This study investigates investment-cash flow sensitivity during the COVID-19 economic crisis. Using an international sample of publicly listed firms, we find that the sensitivity of capital expenditures to cash flows is significantly reduced during the crisis. When we split the sample into strongly and weakly affected countries, we find that firms in countries affected more seriously by COVID-19 exhibit lower investment responsiveness to cash flows. We further find that investment-cash flow sensitivity is diminished when government aid is greater, firms have more cash on hand, and investment opportunities decline. Our results survive a host of robustness checks. This study contributes to the discussion on the impact of COVID-19 on corporate policies within an international framework.

This paper investigates the direct and spillover portfolio effects from the global outbreak of COVID-19. We find that an increase of the newly added cases of one specific country causes investors to significantly decrease their portfolio allocations in the outbreak countries (direct effect). Simultaneously, investors also decrease their allocations to other countries (spillover effect). In addition, we provide evidence and documentation that the transmission mechanism underlying foreign exposures matter to the above-mentioned portfolio effect. Moreover, we provide evidence for phase heterogeneity. The first wave of the COVID-19 pandemic has significant direct and spillover portfolio effects, but the impacts are weakened in second wave of the pandemic. The capital reallocation effect occurs only when the disease becomes global. Finally, our heterogeneities analysis shows that both local and spillover effects are mitigated when the economies are more developed and democratic and when the country has better health care facilities.

In this paper we document that although COVID-19 has brought uncertainties to the overall economy, the Technology (tech) sector is the systematic beneficiary of the pandemic. Using a quasi-natural setup, we find a significant notion that the Stock Price Crash Risk (SPCR) of firms within the Tech sector decreases during the COVID-19 pandemic compared to the recent past and firms belonging to other sectors. Our analyses further reveal that firms in the Tech sector with stronger external monitoring and better information environment receive an even greater advantage from the pandemic. Overall, our study suggests that the higher systemic dependency on the Tech sector during the COVID-19 outbreak results in an economic benefit for this sector.

The paper examines the dynamic spillover among traditional currencies and cryptocurrencies before and during the COVID-19 pandemic and investigates whether economic policy uncertainty (EPU) impacts this spillover. Based on the TVP-VAR approach, we find evidence of spillover effects among currencies, which increased widely during the pandemic. In addition, results suggest that almost all cryptocurrencies remain as "safe-haven" tools against market uncertainty during the COVID-19 period. Moreover, comparative analysis shows that the total connectedness for cryptocurrencies is lower than for traditional currencies during the crisis. Further analysis using quantile regression suggests that EPU exerts an impact on the total and the net spillovers with different degrees across currencies and this impact is affected by the health crisis. Our findings have important policy implications for policymakers, investors, and international traders.

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.

We investigate the impact of macroeconomic surprise and uncertainty on G7 financial markets around COVID-19 pandemic using two real-time, real-activity indexes recently constructed by Scotti (2016). We applies the wavelet analysis to detect the response of the stock markets to the macroeconomic surprise and an uncertainty indexes and then we use NARDL model to examine the asymmetric effect of the news surprise and uncertainty on the equity markets. We conduct our empirical analysis with the daily data from January, 2014 to September, 2020. Our findings indicate that G7 stock markets are sensitive to the macroeconomic surprise and uncertainty and the effect is more pronounced at the long term than the short term. Moreover, we show that the COVID-19 crisis supports the relationship between the macroeconomic indexes and the stock prices. The results are useful for investment decision-making for the investors on the G7 stock indices at different investment horizons.

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.

This paper aims to investigate the regime-switching and time-varying dependence between the COVID-19 pandemic and the US stock markets using a Markov-switching framework. It makes two contributions to the empirical literature by showing that: (a) the variations of the daily reported COVID-19 cases and cumulative COVID-19 deaths induced asymmetric lower (left) and upper (right) tail dependence with the stock markets, and its left and right tail dependence exhibited significant time-varying trends; and (b) the left and right tail dependence between the stock markets and the pandemic exhibited significant regime-switching behaviours, with its switching probabilities in the higher tail dependence stage all being greater than in the lower tail dependence stage after 1 December 2019. Moreover, given that there is concurrent but significant financial market reaction to any unexpected emergence of a transmittable respirational disease or a natural calamity, the outcomes have some vital implications to market players and policymakers.

The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak's impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude.

