The Value of Social Media for Predicting Stock Returns [electronic resource] : Preconditions, Instruments and Performance Analysis /
Nofer, Michael.author.
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.  .
Description based upon print version of record.
Includes bibliographical references.
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.
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.  .
Data mining.
Macroeconomics.
Information technology.
Business—Data processing.
Data Mining and Knowledge Discovery.
Macroeconomics/Monetary Economics//Financial Economics.
IT in Business.