Cette étude tente d’améliorer le modèle standard de prévision de l’indice de volatilité VIX à l’aide du Search Volume Index (SVI) rapporté par Google Trends à l’échelle journalière. La volatilité des marchés étant basée sur la peur et la recherche de gains économiques des agents de marché, Google Trends permet un accès direct et virtuellement immédiat aux désirs et aux inquiétudes de ceux-ci. Durant la période de janvier 2010 à décembre 2016, les séries de 15 termes de recherche liés à l’économie sont utilisées pour tenter d’améliorer les modèles ARIMA et SARIMA recommandés dans la prévision du VIX. Contrairement aux études similaires effectuées à l’échelle hebdomadaire, les résultats démontrent qu’il n’y a pas d’amélioration des prévisions causée par l’addition des données Google Trends aux modèles.

Fear of crime is an important topic in research as well as in public opinion. However, data on fear of crime is limited and difficult to collect, being heavily reliant on surveys with different methods of operationalization yielding different results. This paper aims to investigate if an alternative method can be used to estimate fear of crime. Using a large representative unique data on fear of crime from Google Trends, I analyze if fear of crime can be estimated in Sweden during years 2011-2019, using The Swedish Crime Survey as benchmark. The results show that the method is accurate for country-level and the most populated regions of Sweden, but less so for the lesser populated regions. This method can be used to estimate fear of crime in a time- and money-efficient way, producing daily estimates at little to no cost.

This diploma thesis is based on the theory of behavioral attention and examines the effect of the search for negative words in conjunction with the name of the bank on the price and on the yield of the shares of these banks. As a sample, 12 global, publicly traded and significant banks were selected. In this work, the behavioral attention is identified as the level of search on Google. The panel regression with random effects is used in the work, and Bayesian Model Averaging is used to identify suitable variables. The data proves the effect of negative behavioral attention, when an increased level of attention diminishes yield and share price. The results are then subjected to a robustness analysis where the impact of behavioral attention is examined before, during, and after the financial crisis. Furthermore, the effect of regulation and the level of behavioral attention itself is examined. The diploma thesis corresponds to the knowledge of behavioral economics and confirms a certain irrational behavior of investors on the market.

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Yes / Purpose: We develop new quantitative methods to estimate the level of speculation and long-term sustainability of Bitcoin and Blockchain. Design/Methodology/Approach: We explore the practical application of speculative bubble models to cryptocurrencies. We then show how the approach can be extended to provide estimated brand values using data from Google Trends. Findings: We confirm previous findings of speculative bubbles in cryptocurrency markets. Relatedly, Google searches for cryptocurrencies seem to be primarily driven by recent price rises. Overall results are sufficient to question the long-term sustainability of Bitcoin with the suggestion that Ethereum, Bitcoin Cash and Ripple may all enjoy technical advantages relative to Bitcoin. Our results also demonstrate that Blockchain has a distinct value and identity beyond cryptocurrencies - providing foundational support for the second generation of academic work on Blockchain. However, a relatively low estimated long-term growth rate suggests that the benefi ts of Blockchain may take a long time to be fully realised. Originality/value: We contribute to an emerging academic literature on Blockchain and to a more established literature exploring the use of Google data within business analytics. Our original contribution is to quantify the business value of Blockchain and related technologies using Google Trends.

<p>The purpose of this Bachelor thesis is to analyze if there is a correlation between stock prices and the amount of searches of the companies names on Google. The theories used in the study were Capital Asset Pricing Model (CAPM) and Efficient Market Hypothesis (EMH). Regressions analysis is used as the statistical method to see if there is a significant correlation between the stock prices and the amout of searches of the company name on Google. The data used were the rate of return of three companies (ABB, Oriflame and Sandvik) on the Nasdaq OMX Nordic stock market, the rate of return of the Nasdaq OMX Nordic stock market index (OMX Stockholm_PI) and the Google search frequency from Google Trends on each company. The result showed no significance and the conclusion of the thesis is that there is no significant correlation between the three studied companies and their search frequency on the search engine Google.</p> / <p><strong>Syfte</strong>: Syftet med uppsatsen är att undersöka ifall det finns ett samband mellan företags aktiekurser och sökfrekvens på företagets namn på söktjänsten Google.</p><p><strong>Data: </strong>Daglig avkastning på ABB:s, Oriflames och Sandviks aktier, Aktieindex samt Googels sökfrekvens.</p><p><strong>Teorier: </strong>Capital Asset Pricing Model (CAPM), Effektiva marknadshypotesen (EMH)</p><p><strong>Slutsats: </strong>Det råder inget signifikant samband mellan de undersökta företagens aktiekurser och deras företagsnamns sökfrekvens på söktjänsten Google.</p>

The purpose of this Bachelor thesis is to analyze if there is a correlation between stock prices and the amount of searches of the companies names on Google. The theories used in the study were Capital Asset Pricing Model (CAPM) and Efficient Market Hypothesis (EMH). Regressions analysis is used as the statistical method to see if there is a significant correlation between the stock prices and the amout of searches of the company name on Google. The data used were the rate of return of three companies (ABB, Oriflame and Sandvik) on the Nasdaq OMX Nordic stock market, the rate of return of the Nasdaq OMX Nordic stock market index (OMX Stockholm_PI) and the Google search frequency from Google Trends on each company. The result showed no significance and the conclusion of the thesis is that there is no significant correlation between the three studied companies and their search frequency on the search engine Google. / Syfte: Syftet med uppsatsen är att undersöka ifall det finns ett samband mellan företags aktiekurser och sökfrekvens på företagets namn på söktjänsten Google. Data: Daglig avkastning på ABB:s, Oriflames och Sandviks aktier, Aktieindex samt Googels sökfrekvens. Teorier: Capital Asset Pricing Model (CAPM), Effektiva marknadshypotesen (EMH) Slutsats: Det råder inget signifikant samband mellan de undersökta företagens aktiekurser och deras företagsnamns sökfrekvens på söktjänsten Google.

