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Business & Information Systems Engineering
Erschienen in: Business & Information Systems Engineering 4/2015

01.08.2015 | Research Paper

Using Twitter to Predict the Stock Market

Where is the Mood Effect?

verfasst von: Dr. Michael Nofer, Prof. Dr. Oliver Hinz

Erschienen in: Business & Information Systems Engineering | Ausgabe 4/2015

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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. The paper tries to replicate these findings by measuring the mood states on Twitter. The sample consists of roughly 100 million tweets that were published in Germany between January, 2011 and November, 2013. In a first analysis, a significant relationship between aggregate Twitter mood states and the stock market is not found. However, further analyses also consider mood contagion by integrating the number of Twitter followers into the analysis. The results show that it is necessary to take into account the spread of mood states among Internet users. Based on the results in the training period, a trading strategy for the German stock market is created. The portfolio increases by up to 36 % within a six-month period after the consideration of transaction costs.
Vorheriger Artikel Internationalization of Information Systems Research and Teaching
Nächster Artikel Resource Planning in Disaster Response

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Fußnoten
1
Daniel et al. (2002) provide an extensive literature review showing that investors’ psychology exerts an influence on security prices.
 
2
https://dev.twitter.com.
 
3
See also background mood (Loewenstein et al. 2001).
 
5
More information on VDAX-NEW can be found at the website of the exchange: http://​www.​dax-indices.​com/​EN/​MediaLibrary/​Document/​VDAX_​L_​2_​4_​e.​pdf (accessed 22 June 2014).
 
6
Information on GfK index can be found on http://​www.​gfk.​com.
 
7
We also calculated SMI and WSMI values without the anger dimension and received qualitatively similar results.
 
8
Among others, Antweiler and Frank (2004) report R² value of 0.049; Avery et al. (2009) report R² values between 0.0005 and 0.0151; Das and Chen (2007) report R² value of 0.0027 and 0.0041.
 
9
There are several discount brokers who offer their clients cost-effective access to capital markets (e.g., Cortal Consors in Germany). We are aware that € 5 is at the low end of the range. However, these costs are very easy to realize for the individual investor. Nevertheless, the outperformance against the benchmark indices would persist even if we assume € 10 per trade.
 
Literatur
Ali K, Pazzani M (1992) Reducing the small disjuncts problem by learning probabilistic concept descriptions. In: Petsche T, Judd S, Hanson S (eds) Computational learning theory and natural learning systems, vol 3. MIT Press, Cambridge
Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of Internet stock message boards. J Financ 59(3):1259–1294CrossRef
Avery C, Chevalier J, Zeckhauser R (2009) The ‘CAPS’ prediction system and stock market returns. Working Paper, Harvard Kennedy School, Cambridge
Baker M, Stein J (2004) Market liquidity as a sentiment indicator. J Financ Mark 7(3):271–299CrossRef
Baker M, Wurgler J (2007) Investor sentiment in the stock market. J Econ Perspect 21(2):129–151CrossRef
Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings 4th ACM international conference web search data, pp 65–74
Bell NJ, Schoenrock CJ, O’Neal KK (2000) Self-monitoring and the propensity for risk. Eur J Personal 14(2):107–119CrossRef
Benedict R (1934) Patterns of culture. Houghton Mifflin, Boston
Biemann C, Bordag S, Heyer G, Quasthoff U, Wolff C (2004) Language independent methods for compiling monolingual lexical data. In: Proceedings CIcLING, LNCS 2945, pp 217–228
Bifet A, Frank E (2010) Sentiment knowledge discovery in Twitter streaming data. Discov Sci 6332:1–15CrossRef
Bollen J, Mao H, Zeng X (2010) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8CrossRef
Bono JE, Ilies R (2006) Charisma, positive emotions and mood contagion. Leadersh Q 17:317–334CrossRef
Boucher JD (1979) Culture and emotion. In: Marsella AJ, Tharp R, Ciborowski T (eds) Perspectives on cross-cultural psychology. Academic Press, New York, pp 159–178
Boucher J, Osgood CE (1969) The pollyanna hypothesis. J Verb Learn Verb Behav 8(1):1–8CrossRef
Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: Proceedings of the 43rd Hawaii international conference on social systems (HICSS)
Brown GW, Cliff MT (2005) Investor sentiment and asset valuation. J Bus 78(2):405–440CrossRef
Brown R, Gilman A (1960) The pronouns of power and solidarity. In: Sebeok T (ed) Style in language. MIT Press, Cambridge, pp 253–276
Brown P, Keim DB, Kleidon AW, Marsh TA (1983) Stock return seasonalities and the tax-loss selling hypothesis: analysis of the arguments and Australian evidence. J Financ Econ 12(1):105–112CrossRef
Carton S, Jouvent R, Bungener C, Widlöcher D (1992) Sensation seeking and depressive mood. Personal Individ Differ 13(7):843–849CrossRef
Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the 4th international aaai conference on weblogs and social media
Chan K, Hameed A, Tong W (2000) Profitability of momentum strategies in the international equity markets. J Financ Quant Anal 35(2):153–172CrossRef
Chang S-C, Chen S-S, Chou RK, Lin Y-H (2008) Weather and intraday patterns in stock returns and trading activity. J Bank Financ 32:1754–1756CrossRef
Chang S-C, Chen S-S, Chou RK, Lin YH (2012) Local sports sentiment and returns of locally headquartered stocks: a firm-level analysis. J Empir Financ 19(3):309–318CrossRef
Chen G, Firth M, Rui OM (2001) The dynamic relation between stock returns, trading volume, and volatility. Financ Rev 36(3):153–174CrossRef
Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354CrossRef
Chintagunta PK, Gopinath S, Venkataraman S (2010) The effects of online user reviews on movie box office performance: accounting for sequential rollout and aggregation across local markets. Mark Sci 29(5):944–957CrossRef
Chordia T, Swaminathan B (2000) Trading volume and cross-autocorrelations in stock returns. J Financ 55(2):915–935CrossRef
Chou KL, Lee T, Ho AH (2007) Does mood state change risk taking tendency in older adults? Psychol Aging 22(2):310CrossRef
Conover MD, Gonçalves B, Ratkiewicz J, Flammini A, Menczer F (2011) Predicting the political alignment of twitter users. In: Proceedings of the international conference social computing
Coval JD, Moskowitz TJ (1999) Home bias at home: local equity preference in domestic portfolios. J Financ 54(6):2045–2073CrossRef
Coviello L, Sohn Y, Kramer AD, Marlow C, Franceschetti M, Christakis NA, Fowler JH (2014) Detecting emotional contagion in massive social networks. PLoS One 9(3):e90315CrossRef
Dalbert C (1992) Subjektives Wohlbefinden junger Erwachsener: theoretische und empirische Analysen der Struktur und Stabilität. Zeitschrift für Differentielle und Diagnostische Psychologie 13:207–220
Daniel K, Hirshleifer D, Teoh SH (2002) Investor psychology in capital markets: evidence and policy implications. J Monet Econ 49:139–209CrossRef
Das SR, Chen MY (2007) Yahoo! for Amazon: sentiment extraction from small talk on the web. Manag Sci 53(9):1375–1388CrossRef
De Long JB, Shleifer A, Summers LH, Waldmann RJ (1990) Positive feedback investment strategies and destabilizing rational speculation. J Financ 45(2):379–395CrossRef
Dellarocas C, Zhang X, Awad NF (2007) Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J Interact Mark 21(4):23–45CrossRef
Dichev ID, Janes TD (2003) Lunar cycle effects in stock returns. J Priv Equity 6(4):8–29CrossRef
Edmans A, Garcia D, Norli Ø (2007) Sports sentiment and stock returns. J Financ 62(4):1967–1998CrossRef
