Google search volume's ability to explain and predict stock market activity in Finland

dc.contributorAalto Universityen
dc.contributorAalto-yliopistofi
dc.contributor.advisorLof, Matthijs
dc.contributor.authorRechardt, Toni
dc.contributor.departmentRahoituksen laitosfi
dc.contributor.schoolKauppakorkeakoulufi
dc.contributor.schoolSchool of Businessen
dc.date.accessioned2019-06-02T16:00:48Z
dc.date.available2019-06-02T16:00:48Z
dc.date.issued2019
dc.description.abstractI study the contemporary and predictive effect of Google Trends search volume index (henceforth GSV) on stock market activity in the Finnish stock market, measured by stock return, volatility and trading volume. This study adds to the finance literature that utilizes GSV as a measure for investor attention. The studied companies chosen to represent the Finnish stock market are the OMX Helsinki 25 component companies, that were included in the index during the sampled time period of December 29, 2013 to December 30, 2018, or a complete 261 weeks. The sample also includes joiners and leavers. I find that GSV is able to explain contemporary stock market activity well, and that it can contribute to predicting future stock market activity as well. I follow the methodologies of Kim et al. (2018) and Bijl et al. (2016) and study the effect utilizing panel data regressions, and further create a backtesting trading strategy based on the regression results to test the effect in a more economical setting. I find that GSV is able to explain contemporary stock volatility and trading volume with strong significance. GSV is also a significant predictor of future stock volatility, and indication of its predictive relationship with future stock returns is also found in the results. Namely, indication is found that GSV is able to predict a two-week subsequent stock return reversal effect. The backtesting trading strategy based on this found relationship provide further indication towards an existing effect between GSV and future stock returns, and is able to beat the benchmark index with excluded transaction costs. This research contributes to the existing finance literature by being, to the best of my knowledge, the first to study the relationship between GSV and stock market activity in the Finnish stock market. More generally, this research provides insight on whether GSV explains and predicts stock market activity in a relatively small stock market, in a developed country, with very high internet penetration and activity.en
dc.format.extent50 + 5
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/38189
dc.identifier.urnURN:NBN:fi:aalto-201906023274
dc.language.isoenen
dc.locationP1 Ifi
dc.programmeFinanceen
dc.subject.keywordGoogle searchesen
dc.subject.keywordinvestor attentionen
dc.subject.keywordvolatilityen
dc.subject.keywordtrading volumeen
dc.subject.keywordstock returnsen
dc.subject.keywordpredictabilityen
dc.titleGoogle search volume's ability to explain and predict stock market activity in Finlanden
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotMaisterin opinnäytefi
local.aalto.electroniconlyyes
local.aalto.openaccessno

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