mediating role in economic policy uncertainty and digital finance. According to the threshold regression test, economic policy uncertainty's impact on digital finance demonstrates a double threshold effect dependent on the economic development level. As economic development levels rise, the promoting role of economic policy uncertainty in digital finance gradually strengthens. Further analysis reveals that digital finance has a significant spatial positive correlation and that economic policy uncertainty can drive the development of digital finance in neighbouring areas through the spatial spillover effect. Consequently, financial institutions should actively develop digital finance to strengthen their risk resilience in the face of shocks to economic policy uncertainty. Simultaneously, the government may facilitate the orderly and healthy development of digital finance through augmenting investment, optimising the layout of digital infrastructure, and boosting interregional collaboration.

Do fund managers learn about salient risks? To address this issue, we explore whether managers' exposure to prior disease outbreaks might affect their fund performance during COVID-19. Using data on US equity fund managers, we find that H1N1 funds with managers exposed to the influenza A disease in 2009 (H1N1) generate superior returns during the 2020 COVID-19 crisis compared to other funds. A possible explanation is that H1N1 funds use less human-interaction-based information after H1N1 exposure so that their performance is less affected by the lockdowns that prevented social interaction during the COVID-19 pandemic. Conversely, we do not find that they have changed their exposure through the domestic holdings of firms with international supply chains after H1N1. Overall, our findings support the view that managers' professional experience may affect investment style and explain superior performance for funds with managers exposed to similar exogenous experiences throughout their careers.

This paper explores the information content of the untargeted narratives of the Bank of England (BoE), the European Central Bank (ECB), and the Federal Reserve (Fed) and whether it has the potential to improve forecasting performance. We apply the Latent Dirichlet Allocation (LDA) method to extract topics from the corpus of text data. We then evaluate the impact of these central bank officials' speeches on macroeconomic and financial variables forecasting from 1997 to 2018. Our results suggest that the forecasting model, incorporating information from speeches, produces estimates with a lower forecasting error for several variables in the UK, the EU, and the US. For certain variables, the forecast improvement is more pronounced after the global financial crisis of 2008.

Despite the increasing concentration on the budding, but yet immature, green investment markets, less empirical information is known on their multifractal and efficiency behaviour, with no study particularly linking these to the crude oil market. In addition, how the recent COVID-19 affects the multifractality and cross-correlation between oil price and the green investment assets remains unexamined in the literature. Filling these gaps, this study employs novel multifractal techniques that cut across univariate, multiscale, and cross-correlation analyses. We find that oil price and all the green investment assets are strongly multifractal in behaviour before and during the COVID-19 pandemic, although the pandemic intensifies persistence and market inefficiency. Moreover, the multifractality is established to vary across scales, thereby making it to be complex and heterogeneous. On a final note, oil price has a strong multifractal cross-correlation with all the green investment assets, and this is more pronounced during the pandemic. Thus, the oil and green investment markets are closely knitted. These findings are followed with suitable policy implications for investors who desire to effectively assess and manage financial risks and policymakers that desire optimal performance of the markets to achieve the goal of a carbon-friendly economy.

To address the challenge of reducing carbon emissions and achieving carbon neutrality, assessing the efficacy of total carbon emission control policy (TCP) is imperative for the pursuit of sustainable economic growth in China. This study leverages a dataset comprising 282 Chinese cities across the period from 2010 to 2021 to quantify China's environmental total factor productivity (ETFP). Employing the differences-in-difference (DID) approach, this research scrutinises the influence of TCP on ETFP. The findings reveal that the implementation of TCP significantly enhances ETFP within the regions subject to the policy. Notably, the policy exerts a more pronounced influence on ETFP in the eastern regions, cities reliant on resource extraction and those with higher administrative status. The promotion of ETFP through TCP is largely mediated by advancements in technology and shifts in the industrial landscape. This study offers valuable insights to facilitate the effective rollout of TCP in China.

The study explores the effect of China's Green Financial Reform and Innovation (GFRI) pilot zone policy on corporate ESG performance from 2012 to 2021. Utilising a double/debiased machine learning (DDML) model, we find that the policy enhances ESG performance by mitigating financial constraints, improving environmental practices, and reducing short-sighted management behaviour in Chinese corporates. Heterogeneity analysis reveals varied effects based on resource endowments, geographic locations, ownership properties and life cycles. This study underscores the significance of the GFRI policy in advancing China's dual-carbon goals. It further illustrates the potential of this policy to serve as a model for similar initiatives globally, offering empirical evidence that supports environmental governance and remediation efforts at both regional and corporate levels.

Optimising the allocation of limited research and development (R&D) resources significantly enhances the environmental benefits. Based on the innovation value chain theory, this study defines green R&D efficiency and investigates its impact on industrial carbon intensity from a spatial perspective. Using the spatial Durbin model, the results indicate that green R&D efficiency reduces industrial carbon intensity both locally and in neighbouring provinces. Endogenous bias and robustness check further confirm these findings. The key mechanisms driving this effect include green output efficiency and green industrial transformation. Additional analysis manifests that the reduction effect is more pronounced during the development stage of green R&D activities than during the research stage. However, both stages work synergistically to reduce industrial carbon intensity. Environmental regulation and a free market environment positively influence the environmental performance of green R&D efficiency. Finally, policy recommendations are provided.