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This paper aims to shed light on an impact of Google keywords on the number of new businesses (and an amount of capital registered) in Vietnam, the Southeast Asian country, after the year of an entrepreneur, 2016.

This study uses a rich set of quantitative techniques from VAR Granger and threshold regression. The whole sample period covers the data (keywords, number of new businesses, an amount of capital invested to register) from the first week of 2016 to October 2018, which includes 144 observations in total.

The findings suggest that the relationship between Google does not persist in the long run. There is a short-run shock, might cause a change to the frequency of the other keywords rather than the number of firms (or an amount of capital). However, under the number of firms’ threshold, keywords have the both positive and negative impacts on entrepreneurs whereas a higher threshold of capital, keywords show their roles to predict an amount of money for registering firms.

The Vietnamese Government and executives are advised to consider the Google keywords “entrepreneur” (in Vietnamese) and “start-up”, which cause a decline in entrepreneurial movements. In addition, the current period is going to inverse from the previous one in terms of the number of firms and an amount of capital. Finally, there are two critical thresholds: 1,602 companies and 35,010m VND for the keywords' influence.

This study contributes empirical evidence of technological change and entrepreneurship and contributes to the existing literature by discussing how this relationship under the threshold.

Market volatility is subject to good or bad news and even responses to fake news and policy changes. In this piece of work, the authors consider the effects of the recent COVID-19 pandemic event on the global equity market, commodities and FX market, measured in terms of the investors' fear index.

In this empirical work, the authors employ time series-based regression models followed by augmented dummy regressions and growth of the COVID-19.

COVID-19-induced investors' fear appears to be higher in the equity segment for the first time since the market crash of 1987 and the global financial crisis of 2008–2009. Furthermore, this disease outbreak shock has been more pronounced in terms of crude oil prices. Besides, a market participant in the commodity and FX market has paid a disproportionate premium to protect such pandemic development. Findings show that Options act as the best hedge against an uncertainty like COVID-19 and that option-based implied volatility is the best measure of investors' fear and market volatility.

This study has practical implications for the financial markets, e.g. (1) Contagious disease outbreak news matters for the equity, commodity, and foreign exchange markets – empirical outcome validates the theory of market efficiency valid for the Options. (2) Option's implied volatility is the best indicator of investor fear measured for the unprecedented economic news. Further implication holds for the policymakers and society, e.g. (1) The unavailability of short-selling could be one plausible reason for increased uncertainty and volatility; hence, policymakers should look upon this issue at the exchange level. (2) Any market needs multiple lines of risk management, effective price discovery and attractive liquidity.

The study is novel in terms of presenting market behavior amid COVID-19 across global equity markets and commodities and FX markets.

This study examines the prediction power of investor sentiment on Bitcoin return.

We construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.).

Using a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions.

The findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency.

This study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.

This paper aims to identify the disproportionate impacts of the COVID-19 pandemic on labor markets.

The authors conduct a large-scale survey on 16,000 firms from 82 industries in Ho Chi Minh City, Vietnam, and analyze the data set by using different machine-learning methods.

First, job loss and reduction in state-owned enterprises have been significantly larger than in other types of organizations. Second, employees of foreign direct investment enterprises suffer a significantly lower labor income than those of other groups. Third, the adverse effects of the COVID-19 pandemic on the labor market are heterogeneous across industries and geographies. Finally, firms with high revenue in 2019 are more likely to adopt preventive measures, including the reduction of labor forces. The authors also find a significant correlation between firms' revenue and labor reduction as traditional econometrics and machine-learning techniques suggest.

This study has two main policy implications. First, although government support through taxes has been provided, the authors highlight evidence that there may be some additional benefit from targeting firms that have characteristics associated with layoffs or other negative labor responses. Second, the authors provide information that shows which firm characteristics are associated with particular labor market responses such as layoffs, which may help target stimulus packages. Although the COVID-19 pandemic affects most industries and occupations, heterogeneous firm responses suggest that there could be several varieties of targeted policies-targeting firms that are likely to reduce labor forces or firms likely to face reduced revenue. In this paper, the authors outline several industries and firm characteristics which appear to more directly be reducing employee counts or having negative labor responses which may lead to more cost–effect stimulus.