Skip to main content
Erschienen in: Annals of Data Science 4/2022

22.08.2019

Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market

Erschienen in: Annals of Data Science | Ausgabe 4/2022

Einloggen

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

search-config
loading …

Abstract

The stock market is a popular investment option for investors because of its expected high returns. Stock market prediction is a complex task to achieve with the help of artificial intelligence. Because stock prices depend on many factors, including trends and news in the market. However, in recent years, many creative techniques and models have been proposed and applied to efficiently and accurately forecast the behaviour of the stock market. This paper presents a comparative study of fundamental and technical analysis based on different parameters. We also discuss a comparative Analysis of various prediction techniques used to predict stock price. These strategies include technical analysis like time series analysis and machine learning algorithms such as the artificial neural network (ANN). Along with them, few researchers focused on the textual analysis of stock prices by continuous analysing the public sentiments from social media and other news sources. Various approaches are compared based on methodologies, datasets, and efficiency with the help of visualisation.

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

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!

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!

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!

Literatur
1.
Zurück zum Zitat Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool Publishers, San RafaelCrossRef Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool Publishers, San RafaelCrossRef
2.
Zurück zum Zitat Attigeri GV, Manohara PMM, Pai RM, Nayak A (2015) Stock market prediction: a big data approach. In: Proceedings of the IEEE region 10th conference on TENCON 2015, Macao, China, pp 1–5 Attigeri GV, Manohara PMM, Pai RM, Nayak A (2015) Stock market prediction: a big data approach. In: Proceedings of the IEEE region 10th conference on TENCON 2015, Macao, China, pp 1–5
3.
Zurück zum Zitat Chen R, Lazer M (2013) Sentiment analysis of twitter feeds for the prediction of stock market movement, Stanford. edu. Retrieved January. 2013 Chen R, Lazer M (2013) Sentiment analysis of twitter feeds for the prediction of stock market movement, Stanford. edu. Retrieved January. 2013
4.
Zurück zum Zitat Zhang L (2013) Sentiment analysis on twitter with stock price and significant keyword correlation. The University of Texas at Austin, Austin Zhang L (2013) Sentiment analysis on twitter with stock price and significant keyword correlation. The University of Texas at Austin, Austin
5.
Zurück zum Zitat Adebiyi Ayodele A, Charles AK, Adebiyi MO, Otokiti SO (2012) Stock price prediction using neural network with hybridized market indicators. J Emerg Trends Comput Inf Sci 3(1):1–9 Adebiyi Ayodele A, Charles AK, Adebiyi MO, Otokiti SO (2012) Stock price prediction using neural network with hybridized market indicators. J Emerg Trends Comput Inf Sci 3(1):1–9
6.
Zurück zum Zitat Bollen J, Mao J, Zeng X (2010) Twitter mood predicts the stock market, CoRR. vol. abs/1010.3003 Bollen J, Mao J, Zeng X (2010) Twitter mood predicts the stock market, CoRR. vol. abs/1010.3003
7.
Zurück zum Zitat Rechenthin M, Street WN, Srinivasan P (2013) Stock chatter: using stock sentiment to predict price direction. Algorithm Finance 2(3–4):169–196CrossRef Rechenthin M, Street WN, Srinivasan P (2013) Stock chatter: using stock sentiment to predict price direction. Algorithm Finance 2(3–4):169–196CrossRef
8.
Zurück zum Zitat Zabir HK, Tasnim SA, Md. Hussain MA (2011) Price prediction of share market using artificial neural network (ANN). Int J Comput Appl 22(2):42–47 Zabir HK, Tasnim SA, Md. Hussain MA (2011) Price prediction of share market using artificial neural network (ANN). Int J Comput Appl 22(2):42–47
9.
Zurück zum Zitat Setty DV, Rangaswamy TM, Subramanya KN (2010) A review on data mining applications to the performance of stock marketing. Int J Comput Appl 1:33–43 Setty DV, Rangaswamy TM, Subramanya KN (2010) A review on data mining applications to the performance of stock marketing. Int J Comput Appl 1:33–43
10.
