The results lay the foundation for recommending the insurance market participants to lobby for adoption of public-private protection schemes being able to secure a more efficient response to the pandemic-related losses that may occur in the future.įollowing the exponential growth of the cryptocurrency market, there has emerged a growing interest in the cryptocurrency market. The conducted analyses reveal that the demand for insurance services due to the Covid-19 outbreak in the United States can be expected to increase 2-6 times, with the total amount of the incurred costs for the economy due to the virus ranging from 0.3 to 7 percent of the US-2019 GDP. The impulse responses show a positive relationship between the Google Trends Hits and Initial Claims with the Covid-factor having a significant impact on the responses. In the constructed models, the impact of the exogenous variable New Covid Cases is compared with that of over US billion-dollar natural disasters. Google Trends Hits and Initial Claims for Unemployment Insurance Benefits are used as endogenous variables in the built models. The data was collected and reduced to a single scale by US states within the widest possible time span. This paper is the first attempt to obtain estimates by applying Google Trends with a search key word “Business Interruption Insurance”. The article investigates the Covid-19 pandemic related changes in the demand for insurance services in the Unites States due to business interruptions by employing panel vector autoregression models to a dynamic panel data set of 50 states and District of Columbia for three periods of time: 01 January, 2004 to 28 June, 2020 01 January, 2004 to Janu(pre-Covid period) Januto J(Covid-period). Key words: Stock returns Google searches investor attention/sentiment predictability This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market. We use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. To the best of our knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies. We notice that the domestic investor searches are correlated with higher excess returns than the Worldwide investor searches. The higher quantiles of the Google search volume index have corresponding higher excess returns. The Google search volume index performs as a better predictor of the direction as well as the magnitude of the excess returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. We find that high Google search volumes lead to positive returns. We are motivated by Tetlock (2007) and Bijl et al., (2016) to employ regression approach of econometric estimation. We employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange (NSE) in the Indian stock market. We also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market. This paper investigates whether the investor attention using the Google Search Volume Index (GSVI) can be used to forecast stock returns.
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