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Mathematical Gann Square Model and Elliott Wave Principle with Bi-LSTM for Stock Price Prediction

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

The process involved in predicting the value of stock price is an effort to evaluate the future stock values in order to enhance the profit of the company. The major objective of this research is to utilize Gann square mathematical model and Elliott Wave Principle to predict the stock price values. Moreover, the Elliott Wave Principle, which is used by this research, is combined with Bi-directional Long Short-Term Memory (Bi-LSTM) to analyze the recurrent long-term changes of price patterns in waveforms associated with consistent changes. The performance of the proposed stock prediction model is evaluated based on Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The outcome of Gann square model and Elliott Wave Principle with Bi-LSTM is evaluated in light of these aforementioned metrics and provides MAE of 0.56, MSE of 0.42, and RMSE of 0.54.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Presidency University, Chennai, India

    K. V. Manjunath & M. Chandra Sekhar

Authors
  1. K. V. Manjunath
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  2. M. Chandra Sekhar
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Corresponding author

Correspondence to K. V. Manjunath .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Manjunath, K.V., Chandra Sekhar, M. (2023). Mathematical Gann Square Model and Elliott Wave Principle with Bi-LSTM for Stock Price Prediction. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_49

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