Zurück zum Zitat Rechenthin MD (2014) Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction. Ph.D. thesis, University of Iowa, USA Rechenthin MD (2014) Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction. Ph.D. thesis, University of Iowa, USA
11.
Zurück zum Zitat Buche A, Chandak MB (2016) Stock market prediction using text opinion mining: a survey. Int J Adv Res Comput Sci Softw Eng 6:566–569 Buche A, Chandak MB (2016) Stock market prediction using text opinion mining: a survey. Int J Adv Res Comput Sci Softw Eng 6:566–569
12.
Zurück zum Zitat Twomey JM, Smith AE (1997) Validation and verification. In: Kartam N, Flood I, Garrett JH (eds) Artificial neural networks for civil engineers: fundamentals and applications. ASCE, New York, pp 44–64 Twomey JM, Smith AE (1997) Validation and verification. In: Kartam N, Flood I, Garrett JH (eds) Artificial neural networks for civil engineers: fundamentals and applications. ASCE, New York, pp 44–64
13.
Zurück zum Zitat Cheng CH, Chen YS (2007) Fundamental analysis of stock trading systems using classification techniques. In: International conference on machine learning and cybernetics, vol 3, Hong Kong, China, pp 1377–1382 Cheng CH, Chen YS (2007) Fundamental analysis of stock trading systems using classification techniques. In: International conference on machine learning and cybernetics, vol 3, Hong Kong, China, pp 1377–1382
14.
Zurück zum Zitat Gudelek, MU, Boluk SA, Ozbayoglu AM (2017) A deep learning based stock trading model with 2-D CNN trend detection. In: IEEE (SSCI), Honolulu, HI, USA, pp 1–8 Gudelek, MU, Boluk SA, Ozbayoglu AM (2017) A deep learning based stock trading model with 2-D CNN trend detection. In: IEEE (SSCI), Honolulu, HI, USA, pp 1–8
15.
Zurück zum Zitat Dang M, Duong D (2016) Improvement methods for stock market prediction using financial news articles. In: Proceedings of 3rd national foundation for science and technology development conference on information and computer science (NICS), Danang, Vietnam, pp 125–129 Dang M, Duong D (2016) Improvement methods for stock market prediction using financial news articles. In: Proceedings of 3rd national foundation for science and technology development conference on information and computer science (NICS), Danang, Vietnam, pp 125–129
16.
Zurück zum Zitat Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: International joint conference on artificial intelligence, AAAI Press, pp 2327–2333 Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: International joint conference on artificial intelligence, AAAI Press, pp 2327–2333
17.
Zurück zum Zitat Zhang X, Zhang Y, Wang S, Yao Y, Fang B, Philip SY (2018) Improving stock market prediction via heterogeneous information fusion. Knowl-Based Syst 143:236–247CrossRef Zhang X, Zhang Y, Wang S, Yao Y, Fang B, Philip SY (2018) Improving stock market prediction via heterogeneous information fusion. Knowl-Based Syst 143:236–247CrossRef
18.
Zurück zum Zitat Li JH, Bu, Wu J (2017) Sentiment-aware stock market prediction: a deep learning method. In: International conference on service systems and service management (ICSSSMʼ17), IEEE, Dalian, China, pp 1–6 Li JH, Bu, Wu J (2017) Sentiment-aware stock market prediction: a deep learning method. In: International conference on service systems and service management (ICSSSMʼ17), IEEE, Dalian, China, pp 1–6
19.
Zurück zum Zitat Yunus Y, Halid K, Jamshidi M (2014) Stock market prediction by using artificial neural network. In: World automation congress (WAC), pp 718–722 Yunus Y, Halid K, Jamshidi M (2014) Stock market prediction by using artificial neural network. In: World automation congress (WAC), pp 718–722
20.
Zurück zum Zitat Mehak U, Syed HA, Kamran R, Syed SAA (2016) Stock market prediction using machine learning techniques. In: International conference on computer and information sciences (ICCOINS), pp 322–327 Mehak U, Syed HA, Kamran R, Syed SAA (2016) Stock market prediction using machine learning techniques. In: International conference on computer and information sciences (ICCOINS), pp 322–327
Metadaten
Titel
Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market
Publikationsdatum
22.08.2019
Erschienen in
Annals of Data Science / Ausgabe 4/2022
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-019-00230-7

Weitere Artikel der Ausgabe 4/2022

Annals of Data Science 4/2022 Zur Ausgabe