Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Google Searches and Stock Volatility - Evidence from the Danish market

Nyström, Oliver Albin Wiegaard LU (2021) NEKH03 20211
Department of Economics
Abstract
In this thesis, we investigate the effect of online search activity approximated by Google searches on the volatility of the largest, most actively traded stocks listed on the Danish stock exchange. The interest in this relationship stems from an increasing number of retail investors entering the market in recent years. Driven by low-to-negative deposit rates, easily accessible online trading platforms with limited entry regulation, and most recently, the Covid-19 pandemic, we argue that online information seeking of “amateur investors” with little to no prior trading experience can be linked to the dynamics of return volatility. By conducting both statistical and regression analyses we investigate a dataset containing information on 27... (More)
In this thesis, we investigate the effect of online search activity approximated by Google searches on the volatility of the largest, most actively traded stocks listed on the Danish stock exchange. The interest in this relationship stems from an increasing number of retail investors entering the market in recent years. Driven by low-to-negative deposit rates, easily accessible online trading platforms with limited entry regulation, and most recently, the Covid-19 pandemic, we argue that online information seeking of “amateur investors” with little to no prior trading experience can be linked to the dynamics of return volatility. By conducting both statistical and regression analyses we investigate a dataset containing information on 27 Danish stocks for the years 2016-2021. Contrary to our expectations, we fail to establish a descriptive link between Google search activity and volatility at the market level, nor do we find predictive powers of Google searches on volatility patterns for our sample. Each result is somewhat controversial and lacks support in most of the established body of literature covering developed markets. We hypothesize that this can be explained by the methodology of which search data was obtained, leaving valuable knowledge for future investigators. (Less)
Please use this url to cite or link to this publication:
author
Nyström, Oliver Albin Wiegaard LU
supervisor
organization
course
NEKH03 20211
year
type
M2 - Bachelor Degree
subject
keywords
Stock volatility, Google SVI
language
English
id
9051406
date added to LUP
2021-07-05 13:32:07
date last changed
2021-07-05 13:32:07
@misc{9051406,
  abstract     = {{In this thesis, we investigate the effect of online search activity approximated by Google searches on the volatility of the largest, most actively traded stocks listed on the Danish stock exchange. The interest in this relationship stems from an increasing number of retail investors entering the market in recent years. Driven by low-to-negative deposit rates, easily accessible online trading platforms with limited entry regulation, and most recently, the Covid-19 pandemic, we argue that online information seeking of “amateur investors” with little to no prior trading experience can be linked to the dynamics of return volatility. By conducting both statistical and regression analyses we investigate a dataset containing information on 27 Danish stocks for the years 2016-2021. Contrary to our expectations, we fail to establish a descriptive link between Google search activity and volatility at the market level, nor do we find predictive powers of Google searches on volatility patterns for our sample. Each result is somewhat controversial and lacks support in most of the established body of literature covering developed markets. We hypothesize that this can be explained by the methodology of which search data was obtained, leaving valuable knowledge for future investigators.}},
  author       = {{Nyström, Oliver Albin Wiegaard}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Google Searches and Stock Volatility - Evidence from the Danish market}},
  year         = {{2021}},
}