Τhis paper presents new evidence on connectedness across cryptocurrencies in the era of the Covid-19 pandemic. The results from the TVP-VAR dynamic connectedness approach show that the degree of connectedness is time-varying, indicating a decline during the Covid-19 period. Next, this paper highlights the nonlinear characteristics of the relationship between connectedness and its explanatory variables. The results from the LSTR model indicate that the regression coefficients change smoothly between the low and the upper regimes as anxiety across Twitter users increases. As the latter changes smoothly from low to high values, the impact of higher Disease-based volatility or Twitter-based uncertainty on connectedness turns gradually from positive to negative. The upper regime is dominant during the Covid-19 pandemic. Important implications for investors and policy makers are derived.

This paper examines the impact of bank-specific factors and variations in the context of stringency of government policy responses on bank stock returns because of the COVID-19 pandemic. A sample of 1,927 publicly listed banks from 110 countries is used for the period of the first major wave of COVID-19, that is, January to May 2020. Our findings indicate that stock returns of banks with higher capitalization and deposits, more diversification, lower non-performing loans, and larger size are more resilient to the pandemic. While banks' environment and governance scores do not have a significant impact, higher social and corporate social responsibility strategy scores intensify the negative stock price reaction to COVID-19. We further observe that the pandemic-induced reduction in bank stock prices is mitigated as the strictness of government policy responses increases, mainly through economic responses such as income support, debt and contract relief, and fiscal measures from governments.

This study assesses the role of gold, crude oil and cryptocurrency as a safe haven for traditional, sustainable, and Islamic investors during the COVID-19 pandemic crisis. Using Wavelet coherence analysis and spillover index methodologies in bivariate and multivariate settings, this study examines the correlation of these assets for different investment horizons. The findings suggest that gold, oil and Bitcoin exhibited low coherency with each stock index across almost all considered investment horizons until the onset of the COVID-19. Conversely, with the outbreak of the pandemic, the return spillover is more intense across financial assets, and a significant pairwise return connectedness between each equity index and hedging asset is observed. Hence, gold, oil, and Bitcoin do not exhibit safe-haven characteristics. However, by decomposing the time-varying co-movements into different investment horizons, we find that total and pairwise connectedness among the assets are primarily driven by a higher-frequency band (up to 4 days). It indicates that investors have diversification opportunities with gold, oil, and Bitcoin at longer horizons. The results are robust over different types of equity investors (traditional, sustainable, and Islamic) and various investment horizons.

This article investigates how international decision-making's conditionality aids countries during strenuous economic conditions imposed by the COVID-19 pandemic. It examines and contrasts the European Union's conditionality policies, the International Monetary Fund, and the World Bank as the more influential and leading groups of institutions. The article reveals notable policy differences. As opposed to that of the IMF and WB, the EU's approach is more comprehensive and not confined to economic considerations. Those variations aside, the article draws on the same premise: expectations of compliance with the set conditions. While in-depth, structural requirements could guide ordinary decision-making and build up resilient national institutions and policies, this article questions the merits of large-scale comprehensive terms in the face of a situation created by a or a humanly uncontrollable event such as the COVID-19 pandemic. With no more initial research addressing the specific question of the application and adequacy of conditionality to force majeure emergencies or pandemic situations of the scale of COVID-19, this article argues in favor of a measured and targeted response limited to the development, design, or determination of policy choices that tackle the intended purpose. Also, for validly practical considerations that search for to ensure the better use of aid and avoid distracting or overburdening the recipient countries to the point of risking losses of devastating proportions, the article proposes to revise and limit conditionality during force majeure events to the essential aspects of transparent management of funds for the sole intended purpose. This in itself is a distinct democratic exercise of efficient and accountable public management decision-making.

This study aims to examine the impact of COVID-19 on financial markets, using emerging market data. Specifically, panel data regression is applied on 3200 observations for daily market returns during lockdown in India. The event study methodology is adopted to show abnormal returns registered in the lockdown period. A contrasting breakdown effect of COVID-19 on various Indian industries has been observed through sectoral analysis. The study also provides empirical evidence for lockdown measures taken by the government on stock market returns and post lockdown impact of COVID-19 on daily market returns for over 6550 observations.

In this study, we explore the impact of government intervention to contain the spread of COVID-19 in emerging countries on the performance of their leading stock indices. We retrieved data on the performance of 25 international capital market indices included in the MSCI Emerging Markets Index and data about the closures, economic, and health measures imposed in each country examined. Overall, our findings show that government restrictions are associated with negative market returns, possibly due to the anticipated adverse effect to the economy. The adverse effect is more evident when closures are imposed. The market response to economic stimulus is mild but varies depending on the type of intervention imposed, much as with the health measures. Public campaigns may raise public awareness about COVID-19, but they can also increase the public's fear of the pandemic, reflected in the negative response in capital markets. The results are essential for understanding the trends and fluctuations in emerging markets during this current crisis and for preparing for crises in the future.

