Skip to main content

Advertisement

Springer Nature Link
Log in

RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM)

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 06 June 2022

This article has been updated

Abstract

Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Change history

References

  • Ahmed ST (2017) A study on multi objective optimal clustering techniques for medical datasets. In: 2017 international conference on intelligent computing and control systems (ICICCS), pp 174–177. IEEE. https://doi.org/10.1109/ICCONS.2017.8250704

  • Ahmed SST, Thanuja K, Guptha NS, Narasimha S (2016) Telemedicine approach for remote patient monitoring system using smart phones with an economical hardware kit. In: 2016 international conference on computing technologies and intelligent data engineering, pp 1–4. IEEE. https://doi.org/10.1109/ICCTIDE.2016.7725324

  • Ahmed ST, Sandhya M, Sankar S (2019) A dynamic MooM dataset processing under TelMED protocol design for QoS improvisation of telemedicine environment. J Med Syst 43(8):257. https://doi.org/10.1007/s10916-019-1392-4

    Article  Google Scholar 

  • Ahmed ST, Sandhya M, Sankar S (2020) TelMED: dynamic user clustering resource allocation technique for moom datasets under optimizing telemedicine network. Wirel Pers Commun. https://doi.org/10.1007/s11277-020-07091-x

    Article  Google Scholar 

  • Chandar SK (2019) Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction. J Ambient Intell Humaniz Comput, pp 1–9 pp, https://doi.org/10.1007/s12652-019-01224-2

  • Chouhan L, Agarwal N, Ishita P, Saxena S (2018) Stock market prediction using machine learning. In: First international conference on secure cyber puttinging and communications, National Institute of Technology, JALANDHAR, Dec 2018. https://doi.org/10.1109/ICSCCC.2018.8703332

  • Erdogan Z, Namli E (2019) A living environment prediction model using ensemble machine learning techniques based on the quality of life index. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01432-w

    Article  Google Scholar 

  • Hiransha M, Gopalakrishnan EA, Menon VK, Soman KP (2018) NSE stock market prediction using deep-learning models. Elsevier Procedia Comput Sci 132(2018):1351–1362

    Google Scholar 

  • Kadam VJ, Jadhav SM, Vijayakumar K (2019) Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. J Med Syst 43:263. https://doi.org/10.1007/s10916-019-1397-z

    Article  Google Scholar 

  • Kalra S, Prasad JS (2019) Efficacy of news sentiment for stock market prediction. In: 2019 international conference on machine learning, big data, cloud and parallel computing (Com-IT-Con), India, 14th–16th Feb 2019, pp 491–496

  • Martinsson F, Liljeqvis I (2017) Short-term stock market prediction based on candlestick pattern analysis, Thesis

  • Nakov P, Ritter A, Rosenthal S, Stoyanov V, Sebastiani F (2016) SemEval-2016 task 4: sentiment analysis in Twitter. In: Proceedings of the 10th international workshop on semantic evaluation, ser. SemEval’16

  • Peng Z et al (2019) Stock analysis and prediction using big data analytics. In: 2019 international conference on intelligent transportation, big data & smart City (ICITBS), pp 309–312

  • Sadia KH, Sharma A, Paul A, Padhi S, Sanyal S (2019) Stock market prediction using machine learning algorithms. Int J Eng Adv Technol (IJEAT) 8(4). ISSN: 2249–8958

  • Somani P, Talele S, Sawant S (2014) Stock market prediction using hidden markov model. In: 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, 2014, 978-1-4799-4/14, pp 89–92

  • Sun T, Wang J, Zhang P, Cao Y, Liu B, Wang D (2017) Predicting stock price returns using microblog sentiment for Chinese stock market. In: 2017 3rd international conference on big data computing and communications (BIGCOM). https://doi.org/10.1109/BIGCOM.2017.59

  • Usmani M, Adil SH, Raja K, Ali SSA (2016) Stock market prediction using machine learning techniques. In: 2016 3rd international conference on computer and information sciences (ICCOINS), pp 322–327

  • Vijayakumar K, Arun C (2017) Automated risk identification using NLP in cloud based development environments. J Ambient Intell Humaniz Comput, ISSN 1868-5137. https://doi.org/10.1007/s12652-017-0503-7

  • Vijayakumar K, Arun C (2019) Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Cluster Comput. https://doi.org/10.1007/s10586-017-1176-x

    Article  Google Scholar 

  • Wen M, Li P et al (2019) Stock market trend prediction using high-order information of time series. IEEE Trans Big Data Learn Discov 7(2019):28299–28308

    Google Scholar 

  • Zhao J, Sun N, Cheng W (2019) Logistics forum based prediction on stock index using intelligent data analysis and processing of online web posts. ISSN: 1868-5145. https://doi.org/10.1007/s12652-019-01520-x

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Sri Sairam Engineering College, Chennai, India

    M. Ananthi

  2. Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai, India

    K. Vijayakumar

Authors
  1. M. Ananthi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. K. Vijayakumar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to M. Ananthi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04067-6"

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ananthi, M., Vijayakumar, K. RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM). J Ambient Intell Human Comput 12, 4819–4826 (2021). https://doi.org/10.1007/s12652-020-01892-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01892-5

Keywords