Abstract
Forecasting stock price and intraday direction is the main problem in the area of Quantitative Finance. This paper explores the efficacy of Bayesian Long Short-Term Memory Neural Network Model (to be precise LSTM + BNN) in price forecasting. Performance was tested against ML models like Random Forest, XGBoost, and Vanilla LSTM. Public data on Indian stocks of five companies Reliance, Dr Reddy, Dmart, TCS and Hindunilvr is collected from YahooFinance. Performance of the proposed model is measured using root mean square error and mean absolute error. The model is deployed through a webapp, which displays the predictions for stocks along with statistical metrics such as level of confidence and uncertainty with respect to the predictions. Objective of the proposed research is to create a hybrid model B-LSTM and compare the result with existing models which outperforms with low error rate.
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Pinapatruni, R., Mohammed, F., Mohiuddin, S.A., Patel, D. (2023). A Hybrid Model for Forecasting Stock Prices Using Bayesian and LSTM. 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_55
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DOI: https://doi.org/10.1007/978-981-99-6706-3_55
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