PREDICTING STOCK RETURNS WITH GOOGLE SEARCH DATA: EVIDENCE FROM THE FINNISH MARKET
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School of Business |
Bachelor's thesis
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Authors
Date
2019
Department
Rahoituksen laitos
Major/Subject
Mcode
Degree programme
Rahoitus
Language
en
Pages
16 + 1
Series
Abstract
The attention price pressure theory (Barber and Odean, 2008) states that investor attention can be used to predict stock returns. I quantify investor attention with the amount of Google searches made and study this relation in a sample of 120 of the most searched public stocks listed on the Nasdaq OMX Helsinki during the time period January 2010 to December 2018. I find that the most searched companies indeed do have the highest returns, but they do not earn abnormal returns when controlling for risk-factors. Abnormal returns can’t be found even when looking specifically within the stocks which are hardest to arbitrage, which should be most liable to the relation. The results contradict research that has been done on the US market using the time period 2004-2008, but are in line with more recent research, which fails to find convincing evidence using periods later than 2008. The most likely reasons for this are that the correlation has been arbitraged away according to the tenets of efficient mar-ket hypothesis, and that the boom years of 2004-2008 ending in the financial crisis were a special time period, from which results don’t generalize to other periods.Description
Thesis advisor
Spickers, TheresaKeywords
attention price pressure theory, stock return prediction, Google search data, Finland, market efficiency