Do investors' personalities predict market winners? Experimental setting and machine learning analysis.

This study uses experiments and surveys from 146 participants who participated in equity trading to explore the predictive power of the Big-five personality traits, social behaviours, along with self-attribution and demographic characteristics on trading performance. Interestingly, we found that investors who are more open and neurotic gain higher returns compared to the market benchmark. We also found that other social traits are associated with the effectiveness of stock trading, such as awareness of social and ethical virtues (fairness and politeness). Moreover, instead of using separate characteristics, this study employs machine learning to cluster these personal features to understand the interconnection between socioeconomic determinants and financial decisions. This study contributes new evidence to the existing literature that personalities could explain trading performance.

Dataset of discourses about COVID-19 and financial markets from Twitter.

In this data article, a collection of 11,625,887 tweets on the topic of the COVID-19 pandemic are provided. The data from Twitter were collected through Twitter API from January 2020 to June 2020. In addition, we also provided subsets of tweets containing discourses on both COVID-19 and financial topics. In order to facilitate the research on sentiment analysis, the Sentiment140 dataset containing 1,600,000 tweets that were annotated as positive or negative sentiment was also provided (Go et al., 2009) We used Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to transform documents to numeric vectors and used logistic regression classifier to train and predict sentiments of tweets. These datasets may garner interest from data science, economists, social science, natural language processing, epidemiology, and public health groups.

The race Against Time to Save Human Lives During the COVID-19 With Vaccines: Global Evidence.

Voluminous vaccine campaigns have been used globally, since the COVID-19 pandemic has brought devastating mortality and destructively unprecedented consequences to different aspects of economies. This study aimed to identify how the numbers of new deaths and new cases per million changed after half of the population had been vaccinated. This paper used actual pandemic consequence variables (death and infected rates) together with vaccination uptake rates from 127 countries to shed new light on the efficacy of COVID-19 vaccines. The 50% uptake rate was chosen as the threshold to estimate the real benefits of vaccination campaigns for reducing COVID-19 infection and death cases using the difference-in-differences (DiD) imputation estimator. In addition, a number of control variables, such as government interventions and people's mobility patterns during the pandemic, were also included in the study. The number of new deaths per million significantly decreased after half of the population was vaccinated, but the number of new cases did not change significantly. We found that the effects were more pronounced in Europe and North America than in other continents. Our results remain robust after using other proxies and testing the sensitivity of the vaccinated proportion. We show the causal evidence of significantly lower death rates in countries where half of the population is vaccinated globally. This paper expresses the importance of vaccine campaigns in saving human lives during the COVID-19 pandemic, and its results can be used to communicate the benefits of vaccines and to fight vaccine hesitancy.

Drivers of medical spending behaviour amid the COVID-19 pandemic: Heuristic or systematic.

BACKGROUND: The COVID-19 pandemic has been creating unprecedented chaos and it could forever alter the way people live and work. Experiencing multiple waves of pandemic attacks could make people evolve their perceived risks about the health crisis, change their healthcare behaviours and medical spending to deal with the changing threats over time. OBJECTIVES: Even though there has been a great dealt of research on personal healthcare behaviours during the COVID-19 pandemic, the individual decision on medical spending has not been well explored. This study uses the health belief model and heuristic-systematic information processing theory to study the key drivers of medical spending behaviour as the COVID-19 pandemic evolved in Vietnam. METHODS: Two surveys were conducted during the first (April 2020) and second waves (August 2020) of the COVID-19 pandemic resulted in a sample size of 1037 cases. The partial least square structural equation modeling (PLS-SEM) technique was employed to explore the structural relationships between health-seeking behaviours, pandemic perceived risks, panic buying, and demographic factors and how these sets of factors drive medical spending behaviours over time. RESULTS: Comparing the two pandemic waves, this study finds significant distinctions in how people evaluate the risks of the pandemic and process information to make decisions about their medical spending. People were primarily influenced by the heuristic processes of panic buying patterns (ß = 0.313, p < 0.001) and the health-related established habits in the first wave. Only in the second wave of the pandemic, the impact of the COVID-19 pandemic perceived risk has been recognized as a significant factor on medical spending via the comparison between perceived risks of the first and second pandemic waves (ß = 0.262, p < 0.001). CONCLUSIONS: This study explores how individuals formulate their spending decisions in extreme conditions and provide valuable insights to help governments and institutions plan their policies to combat the COVID-19 pandemic more effectively.

Financial performances, entrepreneurial factors and coping strategy to survive in the COVID-19 pandemic: case of Vietnam.

Vietnam has been one of a few countries that successfully contained the COVID-19 pandemic. However, aggressive measurements against the pandemic were at the expense of economic activities and companies' financial performances. This cross-sectional study uses a survey of 672 companies in Vietnam and the logistic regression model to explore companies' coping strategy choices based on their degree of financial distress, companies' profiles, entrepreneurial factors, and the interactions between them. The results suggest that companies predominantly selected cost-cutting strategies to deal with the economic shutdown. However, the interactions between financial and entrepreneurial factors could significantly increase the likelihood of selecting growth-focused strategies. Besides, when facing a global pandemic such as COVID-19, managers' perceptions about the spillover effects of global risks were much more impactful than local risks on companies' coping strategy selections. This paper can help to inform managers to better deal with the aftermath of the COVID-19 outbreak.