An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets

Author(s)	Dr. Saumendra Mohanty
Country	India
Abstract	This study focuses on the use of Long Short-Term Memory (LSTM) neural networks for stock and currency market forecasting. Accurate projections are difficult to make because of the complex dynamics and non-linear interactions that characterise financial markets. A Recurrent Neural Networks (RNN) variation called LSTM is particularly good at identifying temporal dependencies and long-term patterns in sequential data. The goal of LSTM models is to discover significant insights and produce precise predictions using past price data. In addition to discussing data pretreatment methods, model creation, and assessment metrics related to stock and FX market prediction, this work examines the benefits of LSTM in capturing market dynamics. Case studies and empirical analysis are used to investigate the capabilities and constraints of LSTM models in forecasting market movements.
Keywords	Long Short-Term Memory (LSTM) neural networks, stock market forecasting, currency market forecasting, deep learning, Recurrent Neural Networks (RNN)
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-05-29
Cite This	An International Study of Application of Long Short-Term Memory (LSTM) Neural Networks for the prediction of stock and forex markets - Dr. Saumendra Mohanty - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.3345
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.3345
Short DOI	https://doi.org/gr9rzb

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research