Investor sentiments and stock markets during the COVID-19 pandemic

Market resilience of orthopaedic hip/knee arthroplasty sales during COVID-19

INTRODUCTION

The coronavirus 2019 (COVID-19) pandemic led to a marked decrease in elective surgical volume and orthopaedic device sales. The aim of this paper was to quantify this decrease and the related financial impact on the largest hip/knee arthroplasty companies by: (1) tracking individual hip/knee company valuations; (2) calculating aggregate changes in overall hip/knee arthroplasty market valuations; and (3) quantifying quarterly hip/knee revenues relative to prior years.

MATERIALS AND METHODS

Financial data on the top five hip/knee arthroplasty companies by size between January 1, 2019, and October 1, 2020, was collected from a Wall Street financial database, S&P Capital IQ. Changes in valuation of these companies were compared against benchmark market indices, the S&P500 and Vanguard Healthcare ETF. U.S. hip/knee arthroplasty-specific revenue for Q1 and Q2 of 2019 and 2020 was collected from Securities Exchange Commission 10-Q forms. Quarterly revenue changes were calculated using 1-2Q19 revenues as baselines and aggregate to approximate the overall hip/knee arthroplasty market.

RESULTS

The top five hip/knee companies lost $179.2 billion (32.7% loss) in market value from pre COVID-19 market highs to COVID-19 market lows (March 2020), while S&P500 and Vanguard Healthcare ETF decreased 36.1 and 33.2%, respectively. From market lows to October 2020, arthroplasty companies rallied 38.6% while the S&P500 and Vanguard Healthcare ETF regained 43.5 and 56.4% respectively. Notably, this occurred while aggregate 1Q/2Q20 revenue lagged 7.1/41.8% relative to 2019, with an overall decrease of $1.58B (24.8%).

CONCLUSIONS

Similar to the overall market and healthcare sector, the top five hip/knee arthroplasty companies have recovered from their COVID market lows. Our results reveal that the valuations of hip/knee companies remained robust during COVID, even as revenues fell, likely due to strong investor confidence in the industry outlook and the greater overall healthcare system utilization.

Dynamic analysis and optimal control of a stochastic investor sentiment contagion model considering sentiments isolation with random parametric perturbations

Investor sentiment contagion has a profound influence on economic and social development. This paper explores the diverse influences of various investor sentiments in modern society on the economy and society. It also investigates the interference of various uncertain factors on investor sentiments in the modern economy and society. On this basis, the dual-system stochastic SPA2G2R model was constructed, incorporating positive and negative sentiments, as well as a supervision and isolation mechanism. The global existence of positive solutions was established, and sufficient conditions for the disappearance and steady distribution of investor sentiment were calculated. An optimal control strategy for the stochastic model was put forward, with numerical simulation supporting the theoretical analysis results. A comparison with parameter changes in the deterministic model was also conducted. The research reveals a competitive relationship between different investor sentiments. Enhancing societal guidance mechanisms promotes positive investor sentiment contagion. Timely control by the supervisory department effectively curbs the spread of investor sentiment. Additionally, white noise promotes investor sentiment contagion, suggesting effective regulation through control of noise intensity and disturbance parameters.

Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure

This paper investigates the relationship between investors' attention, as measured by Google search queries, and equity implied volatility during the COVID-19 outbreak. Recent studies show that search investors' behavior data is an extremely abundant repository of predictive data, and investor-limited attention increases when the uncertainty level is high. Our study using data from thirteen countries across the globe during the first wave of the COVID-19 pandemic (January-April 2020) examines whether the search "topic and terms" for the pandemic affect market participants' expectations about future realized volatility. With the panic and uncertainty about COVID-19, our empirical findings show that increased internet searches during the pandemic caused the information to flow into the financial markets at a faster rate and thus resulting in higher implied volatility directly and via the stock return-risk relation. More specifically for the latter, the leverage effect in the VIX becomes stronger as Google search queries intensify. Both the direct and indirect effects on implied volatility, highlight a risk-aversion channel that operates during the pandemic. We also find that these effects are stronger in Europe than in the rest of the world. Moreover, in a panel vector autoregression framework, we show that a positive shock on stock returns may soothe COVID-related Google searches in Europe. Our findings suggest that Google-based attention to COVID-19 leads to elevated risk aversion in stock markets.

