Quantifying Wikipedia Usage Patterns Before Stock Market Moves

Moat, Helen Susannah; Curme, Chester; Avakian, Adam; Kenett, Dror Y.; Stanley, H. Eugene; Preis, Tobias

doi:10.1038/srep01801

Cited by 263 publications

(198 citation statements)

References 37 publications

(36 reference statements)

Supporting

Mentioning

184

Contrasting

Unclassified

Order By: Relevance

“…Social applications include evaluating toponym importance in order to make type size decisions for maps [21], measuring the flow of concepts across the world [22], and estimating the popularity of politicians and political parties [23]. Finally, economic applications include attempts to forecast movie ticket sales [24] and stock prices [25]. The latter two applications are of particular interest because they include a forecasting component, as the present work does.…”

Section: Author Summarymentioning

confidence: 95%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

View full text Add to dashboard Cite

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

show abstract

Section: Author Summarymentioning

confidence: 95%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

View full text Add to dashboard Cite

show abstract

“…More recently and linked to technological development, the Internet and social networks, the relation between information about investor mood derived from the Internet and the evolution of markets has been studied. Gerow and Keane (2011) base their study on the frequency of use of different words on social networks, whilst Moat et al (2013) studied the frequency of word use in Wikipedia. Gómez-Martínez (2013) used Internet search statistics as an indicator of the status of investor confidence or risk aversion, information with which they prepared a Risk Aversion Index (RAI), derived from the volume of Google searches on certain financial or economic terms that correlate negatively with market developments.…”

Section: Societal Beliefsmentioning

confidence: 99%

Twitter investment alerts for Ibex35 securities

Martínez

García²,

Vázquez³

2017

REVISTA PERSPECT. EMPRESARIAL

View full text Add to dashboard Cite

This paper sought to find the statistical relationship between Twitter messages and the evolution of the Spanish stocks mentioned in Tweets. We analyzed information from Twitter to evaluate stock sentiment using Stockbuzz -the first tool to gather information from the social network in Spanish. Stockbuzz has been developed by Spanish bank BBVA and shows the investors' mood for the IBEX 35 Spanish index. We use the application on investment decision making and calculate the average return depending on positive or negative Investors' Mood. We conclude that twitter is a valid tool to generate investment alerts.keywORDS Twitter, social networks, investors' mood, investment alerts, stock markets. HIStORIa Del aRtíCUlO¿CómO CItaR?: Alertas de Inversión en Twitter para Ibex35ReSUmen El presente artículo busca encontrar una relación estadística entre los mensajes de Twitter y la evolución de las bolsas de valores españolas que se menciona en los trinos. Analizamos información de Twitter para evaluar la percepción de la bolsa mediante Stockbuzz -la primera herramienta que recolecta información de la red social en español. Stockbuzz fue desarrollada por el banco español BBVA y muestra el ánimo de los inversores en la bolsa de valores española IBEX 35. Utilizamos la aplicación en las decisiones de inversión hacienda y calculando el retorno promedio dependiendo del ánimo positivo o negativo de los inversores. Concluimos que Twitter es una herramienta válida para generar alertas de inversión.palabRaS ClaVe Twitter, redes sociales, ánimo de los inversores, alertas de inversión, bolsas de valores. Alerta de Investimento em Twitter sobre Segurança no Ibex35ReSUmO Este artigo procurou encontrar a relação estatística entre as mensagens do Twitter e a evolução dos estoques espanhóis mencionados nos Tweets. Analisamos informações do Twitter para avaliar o sentiment do estoque usando Stockbuzz -a primeira ferramenta para coletar informações da rede social em espanhol. Stockbuzz foi desenvolvida pelo banco espanhol BBVA e mostra o clima dos investidores para o índice espanhol IBEX 35. Utilizamos o aplicativo sobre a tomada de decisões de investimento e calculamos o retorno médio dependendo da atitude positiva ou negativa dos investidores. Concluí-mos que Twitter é uma ferramenta válida para gerar alertas de investimento.palaVRaS CHaVe Twitter, redes sociais, atitude dos investidores, alertas de investimento, bolsas de valores.

show abstract

“…A textual analysis approach to Twitter data can be found in other works [15,[40][41][42][43] where the authors find clear relations between mood indicators and Dow Jones Industrial Average. Some other authors have used news, Wikipedia data or search trends to predict market movements [26,[44][45][46].…”

Section: Sentiment Analysismentioning

confidence: 99%

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

et al. 2015

View full text Add to dashboard Cite

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

show abstract

Quantifying Wikipedia Usage Patterns Before Stock Market Moves

Cited by 263 publications

References 37 publications

Global Disease Monitoring and Forecasting with Wikipedia

Global Disease Monitoring and Forecasting with Wikipedia

Twitter investment alerts for Ibex35 securities

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

Contact Info

Product

Resources

About