Eisenberg AE, Baron J, Seligman ME (1998) Individual differences in risk aversion and anxiety. Psychol Bull 87:245–251
Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25(2):383–417CrossRef
Forgas JP (1995) Mood and judgment: the affect infusion model (AIM). Psychol Bull 117(1):39–66CrossRef
Fowler JH, Christakis NA (2008) Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. BMJ 337:a2338CrossRef
French KR, Poterba JM (1991) Investor diversification and international equity markets. Am Econ Rev 81(2):222–226
French KR, Schwert GW, Stambaugh RF (1987) Expected stock returns and volatility. J Financ Econ 19(1):3–29CrossRef
Gallant AR, Rossi PE, Tauchen G (1992) Stock prices and volume. Rev Financ Stud 5(2):199–242CrossRef
Gehm TL, Scherer KR (1988) Factors determining the dimensions of subjective emotional space. In: Scherer KR (ed) Facets of emotion: recent research. Lawrence Erlbaum Associates, Hillsdale, pp 99–113
Ghosh R, Lerman K (2011) A framework for quantitative analysis of cascades on networks. In: Proceedings of the 4th ACM international conference web search data mining
Gilbert E, Karahalios K (2010) Widespread worry and the stock market. In: Proceedings of the 4th international AAAI conference weblogs social media
Grahl J, Hinz O, Rothlauf F (2014) What is the value of a “Like”? – experimental evidence for the influence of popularity signals on shopping behavior. Working Paper
Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380CrossRef
Grinblatt M, Keloharju M (2001) How distance, language, and culture influence stockholdings and trades. J Financ 56(3):1053–1073CrossRef
Grinblatt M, Titman S, Wermers R (1995) Momentum investment strategies, portfolio performance, and herding: a study of mutual fund behavior. Am Econ Rev 85(5):1088–1105
Guillory J, Spiegel J, Drislane M, Weiss B, Donner W, Hancock J (2011) Upset now?: emotion contagion in distributed groups. In: Proceedings of the SIGCHI conference human factors computing system
Hancock JT, Gee K, Ciaccio K, Lin JMH (2008) I’m sad you’re sad: emotional contagion in CMC. In: Proceedings of the 2008 ACM conference computing supported cooperative work
Hatfield E, Cacioppo JT (1994) Emotional contagion. University Press, Cambridge
Heimbach I, Hinz O (2012) How smartphone apps can help predicting music sales. In: Proceedings of the 20th European conference information system (ECIS), Barcelona, Spain
Hill S, Ready-Campbell N (2011) Expert stock picker: the wisdom of (experts in) crowds. Int J Electron Commer 15(3):73–102CrossRef
Hinz O, Spann M (2008) The impact of information diffusion on bidding behavior in secret reserve price auctions. Inf Syst Res 19(3):351–368CrossRef
Hinz O, Skiera B, Barrot C, Becker J (2011) Seeding strategies for viral marketing: an empirical comparison. J Mark 75(6):55–71CrossRef
Hirshleifer D, Shumway T (2003) Good day sunshine: stock returns and the weather. J Financ 58(3):1009–1032CrossRef
Holte RC, Acker L, Porter BW (1989) Concept learning and the problem of small disjuncts. In: Proceedings of the 11th international joint conference Artificial Intelligence, Detroit, Michigan
Huberman G (2001) Familiarity breeds investment. Rev Financ Stud 14(3):659–680CrossRef
Jaffe JF, Westerfield R, Ma C (1989) A twist on the Monday effect in stock prices: evidence from the US and foreign stock markets. J Bank Financ 13(4–5):641–650
Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: implications for stock market efficiency. J Financ 48(1):65–91CrossRef
Johnson EJ, Tversky A (1983) Affect, generalization, and the perception of risk. J Pers Soc Psychol 45:20–31CrossRef
Kamstra MJ, Kramer LA, Levi MD (2000) Losing sleep at the market: the daylight saving anomaly. Am Econ Rev 90(4):1005–1011CrossRef
Kamstra MJ, Kramer LA, Levi MD (2003) Winter blues: a SAD stock market cycle. Am Econ Rev 93(1):324–343CrossRef
Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 59:59–68CrossRef
Karabulut Y (2011) Can Facebook predict stock market activity? Working Paper, University of Frankfurt, Germany
Karpoff JM (1987) The relation between price changes and trading volume: a survey. J Financ Quant Anal 22(1):109–126CrossRef
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD international conference knowledge discovery data mining
Kramer AD (2012) The spread of emotion via facebook. In: Proceedings of the SIGCHI conference human factors computer system, pp 767–770
Krämer W, Runde R (1997) Stocks and the weather: an exercise in data mining or yet another capital market anomaly? Empir Econ 22:637–641CrossRef
Kramer AD, Guillory JE, Hancock JT (2014) Experimental evidence of massive-scale emotional contagion through social networks. In: Proceedings of the National Academy of Science
Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference world wide web
Lemmon M, Portniaguina E (2006) Consumer confidence and asset prices: some empirical evidence. Rev Financ Stud 19(4):1499–1529CrossRef
Lerman K, Ghosh R, Surachawala T (2012) Social contagion: an empirical study of information spread on Digg and Twitter follower graphs. Working Paper
Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M (2007) Cascading behavior in large blog graphs. In: Proceedings of the 7th SIAM international conference data mining (SDM)