Impact of COVID-19 on investor sentiment in China's stock markets

Large-scale public health emergencies may exert significant adverse effects on market sentiment. This study utilizes interrupted time series analysis (ITSA) to explore the shift in Chinese investors' sentiment in response to the uncertainties due to the outbreak of the COVID-19 pandemic. The empirical findings demonstrate that COVID-19 had a notable impact on investor sentiment within China's stock markets. Before the outbreak of COVID-19, investor sentiment had been on an upward trend. However, since the onset of the pandemic, there has been a sustained decline in investor sentiment, aligning with the downward trend observed in China's stock markets. Interestingly, the immediate effect of the COVID-19 intervention was positive, briefly boosting investor sentiment. As of 2023, with the conclusion of the pandemic and the Chinese government's decision to end the zero COVID-19 policy, we anticipate resurgence in investor sentiment within China's stock markets.

How does air pollution affect the stock market performance? Evidence from China

Given its broad impact on human society, air pollution could become a non-economic factor affecting the stock market. But the impact of air pollution on the stock market performance has not received enough attention. This study examines the influence and potential mechanism of air pollution on stock market performance based on the panel data of 1344 A-share listed firms in China covering the period 2013-2019. The result shows that air pollution can negatively affect stock market performance. Second, heterogeneity analysis creatively points out that firms with less analysts, smaller size, stated-owned ownership, polluting related industry are more vulnerable to the negative effects of air pollution. Finally, the result also reveals a mechanism that air pollution could worsen the stock market by depressing investors' sentiments. The above findings enrich current research related to the impact of air pollution on stock market performance and also provide a new perspective for investors to make stock investment decisions.

Firms' exposures on COVID-19 and stock price crash risk: Evidence from China

This study examines the impact of firms' exposures on COVID-19 sentiment on the stock price crash risk. We show the exposure on COVID-19 sentiment related to the medical, travelling and uncertain aspects significantly increases the stock price crash risk, while the exposure on COVID-19 sentiment related to vaccines significantly decreases the risk of stock price crash. Furthermore, our findings are stronger for non-state-owned firms and firms with low information transparency. Overall, we provide timely policy implication for economic impacts of the COVID-19 on the stock market.

COVID-19 and tourism sector stock price in Spain: medium-term relationship through dynamic regression models

The global pandemic, coronavirus disease 2019 (COVID-19), has significantly affected tourism, especially in Spain, as it was among the first countries to be affected by the pandemic and is among the world's biggest tourist destinations. Stock market values are responding to the evolution of the pandemic, especially in the case of tourist companies. Therefore, being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector, thereby improving the response to the crisis by policymakers and investors. Accordingly, a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term. It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.

Exploring the effect of Covid-19 on herding in Asian financial markets

We examine herding behavior before, during, and after the Covid-19 pandemic in eight prominent Asian stock markets. Daily stock returns for the period Jan- 2018 to July- 2022 in the markets were investigated using the models prescribed by Chang et al., (2000) and Chiang and Zheng (2010). The empirical results provide strong support to earlier studies by providing robust evidence of herding in Vietnam, Indonesia, India, South Korea, and Singapore when the market is bullish and Indonesia and Vietnam also exhibit herding when the market is bearish. Herding tendency is dominant for Vietnam, India, and Indonesia during the pandemic with the post-pandemic time being more potent for China and Vietnam. Notably, an anti-herding tendency is found in China, Hong Kong, and Singapore. As a policy measure, efficient information dissemination, deterrence of insider trading, and regulation of mispricing can be undertaken.