The Value of Social Media for Predicting Stock Returns Preconditions, Instruments and Performance Analysis /

Michael Nofer examines whether and to what extent Social Media can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which largely consist of user generated content. For instance, emotions can be extracted in order to identify the invest...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nofer, Michael (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2015.
Edición:1st ed. 2015.
Temas:
Data mining.
Macroeconomics.
Business information services.
Data Mining and Knowledge Discovery.
Macroeconomics and Monetary Economics.
IT in Business.
Acceso en línea:Texto Completo

MARC

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245 1 4 |a The Value of Social Media for Predicting Stock Returns  |h [electronic resource] :  |b Preconditions, Instruments and Performance Analysis /  |c by Michael Nofer. 
250 |a 1st ed. 2015. 
264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Vieweg,  |c 2015. 
300 |a XVII, 128 p. 10 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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505 0 |a Introduction -- Market Anomalies on Two-Sided Auction Platforms -- Are Crowds on the Internet Wiser than Experts? - The Case of a Stock Prediction Community -- Using Twitter to Predict the Stock Market: Where is the Mood Effect? -- The Economic Impact of Privacy Violations and Security Breaches - A Laboratory Experiment -- Literature. 
520 |a Michael Nofer examines whether and to what extent Social Media can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which largely consist of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to identify opinions on certain companies. Taking Social Media platforms as examples, the author examines the forecasting quality of user generated content on the Internet. Contents Market Anomalies on Two-Sided Auction Platforms Are Crowds on the Internet Wiser than Experts? - The Case of a Stock Prediction Community Using Twitter to Predict the Stock Market: Where is the Mood Effect? The Economic Impact of Privacy Violations and Security Breaches - A Laboratory Experiment Target Groups Scientists and students in the field of IT, finance and business Private investors, institutional investors About the Author Michael Nofer wrote his dissertation at the Chair of Information Systems / Electronic Markets at TU Darmstadt, Germany.  . 
650 0 |a Data mining. 
650 0 |a Macroeconomics. 
650 0 |a Business information services. 
650 1 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Macroeconomics and Monetary Economics. 
650 2 4 |a IT in Business. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783658095079 
776 0 8 |i Printed edition:  |z 9783658095093 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-658-09508-6  |z Texto Completo 
912 |a ZDB-2-SCS 
912 |a ZDB-2-SXCS 
950 |a Computer Science (SpringerNature-11645) 
950 |a Computer Science (R0) (SpringerNature-43710) 

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