Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach.

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

Guest editorial

Crisis management across cultures in the COVID-19 pandemic: towards a knowledge-based systems view Introduction Since early 2020, the COVID-19 crisis, classified as a global public health emergency (GPHE), has resulted in a series of devastating impacts, such as the breakdown of industries, massive job losses, natural hazards and social precarity on many national economies, as well as the firms operating and the people dwelling within these nations (Chin et al., 2020). [...]extraordinary crises often lead to intricate systematic failures involving the entire business ecosystem. [...]we propose a special issue (SI) to advance current studies towards a knowledge-based systems view of crisis management to identify, interpret and rationalise the foregoing novel risks whose essence, probability of occurrence, sphere of influence and magnitude of adverse consequences may subvert extant knowledge and cognition. To allow for more innovative submissions, we encouraged authors to conduct broader interdisciplinary theoretical underpinnings with a greater number of mixed varieties of methodologies and to combine multiple levels of analysis through an integrative lens of KM, risk management and IB. (2022a, 2022b) undertake an unconventional, culturally sensitive linguistic approach to make sense of the formation of risk knowledge;more specifically, taking risk discussions in online knowledge communities as a basis, they explored the negative effect of text complex level and the positive effect of text analytic level on user engagement, as well as the positive intervening role of riskification on the above-mentioned mechanisms.

Emerging stock market reactions to shocks during various crisis periods.

This study investigates granger causal linkages among six Asian emerging stock markets and the US market over the period 2002-2020, taking into account several crisis periods. The pairwise Granger causality tests for investigating the short-run causality show significant bi- and uni-directional causal relationships in those markets and evidence that they have become more internationally integrated after every crisis period. An exception is Bangladesh with almost no significant short-term causal linkages with other markets. For understanding, how the financial linkages amplify volatility spillover effects, we apply the GARCH-M model and find that volatility and return spillovers act very inversely over time. However, market interface is weak before the crisis periods and becomes very strong during the financial crisis and US-China economic policy uncertainty periods. The US market plays a dominant role during the financial crisis and COVID-19 periods. Further analysis using the VAR model shows that a large proportion of the forecast variance of the Asian emerging stock markets is affected by the S&P 500 and that market shock starts to rise notably from the 1 to 10 period. The overall findings could provide important policy implications in the six countries under study regarding hedging, trading strategies, and financial market regulation.

Coronavirus: the not-so-trivial loss of well-being

Abstract The COVID-19 outbreak has been one of the world?s most severe challenges since World War II, which not only reflected in the observable economic depression, but also in emotional damage that is scarcely underscored. This paper estimates the effect of the epidemic on happiness of Chinese residents by capturing the emotions conveyed via massive social media, combined with city-level epidemic data. We confirm the significant negative impact of the outbreak on life satisfaction, with an extra 1% of cumulative death cases associated with a 0.54% decrease in expressed happiness. Meanwhile, we find that well-being drops the most in regions with high-level risk of COVID-19, and happiness in regions with higher or lower income levels are more vulnerable to the effects of the epidemic, in comparison to the intermediate-level income regions. Females? sentiment exhibits more sensitivity to the epidemic situation than does males? sentiment. Furthermore, the timing of major public events also plays a sizable role in determining self-reported happiness, and our results suggest that pandemic alerts announced by the WHO significantly increased the loss of well-being, while announcements of city-unlocking had the opposite impact. The interaction between the outbreak and real-time expressed happiness provides an important opportunity for reflection on the future governance policies of epidemic containment. This article is protected by copyright. All rights reserved.

The hit of the novel coronavirus outbreak to China's economy.

Broke out at the end of 2019, the novel coronavirus pneumonia (COVID-19) has been spreading throughout the world, leading to more than 87 million confirmed infections and 1.88 million fatalities. Motivated by this, we evaluate the economic impacts of COVID-19 outbreak on both national and industrial levels by employing quarterly computable general equilibrium (CGE) model. Our results reveal that the epidemic may lower China's economic growth in 2020 by 3.5%, versus 4.4% for final consumption (relative to baseline). The service industry suffers the most from the outbreak, and the Accommodation-Food-Beverage service, Wholesale-Retail Trade, and Transport-Storage-Post are identified as the most vulnerable sectors, with the negative impact on output reaching as high as 14.6%. When moving to 2021, the hit to economy shrinks to 2% (1.2-2.7%), with industry estimated to be the most affected sector instead. This study indicates that implementing effective measures for preventing and controlling the epidemic and policies for post-disease economic recovery play critical role in curbing the potential economic damage.

Global supply-chain effects of COVID-19 control measures.

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A 'go-slow' approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.