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

Autor Michael Nofer
en Limba Engleză Paperback – 5 mai 2015
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.
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Specificații

ISBN-13: 9783658095079
ISBN-10: 3658095075
Pagini: 148
Ilustrații: XVII, 128 p. 10 illus.
Dimensiuni: 148 x 210 x 12 mm
Greutate: 0.2 kg
Ediția:2015
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Vieweg
Locul publicării:Wiesbaden, Germany

Public țintă

Research

Cuprins

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.

Notă biografică

Michael Nofer wrote his dissertation at the Chair of Information Systems | Electronic Markets at TU Darmstadt, Germany.

Textul de pe ultima copertă

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.  

Caracteristici

Publication in the field of technical sciences Includes supplementary material: sn.pub/extras