Skip to main content
SpringerLink
Log in

An Automated Dual-Module Pipeline for Stock Prediction: Integrating N-Perception Period Power Strategy and NLP-Driven Sentiment Analysis for Enhanced Forecasting Accuracy and Investor Insight

  • Conference paper
  • First Online:
Deep Learning Theory and Applications (DeLTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1875))

Included in the following conference series:

  • 466 Accesses

Abstract

The financial sector has witnessed considerable interest in the fields of stock prediction and reliable stock information analysis. Traditional deterministic algorithms and AI models have been extensively explored, leveraging large historical datasets. Volatility and market sentiment play crucial roles in the development of accurate stock prediction models. We hypothesize that traditional approaches, such as n-moving averages, may not capture the dynamics of stock swings, while online information influences investor sentiment, making them essential factors for prediction. To address these challenges, we propose an automated pipeline consisting of two modules: an N-Perception period power strategy for identifying potential stocks and a sentiment analysis module using NLP techniques to capture market sentiment. By incorporating these methodologies, we aim to enhance stock prediction accuracy and provide valuable insights for investors.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vargas, M.R., Dos Anjos, C.E.M., Bichara, G.L.G., Evsukoff, A.G.: Deep leaming for stock market prediction using technical indicators and financial news articles. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)

    Google Scholar 

  2. Thakkar, A., Chaudhari, K.: A comprehensive survey on deep neural networks for stock market: the need, challenges, and future directions. Expert Syst. Appl. 177, 114800 (2021)

    Article  Google Scholar 

  3. Sharpe, W.F.: Efficient capital markets: a review of theory and empirical work: discussion. J. Financ. 25(2), 418–420 (1970)

    Google Scholar 

  4. Granger, C.W.J.: Long memory relationships and the aggregation of dynamic models. J. Econom. 14(2), 227–238 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  5. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  6. Xu, F., Yang, F., Fan, X., Huang, Z., Tsui, K.L.: Extracting degradation trends for roller bearings by using a moving-average stacked auto-encoder and a novel exponential function. Measurement 152, 107371 (2020)

    Article  Google Scholar 

  7. Liu, X., An, H., Wang, L., Jia, X.: An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms. Appl. Energy 185, 1778–1787 (2017)

    Article  Google Scholar 

  8. Rubi, M.A., Chowdhury, S., Rahman, A.A.A., Meero, A., Zayed, N.M., Islam, K.M.A.: Fitting multi-layer feed forward neural network and autoregressive integrated moving average for dhaka stock exchange price predicting. Emerg. Sci. J. 6(5), 1046–1061 (2022)

    Google Scholar 

  9. Alam, T.: Forecasting exports and imports through artificial neural network and autoregressive integrated moving average. Decis. Sci. Lett. 8(3), 249–260 (2019)

    Article  Google Scholar 

  10. Engle, R.F., Granger, C.W.J.: Co-integration and error correction: representation, estimation, and testing. Econom.: J. Econom. Soc. 251–276 (1987)

    Google Scholar 

  11. Cakra, Y.E., Trisedya, B.D.: Stock price prediction using linear regression based on sentiment analysis. In: 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 147–154. IEEE (2015)

    Google Scholar 

  12. Vachhani, H., et al.: Machine learning based stock market analysis: a short survey. In: Raj, J.S., Bashar, A., Ramson, S.R.J. (eds.) ICIDCA 2019. LNDECT, vol. 46, pp. 12–26. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38040-3_2

    Chapter  Google Scholar 

  13. Xie, Y., Jiang, H.: Stock market forecasting based on text mining technology: a support vector machine method. arXiv preprint arXiv:1909.12789 (2019)

  14. Moghar, A., Hamiche, M.: Stock market prediction using LSTM recurrent neural network. Procedia Comput. Sci. 170, 1168–1173 (2020)

    Article  Google Scholar 

  15. Oncharoen, P., Vateekul, P.: Deep learning for stock market prediction using event embedding and technical indicators. In: 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), pp. 19–24. IEEE (2018)

    Google Scholar 

  16. Kumar, D., Sarangi, P.K., Verma, R.: A systematic review of stock market prediction using machine learning and statistical techniques. Mater. Today Proc. 49, 3187–3191 (2022)

    Article  Google Scholar 

  17. Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Financ. 62(3), 1139–1168 (2007)

    Article  Google Scholar 

  18. Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia Soc. Behav. Sci. 26, 55–62 (2011)

    Article  Google Scholar 

  19. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  20. Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  21. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  22. Aasi, B., Imtiaz, S.A., Qadeer, H.A., Singarajah, M., Kashef, R.: Stock price prediction using a multivariate multistep LSTM: a sentiment and public engagement analysis model. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–8. IEEE (2021)

    Google Scholar 

  23. Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., Anastasiu, D.C.: Stock price prediction using news sentiment analysis. In: 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 205–208. IEEE (2019)

    Google Scholar 

  24. Chiong, R., Fan, Z., Hu, Z., Adam, M.T.P., Lutz, B., Neumann, D.: A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 278–279 (2018)

    Google Scholar 

  25. Deléglise, H., Interdonato, R., Bégué, A., d’Hôtel, E.M., Teisseire, M., Roche, M.: Food security prediction from heterogeneous data combining machine and deep learning methods. Expert Syst. Appl. 190, 116189 (2022)

    Google Scholar 

  26. Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7), e0180944 (2017)

    Article  Google Scholar 

  27. Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  28. Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning (2000)

    Google Scholar 

  29. Waqar, M., Dawood, H., Guo, P., Shahnawaz, M.B., Ghazanfar, M.A.: Prediction of stock market by principal component analysis. In: 2017 13th International Conference on Computational Intelligence and Security (CIS), pp. 599–602. IEEE (2017)

    Google Scholar 

  30. Lahmiri, S.: Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl. Math. Comput. 320, 444–451 (2018)

    MathSciNet  MATH  Google Scholar 

  31. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)

    Article  MATH  Google Scholar 

  32. Agarwal, V., Madhusudan, L., Babu Namburi, H.: Method and apparatus for stock performance prediction using momentum strategy along with social feedback. In: 2nd International Conference on Intelligent Technologies (CONIT). IEEE (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Memorial High School, Frisco, TX, USA

    Siddhant Singh

  2. Panther Creek High School, Frisco, TX, USA

    Archit Thanikella

Authors
  1. Siddhant Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Archit Thanikella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Siddhant Singh or Archit Thanikella .

Editor information

Editors and Affiliations

  1. Université de Tours, Tours, France

    Donatello Conte

  2. Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

  3. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

  4. University of Naples Federico II, Naples, Italy

    Carlo Sansone

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, S., Thanikella, A. (2023). An Automated Dual-Module Pipeline for Stock Prediction: Integrating N-Perception Period Power Strategy and NLP-Driven Sentiment Analysis for Enhanced Forecasting Accuracy and Investor Insight. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-39059-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39058-6

  • Online ISBN: 978-3-031-39059-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation