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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Google Trends as a Signal for UK Stock Market Movements |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 38 |
| Date | 2016 |
| Zusammenfassung | Financial markets and traders active in the market are producing a large amount of information. The recorded data can be used to determine how many decisions to buy or sell are made. However, this data doesn’t reveal what drives the traders to reach those decisions. Almost every decision is preceded by some sort of information gathering. With the advancement in technology, this process of collecting information nowadays occurs typically online, mostly by using an internet search engine like Google. Google records which words people are looking for and offers this data to the public via their software Google Trends. If future decisions are in fact connected to online information gathering, it should be possible to anticipate investor’s decisions and thus future stock price movements by analysing online data. The objective of this thesis is to examine whether there is a possibility to use Google queries as an indicator for subsequent stock market moves in the United Kingdom. In addition, the study aims to analyse the predictive power of Google in times of investor’s confidence and doubt. This is achieved by evaluating either positive or negative financial words using Google Trends and creating an investment strategy based on the weekly query volume change of those terms. If investors are confident and search for more “good” words in a week than the previous week, the returns of the British Index FTSE 100 is expected to rise subsequently. It should also rise when the search volume of negative words is declining since this would imply that people are less concerned and invest more in the stock market. The other way around, it is predicted that when investors gather less positive or more negative information, they are considering selling their shares and the Index return declines in the upcoming period. The trading strategy utilizes those potential indications and takes a weekly long or short position in the index depending on the Google data. The backtesting was conducted every week over ten years from 2006-2016 and compared to a buy-and-hold performance of the British Stock Index FTSE 100. Another question, that this thesis attempts to answer, is why an individual might buy or sell and thus influence the market prices after obtaining the online information. The explanations are based on behavioural finance concepts. The theoretical basis derives from several researches which have analysed the interaction between internet data and the economic status. As many findings show, there often exists a correlation, whether this is for instance between the online searching behaviour and the level of consumption or the financial markets. The results point out that there is an evident connection between Google search query volume and the UK stock markets. This correlation is almost always negative. This means that it doesn’t matter if the information collecting is based on positive or negative words, but only on the quantity of the words which are searched for. This finding is supported by several experiments, based on different word lists. With new technological systems, trails of people’s everyday behaviour can be recorded and observed. The results in this paper provide support that in the UK, these trails can be used to analyse the behaviour in stock market movements and even anticipate how they will change in the near future. The findings open up interesting possibilities to construct a simple but profitable investment strategy in the long run based on Google Trends data. |
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