Levy T, Yagil J (2011) Air pollution and stock returns in the US. J Econ Psychol 32(3):374–383CrossRef
Lewis MP (2009) Ethnologue: languages of the world. SIL International, Dallas
Lo AW, Repin DV (2002) The psychophysiology of real-time financial risk processing. J Cogn Neurosci 14(3):323–339CrossRef
Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psycholl Bull 127(2):267–286CrossRef
McNair DM, Lorr M, Droppleman LF (1971) Profile of mood states. Educational and Industrial Testing Service, San Diego
Mitchel RLC, Philipps LH (2007) The psychological, neurochemical and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions. Neuropsychol 45:617–629CrossRef
Nann S, Krauss J, Schoder D (2013) Predictive analytics on public data – the case of stock markets. In: Proceedings of the 21st European conference information system
Neumann R, Strack F (2000) “Mood contagion”: the automatic transfer of mood between persons. J Pers Soc Psychol 79(2):211–223CrossRef
Nofsinger JR (2005) Social mood and financial economics. J Behav Financ 6(3):144–160CrossRef
Oh C, Sheng O (2011) Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In: Proceedings of the 32nd international conference information system
Pavlenko A (2008) Emotion and emotion-laden words in the bilingual lexicon. Biling Lang Cogn 11(2):147–164
Qiu L, Welch I (2004) Investor sentiment measures. National Bureau of Economic Research
Rao T, Srivastava S (2012) Using Twitter sentiments and search volumes index to predict oil, gold, forex and markets indices. Working Paper
Ruiz EJ, Hristidis V, Castillo C, Gionis A, Jaimes A (2012) Correlating financial time series with micro-blogging activity. Working Paper
Saunders EM (1993) Stock prices and Wall Street weather. Am Econ Rev 83(5):1337–1345
Schwarz N (1990) Feelings as information: Informational and motivational functions of affective states. In: Sorrentino RM, Higgins ET (eds) Handbook of motivation and cognition: foundations of social behavior, vol 2. Guilford, New York, pp 527–561
Sharpe W (1966) Mutual fund performance. J Bus 39(1):119–138
Shiller RJ (2003) From efficient markets theory to behavioral finance. J Econ Perspect 17(1):83–104CrossRef
Sprenger TO, Tumasjan A, Sandner PG, Welpe IM (2013) Tweets and trades: the information content of stock microblogs. Eur Financ Manag 20(5):926–957CrossRef
Sy T, Cote S, Saavedra R (2005) The contagious leader: impact of the leader’s mood on the mood of group members, group affective tone, and group processes. J Appl Psychol 90:295–305CrossRef
Tirunillai S, Tellis GJ (2012) Does chatter really matter? Dynamics of user-generated content and stock performance. Mark Sci 31(2):198–215CrossRef
Trombley MA (1997) Stock prices and Wall Street weather: additional evidence. Q J Bus Econ 36(3):11–21
Tseng KC (2006) Behavioral finance, bounded rationality, neuro-finance, and traditional finance. Invest Manag Financ Innov 3(4):7–18
Tversky A, Kahneman D (1991) Loss aversion in riskless choice: a reference-dependent model. Q J Econ 106(4):1039–1061CrossRef
Van Eck M, Nicolson NA, Berkhof J (1998) Effects of stressful daily events on mood states: relationship to global perceived stress. J Pers Soc Psychol 75(6):1572–1585CrossRef
Vittengl JR, Holt CS (1998) A time-series diary study of mood and social interaction. Motiv Emot 22(3):255–275CrossRef
Wann D, Dolan T, Mcgeorge K, Allison J (1994) Relationships between spectator identification and spectators’ perceptions of influence, spectators’ emotions, and competition outcome. J Sport Exerc Psychol 16(4):347–364CrossRef
Weinberg P, Gottwald W (1982) Impulsive consumer buying as a result of emotions. J Bus Res 10(1):43–57CrossRef
Whaley RE (2000) The investor fear gauge. J Portf Manag 26(3):12–17CrossRef
Wong A, Carducci BJ (1991) Sensation seeking and financial risk taking in everyday money matters. J Bus Psychol 5(4):525–530CrossRef
Worthington A (2009) An empirical note on weather effects in the Australian stock market. Econ Pap J Appl Econ Policy 28(2):148–154CrossRef
Wright WF, Bower GH (1992) Mood effects on subjective probability assessment. Organ Behav Hum Decis Process 52:276–291CrossRef
Ye S, Wu SF (2010) Measuring message propagation and social influence on twitter.com. Soc Inform 6430:216–231CrossRef
Yuen KS, Lee T (2003) Could mood state affect risk-taking decisions? J Affect Disord 75(1):11–18CrossRef
Zhang X, Fuehres H, Gloor P (2010) Predicting stock market indicators through Twitter – “I hope it is not as bad as I fear”. In: Proceedings collaborative innovations networks conference, Savannah, GA
Zhu F, Zhang X (2010) Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. J Mark 74(2):133–148CrossRef
Zuckerman M (1984) Sensation seeking: a comparative approach to a human trait. Behav Brain Sci 7(3):413–471CrossRef
Metadaten
Titel
Using Twitter to Predict the Stock Market
Where is the Mood Effect?
verfasst von
Dr. Michael Nofer
Prof. Dr. Oliver Hinz
Publikationsdatum
01.08.2015
Verlag
Springer Fachmedien Wiesbaden
Erschienen in
Business & Information Systems Engineering / Ausgabe 4/2015
Print ISSN: 2363-7005
Elektronische ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-015-0390-4

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