Effects of investor sentiment and country governance on unexpected conditional volatility during the COVID-19 pandemic: Evidence from global stock markets

COVID19-MLSF: A multi-task learning-based stock market forecasting framework during the COVID-19 pandemic

The sudden outbreak of COVID-19 has dramatically altered the state of the global economy, and the stock market has become more volatile and even fallen sharply as a result of its negative impact, heightening investors' apprehension regarding the correlation between unexpected events and stock market volatility. Additionally, internal and external characteristics coexist in the stock market. Existing research has struggled to extract more effective stock market features during the COVID-19 outbreak using a single time-series neural network model. This paper presents a framework for multitasking learning-based stock market forecasting (COVID-19-MLSF), which can extract the internal and external features of the stock market and their relationships effectively during COVID-19.The innovation comprises three components: designing a new market sentiment index (NMSI) and COVID-19 index to represent the external characteristics of the stock market during the COVID-19 pandemic. Besides, it introduces a multi-task learning framework to extract global and local features of the stock market. Moreover, a temporal convolutional neural network with a multi-scale attention mechanism is designed (MA-TCN) alongside a Multi-View Convolutional-Bidirectional Recurrent Neural Network with Temporal Attention (MVCNN-BiLSTM-Att), adjusting the model to account for the changing status of COVID-19 and its impact on the stock market. Experiments indicate that our model achieves superior performance both in terms of predicting the accuracy of the China CSI 300 Index during the COVID-19 period and in terms of sing market trading.

The COVID-19 pandemic and Bitcoin: Perspective from investor attention

The response of the Bitcoin market to the novel coronavirus (COVID-19) pandemic is an example of how a global public health crisis can cause drastic market adjustments or even a market crash. Investor attention on the COVID-19 pandemic is likely to play an important role in this response. Focusing on the Bitcoin futures market, this paper aims to investigate whether pandemic attention can explain and forecast the returns and volatility of Bitcoin futures. Using the daily Google search volume index for the "coronavirus" keyword from January 2020 to February 2022 to represent pandemic attention, this paper implements the Granger causality test, Vector Autoregression (VAR) analysis, and several linear effects analyses. The findings suggest that pandemic attention is a granger cause of Bitcoin returns and volatility. It appears that an increase in pandemic attention results in lower returns and excessive volatility in the Bitcoin futures market, even after taking into account the interactive effects and the influence of controlling other financial markets. In addition, this paper carries out the out-of-sample forecasts and finds that the predictive models with pandemic attention do improve the out-of-sample forecast performance, which is enhanced in the prediction of Bitcoin returns while diminished in the prediction of Bitcoin volatility as the forecast horizon is extended. Finally, the predictive models including pandemic attention can generate significant economic benefits by constructing portfolios among Bitcoin futures and risk-free assets. All the results demonstrate that pandemic attention plays an important and non-negligible role in the Bitcoin futures market. This paper can provide enlightens for subsequent research on Bitcoin based on investor attention sparked by public emergencies.