Research in International Business and Finance最新文献

This study introduces a finetuned hybrid forecasting model combining both Discrete Wavelet Transform (DWT) and Long Short-Term Memory network (LSTM) to predict dirty and clean cryptocurrency returns (Bitcoin and Ripple). The findings show that the proposed DWT-LSTM model outperforms a large set of benchmark models in terms of forecasting accuracy. We investigate a broader set of predictors involving financial markets (other cryptocurrencies and commodities) and external variables (blockchain information, Twitter economic uncertainty, and CO2 emissions). Our findings underline the comparable performance of the considered predictors, with the Twitter Economic Uncertainty index being the best predictor of Bitcoin returns and S&P GSCI Energy being the best predictor of Ripple returns. We also highlight the superior performance of the trading strategies based on our forecasting results.

This study examined the influence of environmental, social, and governance (ESG) information disclosure on firm value and the mediation effect of financing constraints using a panel of Chinese A-listed firms from 2013 to 2020. Based on linear regressions, we found that the relationship between ESG disclosure scores and firm value was significantly positive, and the results remained after robustness checks and endogenous tests. Furthermore, the mediation effect analysis indicates that the relationship is achieved by alleviating financing constraints. The subsample analyses showed that ESG disclosure scores positively affect firm value in non-state-owned, nonheavy polluting, and high-transparency firms. The results of each dimension’s relationship to firm value indicate that environmental and social dimensions significantly affected firm value in the panel data, whereas governance did not. Therefore, this paper determines the influence of ESG information disclosure and firm value in China and reveals the mediation effect of financing constraints, supplementing the influence mechanism of information disclosure and extending the previous studies bsed on the developed stock markets.

Green finance plays a pivotal role in driving sustainable development. While its effectiveness in promoting green development is well-established, the impact of regional heterogeneity on its outcomes warrants further investigation. By introducing the Spatial Durbin Model (SDM) model, we analyze the causal relationship between provincial carbon emissions and green finance development in China, accounting for spatial spillovers across provinces. A geographic mismatch between regions of green finance development and carbon emission concentration is revealed. Central and Western areas exhibit higher carbon emissions, indicating a greater need for targeted green finance initiatives to promote sustainable development. However, green finance growth in these areas remains sluggish. Moreover, carbon emissions hinder local green finance development and negatively impact neighboring regions. We present new insights into the influence of carbon emissions on green finance, highlighting the importance of inter-regional collaboration and the potential applicability to other countries facing similar challenges.

Vietnam is actively pursuing sustainable economic development while transitioning to a greener economy, facing challenges in balancing economic growth with environmental sustainability. The evaluation of current sustainable development efforts and the identification of effective green financial tools are critical for Vietnam's progress. This study introduces the Sustainable Economic Development (SED) Index, a comprehensive measure of sustainable economic development quality across Vietnam's 63 provinces from 2015 to 2022. Using a range of analytical techniques, we explore the relationships between green credit, environmental pollution, and sustainable economic development during this period. Our findings indicate rapid growth in sustainable economic development until 2019, followed by a deceleration due to the COVID-19 pandemic and the Ukraine war. Green credit emerges as a pivotal factor in supporting sustainable economic growth, managing climate risks, and mitigating environmental pollution, particularly during times of uncertainty. Additionally, we observe spatial spillover effects, where the benefits of green credit and the challenges of environmental pollution transcend provincial boundaries. The study recommends promoting green credit policies, fostering regional cooperation, and enhancing local competitiveness, digital transformation, and social equality to advance Vietnam's journey toward sustainable economic development.

This study examines the impact of place-based policies on local technology spillovers, using a sample from the Chinese A-share market. The findings indicate that firms experience greater local technology spillovers following the establishment of national high-tech zones (HTZs). Channel analysis shows that HTZ policies facilitate the formation of R&D alliances and promote technology transactions. The positive effects are more pronounced in firms with higher knowledge absorptive capacity, those led by executives with academic backgrounds, and in environments with weaker market competition and intellectual property rights enforcement. Additionally, the research reveals that the technology spillovers generated by HTZ policies primarily spread between industries rather than within them.

There is substantial evidence indicating that stocks with lottery-like payoffs have lower returns. Unlike existing studies that focus on the role of investor behavior in accelerating lottery premiums, we propose that investors’ social preference toward corporate social responsibility (CSR) could alleviate the underperformance of lottery-like stocks. Using a sample of all Chinese listed A-share stocks, we show that a negative relationship between lottery preference and stock returns does not exist among stocks that behave well regarding CSR performance. Furthermore, we show that better CSR performance and higher institutional ownership mitigate the overpricing of lottery-like stocks. Our research contributes to CSR literature by showing that behaving socially responsible can prevent stock prices from being overpriced when the stock exhibits lottery-like payoffs.

The Efficient Market Hypothesis (EMH) is still a debated subject in the financial area. Particularly, no conclusions are drawn to date in link with the Google Search Volume Index (GSVI). To conclude on this question, our paper takes up the work of Škrinjarić (2019) by proposing robustness tests, various econometric improvements and the inclusion of additional explanatory variables. On a database of ten emerging European indices studied by Škrinjarić (2019), a dynamic panel model was applied. Unlike Škrinjarić (2019) who modeled the time-series separately and thus neglected any possible dependence or homogeneity between countries, our study operates within the framework of panel data. Drawing from a robust estimation approach, our findings indicate that the GSVI has no impact on market returns. In essence, this suggests that internet search queries fail to provide avenues for investors to seize arbitrage opportunities. Such findings support the EMH in the studied markets and underline the exposure of prior studies to robustness challenges.