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

140 Pages·2015·47.746 MB·English

by  Michael Nofer (auth.)

The Value of Social Media for Predicting
Stock Returns
Michael Nofer
The Value of Social
Media for Predicting
Stock Returns
Preconditions, Instruments
and Performance Analysis
With a Foreword by Prof. Dr. Oliver Hinz
Michael Nofer
Darmstadt, Germany
Dissertation, TU Darmstadt, Germany, 2014
ISBN 978-3-658-09507-9 ISBN 978-3-658-09508-6 (eBook)
DOI 10.1007/978-3-658-09508-6
Library of Congress Control Number: 2015935424
Springer Vieweg
© Springer Fachmedien Wiesbaden 2015
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or
part of the material is concerned, speci(cid:191) cally the rights of translation, reprinting, reuse of illus-
trations, recitation, broadcasting, reproduction on micro(cid:191) lms or in any other physical way, and
transmission or information storage and retrieval, electronic adaptation, computer software, or by
similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a speci(cid:191) c statement, that such names are
exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained
herein or for any errors or omissions that may have been made.
Printed on acid-free paper
Springer Vieweg is a brand of Springer Fachmedien Wiesbaden
Springer Fachmedien Wiesbaden is part of Springer Science+Business Media
(www.springer.com)
Foreword
Firms like Facebook or Pinterest !bat have access to a large number of users
impact our daily life in many aspects. Facebook for example has over one billion
users registered rof their online service allowing their user base to manage social
contacts, ot post and share content and to communicate their taste by clicking on
"Like" buttons !bat are nowadays available on many websites. Many agree that
their market capitalization cannot be justified by their tangible assets like ma
cbines or inventories, but bases mainly on their access ot unique consumer data
that makes these firms special. Users of these networks leave digital ,stnirptoof
reveal their preferences or make social recommendations that seem valuable rof
.ssenisub-e
nO the one hand a lot of these data is freely accessible and on the other hand
these data can be used ot forecast future developments dna can thereby potential
ly be monetized. This potential makes this dleif os attractive these .syad Big Data
is assumed to be the new oil and we are currently in the middle of a gold rush.
Researchers and practitioners alike believe !bat Internet data are also valua
ble rof beating eht stock market. Michael Nofer's dissertation tries ot assess the
value of social media rof predicting stock retorns and examines the precondi
tions, instruments and finally assesses the potential performance gains. It is one
of eht tsrif dissertations !bat examines this phenomenon with such a great care,
with such a huge data base and with a different set of methods.
A common theme of this book is the thoughtful approach in all essays in
identifying the important and timely research questions and the depth at which
the authors examines the issue at hand. This si not an easy undertaking dna I laud
the nice empirical work !bat has been carried .tuo
I highly recommend this book ot both, practitioners and researchers who are
interested in predicting eht development of the stock market. The book sah the
potential ot be one of the milestones in this domain and in my opinion the read
sre can highly benefit from Michael Nofer's work. I wish the author all the best
with this publication and I believe that the book will be a huge !sseccus
Tecbnische Universitiit Darmstadt Prof. .rD Oliver Hinz
Acknowledgements
This dissertation was accepted by TU Darmstadt in November 2014. I would like
to take this opportunity to thank those people who supported me during my time
at eht Chair of Information Systems I Electronic Markets.
I would particularly like to thank my supervisor, Prof. Dr. Oliver Hinz, for
giving me the chance to conduct research under outstaoding conditions in such
an interesting field of stody. When he founded the Chair in 2011, I was fortonate
to be among the first PhD stodents. Despite his numerous responsibilities, Prof.
Hinz fully dedicates his time to his stodents whenever needed. Every day I en
joyed working in sucb a pleasant atmosphere, gaining new insights again dna
again. It was also a great pleasure to see the team grow over the years. I addi
tionally thank Prof. Dr. Alexander Ben1ian (Chair of Information Systems and
Electronic Services, TU Darmstadt) for co-supervising my dissertation.
One article of this dissertation was written alongside Prof. Dr. Jan Munter
mano (University of GOttingen) and Dr. Heiko RoBnagel (Frauohofer-Gesell
schaft). I am very grateful for the opportunity to work in conjunction with such
notable researchers.
I would also like to thank my colleagues at TU Darmstadt. We have sup
ported each other not only with technical koowledge, but with our interpersonal
support. The advice I received during the scientific colloqniums was especially
valuable to me. I particularly thank Markos Franz for comforting me after the
defeats snfIered by my favorite football club Karlsruher SC. I am also grateful to
have met talented stodents who supported me in terms of data collection and
programming tasks.
Finally, I want to thank my friends and especially my wonderful family for
accompanying me on this journey. This dissertation would not have been possi
ble without the encouragements of those people who unconditinna1ly supported
me over the years.
Darmstadt Michael Nofer
Table of Contents
Foreword .............................................................................................................. V
stnemegdelwonkcA ............................................................................................ IIV
tsiL of serugiF .................................................................................................. IIIX
tsiL of selbaT ..................................................................................................... XV
tsiL of snoitaiverbbA ...................................................................................... IIVX
1 noitcudortnI ............................................................................. 1
1.1 sysponyS ............................................................................................... 1
2.1 hcraeseR stxetnoC ................................................................................. 3
1.2.1 tekraM ycneiciffE ...................................................................... 3
2.2.1 Wisdom of sdworC .................................................................... 4
3.2.1 dooM sisylanA ........................................................................... 5
4.2.1 ycavirP dna ytiruceS .................................................................. 5
3.1 erutcurtS of eht Dissertation .................................................................. 6
2 tekraM seilamonA no dediS-owT noitcuA Platforms ••.••.•• 11
Abstract ....................................................................................................... 11
1.2 noitcudortnI ......................................................................................... II
