An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors.

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

Is Bitcoin a better hedging and safe-haven investment than traditional assets against currencies? Evidence from the time-frequency domain approach

Many studies have discussed hedges and safe havens against stocks, but few studies focus on the hedging/safe-haven performance of assets against the currency market over different time horizons. This paper studies the connectedness, hedging and safe-haven properties of Bitcoin/gold/crude oil/commodities against six currencies across multiple investment horizons, placing a particular focus on the performance of these assets during the recent COVID-19 outbreak. Our findings suggest that the overall dependence between assets and the currency market is the strongest in the short term, and Bitcoin is the least dependent across all investment horizons. The dynamic relationships between the four assets and the currency market vary with timescales. Bitcoin offers better hedging capability in the long term and commodities emerge as the most favorable option for the optimal portfolio of currency over all time horizons. Further analysis shows that assets are better at helping investments reduce risk in the initial stages of the pandemic, and gold is an effective and robust safe haven for currencies.

The time-varying effects of trade policy uncertainty and geopolitical risks shocks on the commodity market prices: Evidence from the TVP-VAR-SV approach

The paper focuses on investigating the time-varying influence of geopolitical risks (GPR) and trade policy uncertainty (TPU) on commodity prices by using time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV). We find that (i) TPU and GPR have significant time-varying effects on the aggregate (classified) commodity market, and the former is a short-term effect before 2006, and it becomes a medium-to-long-term effect after 2006, while the latter is mainly a short-term effect;(ii) TPU shock has a significant positive and time-varying impact on GPR, and the relatively long-term impact is more obvious before 2017, while the short-term impact will dominate after 2017. Additionally, the GPR shock has a short-term negative impact on TPU, and a medium- and long-term positive impact except for the period from 2002 to 2006;(iii) the impact of geopolitical threats (GPT) and geopolitical acts (GPA) on aggregate commodity market have positive and negative alternating shock effects with time variability and a significant short periods impact;(iv) there is a certain degree of heterogeneity in the response of different commodity prices, and the response of individual commodities is related to specific external shocks, such as the COVID-19 pandemic.

Research and Prospect Analysis of Sports Consumption Willingness Based on Public Health Emergencies

In 2020, the sudden outbreak of coronavirus disease 2019 (COVID-19) has had a great impact on the health and life of people all over the world, and the sports industry is facing unprecedented challenges due to its participation and strong clustering. Based on the questionnaire survey, literature analysis, and other research methods, this study introduces the stimulus-organism-response (SOR) theory, takes the sports and consumption of Kunshan citizens as the research subject, and draws lessons from the structural equation model (SEM) to build a theoretical model of sports consumption characteristics and future consumption willingness. The results of empirical analysis show that physical sports consumption has been greatly affected by the epidemic, but because people realize the importance of sports, the willingness of residents to consume sports increases, and the venue and other factors affect the ornamental and participating sports consumption willingness decreases. At the same time, the restrictive factors, such as lower educational background, increased age, and lack of time, make the sports consumption willingness of this characteristic group significantly lower than that of other citizens. This study puts forward some suggestions for relevant government departments to improve the sports consumption willingness of citizens. In order to expand the development prospect of sports industry from a long-term perspective, it can provide reference for the development of sports consumption.

Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains

Many scholars have explored the COVID-19 impact on the crude oil, gold, and Bitcoin markets, whereas most have ignored the media coverage influence. This paper focuses on examining information spillover from epidemic-related news to the crude oil, gold, and Bitcoin markets with the time-frequency analysis method. The empirical results reveal that both the return and volatility spillovers from epidemic-related news to the crude oil, gold, and Bitcoin markets are stronger in the short term (less than 1 week). In the long term, only the media sentiment index notably impacts crude oil, gold, and Bitcoin market returns. The volatility spillover from media coverage to crude oil mainly occurs in the short term. Regarding the gold and Bitcoin markets, the long-term volatility spillovers are significant. An obvious risk contagion path is found. Media hype is the main risk transmitter and transmits vast shocks to these three markets, especially the Bitcoin market, which subsequently transmits these shocks to the gold market. Risk accumulates systemically in the gold and Bitcoin markets. These findings have crucial empirical implications for policymakers and investors when formulating related short- or long-term decisions during the pandemic.

Melatonin mitigates Chloroquine-induced defects in porcine immature Sertoli cells.

Chloroquine (CQ) could function as a lysosomotropic agent to inhibit the endolysosomal trafficking in the autophagy pathway, and is widely used on malarial, tumor and recently COVID-19. However, the effect of CQ treatment on porcine immature Sertoli cells (iSCs) remains unclear. Here we showed that CQ could reduce iSC viability in a dose-dependent manner. CQ treatment (20 µM) on iSCs for 36h could elevate oxidative stress, damage mitochondrial function and promote apoptosis, which could be partially rescued by melatonin (MT) (10 nM). Transcriptome profiling identified 1611 differentially expressed genes (DEGs) (776 up- and 835 down-regulated) (20 µM CQ vs. DMSO), mainly involved in MAPK cascade, cell proliferation/apoptosis, HIF-1, PI3K-Akt and lysosome signaling pathways. In contrast, only 467 (224 up- and 243 down-regulated) DEGs (CQ + MT vs. DMSO) could be found after MT (10 nM) addition, enriched in cell cycle, regulation of apoptotic process, lysosome and reproduction pathways. Therefore, the partial rescue effects of MT on CQ treatment were confirmed by multiple assays (cell viability, ROS level, mitochondrial function, apoptosis, and mRNA levels of selected genes). Collectively, CQ treatment could impair porcine iSC viability by deranging the signaling pathways related to apoptosis and autophagy, which could be partially rescued by MT supplementation.

COVID-19-related government interventions and travel and leisure stock

COVID-19-related government interventions have significantly affected tourism, while the impact of government interventions on the tourism financial market remains essentially unexplored. This paper comprehensively evaluates how COVID-19-related government interventions affected the travel and leisure stock markets based on a panel quantile regression model. Three government interventions (stringency index, containment and health index and economic support index) and two important stock market features (return and volatility) are discussed. The results reveal that the three government interventions are beneficial to the travel and leisure stock market, especially when the market is under adverse conditions. Specifically, containment and health measures lead to an increase in stock returns. Stringency measures and economic support measures promote stock return and restrain stock market volatility. This study provides significant insights for protecting and recovering the travel and leisure stock market by considering when and which government interventions should be implemented.

Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic.

The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this age of rapid dissemination of information. The ensuing uncertainty has led to the emergence of heightened volatility in prices of crude oil futures. Whether such news has predictive value for the volatility of crude oil futures during the COVID-19 pandemic is examined in this research. We proposed a modeling framework, genetic algorithm regularization online extreme learning machine with forgetting factor (GA-RFOS-ELM), to estimate the effects of news during the COVID-19 pandemic on the volatility of crude oil futures. GA-RFOS-ELM could learn block-by-block with fixed or varying block size when considering the block own valid period. The experimental results illustrate that news during the COVID-19 pandemic has more predictive information, which is crucial for short-term volatility forecasting of crude oil futures. The novel approach illustrates that online update learning ability is needed during the COVID-19 pandemic, which could be effective and efficient in volatility forecasting of crude oil futures. The contributions of our study are significant for investors and administrators to predict and understand the behavior of volatility during the COVID-19 pandemic.

Complement C3 identified as a unique risk factor for disease severity among young COVID-19 patients in Wuhan, China.

Given that a substantial proportion of the subgroup of COVID-19 patients that face a severe disease course are younger than 60 years, it is critical to understand the disease-specific characteristics of young COVID-19 patients. Risk factors for a severe disease course for young COVID-19 patients and possible non-linear influences remain unknown. Data were analyzed from COVID-19 patients with clinical outcome in a single hospital in Wuhan, China, collected retrospectively from Jan 24th to Mar 27th. Clinical, demographic, treatment and laboratory data were collected from patients' medical records. Uni- and multivariable analysis using logistic regression and random forest, with the latter allowing the study of non-linear influences, were performed to investigate the clinical characteristics of a severe disease course. A total of 762 young patients (median age 47 years, interquartile range [IQR] 38-55, range 18-60; 55.9% female) were included, as well as 714 elderly patients as a comparison group. Among the young patients, 362 (47.5%) had a severe/critical disease course and the mean age was statistically significantly higher in the severe subgroup than in the mild subgroup (59.3 vs. 56.0, Student's t-test: p < 0.001). The uni- and multivariable analysis suggested that several covariates such as elevated levels of serum amyloid A (SAA), C-reactive protein (CRP) and lactate dehydrogenase (LDH), and decreased lymphocyte counts influence disease severity independently of age. Elevated levels of complement C3 (odds ratio [OR] 15.6, 95% CI 2.41-122.3; p = 0.039) are particularly associated with the risk of developing severe COVID-19 specifically in young patients, whereas no such influence seems to exist for elderly patients. Additional analysis suggests that the influence of complement C3 in young patients is independent of age, gender, and comorbidities. Variable importance values and partial dependence plots obtained using random forests delivered additional insights, in particular indicating non-linear influences of risk factors on disease severity. This study identified increased levels of complement C3 as a unique risk factor for adverse outcomes specific to young COVID-19 patients.