[pt] Investidores estão sempre à procura de uma vantagem. Porém, tradicionais teorias financeiras nos dizem que tentar predizer tendências na bolsa de valores é um esforço em vão, uma vez que seguem um passeio aleatório, i.e., um processo estocástico ou randômico. Além disso, afirma-se que o mercado é eficiente de maneira que sempre incorpora e reflete toda informação relevante, o que torna impossível bater o mercado. Recentemente, com o crescimento da web e aumento da disponibilidade de dados em conjunto com a evolução dos algoritmos de Aprendizado de Máquina, diversos trabalhos tem aplicado técnicas de Processamento de Linguagem Natural em notícias financeiras e dados de redes sociais para prever variações do preço de ações. Consequentemente, estão surgindo fortes evidências que o mercado pode, em algum grau, ser previsto. Este trabalho descreve o desenvolvimento de uma aplicação baseada em Aprendizado de Máquina para realizar a predição de tendências no mercado de ações, i.e., variações negativas, positivas ou neutras de preços com granularidade de minuto. Avaliamos o sistema usando dados de cotação de ações da B3 (Brasil Bolsa Balcão), antiga BM&FBOVESPA, e um dataset de tópicos mais relevantes buscados no Google Search e seus artigos relacionados, que são disponibilizados pela plataforma Google Trends e coletados, minuto a minuto, de 15/08/2016 até 10/07/2017. Os experimentos mostram que esses dados provêem informação relevante para a tarefa em questão, onde conseguimos uma acurácia de 69.24 porcento para a predição de tendências do ativo PETR4, criando alguma / [en] Investors are always looking for an edge. However, traditional economic theories tell us that trying to predict short-term stock price movements is wasted effort, since it approximate a random walk, i.e., a stochastic or random process. Besides, these theories state that the market is efficient enough to always incorporate and reflect all relevant information, making it impossible to beat the market. In recent years, with the growth of the web and data availability in conjunction with advances in Machine Learning, a number of works are using Natural Language Processing to predict share price variations based on financial news and social networks data. Therefore, strong evidences are surfacing that the market can, in some level, be predicted. This work describes the development of an application based on Machine Learning to predict trends in the stock market, i.e., positive, negative or neutral price variations with minute granularity. We evaluate our system using B3 (Brasil Bolsa Balcão), formerly BM&FBOVESPA, stock quotes data, and a dataset with the most relevant topics of Google Search and its related articles, provided by the Google Trends platform and collected, minute by minute, from 08/15/2016 to 07/10/2017. The experiments show that this data provides useful information to the task at hand, in which we achieve 69.24 per cent accuracy predicting trends for the PETR4 stock, creating some leverage to make profits possible with intraday trading.

[pt] Neste trabalho, estudo o comportamento dos arbitradores ao se depararem com variações nos níveis de preços das ações, mais especificamente, analisando a forma como eles buscam informações sobre esses ativos. Para isso, testo e confirmo a hipótese de que os retornos anormais das ações levam os investidores a buscarem ativamente mais informações sobre essas empresas, usando dados de volume de buscas no Google. Adicionalmente, analiso de forma separada o impacto de retornos anormais negativos e de retornos anormais positivos sobre o volume de buscas do Google, chegando à conclusão de que os retornos negativos têm um efeito maior sobre o volume de buscas que os efeitos positivos. / [en] I study the behavior of arbitrageurs when they are faced with changes in stock price levels, more specifically analyzing the way they seek information about these assets. I test and confirm the hypothesis that abnormal stock returns prompt investors to seek actively information about these companies by using Google search volume data. Furthermore, I study the separate effects of negative abnormal returns and positive abnormal returns on Google search volumes, and conclude that negative returns cause a greater impact on the search volumes than positive returns.

In this paper, I analyze Bitcoin price formation and adoption rates at a global and national level. In determining Bitcoin prices, I consider contemporaneous and lagged values of traditional determinants of currencies, such as inflation and industrial production, and digital currency specific factors, primarily public interest. Using monthly time-series data across five years (2011 – 2016), I find that global public interest in Bitcoin, measured by Google searches for the keyword ‘Bitcoin,’ has a positive and significant impact on Bitcoin prices. I extend the analysis to a country level by employing a proxy for adoption rates, represented by the number of local Bitcoin client downloads, which is a useful predictor of prices. I examine pooled data across 12 countries to show that searches for ‘Bitcoin’ can be used to predict adoption rates and, consequently, prices. To the best of my knowledge, this is the first academic article to study Bitcoin usage at a national level. I find that contemporaneous values of traditionally used macroeconomic determinants of currency prices, except inflation, do not have a significant impact on Bitcoin prices.