2.2 suoiverP hcraeseR ............................................................................... 31
1.2.2 dediS-owT stekraM .................................................................. 31
2.2.2 tneiciffE stekraM dna tekraM seilamonA ................................ 41
3.2 laciripmE ydutS .................................................................................. 61
1.3.2 Platform noitpircseD ................................................................ 71
2.3.2 sevitpircseD ............................................................................. 71
3.3.2 sisylanA ................................................................................... 02
4.2 noissucsiD ........................................................................................... 32
1.4.2 snoitatimiL dna erutuF hcraeseR ............................................. 52
2.4.2 noisulcnoC ............................................................................... 52
X elbaT of Contents
3 Are Crowds on the Internet Wiser than Experts?
- The Case of a Stock Prediction Community .................... 27
Abstract ....................................................................................................... 72
1.3 Introduction ......................................................................................... 72
3.2 Previous Research ............................................................................... 30
1.2.3 Domain Background ................................................................ 30
2.2.3 Theoretical dnuorgkcaB ........................................................... 30
3.3 puteS of Empirical Study .................................................................... 63
1.3.3 Data Collection ........................................................................ 63
2.3.3 Data Analysis ........................................................................... 24
3.4 stluseR of Empirical Study ................................................................. 44
1.4.3 Comparison of Forecast Accuracy between Professional
Analysts and the Crowd ........................................................... 44
2.4.3 Diversity and Independence .................................................... 84
5.3 Discussion ........................................................................................... 15
1.5.3 snoitacilpmI ............................................................................. 15
2.5.3 Sunnnary and .kooltUO ............................................................ 25
3.6 Appendix ............................................................................................. 55
4 Using Twitter to Predict the Stock Market: Where si
the Mood Effect? .................................................................... 36
Abstract ....................................................................................................... 36
1.4 Introduction ......................................................................................... 36
2.4 Previous Research ............................................................................... 56
1.2.4 laroivaheB Finance .................................................................. 56
2.2.4 ecneulfnI of Mood on erahS snruteR ....................................... 76
3.2.4 evitciderP eulaV of laicoS Media ............................................ 96
3.4 Empirical Study .................................................................................. 17
1.3.4 Data Collection and Method .................................................... 17
4.4 stluseR ................................................................................................. 67
1.4.4 evitpircseD scitsitatS ............................................................... 67
2.4.4 pihsnoitaleR between laicoS Mood and eht kcotS Market ...... 77
3.4.4 pihsnoitaleR between Follower-Weighted laicoS Mood
and the Stock Market ............................................................... 77
5.4 Trading Strategy ................................................................................. 80
6.4 Conclusion .......................................................................................... 28
7.4 Appendix ............................................................................................. 58
elbaT of Contents XI
5 The Economic Impact of Privacy Violations
and Security Breaches - A Laboratory Experiment .......... 98
Abstract ....................................................................................................... 89
1.5 Introduction ......................................................................................... 89
5.2 Related Work ...................................................................................... 19
5.3 Theoretical Background ...................................................................... 29
1.3.5 Privacy ..................................................................................... 29
5.3.2 Security .................................................................................... 39
5.3.3 Trust ......................................................................................... 59
5.4 Research Model .................................................................................. 69
5.5 Laboratory Experiment ....................................................................... 99
1.5.5 Method ..................................................................................... 99
5.5.2 Results ................................................................................... 201
5.5.3 Robustness Check .................................................................. 401
5.6 Discussion ......................................................................................... 501
1.6.5 yrammuS ................................................................................ 501
5.6.2 Limitations and Future Research ........................................... 601
7.5 xidneppA ........................................................................................... 801
6 Literature ............................................................................. 901
List of Figures
Figure :1-2 ycneuqerF of snoitcasnarT per ,raeY htnoM dna yadkeeW ...... 91
Figure :2-2 ycneuqerF of snoitcasnarT per yaD of eht htnoM ..................... 02
Figure :1-3 tohsneercS of na Analyst's noitadnemmoceR ........................... 73
Figure :2-3 tnempoleveD of dradnatS noitaiveD of egA revo emiT ............ 55
Figure :3-3 tnempoleveD of eht egarevA egA revo emiT ........................... 65
Figure :4-3 tnempoleveD of redneG ytisreviD revo emiT ........................... 65
Figure :1-4 laciteroehT krowemarF ............................................................. 96
Figure :2-4 IMS dna IMSW seulaV revo emiT ........................................... 58
Figure :3-4 L&P Chart of gnidarT seigetartS neewteb enuJ ,1 3102 dna
rebmevoN ,03 3102 ................................................................... 68
Figure :1-5 lautpecnoC krowemarF ............................................................. 79