ABSTRACT
Using a new investor sentiment metric derived from Twitter, this paper examines how the pandemic's death rate influences the impact of investor sentiment on stock liquidity. Recent literature remains inconclusive regarding the effect of COVID-19 information and investor sentiment on financial markets. Using panel smooth transition regression (PSTR) for daily data on 338 listed firms in the S&P500 from January 2, 2020, to May 26, 2021, the findings reveal that the impact of Twitter sentiment on stock liquidity is nonlinear and changes over time and across firms in the function of the pandemic's death rate in the US. The results exhibit a threshold level of 4.32%, above which investor sentiment boosts stock liquidity. The speed of the transition from low to high pandemic death rate regime occurred abruptly rather than smoothly. This translates to severe changes in investor perception and demonstrates that investors are rapidly updating their beliefs during the COVID-19 outbreak. © 2023 Elsevier Inc.
ABSTRACT
The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.