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. 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.

Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

This paper aims to develop an artificial neural networkbased forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural networkbased models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.

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. Thus, we argue that they can serve as mediators during the forecasting process and we expect a strong significant relationship between the effectiveness of corporate governance mechanisms and analyst activity. Five hypotheses are tested with a large sample of 154 US market firms over a 17-year period (2004–2020). Our empirical findings point out some special features of US stock markets. We find evidence that analysts tend to issue favorable recommendations, more accurate, less dispersed and more optimistic earnings forecasts for most well-governed firms. Furthermore, we show that higher-quality governance transparency is an important determinant of financial analysts’ behavior in the USA. The results also indicate that higher-quality governance appears valuable with financial analysts during pre- and post-crisis period, while it is not generally detected in COVID-19 times. However, we report the weakness of analysts’ outputs–governance quality for small firms. Thus, our findings cast doubts over the corporate governance-based analyst practices of US small and unaffiliated firms. The main implication of these findings is to improve understanding of how investors’ behavioral characteristics affect the transmission mechanism of information in money market and capital market prices. This paper has important implications for the decision making of financial analysts and investors by requesting firms to significantly improve their information environments in the good and bad times. It also offers insights into how firms establishing good corporate governance mechanisms can help the analysts to predict future stock prices.

Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic

In this paper, we examine the impact of investor sentiment on Bitcoin returns. Using a large dataset of messages discussed on social media and several financial indicators, we create a sentiment indicator based on computational text analysis and driven by the principal component analysis (PCA) method. We utilize a vector autoregressive analysis and other analytical methods to examine the sentiment index–bitcoin return nexus. Our findings reveal that the sentiment index is a strong predictor of cryptocurrency market returns in the short term. Furthermore, we confirm that during the COVID-19 pandemic, investors' sentiments significantly impacted Bitcoin returns. Our results show that the proposed sentiment index can generate excess returns for investors who utilize it as a return predictor. Our empirical findings suggest important policy implications.

Investor sentiment and Bitcoin relationship: A quantile-based analysis

This paper applies a quantile-based analysis to investigate the causal relationships between Bitcoin and investor sentiment by considering the possible effects of the ongoing COVID-19 pandemic. Such an analysis allows investigating the predictive power of investor sentiment (Bitcoin) on Bitcoin (investor sentiment) at different levels of the distributions. Results emphasize that only Bitcoin returns/volatility have significant predictive power on the investor sentiment whether investors are fear or greed before and over the COVID-19 period. Moreover, the COVID-19 crisis has no effect on the causal relationship between the two variables. Further analysis shows an asymmetric causality observed only during the pandemic period. Furthermore, the quantile autoregressive regression model shows a significant positive relationship between investor sentiment and Bitcoin returns.