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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Analysis of Genetic Algorithm for Effective Power Delivery and with Best Upsurge Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Indian Stock Market Analysis Using CHAID Regression Tree

verfasst von : Udit Aggarwal, Sai Sabitha, Tanupriya Choudhury, Abhay Bansal

Erschienen in: Data Engineering and Intelligent Computing

Verlag: Springer Singapore

Einloggen

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

search-config
loading …

Abstract

Data mining is the technique which utilized to extract concealed “analytical” and “predictive” facts and figures from a big set of datasets and databases. It is being applied in various research areas by data scientists and analysts such as mathematics, marketing, genetics, cybernetics, etc. In this paper, A Chi Squared Automatic Interaction Detection (CHAID) regression tree model has been proposed to infer the volatility of the Stock Exchange Sensitive Index (SENSEX) data while explicitly accounting for dependencies between multiple derived attributes. Using real stock market data, dynamic time varying graphs are constructed to further analyze how the volatility depends on various factors such as Lok Sabha Elections, Domestic Riots, Union Budget of India, Indian Monsoon and Global factors. Factors have been analyzed to understand their role in the fluctuations seen over time in the market and how the SENSEX behave over these factors.

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 Novel Context and Load-Aware Family Genetic Algorithm Based Task Scheduling in Cloud Computing
Nächstes Kapitel A Novel Approach to Improve the Performance of Divisive Clustering-BST
Literatur
1.
Abdullah, S.S., Rahaman, M.S., Rahman, M.S.: Analysis of stock market using text mining and natural language processing. In: 2013 International Conference on Informatics, Electronics and Vision (ICIEV), pp. 1–6. IEEE (2013)
2.
Babu, C.N., Reddy, B.E.: Selected Indian stock predictions using a hybrid ARIMA-GARCH model. In: 2014 International Conference on Advances in Electronics, Computers and Communications (ICAECC), pp. 1–6. IEEE (2014)
3.
Bebarta, D.K., Biswal, B., Rout, A.K., Dash, P.K.: Forecasting and classification of Indian stocks using different polynomial functional link artificial neural networks. In: 2012 Annual IEEE India Conference (INDICON), pp. 178–182. IEEE (2012)
4.
Fonseka, C., Liyanage, L.: A Data mining algorithm to analyse stock market data using lagged correlation. In: ICIAFS 2008. 4th International Conference on Information and Automation for Sustainability, 2008, pp. 163–166. IEEE (2008)
5.
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier (2011)
6.
Hendahewa, C., Pavlovic, V.: Analysis of causality in stock market data. In: 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol. 1, pp. 288–293. IEEE (2012)
7.
Huang, K.Y., Wan, S.: Application of enhanced cluster validity index function to automatic stock portfolio selection system. Inf. Technol. Manage. 12(3), 213–228 (2011)CrossRef
8.
9.
10.
11.
Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 119–127 (1980)
12.
Kim, H.J., Ryu, C.K., Ryu, D.S., Cho, H.G.: A phylogenetic analysis for stock market indices using time-series string alignments. In: ICCIT’08. Third International Conference on Convergence and Hybrid Information Technology, 2008, vol. 1, pp. 487–492. IEEE (2008)
13.
Kumar, D.A., Murugan, S.: Performance analysis of Indian stock market index using neural network time series model. In: 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME), pp. 72–78. IEEE (2013)
14.
Mukherjee, D.: Comparative analysis of Indian stock market with international markets. Great Lakes Herald 1(1), 39–71 (2007)
15.
Saxena, P., Pant, B., Goudar, R.H., Srivastav, S., Garg, V., Pareek, S.: Future predictions in Indian stock market through linguistic-temporal approach. In: 2013 7th International Conference on Intelligent Systems and Control (ISCO) (2013)
16.
Tirea, M., Negru, V.: Stock market analysis-strongest performing stocks influence on an evolutionary market. In: Proceedings of the ITI 2013 35th International Conference on Information Technology Interfaces (ITI), pp. 263–270. IEEE (2013)
17.
Troiano, L., Kriplani, P.: Predicting trend in the next-day market by hierarchical hidden Markov model. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 199–204. IEEE (2010)
18.
Zhongxing, Y., Liting, G.: A hybrid cognition system: application to stock market analysis. In: IJCNN’93-Nagoya. Proceedings of 1993 International Joint Conference on Neural Networks, 1993, vol. 3, pp. 3000–3003. IEEE (1993)
Metadaten
Titel
Indian Stock Market Analysis Using CHAID Regression Tree
verfasst von
Udit Aggarwal
Sai Sabitha
Tanupriya Choudhury
Abhay Bansal
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Data Engineering and Intelligent Computing

Print ISBN: 978-981-10-3222-6

Electronic ISBN: 978-981-10-3223-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-3223-3_52