COVID-19 has had far-reaching global effects on the health and wellbeing of individuals on every continent. The economic and financial market response has been equally disastrous with high levels of volatility observed. This study explores the temporal relations between structural breaks, market volatility and government stay-at-home policy interventions and social distancing measures for 28 countries and their respective indices. We present results which indicate the establishment of stay-at-home policies influence sharp discontinuities in 15 of 28 markets (53.57 %) and increase market efficiency in 30 of 49 cases observed (61.22 %). These results indicate a small, statistically significant degree of stabilization in international financial markets responding to government stay-at-home policies and social distancing measures, a promising result for political actors concerned with economic performance during the public health response to the coronavirus 2019 pandemic.

This paper examines the daily abnormal stock price returns of a sample of 154 publicly-traded hospitality firms from 23 different countries representing over $400 billion in combined market capitalization around the time that COVID-19 was first viewed by stock market participants as a major-possibly even -threat. The findings of the study suggest that, financially, hotels performed better than restaurants, which themselves performed better than casinos. These findings are consistent with medical recommendations concerning the relative safety of various hospitality-related activities and, therefore, also with the tenets of financial market efficiency in the hospitality sector. Additional findings suggest that hospitality firms with strong balance sheets and income statements characterized by relatively low leverage ratios, high market value (consistent with a "too big to fail" mentality), and higher price/earnings ratios (implying higher relative profitability) all fared better than smaller, weaker firms. Although, in no case, did Bloomberg's proprietary environmental, social, and governance (ESG) variable possess any predictive power, variables reflecting cross-country cultural differences support Huynh's (2020) finding that "individualism" was an important factor in explaining the economic impact of the COVID-19 pandemic on hospitality firms.

As the COVID-19 outbreak became a global pandemic, traditional financial market indicators were significantly affected. We examine the price efficiency and net cross-correlations among Bitcoin, gold, a US dollar index, and the Morgan Stanley Capital International World Index (MSCI World) during the four months after the World Health Organization officially designated COVID-19 as a global pandemic. Using intraday data, we find that Bitcoin prices were more efficient than the US dollar and MSCI World indices. Using a detrended partial-cross-correlation analysis, our results show that net cross-correlations vary across time scales. Our results suggest that when the time scale is greater than two months, gold can be considered as a safe haven for investors holding the MSCI World and US dollar indices and when the time scale exceeds three months, Bitcoin can be considered a safe haven for the MSCI World index.

On 20 April 2020, the West Texas Intermediate (WTI) crude oil price dropped to negative levels for the first time in history. This study examines the factors underlying the historic oil price fluctuation during the Covid-19 pandemic. The autoregressive distributed lag (ARDL) bounds testing approach incorporating a structural break is applied to the daily series from 17 January to 14 September 2020 to analyze long-run relationships and short-run dynamics. The results reveal that increases in Covid-19 pandemic cases, US economic policy uncertainty, and expected stock market volatility contributed to the fall in the WTI crude oil price, whereas the fall in the global stock markets appears to significantly reduce the fall. Furthermore, the Russia-Saudi Arabia oil price war and speculation on oil futures are shown to play a critical part in the collapse of the oil markets. The findings are consistent with our expectations. Although it is reasonable to assume that the solution to this oil crisis is a pick-up in global oil demand, which will occur only when the novel coronavirus is defeated, this study proposes policy recommendations to cope with the current oil price crash.

Using a sample of Chinese firms, we examine stock market reaction to firms that announce a change in their product lines to those related to COVID-19 management (medical masks and ventilators, among others). We find the market reacts positively to the announcements. In addition, when a firm ordinarily has a large share of export sales, the stock market reaction is more salient, indicating that export sales provide a certification effect that positively signals investors. Additional analysis on moderating effects suggest that, conditional on foreign sales, prior experience with medical product lines or less uncertainty about supply availability enhances the cumulative announcement returns (CARs), while the adverse impact of firm size on CAR magnifies.

This study quantitatively measures the Chinese stock market's reaction to sentiments regarding the Novel Coronavirus 2019 (COVID-19). Using 6.3 million items of textual data extracted from the official news media and blogsite, we develop two COVID-19 sentiment indices that capture the moods related to COVID-19. Our sentiment indices are real-time and forward-looking indices in the stock market. We discover that stock returns and turnover rates were positively predicted by the COVID-19 sentiments during the period from December 17, 2019 to March 13, 2020. Consistent with this prediction, margin trading and short selling activities intensified proactively with growth sentiment. Overall, these results illustrate how the effects of the pandemic crisis were amplified by the sentiments.