Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2015 | Book

Introduction Read chapter Read first chapter

The Value of Social Media for Predicting Stock Returns

Preconditions, Instruments and Performance Analysis

Author: Michael Nofer

Publisher: Springer Fachmedien Wiesbaden

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

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.

Table of Contents

Frontmatter
1. Introduction
Abstract
Social Media applications have gained increasing importance in recent years, thanks largely to broadband connections allowing people faster, easier access to the Internet. According to an eMarketer report, 1.97 billion people worldwide are using Social Media applications in 2014, and this number is expected to grow to 2.55 billion by the year 2017 (eMarketer 2013). The most prominent examples of Social Media websites include social networks (e.g., Facebook, Google+), blogs and microblogs (e.g., Twitter), content communities (e.g., Flickr, YouTube), and virtual worlds (e.g., Second Life, World of Warcraft). This dissertation builds upon the work of Kaplan and Haenlein (2010), who define Social Media as a “group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content.”
Michael Nofer
2. Market Anomalies on Two-Sided Auction Platforms
Abstract
A market anomaly (or market inefficiency) is a price distortion typically on a financial market that seems to contradict the efficient-market hypothesis. Such anomalies could be calendar, technical or fundamental related and have been shown empirically in a number of settings for financial markets. This paper extends this stream of research to two-sided auction platforms in Electronic Commerce and empirically analyzes whether calendar anomalies are persistent on such markets. Our empirical study analyzes 78,068 transactions completed between buyers and sellers on a German auction platform and covers the period between April 2005 and May 2009. We observe a persistent turn-of-the-month effect and a day-of-the-week effect that would allow buyers to realize small additional surpluses (0.3 percent price discount). Prices are also persistently lower in the highly competitive Christmas trade period while sellers benefit from higher prices at the beginning of every year. Overall our results support the common notion that two-sided auction platforms are rather efficient markets on which we however can observe some marginal market inefficiencies.
Michael Nofer
3. Are Crowds on the Internet Wiser than Experts? – The Case of a Stock Prediction Community
Abstract
According to the “Wisdom of Crowds” phenomenon, a large crowd can perform better than smaller groups or few individuals. This article investigates the performance of share recommendations, which have been published by members of a stock prediction community on the Internet. Participants of these online communities publish buy and sell recommendations for shares and try to predict the stock market development. We collected unique field data on 10,146 recommendations that were made between May 2007 and August 2011 on one of the largest European stock prediction communities. Our results reveal that on an annual basis investments based on the recommendations of Internet users achieve a return that is on average 0.59 percentage points higher than investments of professional analysts from banks, brokers and research companies. This means that on average investors are better off by trusting the crowd rather than analysts. We furthermore investigate how the postulated theoretical conditions of diversity and independence influence the performance of a large crowd on the Internet. While independent decisions can substantially improve the performance of the crowd, there is no evidence for the power of diversity in our data.
Michael Nofer
4. Using Twitter to Predict the Stock Market: Where is the Mood Effect?
Abstract
Behavioral finance researchers have shown that the stock market can be driven by emotions of market participants. In a number of recent studies mood levels have been extracted from Social Media applications in order to predict stock returns. We try to replicate these findings by measuring the mood states on Twitter. Our sample consists of roughly 100 million tweets that have been published in Germany between January, 2011 and November, 2013. In our first analysis we do not find a significant relationship between aggregate Twitter mood states and the stock market. However, in further analyses we also consider mood contagion by integrating the number of Twitter followers into the analysis. Our results show that it is necessary to consider the spread of mood states among Internet users. Based on our results in the training period, we created a trading strategy for the German stock market. Our portfolio increases by up to 36 percent within a six-month period after the consideration of transaction costs.
Michael Nofer
5. The Economic Impact of Privacy Violations and Security Breaches – A Laboratory Experiment
Abstract
Privacy and security incidents represent a serious threat for a company’s business success. While previous research in this area mainly investigated secondorder effects (e.g., capital market reactions to privacy or security incidents), this study focuses on first-order effects, that is, the direct consumer reaction. In a laboratory experiment, the authors distinguish between the impact of privacy violations and security breaches on the subjects’ trust and behavior. They provide evidence for the so-called “privacy paradox” which describes that people’s intentions, with regard to privacy, differ from their actual behavior. While privacy is of prime importance for building trust, the actual behavior is affected less and customers value security higher when it comes to actual decision making. According to the results, consumers’ privacy related intention-behavior gap persists after the privacy breach occurred.
Michael Nofer
Backmatter
Metadata
Title
The Value of Social Media for Predicting Stock Returns
Author
Michael Nofer
Copyright Year
2015
Electronic ISBN
978-3-658-09508-6
Print ISBN
978-3-658-09507-9
DOI
https://doi.org/10.1007/978-3-658-09508-6

Premium Partner

    Image Credits