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Simulation Based Exploration of Stock Market Using LSTM Model

Rohit Tetarwal1 , Rohit Tushir2

  1. Computer Science Department, Sharda University, Greater Noida, India.
  2. Computer Science Department, Sharda University, Greater Noida, India.

Section:Research Paper, Product Type: Journal Paper
Volume-11 , Issue-4 , Page no. 26-29, Apr-2023

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v11i4.2629

Online published on Apr 30, 2023

Copyright © Rohit Tetarwal, Rohit Tushir . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Rohit Tetarwal, Rohit Tushir, “Simulation Based Exploration of Stock Market Using LSTM Model,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.4, pp.26-29, 2023.

MLA Style Citation: Rohit Tetarwal, Rohit Tushir "Simulation Based Exploration of Stock Market Using LSTM Model." International Journal of Computer Sciences and Engineering 11.4 (2023): 26-29.

APA Style Citation: Rohit Tetarwal, Rohit Tushir, (2023). Simulation Based Exploration of Stock Market Using LSTM Model. International Journal of Computer Sciences and Engineering, 11(4), 26-29.

BibTex Style Citation:
@article{Tetarwal_2023,
author = {Rohit Tetarwal, Rohit Tushir},
title = {Simulation Based Exploration of Stock Market Using LSTM Model},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2023},
volume = {11},
Issue = {4},
month = {4},
year = {2023},
issn = {2347-2693},
pages = {26-29},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5553},
doi = {https://doi.org/10.26438/ijcse/v11i4.2629}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v11i4.2629}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5553
TI - Simulation Based Exploration of Stock Market Using LSTM Model
T2 - International Journal of Computer Sciences and Engineering
AU - Rohit Tetarwal, Rohit Tushir
PY - 2023
DA - 2023/04/30
PB - IJCSE, Indore, INDIA
SP - 26-29
IS - 4
VL - 11
SN - 2347-2693
ER -

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Abstract

In today’s world the stock market has a huge impact on the economy making it difficult for stock market investors to predict stock prices. Financial market investors cannot use simple models to more accurately predict stock prices to invest in stocks. Deep learning helps computer to solve complex problems which humans takes more time to solve. This paper is based on developing a model to predict inventory value using recurrent neural network (RNN) and long- short term memory model (LSTM).

Key-Words / Index Term

Stock Market, Predicting, LSTM Model, RNN Model, Prices, Complex Data, Density

References

[1]. A S Pradeep, Soren Goyal and M. Miller, “Detection of statistical arbitrage using machine learning techniques in Indian Stock market”, PhD Thesis, Computer Science Department, IIT Kanpur, 2019.
[2]. Dhiraj Mundada, Gaurav Chhaparwal, Sachin Chaudhari, Trupti Bhamare, “Stock Value Prediction System”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3, Issue 4, pp. 2217 - 2219, 2015. https://doi.org/10.17762/ijritcc.v3i4.4214
[3]. Rakhi Mahant, Trilok Nath Pandey, Alok Kumar Jagadev and, Satchidananda Dehuri, “Optimized Radial Basis Functional Neural Network for Stock Index Prediction”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Vol. 7, Issue 11, pp. 1252 - 1257, 2016. Doi: 10.1109/ICEEOT.2016.7754884
[4]. S. Hochreiter and J. Schmid Huber, “Long Short- Term Memory”, Neural Computation, Vol.9, Issue 8, pp. 1735 - 1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
[5]. S. Sarode, H. G. Tolani, P. Kak and C. S. Lifna, "Stock Price Prediction Using Machine Learning Techniques”, International Conference on Intelligent Sustainable Systems (ICISS), Vol 5, Issue 3, pp. 177 - 181, 2019. Doi: 10.1109/ISS1.2019.8907958.