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Google Trends and Stock Returns

Due Rosén, Daniel LU (2021) NEKH03 20211
Department of Economics
Abstract
The purpose of this thesis is to use social data in the form of Google Trends for companies listed on the S&P 100 to see if they contain information that allows us to predict future returns in the stock market. Using econometric models and trading strategies, the predictive power of Google Trends is investigated both if it can predict future stock returns but also if this knowledge can be used to make arbitrage profits above the market. The results of this study are positive, Google Trends can predict future returns of individual companies and it seems that increases in Google Trends for a company on aggregate predict future negative returns. The study also proves that this knowledge can be used to make arbitrage profits in times of... (More)
The purpose of this thesis is to use social data in the form of Google Trends for companies listed on the S&P 100 to see if they contain information that allows us to predict future returns in the stock market. Using econometric models and trading strategies, the predictive power of Google Trends is investigated both if it can predict future stock returns but also if this knowledge can be used to make arbitrage profits above the market. The results of this study are positive, Google Trends can predict future returns of individual companies and it seems that increases in Google Trends for a company on aggregate predict future negative returns. The study also proves that this knowledge can be used to make arbitrage profits in times of economic stability. (Less)
Please use this url to cite or link to this publication:
author
Due Rosén, Daniel LU
supervisor
organization
alternative title
Google Trender och Aktieavkastning
course
NEKH03 20211
year
type
M2 - Bachelor Degree
subject
keywords
Social data, Online search queries, Google Trends, Arbitrage profits, Stock market prediction
language
English
id
9064082
date added to LUP
2021-10-14 10:15:30
date last changed
2021-10-14 10:15:30
@misc{9064082,
  abstract     = {{The purpose of this thesis is to use social data in the form of Google Trends for companies listed on the S&P 100 to see if they contain information that allows us to predict future returns in the stock market. Using econometric models and trading strategies, the predictive power of Google Trends is investigated both if it can predict future stock returns but also if this knowledge can be used to make arbitrage profits above the market. The results of this study are positive, Google Trends can predict future returns of individual companies and it seems that increases in Google Trends for a company on aggregate predict future negative returns. The study also proves that this knowledge can be used to make arbitrage profits in times of economic stability.}},
  author       = {{Due Rosén, Daniel}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Google Trends and Stock Returns}},
  year         = {{2021}},
}