To capture house price fluctuations, it is important to combine appropriate factors into the price forecasting model. Fundamental macroeconomic variables have been considered quite completely in many housing price models. However, the ability of these models to predict housing prices are still limited. According to Shiller (2007), it is the psychological factors that cause fluctuations in the housing market. To fully present the current psychological state of the market, researchers need a more powerful database. To solve this problem, Google Trends seems to be a useful tool that provides us the Google search engine indices of specific terms or predetermined categories within a specified time and region. By tracking the search intensity on the Internet, this study aimed to uncover the intentions of potential buyers and used this information to analyze the dynamics of the housing market. This study performed an empirical analysis with data from Taiwan. The purpose is to detect the interaction between the Internet search index and house price and transaction volume in Taiwan and the six main Taiwanese cities. The Internet search information provided by Google Trends is presented in an index form. The data is anonymous, free of charge, and frequently updated. The VEC models were performed to measure the explanatory power of the search volume indices on house prices and sales volume. Moreover, variance decomposition and impulse response were used to examine the dynamic of the variables in the model. The findings reveal that, in Taiwan and its five out of six cities, the Google indicator using the names of estate agencies as the search query could serve as an indicator of transaction volume but not for house price. By proving that the Internet search volume could capture the market sentiment for transaction volume in Taiwan and its cities, it would help the local government and decision-makers in related companies to make more precise predictions of housing market based on market sentiment with a lower cost. / För att fånga upp husprisfluktuationer är det viktigt att kombinera lämpliga faktorer i prisprognosmodellen. Grundläggande makroekonomiska variabler har beaktats ganska fullständigt i många bostadsprismodeller. Dessa modellers förmåga att förutsäga bostadspriserna är dock fortfarande begränsade. Enligt Shiller (2007) är det de psykologiska faktorerna som orsakar fluktuationer på bostadsmarknaden. För att fullt ut kunna presentera det nuvarande psykologiska tillståndet på marknaden behöver forskare en kraftfullare databas. För att lösa detta problem verkar Google Trender vara ett användbart verktyg som ger oss Googles sökmotorindex för specifika termer eller förutbestämda kategorier inom en viss tid och region. Genom att spåra sökintensiteten på Internet syftade denna studie till att avslöja potentiella köpares avsikter och använde denna information för att analysera dynamiken på bostadsmarknaden. Denna studie utförde en empirisk analys med data från Taiwan. Syftet är att upptäcka interaktionen mellan sökindex på Internet och huspris och transaktionsvolym i Taiwan och de sex största taiwanesiska städerna. Internetsökningsinformationen från Google Trends presenteras i en indexform. Uppgifterna är anonyma, kostnadsfria och uppdateras ofta. VECmodellerna utfördes för att mäta förklaringskraften hos sökvolymindexen på huspriser och försäljningsvolym. Variansupplösning och impulssvar användes också för att undersöka dynamiken hos variablerna i modellen. Resultaten avslöjar att i Taiwan och dess fem av sex städer kan Google-indikatorn som använder namnen på fastighetsbyråer som sökfråga fungera som en indikator på transaktionsvolymen men inte för huspriset. Genom att bevisa att sökvolymen på Internet kan fånga marknadssentimentet för transaktionsvolymen i Taiwan och dess städer, skulle det hjälpa de lokala myndigheterna och beslutsfattare i relaterade företag att göra mer exakta förutsägelser om bostadsmarknaden baserade på marknadssentiment till en lägre kostnad .

