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This study applies the PSM-DID approach to investigate whether the energy price has been impacted by the Russia-Ukraine war among OECD countries during 1999–2022. Compared with previous studies, we obtain robust and heterogeneous results by considering endogeneity from the perspective of quantitative policy assessment. It concludes the Russia-Ukraine war has significantly raised energy prices, leading to a 9% increase in energy prices in OECD countries. The heterogeneity results show that the Russia-Ukraine war impacted the energy prices of EU and NATO members but not those of non-EU and non-NATO countries. Additionally, only the energy prices in Southern and Western European countries have been significantly increased by the Russia-Ukraine war, in which Southern Europe has been the most affected, rising by 22%. These findings have great practical significance for analyzing the degree of influence of war on fluctuations in energy prices and geopolitical risks in regions. [ABSTRACT FROM AUTHOR]

We examined the impact of compulsory disclosure regulations on the relationship between the Environmental, Social and Governance (ESG) and firm-specific extreme risk. We used a difference-in-difference approach to solve endogenous concerns and found that the negative impact of the ESG score on extreme risk is more significant after announcing compulsory disclosure regulations. Additionally, we demonstrated that this effect becomes more substantial when firms issue green bonds. Finally, the subsample tests showed that the compulsory influence of the government is more evident in firms with high financial transparency and firms with low crash risk. Our empirical findings had policy implications for governments, regulators, and investors. [ABSTRACT FROM AUTHOR]

The rapid growth of online media and communities has increased and accelerated the spread of unverified and false information ("fake news"), with significant political, economic, and social impacts, leading the European Commission to promulgate a "Code of Practice on Disinformation." Identifying and countering such false information is time- and labor-intensive, and could benefit from the development of tools that automatically identify and flag such information. This study explores the use of deep learning techniques to detect fake news, using decreases in the incidence of emotional vocabulary and subjectivity to enhance detection accuracy, and examines potential correlations between the emotional sentiment of news content and the movement of stock price indexes. Empirical results show that deep learning techniques can be used to effectively detect fake news, with multiple trainings effectively improving detection accuracy and reducing the loss rate. In addition, increased objectivity and the use of fewer words with high emotional sentiment increases news credibility. Finally, news sentiment was found to be correlated with the movement of three of five stock indexes examined. [ABSTRACT FROM AUTHOR]

Previous studies document that investing in research and development is a key factor to keep firm survive in the long-run. This study instead focuses on the relationship between managerial role and R&D decisions. Specifically, we investigate whether more talented managers facilitate firms' R&D investment. Using the MA-score as a proxy for managerial ability, we find that more able managers increase R&D spending. Furthermore, the results show that more talented managers decide to invest more in R&D investment depending on several conditions, such as whether firms are financially unconstrained or in non-crisis period. Other conditions include whether firms are facing lower default probability, in competitive industries, and with some CEOs' characteristics, such as male, young age, higher granted stock option and higher salary. [ABSTRACT FROM AUTHOR]

Using data on Chinese non-financial listed firms covering 2009 to 2022, we explore the effect of supply chain transparency on stock price crash risk. Two proxies for supply chain transparency are constructed using the number of supply chain partners' names and the proportion of their transactions disclosed in annual reports. The results reveal that enhancing supply chain transparency can decrease crash risk, specifically by mitigating tax avoidance and earnings management. Moreover, the analysis suggests that this risk-reduction effect is more prominent in companies where managers are more incentivized to hide negative information and investors possess superior abilities to acquire information. Interestingly, supplier transparency is more influential in mitigating crash risk than customer transparency. These findings emphasize the significance of supply chain transparency in managing financial risk. [ABSTRACT FROM AUTHOR]

This study offers additional evidence on the self-employment of rural-to-urban migrants from the perspective of cultural barriers in China. By employing seven waves of the China Migrants Dynamic Survey data from 2010 to 2016, we find that cultural distance has a negative and significant effect on the self-employment of rural-to-urban migrants in eight subdivision industries. In particular, such negative effect is greater in the service sectors. [ABSTRACT FROM AUTHOR]

It is widely recognized that the tourism industry is susceptible to crisis or natural disaster. Although some literature has studied the consequences of the crisis and disaster, there remains a lack of study on the effect of COVID‐19. Against this background, this paper investigates the tourist flow forecasting by adopting an advanced nonparametric mixed‐frequency vector autoregressions model using Bayesian additive regression trees. This is particularly suitable for forecasting the presence of extreme observations, for example, the COVID‐19 pandemic. We investigate tourism demand forecasting using a large number of predictors, including industrial production index, CPI, exchange rate, economic policy uncertainty, Google trends index, and COVID‐19 infection rate. The data used for this study relate to tourist flows in Chinese Hong Kong, Japan, and South Korea. Empirical study demonstrates that this novel model significantly outperforms the traditional mixed‐frequency vector autoregressions model to quarterly tourist flow forecasting. Therefore, this model can significantly enhance tourism forecast accuracy in the face of extreme events. This study contributes to the literature on tourism forecasting and provides policymakers with policy implications. [ABSTRACT FROM AUTHOR]

The digital economy has a significant influence on the financial behavior of enterprises. According to the panel data of A-share listed enterprises in the manufacturing industry from 2010 to 2020, we discuss how manufacturing enterprises allocate their financial assets in the process of digitalization from the perspective of operating profit and risk taking. The results reveal that ICT investment significantly reduces financial asset allocation, while digital transformation improves the financial asset allocation of manufacturing enterprises. Because ICT investment increases operating profit from the main business, manufacturing enterprises reduce the allocation of short-term and long-term financial assets. We thus propose two phenomena: precautionary saving and substitutional investment. As digital transformation increases risk taking, financial assets are elevated by manufacturing enterprises for the precautionary saving motivation, boosting the short-term financial assets in their portfolios. These findings are further supported by the heterogeneity of innovation characteristics and ownership. [ABSTRACT FROM AUTHOR]