Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
What Do People Expect from a Financial Awareness Platform? Insights from an Online Survey Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Predicting Euro Stock Markets

verfasst von : Ioannis Praggidis, Vasilios Plakandaras, Eirini Karapistoli

Erschienen in: Collective Online Platforms for Financial and Environmental Awareness

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Forecasting exercises are mostly concentrated on the point estimation of future realizations of stock returns. In this paper we try to forecast the direction of the Eurostoxx 50. Under a Dynamic Probit framework we test whether subsequent sign reversals can be accurately forecasted. To this end, we make use of industrial portfolios constructed in the spirit of Fama and French. Furthermore, we augment the forecasting models with macroeconomic variables. Finally, we construct a new sentiment index based on the news for Oil prices. Results show, that the out-of-sample forecasting accuracy approximates 80%.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Reputation-Based Incentive Mechanism for a Crowdsourcing Platform for Financial Awareness
Nächstes Kapitel On the Quality of Annotations with Controlled Vocabularies
Fußnoten
4
The selected countries and the number of stocks are upon availability of the data.
 
Literatur
1.
Alistair, K., Diana, I.: Sentiment classification of movie and product reviews using contextual valence shifters. In: Proceedings of FINEXIN (2005)
2.
Aue, A., Gamon, M.: Customizing sentiment classifiers to new domains: a case study. In: Proceedings of Recent Advances in Natural Language Processing (RANLP), pp. 1–2 (2005)
3.
Bartlett, J., Albright, R.: Coming to a theater near you! Sentiment classification techniques using SAS text miner. In: SAS Global Forum 2008 (2008)
4.
Boiy, E., Hens, P., Deschacht, K., Moens, M.F.: Automatic sentiment analysis in on-line text. In: ELPUB, pp. 349–360 (2007)
5.
Cadilhac, A., Benamara, F., Aussenac-Gilles, N.: Ontolexical resources for feature based opinion mining: a case-study. In: 23rd International Conference on Computational Linguistics, p. 77. Citeseer (2010)
6.
Chen, S.S.: Predicting the bear stock market: macroeconomic variables as leading indicators. J. Bank. Finance 33(2), 211–223 (2009)CrossRef
7.
Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: Gate: a framework and graphical development environment for robust NLP tools and applications. In: Proceedings of 40th Anniversary Meeting of the Association for Computational Linguistics (ACL) (2002)
8.
Driesprong, G., Jacobsen, B., Maat, B.: Striking oil: another puzzle? J. Financ. Econ. 89(2), 307–327 (2008)CrossRef
9.
Fama, E.: The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)CrossRef
10.
Fama, E., French, K.: Industry costs of equity. J. Financ. Econ. 43(2), 153–193 (1997)CrossRef
11.
Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013)CrossRef
12.
Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the first ACM Conference on Online Social Networks, pp. 27–38. ACM (2013)
13.
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics (1997)
14.
Hong, H., Torous, W., Valkanov, R.: Do industries lead stock markets? J. Financ. Econ. 83(2), 367–396 (2007)CrossRef
15.
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
16.
Kauppi, H., Saikkonen, P.: Predicting us recessions with dynamic binary response models. Rev. Econ. Stat. 90(4), 777–791 (2008)CrossRef
17.
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRef
18.
Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41(16), 7653–7670 (2014)CrossRef
19.
Nyberg, H.: Forecasting the direction of the us stock market with dynamic binary probit models. Int. J. Forecast. 27(2), 561–578 (2011)CrossRef
20.
Nyberg, H.: Predicting bear and bull stock markets with dynamic binary time series models. J. Bank. Finance 37(9), 3351–3363 (2013)CrossRef
21.
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
22.
Prabowo, R., Thelwall, M.: Sentiment analysis: a combined approach. J. Inf. 3(2), 143–157 (2009)CrossRef
23.
Salvetti, F., Reichenbach, C., Lewis, S.: Opinion polarity identification of movie reviews. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theory and Applications, pp. 303–316. Springer, Netherlands (2006)CrossRef
24.
Schmeling, M.: Investor sentiment and stock returns: some international evidence. J. Empirical Finance 16(3), 394–408 (2009)CrossRef
25.
Stone, P., Dunphy, D.C., Smith, M.S., Ogilvie, D.: The general inquirer: a computer approach to content analysis. J. Reg. Sci. 8(1), 113–116 (1968)CrossRef
26.
Taboada, M., Anthony, C., Voll, K.: Methods for creating semantic orientation dictionaries. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), pp. 427–432 (2006)
27.
Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Finance 62(3), 1139–1168 (2007)CrossRef
28.
Tong Richard, M.: An operational system for detecting and tracking opinions in on-line discussions. In: Working Notes of the ACM SIGIR Workshop on Operational Text Classification, New York, pp. 1–6 (2001)
29.
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)
30.
Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. (TOIS) 21(4), 315–346 (2003)CrossRef
31.
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)
32.
Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138–1152 (2011)CrossRef
33.
Zhou, L., Chaovalit, P.: Ontology-supported polarity mining. J. Am. Soc. Inf. Sci. Technol. 59(1), 98–110 (2008)CrossRef
Metadaten
Titel
Predicting Euro Stock Markets
verfasst von
Ioannis Praggidis
Vasilios Plakandaras
Eirini Karapistoli
Copyright-Jahr
2016
Buch
Collective Online Platforms for Financial and Environmental Awareness

Print ISBN: 978-3-319-50236-6

Electronic ISBN: 978-3-319-50237-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50237-3_3

Premium Partner

    Bildnachweise