2015~2016年間台灣金融業發生許多重大新聞事件，隨著資訊科技普及，網路搜尋已成為大眾獲取資訊的重要管道。本文利用Google Trends關鍵字搜尋指數作為網路關注度的代理變數，進行與台灣上市金控公司股價報酬相關之研究。 本文使用三種研究方法進行探討，首先利用圖表式比對法，初步觀察異常搜尋指數與異常報酬出現時間之關聯性，結果並未發現搜尋指數與台灣上市金控股價報酬間有明顯且一致的關係；接著套用向量自我迴歸模型進行分析，然而14家台灣上市金控公司中，僅從兆豐金數據可發現前一期搜尋指數的異常變動量增加1%將使下一期異常報酬率下降約2.67%；最後參考相關文獻使用Fama Macbeth兩階段迴歸模型，結果發現平均而言搜尋指數的異常變動量每上升一個標準差會顯著影響兩週後股價的異常報酬率下降約0.17%，SVI對於股價報酬影響為負向符合本文研究動機與背景，且有相關文獻指出投資人對於壞消息的反應較慢，因此使股價報酬有延後反應的現象，但無法解釋兩週的反應時間，因此對於這樣的研究結果持保留的態度。 總結三種研究方法所得結果，本文認為網路關注度對於目前台灣上市金控公司股價的影響仍然有限。 / It’s unquiet for Taiwanese Financial industry between 2015 and 2016. There has been a lot of major news. With the popularity of information technology, Internet search has become an important channel for public access to information. Therefore, we use Search Volume Index (SVI) as a proxy for public online attention and conducts research related to the stock returns of listed financial holding companies in Taiwan. In this paper, three kinds of research methods are used. The first way is chart comparison method for preliminary data analysis. The results couldn’t show a clear and consistent relationship between SVI and stock returns. The second method is vector self-regression model. However, only Mega financial holding company’s result indicates abnormal search volume index(ASVI) increase 1% will decrease next week abnormal return by 2.67%. At last, we use Fama Macbeth two-stage regression model and find that on average 1 standard deviation increased in ASVI will decrease abnormal return by 0.17% after two weeks. The negative impact of SVI on the stock returns of financial holding companies is in line with the research motivation and background, and some relevant literatures prove that investors’ response to the bad news is slow, which leads to the delayed response of stock returns. However, the two weeks of reaction time for stock returns is unknown. In conclusion, this paper finds out that the impact of public online attention on share price of listed financial holding companies in Taiwan is still limited currently.

[pt] Este estudo explora fontes de dados não convencionais como dados textuais de jornais e pesquisas de internet do Google Trends em dois problemas empíricos: (i) analisar o impacto da mobilidade sobre o número de casos e mortes por Covid-19; (ii) nowcasting do PIB em alta-frequência. O primeiro artigo usa fontes de dados não estruturados como controle para fatores comportamentais não observados e encontra que um aumento na mobilidade residencial diminui significativamente o número de casos e mortes num horizonte de quatro semanas. O segundo artigo usa fontes de dados não estruturadas para fazer um nowcasting semanal do PIB, mostrando que dados textuais e Google Trends pode aumentar a qualidade das projeções (medido pelo EQM, EAM e outras métricas) comparado com as expectativas de mercado do Focus como base. Em ambos casos, dados não estruturados reveleram-se fontes ricas de informação não codificadas em indicadores estruturados convencionais. / [en] This study exploits non-conventional data sources such as newspaper textual data and internet searches from Google Trends in two empirical problems: (i) analysing the impacts of mobility on cases and deaths due to Covid-19; (ii) nowcasting GDP in high-frequency. The first paper resorts to unstructured data to control for non-observable behavioural effects and finds that an increase in residential mobility significantly reduces Covid-19 cases and deaths over a 4-week horizon. The second paper uses unstructured data sources to nowcast GDP on a weekly basis, showing that textual data and Google Trends can significantly enhance the quality of nowcasts (measured by MSE, MAE and other metrics) compared to Focus s market expectations as a benchmark. In both cases, unstructured data was revealed to be a valuable source of information not encoded in structured indicators.

