Abstract
Financial markets are highly unpredictable owing to their volatile nature. Due to this complexity, Financial time series forecasting is one of the most challenging fields of study. In the past 20 years many approaches were used to solve time series forecasting. However in the recent years, the implementation of Deep Learning algorithms for financial market predictions has proven to yield better accuracy and returns in the field of financial analysis when used with the rightful knowledge.
In this paper, we propose a novel method for forecasting stock price movements using LSTM. Long Short Term Networks (LSTM) are highly efficient for time series predictions due to their ability to store historical price information, which is important for our approach as previous prices of a stock is essential for predicting its future price. We choose 3 stocks from NIFTY 50 index, deploy our LSTM networks and train on its historical prices and evaluate the performance of the trained model.
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Manjunath, S., Halasuru Manjunath, P. (2023). A Novel Approach for Financial Markets Forecasting Using Deep Learning with Long Short Term Networks. In: Auer, M.E., El-Seoud, S.A., Karam, O.H. (eds) Artificial Intelligence and Online Engineering. REV 2022. Lecture Notes in Networks and Systems, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-031-17091-1_46
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DOI: https://doi.org/10.1007/978-3-031-17091-1_46
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