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Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market
by
Chang, Tzu-Pu
, Chou, Po-Ching
, Lai, Huei-Hwa
, Hu, Cheng-Han
in
Abnormal trading volumes
/ Asymmetric information
/ Companies
/ Dow Jones averages
/ Economics
/ Excess returns
/ Google search volume index
/ Indexes
/ Influenza
/ Institutional investments
/ Internet stocks
/ Investor attention
/ Investors
/ Keywords
/ Securities markets
/ Stock exchanges
/ Stock prices
/ Stocks
/ TPEx 50 index
/ Trading
/ Trends
2022
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Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market
by
Chang, Tzu-Pu
, Chou, Po-Ching
, Lai, Huei-Hwa
, Hu, Cheng-Han
in
Abnormal trading volumes
/ Asymmetric information
/ Companies
/ Dow Jones averages
/ Economics
/ Excess returns
/ Google search volume index
/ Indexes
/ Influenza
/ Institutional investments
/ Internet stocks
/ Investor attention
/ Investors
/ Keywords
/ Securities markets
/ Stock exchanges
/ Stock prices
/ Stocks
/ TPEx 50 index
/ Trading
/ Trends
2022
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Do you wish to request the book?
Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market
by
Chang, Tzu-Pu
, Chou, Po-Ching
, Lai, Huei-Hwa
, Hu, Cheng-Han
in
Abnormal trading volumes
/ Asymmetric information
/ Companies
/ Dow Jones averages
/ Economics
/ Excess returns
/ Google search volume index
/ Indexes
/ Influenza
/ Institutional investments
/ Internet stocks
/ Investor attention
/ Investors
/ Keywords
/ Securities markets
/ Stock exchanges
/ Stock prices
/ Stocks
/ TPEx 50 index
/ Trading
/ Trends
2022
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Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market
Journal Article
Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market
2022
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Overview
This research examines whether Google search volume index (GSVI), a proxy of investor attention, can predict the excess returns and abnormal trading volumes of TPEx 50 index constituents. It also explores the motive underlying GSVI based on positive or negative shocks to stock prices. The empirical data include 48 companies from TPEx 50 index constituents and cover a period from 1 September 2016 to 31 August 2019. The empirical results present that (1) lagged GSVI negatively affects current excess returns, perhaps due to the characteristics of TPEx, in which there are a higher proportion of retail investors, smaller listed companies, and a higher information asymmetry problem. (2) Lagged GSVI can positively affect abnormal current trading volumes. (3) If GSVI is driven by positive shocks, then it can predict excess returns and abnormal trading volumes positively.
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