Submitted by Davi Torres de Carvalho (davi.torres@gmail.com) on 2018-08-30T14:14:52Z No. of bitstreams: 1 PERFIL DOS INVESTIDORES DE CRIPTOMOEDAS - ANÁLISE DE BUSCAS CORRELACIONADAS AO BITCOIN.pdf: 1321448 bytes, checksum: d7e782a8be37d95ad193a89108224ae8 (MD5) / Approved for entry into archive by Joana Martorini (joana.martorini@fgv.br) on 2018-08-30T14:24:27Z (GMT) No. of bitstreams: 1 PERFIL DOS INVESTIDORES DE CRIPTOMOEDAS - ANÁLISE DE BUSCAS CORRELACIONADAS AO BITCOIN.pdf: 1321448 bytes, checksum: d7e782a8be37d95ad193a89108224ae8 (MD5) / Rejected by Suzane Guimarães (suzane.guimaraes@fgv.br), reason: Prezado Davi O seu trabalho foi rejeitado pois o título que consta nele está diferente do título autorizado pela banca examinadora, sendo assim é necessário fazer a correção e submeter o arquivo novamente. Quaisquer dúvidas entrar em contato com o telefone 11 3799-7732. Atenciosamente, on 2018-08-30T14:52:44Z (GMT) / Submitted by Davi Torres de Carvalho (davi.torres@gmail.com) on 2018-08-30T18:31:17Z No. of bitstreams: 1 PERFIL DOS INVESTIDORES DE CRIPTOMOEDAS - ANÁLISE DE BUSCAS CORRELACIONADAS AO BITCOIN.pdf: 1321448 bytes, checksum: d7e782a8be37d95ad193a89108224ae8 (MD5) / Approved for entry into archive by Joana Martorini (joana.martorini@fgv.br) on 2018-08-30T18:43:52Z (GMT) No. of bitstreams: 1 PERFIL DOS INVESTIDORES DE CRIPTOMOEDAS - ANÁLISE DE BUSCAS CORRELACIONADAS AO BITCOIN.pdf: 1321448 bytes, checksum: d7e782a8be37d95ad193a89108224ae8 (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-08-31T12:02:00Z (GMT) No. of bitstreams: 1 PERFIL DOS INVESTIDORES DE CRIPTOMOEDAS - ANÁLISE DE BUSCAS CORRELACIONADAS AO BITCOIN.pdf: 1321448 bytes, checksum: d7e782a8be37d95ad193a89108224ae8 (MD5) / Made available in DSpace on 2018-08-31T12:02:00Z (GMT). No. of bitstreams: 1 PERFIL DOS INVESTIDORES DE CRIPTOMOEDAS - ANÁLISE DE BUSCAS CORRELACIONADAS AO BITCOIN.pdf: 1321448 bytes, checksum: d7e782a8be37d95ad193a89108224ae8 (MD5) Previous issue date: 2018-07-31 / O Bitcoin é a maior criptomoeda em valor de mercado (USD 140 bilhões) e volume de negócios diário (USD 5,7 bilhões) de um grande grupo de criptomoedas. Sua proposta é baseada em uma rede ponto a ponto para permitir que pagamentos online sejam enviados diretamente de uma parte para outra sem passar por uma instituição financeira. Essa proposta também se apresenta como método seguro para evitar o duplo gasto e proteger a rede de ataques cibernéticos. O Bitcoin também usa um sistema de incentivo para a manutenção da rede. Neste artigo, é possível compreender suas características técnicas, bem como analisar a evolução histórica do Bitcoin, incluindo alguns aspectos financeiros como investimento. O principal objetivo é analisar os dados do Google Trends, onde a quantidade de pesquisas correlacionadas ao Bitcoin é usada para encontrar os perfis de investidores que buscaram Bitcoin na internet. Alguns termos como 'mineração', 'mercado' e 'programação de computadores' tiveram uma associação positiva com o interesse do Bitcoin (medido pela quantidade de buscas). Por outro lado, a associação com o termo 'dólar' foi negativa. Os efeitos placebo relativos aos cantores 'Caetano Veloso' e 'Roberto Carlos' não foram significativos. / Bitcoin is the largest cryptocurrency in terms of total market value (USD 140 billion) and daily traded volume (USD 5.7 billion). Its solution is based on a peer-to-peer network to enable online payments to be sent directly from one party to another without going through financial institutions. This proposal presents itself as a safe method to avoid double expenses and to protect the network from cyber attacks. Bitcoin also uses an incentive system for the maintenance of the network. In this article, it is possible to understand its technical characteristics as well as analyze the historical evolution of the bitcoin, including some financial aspects as an investment. The main objective is to analyze the data from Google Trends where the quantity of searches correlated to Bitcoin is used to find the profiles of investors that looked up to bitcoin on the internet. Some terms as 'mining', 'market', and 'computer programming' had a positive association with Bitcoin's interest (measured by the quantity of searches). On the other hand, the association with the term 'dollar' was negative. The placebo effects relative to the singers 'Caetano Veloso' and 'Roberto Carlos' were not significant.

本篇目的---對於精實企業來說資訊預測的能力扮演舉足輕重的角色，如汽車製造商須要有可靠的資訊來完成各項重要的決策以保持企業競爭力，市場以及消費者的活動提供了新型態的資料可以透過現代科技來處理分，本篇論文希望從2008年至2016年整合的Google 搜尋趨勢資料來建構預測模型。 設計/方法論/方法---基於五階段消費者購買行為，此研究檢視整個過程中合適的Google關鍵字，並利用滯後變數模型和Google搜尋趨勢來驗證銷售和各種經濟變數之間的關係，預測的銷售會更進一步檢視其正確性。 結論與發現---用來檢視預測正確性的兩種最常見的方法指出Google搜尋趨勢可以作為有效的銷售預測依據，研究發現總體經濟變數和時間序列在預測上相較於Google搜尋趨勢在短期相對有效性小。 研究貢獻---僅有少許在汽車銷量預測上的研究將Google搜尋趨勢和合適的時間滯留列入考量，本篇研究提供消費者行為和銷售資料關係的新視角。 / Purpose – The role of forecasting in a lean enterprise is immense. It is crucial for car manufacturers to have reliable information about the future to make important decisions and stay competitive. Developing markets and consumers provide new types of data that demand modern approaches to be handled. This paper aims to create reliable forecasting models through integration of Google Trends data from 2008 to 2016. Design/methodology/approach – Building on the 5-stage-model of consumer buying behavior, the study identifies suitable Google keywords for this process. Autoregressive distributed lag models are used to examine the relationship between sales and macro-economic variables as well as Google Trends. Predicted sales are used to test for accuracy. Findings – Two most common evaluation measurements for forecasting accuracy suggest the use of Google Trends, as predictors for future sales, is outstanding. The finding concludes that macro-economic variables and seasonality are not as valuable as Google Trends in short-term, up to one year, forecasting. Value – Only little research on car sales forecasting takes Google Trends and their appropriate time lags into account. This analysis provides new insights into the linkage of consumer behavior and sales data.