Progressive liver injury and increased mortality risk in COVID-19 patients: A retrospective cohort study in China.

BACKGROUND: Liver injury is common and also can be fatal, particularly in severe or critical patients with coronavirus disease 2019 (COVID-19). AIM: To conduct an in-depth investigation into the risk factors for liver injury and into the effective measures to prevent subsequent mortality risk. METHODS: A retrospective cohort study was performed on 440 consecutive patients with relatively severe COVID-19 between January 28 and March 9, 2020 at Tongji Hospital, Wuhan, China. Data on clinical features, laboratory parameters, medications, and prognosis were collected. RESULTS: COVID-19-associated liver injury more frequently occurred in patients aged ≥ 65 years, female patients, or those with other comorbidities, decreased lymphocyte count, or elevated D-dimer or serum ferritin (P < 0.05). The disease severity of COVID-19 was an independent risk factor for liver injury (severe patients: Odds ratio [OR] = 2.86, 95% confidence interval [CI]: 1.78-4.59; critical patients: OR = 13.44, 95%CI: 7.21-25.97). The elevated levels of on-admission aspartate aminotransferase and total bilirubin indicated an increased mortality risk (P < 0.001). Using intravenous nutrition or antibiotics increased the risk of COVID-19-associated liver injury. Hepatoprotective drugs tended to be of assistance to treat the liver injury and improve the prognosis of patients with COVID-19-associated liver injury. CONCLUSION: More intensive monitoring of aspartate aminotransferase or total bilirubin is recommended for COVID-19 patients, especially patients aged ≥ 65 years, female patients, or those with other comorbidities. Drug hepatotoxicity of antibiotics and intravenous nutrition should be alert for COVID-19 patients.

Autistic traits and gender modulate emotion changes before and during the COVID-19 outbreak: a preliminary evidence based on quasi-experimental design (preprint)

Individuals with autism spectrum disorder (ASD) show an enhanced response to stressors, and gender plays an important role in stress response. Thus, autistic traits (ATs) in the general population and gender may regulate the emotion changes before and during the outbreak of COVID-19. The present study addressed this issue through a participants between quasi-experimental design, in which the epidemic status (before, during), gender (male, female), and AT groups (high ATs, low ATs) were independent variables, and positive and negative emotions were dependent variables. We used generalized linear models to estimate the effects of the independent variables and their interactions on emotions. The results showed that the COVID-19 outbreak reduced positive emotions and increased fear and anger. Furthermore, compared with before the COVID-19 outbreak, individuals with high ATs and females experienced stronger anger and fear than individuals with low ATs and males during the epidemic. The present study revealed the emotional impacts of COVID-19 and greater emotional susceptibility to COVID-19 pandemic among individuals with high ATs and females. Our findings provide prospective evidence for understanding the ASD/ATs-related enhanced response to pathogen threat-related stressors and have implications for COVID-19 crisis interventions.  

Research on pneumonia exacerbation in patients infected with SARS-CoV-2 in Wuhan, China (preprint)

Background SARS-CoV-2 pneumonia occasionally exacerbates to critical condition that is hard to manage. We aim to describe exacerbations of SARS-CoV-2 pneumonia among inpatients.Methods We included confirmed SARS-CoV-2 patients with pneumonia exacerbation admitted to Wuhan Pulmonary Hospital, Hubei Province, China between January 6 and February 17, 2020 and discharged or died before February 25. Their demographic characteristics, clinical symptoms, laboratory tests, CT manifestations, complications and clinical outcomes were collected.Results A total of 158 patients were collected, among them 107 patients were stable and discharged after recovery, 24 patients were already critically severe at hospital admission. 14 patients were excluded for insufficient clinical data. Eventually, 13 confirmed cases were included. The mean age was 65 (± 9.81) years. Ten of the 13 (76.9%) patients were female. Nine (69.2%) had underlying comorbidities. Fever and cough were the most common symptoms (12/13, 92.3%). 10/13(76.9%) patients had their exacerbation in the second week of disease course. All patients had both negative and positive nucleic acid test (NAT) results during the course. Increased range of ground-glass opacity (GGO) on CT imaging are consistent to disease exacerbation. ARDS, MODS, respiratory failure were found in 5/13(38.5%), 3/13(23.1%), 6/13(46.2%) patients respectively. Five (38.5%) patients did not survive.Conclusions SARS-CoV-2 pneumonia exacerbations often occurs in the second week of disease course. Negative NAT result could not exclude exacerbation. CT manifestation is consistent with disease progression. Early admissions have positive effects on reducing complications and mortality.