The value of social media for predicting stock returns preconditions, instruments and performance analysis

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¿ cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on micro¿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¿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) Forew d Firms impact user conta s, "Like" their cbines tha rev al s enisub-e like Tecbnis h special the can Univers t Darmst d Inter poten ial ap ro ch in question and si not tuo an easy stock work wil al es ay in depth at whic dna and the domain bo k the undertaki g practi oners this such methods thoug f l of the tries with of set hand syad Big Dat a gold rush are also valu ot a s e the the precondiI t is o n e gains a great care, examin s phenom a dif er nt is t h e res a ch This car ied both, other poten ial- dat and perfo mance reto ns the of dis erta on stock in Nofer's mid le !bat this on rof ther by thes the Nofer's the valu b e can at r c ive beli v ,stnirp o f se m and in madat dig tal dna os like consumer ac es ibl dleif on tha as et leav cur ently social cli k ng agre uniq e tha this are man ge by tangible ot networks i s fre ly dev lopm nts used to bil on Many their user one tas e recom ndati s hand be base ac es thes of over websit their of social a lot o f thes dat ot f o r e c a s t fut re monetiz d This poten ial makes t o b e the n e w oil a n d we Res arch and practi oners alike beating eht stock market Michael o f social media rof predict ng instrume and final y as e tsrif dis erta on !bat examin s such a huge dat base and with o f this bo k A com n them the importan and timely examin s t h e is ue a t hand empir cal work !bat has be n I hig ly recom end this bo k ot i n predict ng eht dev lopm nt o f the mileston ot b e o n e hig ly ben fit from Michael this publicat on and I beli v tha dat one make many by on User or user their justif ed mainly number has com uni ate on be a large their to av il b e can ot firms to exampl al owing and base pref nc s ac es for servic nowad ys but thes have Facebo k conte capit l z on their can onli e are inve tor s, makes authors nice !bat aspect share !bat or eht Pinter s many their and market rof in rof post but ons ident fy g inter s d poten ial sre with life ot nO the the or daily regist red thes ly be is a s u m e d ble value tions, of with Facebo k our I laud res a ch who market The and in m y I wish the b e a huge Prof bo k are the sah opin the author al readthe best !s ec u rD Oliver Hinz Acknowledg ments This dis erta ion was t o take this op rtunity a t eht Chair o f Information I would particular y giv ng me the chance a n inter sting field to be among the first Hinz ful y de icates joyed working in ag in It was also thank Prof tional y Electronic Services, of One article mano (University very schaft) I am notable res arche s I would also ported each other sup ort The advice valu ble to me I by def ats snfIer d have met talented program ing task Final y, I want ac ompanyi g me ble without the encouragem nts me over t h e years Darmstadt ac epted by to TU thank Darmstadt those I Systems like conduct to of to he time to like to the writ en thank my those Jan Munter- We our my such have sup- especial y me I am dat of would with interp sonal was comf rting SC terms who and conju ction with for esp cial y people Dr Darmstad Karls uher and dis erta ion This of in TU in I ad iSystems col qniums me friends journey work Franz sup orted dna years (Frauoh fer-Ges l - scient fic club Prof I en- ag in the dis erta ion Prof but Markos fort nate ins ghts RoBnagel at for such day Information ko wledg , the I was over alongside Heiko Hinz, Every my col eagues who this grow of to fo tbal time in 201 , responsib lit es, new team (Chair during Oliver ne d co-supervis ng Dr thank to on in gain g the technical favorite Chair when ver se my I rec ived Dr numerous op rtunity with like my condit ons the Ben1ia for was particularly Prof his and I would during outs aoding founde atmospher , to thank only my stodents supervisor, under Despite stodents his 2014 me Markets my When sucb a pleas nt a great pleasure Dr Alexander TU Darmstad ) this dis erta ion o f GOt inge ) grateful for not Electronic thank stodents November sup orted who res arch stody PhD his in people wonderful not after also the grateful to col ection and family have be n unco dit n a1ly for pos i- sup orted Michael Nofer Table of Forew d stnem gd lwo kcA Conte s V o f serugiF o f selbaT o f snoita verb A tsiL tsiL tsiL IX XV noitcud r I 2.1 3.1 IV sy pon S hcraes R 1.2 3.2 4.2 erutc S tekraM Abstrac 1.2 3.2 4.2 I VX stxe noC tekraM ycnei f E o f sdworC Wisdom M ycavirP si ylanA dna o f eht noitcud r I suoiverP 1.2 lacir pmE 1.3 2.3 sevitp rc D nois uc D 1.4 2.4 ytiruceS Dis erta on seilamonA no de iS-owT noitcuA Platforms •.• 1 I hcraes R stekraM de iS-owT tneic f E 31 stekraM ydutS dna tekraM seilamonA 31 41 61 71 71 Platform noitp rcseD si ylanA snoita m L noisulc C dna erut F hcraes R 02 32 52 52 X elbaT Are Crowds - The Abstrac 1.3 3.2 3.4 Case Using the Mo d 3.4 5.4 6.4 7.4 of Introduc i Previous 1.2 3 puteS 1.3 2.3 stlu eR 1.4 3.6 Abstrac 1.4 2.4 the Inter Wiser a Stock than Predict on Conte s Experts? Com unity 27 2.4 Discu on 1.5 2.5 Ap endix 5.3 on of 72 72 Res arch Domain Theor tical 30 Background 30 dnuorgkcaB o f Empir cal Dat Col ecti n Dat Analysi o f Empircal Comparis n Analyst Divers ty snoitac lpmI Sun ary Study 30 63 63 24 Study o f Forecast the C r o w d Indep c and and Ac ura y betw n Profes i nal 84 15 15 25 and Twit er to ko ltUO Predict Ef ect? the Stock Market: Wher si 36 Introduc i Previous 1.2 4 3.2 Empircal 1.3 stlu eR 1.4 2.4 3.4 Trading Conclusi Ap endix 36 Res arch laroiv heB ecn ulf I evitc d rP Fina ce o f Mo d eulaV Study Dat 36 56 of Col ecti n evitp rcs D pihsno taleR pihsno taleR and 56 on and scit a S erahS laicoS the Stock Method 76 laicoS Fol wer-W ight d Market Media betw n betw n Stra egy snruteR Mo d and eht kcotS laicoS Market 96 17 17 67 67 Mo d 80 28 58 elbaT of The and Abstrac 1.5 5.2 5.3 5.4 5.6 7.5 XI Conte s Security Impact Breach s of Privacy - A Violat ns Labor t y Experim nt 98 Introduction Relat d Theor tical 1.3 5.3 5.3 Res arch Labor t y 1.5 5 Discu ion 1.6 5.6 xidnep A Litera u Econ mi 89 Work 89 Background Privacy Security Trust 19 29 29 39 59 69 9 Model Experim nt Method Result Robustne Check yram uS Lim ta ons 201 401 501 501 601 801 and Fut re Res arch 901 List of Figures Figure :1-2 ycneuq rF o f snoitcasn rT per Figure :2- ycneuq rF o f snoitca rT per Figure :1-3 tohsne rcS o f na Analyst' Figure :2-3 tnempol v D o f dra n tS Figure :3- tnempol v D o f eht o f redn G Figure :4-3 tnempol v D Figure :1-4 lacitero hT Figure :2-4 IMS Figure :3-4 L&P rebm voN Figure :1-5 lautpecnoC yadke W htnoM o f egA egA revo ytisrev D revo emiT revo emiT 73 emiT 65 65 96 revo seig tar S 3102 91 02 egar vA o f gnidarT krowema F o f eht noita veD seulaV ,03 yaD dna IMSW Chart htnoM noitad em oc R krowema F dna ,raeY emiT ne wt b 58 enuJ ,1 3102 dna 68 79 Utem ure 14 Culna MJ, Armstrong Fairnes , Scien PK and (19 ) Information Impersonal Privacy An Trust: Concer s, Empir cal Procedural Investiga on Organization 10( ):104- Culna MJ, cy: C Wil ams Les ons (20 9) from How the Choice Ethics Can Point Enha ce Dat TJX and Organiz t onal Priva- Breach s MIS Quarte ly (4):673- 87 D'Onfro J (2013) Twit er 2014-06 , user -a e fake-20 Dalbert C (19 2) dnu empir sche N, to Daniel K, kets: 49:13 -209 K, Daniel De M, on Bondt the JB, Deaux ,A 45(2):379-3 Far is K, DeBondt WFM and of es Amazon: C (20 3) Onli e LB, Mutoal on (19 0) Own Fe dback of Fi- Perfo mance: of of Mechanism of Journal The Res arch Forecast 9(3): -371 Forecasting of Word Smal Journal Posit ve One's Int i ve of Dig tization Fe dback for Journal Trends: from Speculation Cause Journal Working Over act? 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