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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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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DOI: https://doi.org/10.1007/978-981-99-6706-3_49
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