Recent studies suggest that networks among firms (sectors) play a vital role in asset pricing. This paper investigates these implications and develops a novel end-to-end graph neural network model for asset pricing by combining and modifying two state-of-the-art machine learning techniques. First, we apply the graph attention mechanism to learn dynamic network structures of the equity market over time and then use a recurrent convolutional neural network to diffuse and propagate firms' information into the learned networks. This novel approach allows us to model the implications of networks along with the characteristics of the dynamic comovement of asset prices. The results demonstrate the effectiveness of our proposed model in both predicting returns and improving portfolio performance. Our approach demonstrates persistent performance in different sensitivity tests and simulated data. We also show that the dynamic network learned from our proposed model captures major market events over time. Our model is highly effective in recognizing the network structure in the market and predicting equity returns and provides valuable market information to regulators and investors.

This paper investigates how corporate insiders respond to the initial COVID-19 outbreaks. Using comprehensive insider transaction data from 25 countries, we document a consistent pattern of insider selling during the month after the first COVID-19 case is confirmed in a given country. Insider selling during these disease outbreaks is less pronounced in countries with higher information disclosure requirements, higher public enforcement index, a more efficient judiciary system, and stronger investor protection. Furthermore, cultural differences and the stringency levels of government responses to the COVID-19 outbreaks help moderate insider panic selling when health disasters strike. The findings suggest that a transparent, reliable business system contributes to rebuilding investor trust and corporate resilience during crises.

The aim of this paper is two-fold. First, we investigate the nexus between investor attention to COVID-19 and daily returns in 59 countries. We use Google Search Volume Index to account for investor attention. Our empirical findings suggest that the search volume of the pandemic is negatively associated with daily returns. The effect was strong in the week that the World Health Organization declared it as pandemic and among advanced countries. Second, we explore the relationship between search volume and market volatility. The findings suggest that COVID-19 sentiment generated excess volatility in the market. Our findings remain robust with alternative specifications.

Does face-to-face interaction still facilitate information transfer despite proliferating communication technologies? We use the COVID-19 collapse in such interactions to examine their influence on information flow in the stock market around earnings announcements. Using daily, county-level abnormal mobility of U.S. residents to proxy for face-to-face interaction, we find that firms located in counties with lower abnormal mobility experience a weaker immediate price reaction to earnings announcements and a larger post-announcement drift. Our findings suggest that lower face-to-face interactions dampen price discovery in financial markets, and that investor attention is a potential mechanism of this effect.

With a financial market dominated by indirect financing, China's banking system played a critical role in the government's response to COVID-19, which piqued our interest in the short-term impact of COVID-19 on the risk of China's banks. Examining the stock price of A-share listed banks and the number of confirmed cases in China and the US during the short time window surrounding the COVID-19 pandemic's outbreak, this study reveals that COVID-19 increased the A-share banking price volatility in both China and the US, reflecting a strong spillover effect of the US economic and financial system. Furthermore, COVID-19 in China has a smaller impact on the stock price volatility of China's state-owned banks (SOBs) than that of medium- and small-sized (M&S) banks, reflecting the higher risk resistance capability of large SOBs. Further analysis confirms that the impact primarily reflected systematic risk rather than idiosyncratic risk, as small and micro enterprises and M&S banks received more targeted financial support from the government. In contrast, large banks took on more responsibilities in the emergency financial stimulus, narrowing the idiosyncratic risk gap between the two types of banks and allowing the banking industry to better play its core role in the recovery of real economy in China. These findings will assist us in better understanding the effectiveness of financial assistance policies during the epidemic and will provide insights for future policymaking during similar crises.

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.

We investigate how the COVID-19 pandemic affected people's health-related choices and spending habits in Italy, the first European country to be heavily affected by the pandemic. We collected about 3000 questionnaires in May and June 2020 (that is, during the stabilization phase that followed the country's lockdown), asking questions taken from the "Survey tool and guidance: rapid, simple, flexible behavioural insights on COVID-19" issued by the World Health Organization (WHO), and correlated the responses with respondents' demographic and socioeconomic profiles. A principal component analysis (PCA) shows three main components that we label "Unusual behavior," "Precautionary spending," and "Augmented social distancing," which vary with demographic and socioeconomic characteristics.

This study has been inspired by the emergence of socially responsible investment practices in mainstream investment activity as it examines the transmission of return patterns between green bonds, carbon prices, and renewable energy stocks, using daily data spanning from 4th January 2015 to 22nd September 2020. In this study, our dataset comprises the price indices of S&P Green Bond, Solactive Global Solar, Solactive Global Wind, S&P Global Clean Energy and Carbon. We employ the TVP-VAR approach to investigate the return spillovers and connectedness, and various portfolio techniques including minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio to test portfolio performance. Additionally, a LASSO dynamic connectedness model is used for robustness purposes. The empirical results from the TVP-VAR indicate that the dynamic total connectedness across the assets is heterogeneous over time and economic event dependent. Moreover, our findings suggest that clean energy dominates all other markets and is seen to be the main net transmitter of shocks in the entire network with Green Bonds and Solactive Global Wind, emerging to be the major recipients of shocks in the system. Based on the hedging effectiveness, we show that bivariate and multivariate portfolios significantly reduce the risk of investing in a single asset except for Green Bonds. Finally, the minimum connectedness portfolio reaches the highest Sharpe ratio implying that information concerning the return transmission process is helpful for portfolio creation. The same pattern has been observed during the COVID-19 pandemic period.

This paper empirically investigates the impact of technology-seeking outward foreign direct investment (OFDI) on firms' productivity under the influence of negative external shocks, taking as a sample the investment data of Chinese firms before and during COVID-19. The results show that technology-seeking OFDI improves productivity, but not under negative external shocks. The dampening effect of such shocks is more significant when the host country is a developed country and in firms with multiple branches. Technology-seeking OFDI particularly improves the productivity of research and development and processing firms, and (among the productivity measures tested) most prominently affects total factor productivity.

This study evaluates the safe-haven role of twelve assets against the US stock market during the 2008 global financial crisis (GFC) and the COVID-19 pandemic. Our results show that silver and the Islamic stock index were safe havens during the 2008 GFC, and the Islamic stock index and Tether have been safe havens during COVID-19. We observe that the Islamic stock index and Tether have emerged as strong new safe havens. However, our supplementary analysis reveals that gold and Bitcoin still exhibit safe-haven behavior during severe market downturns. Overall, our findings suggest that safe-haven assets may vary over time.

Against the backdrop of the exponentially growing trend in green finance investments and the calls for green recovery in the post-COVID world, this study presents the time-frequency connectedness between green and conventional financial markets by using the spillover models of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018). Covering a sample period from January 01, 2008, to July 31, 2020, we aim to explore the dynamics of connectedness between conventional and green investments in fixed income, equity, and energy markets. Additionally, we determine the role of market-wide uncertainty in altering the connectedness structure by performing a subsample analysis for the ongoing COVID-19 pandemic crisis period. Our results show that competing energy investments are not connected, and there is only one-way spillovers from the conventional bonds in the fixed-income investments. Additionally, we observe a low (high) intergroup connectedness for conventional (green) investments. Moreover, the frequency-based analysis shows that connectedness between these competing markets is more pronounced during the short-run. The subsample analysis for the pandemic crisis period shows similar results except for the disconnection between bond markets in the short-run frequency. Our time-varying analysis shows peaks and troughs in the connectedness between climate-friendly and conventional investments that suggest different global events such as the Eurozone Debt Crisis and Shale Oil Revolution drives the association between alternate investments. Similarly, we observe an enhanced connectedness during the recent COVID-19 period, suggesting that financial stability would be a significant factor in determining the smooth transition to green investments.

In an era during which the COVID-19 pandemic continues to spread, high-speed railway (HSR), as one of the key influencers of urban green development, has a significant impact on urban green finance and green productivity. This paper uses HSR as a quasi-natural experiment to study the effect of HSR openings on green productivity in Chinese cities. The empirical results show that, first, the opening of HSR is conducive to the sustained improvement of green productivity in Chinese cities. Second, the opening of HSR makes a significant contribution to the improvement of green productivity in large-scale cities as well as cities in the east and central regions. Third, the opening of HSR can positively impact urban green productivity through the mechanism of green finance development. However, this positive impact tends to first increase and then decrease over time. As the relationship between "finance" and "environment," green finance has an important impact on the green development of cities. These findings will provide positive and useful references for cities to formulate reasonable green development plans in the post-COVID-19 era.

This study investigates the impact of foreign bank entry on bank competition in the host countries. Using data for 148 countries over 1987-2015, I find that although on average an increase in the number of foreign banks is associated with more competition in the host country, competition increases in developed but decreases in developing countries. Stringent capital requirements, higher market entry barriers, and effective credit information sharing can mitigate the impact of foreign bank entry, while better supervision and external governance strengthen the link between foreign bank presence and competition. The findings justify the regulations on bank capital adequacy and call for an effective credit information sharing mechanism.