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With the goal of helping consumers bounce back from the financial challenges they faced as a result of the COVID-19 pandemic, many firms developed and announced consumer-targeted resiliency programs (e.g. Walgreens waived delivery fees, Associated Bank allowed deferred mortgage payments). However, there is a paucity of research examining the unique features of these programs, and whether firms' investors (the first external stakeholder group to provide them with feedback regarding their strategies) were receptive to these programs during a period of time in which firms themselves were suffering financially. Drawing on resilience theory and stakeholder theory, the present research incorporates an event study of consumer-targeted resiliency program announcements to understand their financial implications for firms, and to learn whether firms witnessed different financial effects as a result of firm- and program-specific factors.

This study referred to business news publications and newswire services to collect a comprehensive list of consumer-targeted resiliency programs announced by publicly traded U.S. firms during the pandemic. The resulting dataset consisted of 145 announcements made during the period of February–June 2020. An event study was conducted in order to precisely measure the main effect of consumer-targeted resiliency programs on firm value, as manifested through abnormal stock returns. Finally, a moderation analysis (regression) was conducted to uncover whether firm characteristics or specific features of firms' consumer-targeted resiliency programs lead certain firms to witness stronger financial effects than others.

The main effect of consumer-targeted resiliency programs on firm value was found to be positive – a 1.9% increase on average. The moderation analysis finds that non-financial firms were rewarded more positively than financial firms (e.g. banks and credit card companies). In addition, financial aid (i.e. allowing customers to defer their payments to a firm for its products/services, versus a reduction in the price of a product/service or offering it for free or giving cash back to customers) and temporal characteristics (i.e. an offer being framed as limited-time, vs being indefinite or for the foreseeable future) are not found to have a moderating effect.

This theory-driven empirical study uncovers practical implications for managers of firms interested in whether investing in corporate social responsibility during times of crisis is a wise allocation of resources. Any form of financial aid for consumers, regardless of temporal limitations, is received positively by investors.

Considering rapid digitalization development, this study examines the impacts of digital technology innovation on social responsibility in platform enterprises.

The study applies the event study method and cross-sectional regression analysis, taking 168 digital technology innovations for social responsibility issued by 88 listed platform enterprises from 2011 to 2022 to study the impact of digital technology innovations for social responsibility announcements of different announcement content and platform attributes on the stock market value of platform enterprises.

The results show that, first, the positive stock market reaction is produced on the same day as the digital technology innovation announcement. Second, the announcement of the platform’s public social responsibility and the announcement of co-innovation and radical innovation bring more positive stock market reactions. In addition, the announcements mentioned above issued by trading platforms bring more positive stock market reactions. Finally, the social responsibility attribution characteristics of the announcement did not have a significant differentiated impact on the stock market reaction.

Most scholars have studied digital technology innovation for social responsibility through modeling rather than second-hand data to empirically examine. This study uses second-hand data with the instrumental stakeholder theory to provide a new research perspective on platform social responsibility. In addition, in order to explore the different impacts of digital technology innovation on social responsibility, this study has classified digital technology innovation for social responsibility according to its social responsibility and digital technology innovation characteristics.

This study undertakes a comprehensive investigation into the far-reaching repercussions of Covid-19 stimulus packages and containment policies on stock returns, meticulously examining a diverse array of 14 distinct markets.

This study employed the Panel SVAR model to analyze the relationships between various policies and stock market performance during the Covid-19 outbreak. The sample comprises 5432 daily observations spanning from December 2020 to January 2022 for the 14 selected markets, with missing data excluded.

The findings reveal three consistent impacts across all 14 markets. Firstly, stock returns immediately reversed and decreased within a day when Governments tightened containment policies. Secondly, economic stimulus packages led to a fall in stock returns. Thirdly, an increasing death rate caused the stock return to decrease in the following two days. These findings are supported by the uniform impulse responses in all three shocks, including common, composite and idiosyncratic shocks. Furthermore, all inverse root tests satisfy the stability conditions, indicating the stability and reliability of Panel SVAR estimations.

One vital implication is that all government decisions and measures taken against the shock of Covid-19 must consider economic impacts to avoid unnecessary financial losses and support the effective functioning of stock markets during similar shocks. Secondly, investors should view the decline in stock returns due to Covid-19 effects as temporary, resulting from anxiety about the outbreak. The study highlights the importance of monitoring the impact of policies on financial markets and the broader economy during crises. Overall, these insights can prove helpful for investment decisions and policymaking during future crises.

This study constitutes a noteworthy addition to the literature on behavioural finance and the efficient market hypothesis, offering a meticulous analysis of the multifaceted repercussions of Covid-19 on market interactions. In particular, it unveils the magnitude, duration and intricate patterns of market volatilities linked to significant shock events, encompassing a comprehensive dataset spanning 14 distinct markets.

This paper assesses the environment of legitimacy by determining the role of institutional quality and policy uncertainty on the performance of five major South Asian stock markets (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using 21 years data from 2000 to 2020. The focus of this study is to approach the issue of the environment of legitimacy that leads to sustained market returns.

Panel cointegration tests of Kao and Pedroni are applied, and the Dynamic Panel Vector Autoregressive (PVAR) model is used to determine the estimates.

ADF P-Values of both Kao and Pedroni tests show that the panels are cointegrated; the statistical significance of the results of the Kao and Pedroni panel cointegration test confirms cointegration among the variables. After determining the most appropriate lag, the analysis is done using PVAR. The results indicate that institutional quality, policy uncertainty, and GDP positively affect stock market return. Meanwhile, government actions and inflation negatively affect stock market returns. On the other hand, stock market return positively affects institutional quality, government action, policy uncertainty, and GDP. While stock market return negatively affects inflation.

The sample is taken only from a limited number of South Asian countries, and the period is also limited to 21 years.

Based on our research findings, we have identified several policy implications recommended to enhance and sustain the performance of stock markets.

This paper uses a unique analytical tool, which gives a better insight into the problem. The value of this work lies in its findings, which also have practical implications and theoretical significance.

According to the Government Response tracker (oxCGRT) index, the overall government response, stringency, economic support, containment and health policies to COVID-19 from January 2020 to December 2022. The main objective of this paper is to explore how stock market performance is affected by these polices, respectively.

The authors employ EGARCH and autoregressive distributional lag (ARDL) models to test the impact of epidemic prevention policy implementation on stock market returns, volatility and liquidity and make cross-country comparisons for six important world economies.

Firstly, the implementation of various preventive policies hurts stock market returns and increases volatility, but there are a few indicators that have no effect or have an easing effect in some countries. Secondly, health policies exacerbate market volatility and have a stronger effect than other policy indicators. Thirdly, In China and the USA, anti-epidemic policies have been shown to worsen liquidity, while in Japan they have been shown to improve liquidity.

First, enrich the growing body of COVID-19 research by comprehensively examining whether and how government prevention policies affect stock market returns, volatility and liquidity. Second, explore the impact of different types of intervention policies on stock market performance, separately.

This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

The purpose of this study is to examine within a unified framework the timeliness and conservatism of accounting disclosure accommodating the transmission of news among the Scandinavian stock markets.

To this end the authors have used an augmented ordinary least squares (OLS) approach and univariate generalized autoregressive conditional heteroskedastic and vector autoregressive (VAR) modeling. The sample covers the period from 1987 to 2020, totaling 1452 observations. The sample was collected from the datastream database.

The empirical results of this study are consistent with previous findings and provide evidence that accounting reporting is timely and conservative while news is transmitted amongst the Scandinavian stock markets.

The findings could be important for investors, firms and regulators since failure of considering information that is derived from more advanced approaches could result in lower quality of annual reports of companies.

The authors examined the relationship between earnings yield and conditional risk using an augmented OLS model and the transmission of news among Scandinavian stock markets using a VAR model.

According to the Government Response tracker (oxCGRT) index, the strictest policy responses to the coronavirus pandemic from January 2020 to May 2022 belong to Italy, China, Hong Kong, Greece, Austria, Peru, Singapore and Malaysia. The main question is: “this level of strictness has been able to reduce the uncertainty of the stock market?”

To achieve this goal, the authors investigated the effect of oxCGRT index, and the growth rate of COVID-19 confirms cases on stock market uncertainty from January 2020 to May 2022 in the GARCH, EGARCH and TGARCH models.

Among these countries, the oxCGRT index has reduced uncertainty in the stock market only in Malaysia and Singapore. This result says an appropriate pattern of applying government policy responses is more important than the degree of stringency.

The study will contribute to the existing literature by examining the impact of the comprehensive oxCGRT index on the uncertainty of the stock market.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The authors analyze the relationship between exchange rate fluctuations of a number of major currencies and its impact on US stock market returns, as proxied by the S&P 500. Many studies have explored this topic since the early 1970s with varied results and with no evidence that clearly explains the relationship between exchange rates and stock market returns. This study takes a different look at this hypothesis and investigates the pairwise relationship between various exchange rates and the United States stock market returns (S&P 500 INDEX) from January 2000 to December 2019.

The authors test the data for unit roots using Phillip-Perron method. They use Johansen cointegration model to determine whether returns on S&P 500 are integrated with S&P 500. They use the VAR/VECM analysis to test whether there are any interdependencies between exchange rates and stock market return. Finally, they use various GARCH models, including the EGARCH and TGARCH models, to determine whether there exist volatility spillovers from exchange rate fluctuations in various markets to the volatility in the US stock market.

Using GARCH modeling, the authors find volatility in Australian dollar, Canadian dollar and the euro impact market return, and the volatility of Australian dollars and euro spills over to the volatility of S&P 500. They also find that the spillover is asymmetric for Australian dollars.

One of the limitations could be that the authors use different bivariate GARCH models rather than the MV-GARCH models. For future project(s), they plan to do this analysis from the perspective of a European Union or a British investor and use returns in those markets to see the impact of exchange rates on those markets. It would be interesting to know how the relationship will change during periods of financial crises. This could be achieved by employing structural break methodology.

Many studies have explored the relation between stock market returns and exchange rates since the early 1970s with varied results and with no evidence that clearly explains the relationship between exchange rates and stock market returns. This paper contributes by adding to the existing literature on impact of exchange rate on stock returns and by providing a detailed and different empirical analysis to support the results.