L’accès à l’information financière est un déterminant important pour la prévision des rendements boursiers (mesurés par la variation de la capitalisation boursière) et la littérature propose d’utiliser les comportements de recherches numériques des individus comme indicateur de l’attention des marchés financiers. Ce mémoire explore la relation entre les volumes de recherches pour trois indices de marchés boursiers (Dow Jones, Nasdaq et Nyse) et sept entreprises cotées en bourse (Amazon, Google, Apple, Microsoft, Johnson& Jonhson, Berkshire Hathaway et JP Morgan& Chase) et les rendements boursiers (du 1er janvier 2006 au 31 décembre 2019) sans apporter de preuves catégoriques pour l’utilité des volumes de recherche. On trouve une relation statistiquement significative entre les volumes de recherches et les rendements boursiers absolus des indices de marché (Dow Jones, Nasdaq et Nyse) pendant la période de crise économique. On trouve également une relation statistiquement significative pour la prédiction des rendements bruts d’Apple sur toute la période à l’étude. Les relations significatives suggèrent un comportement économique cohérent avec la littérature : les chocs d’information deviennent progressivement moins importants alors qu’ils sont intégrés par le marché.

Submitted by Fernando Ferreira da Luz Barbosa (fernando.luz@outlook.com) on 2015-07-20T19:07:10Z No. of bitstreams: 1 Fernando Ferreira da Luz Barbosa.pdf: 927554 bytes, checksum: e29a7f5ad6e3cdd15bb6adafc98a4cb6 (MD5) / Approved for entry into archive by BRUNA BARROS (bruna.barros@fgv.br) on 2015-07-21T12:46:06Z (GMT) No. of bitstreams: 1 Fernando Ferreira da Luz Barbosa.pdf: 927554 bytes, checksum: e29a7f5ad6e3cdd15bb6adafc98a4cb6 (MD5) / Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2015-07-30T19:08:34Z (GMT) No. of bitstreams: 1 Fernando Ferreira da Luz Barbosa.pdf: 927554 bytes, checksum: e29a7f5ad6e3cdd15bb6adafc98a4cb6 (MD5) / Made available in DSpace on 2015-07-30T19:08:57Z (GMT). No. of bitstreams: 1 Fernando Ferreira da Luz Barbosa.pdf: 927554 bytes, checksum: e29a7f5ad6e3cdd15bb6adafc98a4cb6 (MD5) Previous issue date: 2015-04-27 / Disagreement between economists is a well know fact. However, it took a long time for this concept to be incorporated in economic models. In this survey, we review the consequences and insights provided by recent models. Since disagreement between market agents can be generated through different hypotheses, the main differences between them are highlighted. Finally, this work concludes with a short review of nowcasting using google trends, emphasizing advances connecting both literatures.

Stock market analysis is a hot-button topic, especially with the growth of online communities surrounding trading and investment. The goal of this paper is to examine the sentiment of r/wallstreetbets and the Google Trends score for a number of stocks – and then understanding whether the herding nature of investors on r/wallstreetbets is better at predicting the movement of the stock market than the WOC nature of Google Trends scores. Some combination of the herding and WOC values will also be used in predicting stock market fluctuations. Analysis will be done through the machine learning algorithms RFC and MLP. Through the mean and median precisions presented by the different machine learning algorithms the effectiveness of the predictor can be understood. This paper finds no real connection between either r/wallstreetbets sentiment or Google Trends data regarding predicting stock value fluctuations – with r/wallstreetbets yielding approximately 51%-52% mean precision depending on the machine learning algorithm used, and Google Trends precisions sitting at around 51%. The combination of r/wallstreetbets data and Google Trends data did not produce any significantly higher precision either, being between 51%-52%.

