Abstract: In The Financial Markets, Predicting Stock Prices Is Essential For Risk Management And Investing Strategies. The Long Short-Term Memory (LSTM) Algorithm And Deep Learning Techniques Are Used In This Study To Provide A Novel Method For Forecasting Nifty Stock Prices. The Study Makes Use Of A Broad Dataset From Yahoo Finance That Spans Five Years, From 2017 To 2023.Data Pre-Processing, Which Includes Data Cleansing, Normalisation, And Feature Engineering, Is The First Step In The Research Process. The Performance Of Elastic Net And LSTM, Two Distinct Stock Price Prediction Systems, Is Then Contrasted. Our Findings Show......
[1]. Brownlee, J. (2020). How to Develop LSTM Models for Time Series Forecasting. Machine Learning Mastery. Available online: Link
[2]. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
doi:10.1162/neco.1997.9.8.1735
[3]. Karpathy, A. (2015). The Unreasonable Effectiveness of Recurrent Neural Networks. Andrej Karpathy Blog. Available online: Link
[4]. Liu, W., Luo, Y., & Wang, Z. (2020). Stock Price Prediction Based on LSTM Deep Learning Algorithm. International Journal of Online and Biomedical Engineering, 16(13), 104-115. doi:10.3991/ijoe.v16i13.17861
[5]. Nair, A., & Abraham, A. (2010). Stock Market Forecasting Using Hidden Markov Model: A New Approach. Expert Systems with Applications, 37(10), 7893-7897. doi:10.1016/j.eswa.2010.04.049
