COVID-19 and stock returns: Evidence from the Markov switching dependence approach.

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.

Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic.

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.

Does corporate governance affect financial analysts' stock recommendations, target prices accuracy and earnings forecast characteristics? An empirical investigation of US companies.

This paper investigates how corporate governance quality affects the analyst's stock recommendations, forecast efficiency and target price accuracy on New York Stock Exchange. In particular, as corporate governance is often uncertain and ambiguous to investors, expert financial advisors may use transparent corporate governance information to set their recommendations and improve the level of accuracy of their earnings forecasts. According to agency and signaling theories, good governance mechanisms aim to mitigate agency conflicts and boost corporate transparency.