Submitted by Samuel Guimarães Filho (samuelgf@gmail.com) on 2017-03-07T01:39:40Z No. of bitstreams: 1 tese_samuel_revisao_1.pdf: 2212736 bytes, checksum: eef717244d02ccf54ceb936354c64525 (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2017-03-07T23:43:25Z (GMT) No. of bitstreams: 1 tese_samuel_revisao_1.pdf: 2212736 bytes, checksum: eef717244d02ccf54ceb936354c64525 (MD5) / Made available in DSpace on 2017-03-08T15:56:07Z (GMT). No. of bitstreams: 1 tese_samuel_revisao_1.pdf: 2212736 bytes, checksum: eef717244d02ccf54ceb936354c64525 (MD5) Previous issue date: 2017-02-10 / This work aims to test if the use of Google Trends as an exogenous variable improves the prediction of the monthly data for Brazilian Formal Job Creation (CAGED) compared to a model that uses only the lags themselves. For the selection of the model was used the algorithm Autometrics and for model comparison the Model Confidence Set. In addition, the model that uses Google Trends data will be compared with some market analyst’s forecasts. The results show that the model the uses the Google data as an exogenous variable is superior to the model that only uses the lag itself. However, this model was not able to overcome the market analysts. / Este trabalho tem como objetivo testar se o uso do Google Trends como variável exógena melhora a previsão do dado mensal do CAGED em relação a modelos que usam apenas as próprias defasagens. Para a seleção do modelo foi utilizado o algoritmo Autometrics e para comparação de modelos o utilzado o Model Confidence Set. Além disto, o modelo que utiliza o Google Trends foi comparado com previsões dos analistas de Mercado. Os resultados encontrados apontam que o modelo que utliza o Google Trends como variável exógena é superior ao modelo que utiliza apenas a própria defasagem. No entanto, este modelo, não foi capaz de superar os analistas de mercado.

Online marketing has resulted in a paradigm shift in the advertisement industry. Programmatic buying is an emerging business model that is very promising for online advertising. In online advertising, revenue maximization is always a key matter for publishers. The purpose of this report is to examine whether programmatic buying can be used in conjunction with other parameters such as Google Adwords or Google Trends to increase profit. This research will provide valuable information regarding how to obtain site visitors at a cheap price while maximizing profit on advertisement shown to those users. We investigate a revenue maximization model that calculates the popularity of a set of news in different countries and compares it to the CPM, Cost-Per-Mille, of the corresponding country. To calculate the popularity, the program uses an API from Google Trends and the CPM data is obtained from the company Adform. Furthermore, we originally planned to also include Google Adwords to estimate the price of traffic acquisition. However, since we found several problems with achieving reasonable estimates for our purpose this parameter has therefore been excluded from the final product. The final product can therefore be seen as a soft indicator of how popular different news are in different countries and what revenue can be expected from corresponding countries. / Digital marknadsföring har resulterat i ett paradigmskifte inom reklambranschen. Programmatisk handel är en mycket lovande affärsmodell för automatisk annonsering online och är under stark tillväxt. Inom digital marknadsföring är vinstmaximering alltid en nyckelfråga för utgivare av annonsplatser. Denna rapport ämnar undersöka huruvida programmatisk handel kan användas tillsammans med andra parametrar som Google Adwords eller Google Trends för att öka vinsten från video-reklamannonser. Den grundläggande idén är att skapa trafik till en specifik hemsida för ett så lågt pris som möjligt samtidigt som vi vill att reklamvisningarna ska ge så höga intäkter som möjligt. Rapporten utreder parametrar som videopris för programmatisk handel i olika länder, Bounce Rate, Cost Per Click och Google Trends Score. Dessa parametrar används för att skapa en sammanvägning för att indikera i vilka länder och för vilka sökord den ekonomiska vinsten potentiellt är störst.        Arbetet har resulterat i ett program som beräknar populariteten för ett antal nyheter i olika länder och jämför med CPM, Cost-Per-Mille, priset för motsvarande land. För att beräkna populariteten används ett API från Google Trends och CPM datan kommer från företaget Adform. Från början var tanken att även väga in Google Adwords för att skapa en prisbild över kostnaden att inbringa trafik. Begränsningar som behövt genomföras under arbetets gång är att exkludera Google Adwords prissättning i det färdiga programmet, då det finns svårigheter i att utröna exakta prisuppgifter från Google Adwords. Slutprodukten är därmed en indikator på vilka nyheter som är populära i olika länder och intäkterna som kan förväntas därifrån.