“Predicting Stock Market Trends Using Deep Long Short-Term Memory Networks”

Authors

  • Prashant Chordiya, Atvir Singh

Keywords:

Stock Market Prediction, LSTM Networks, Time Series Forecasting, Deep Learning, Financial Market, Feature Engineering, Model Evaluation

Abstract

Predicting stock market trends is a highly challenging task due to the complex and volatile nature of financial markets. This project aims to utilize Long Short-Term Memory (LSTM) networks to predict stock market trends based on historical data. LSTMs, a type of recurrent neural network (RNN), are particularly suited for time series forecasting due to their ability to learn from sequential data. By collecting and preprocessing a dataset of historical stock prices and market indicators, we develop and train an LSTM model to predict future stock prices. The model's performance is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrating its accuracy and effectiveness in predicting stock market trends. The insights derived from the model can be valuable for investors and traders in making informed decisions.

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References

“Short term stock market price trend prediction using a comprehensive deep learning system”, Jingyi Shen and M. Omair Shaf, Journal of Big Data ,2022.

“LSTM-based Stock Prediction Modeling and Analysis”, Ruobing Zhang et al., Atlantis Press 2022.

” Stock Market Prediction Using LSTM Recurrent Neural Network”, Adil MOGHAR Elsevier,2020.

“Activity Recognition in Smart Homes using UWB Radar”, Kevin Bouchard, Elsevier,2020.

“Impact of the geometric field of view on drivers’ speed perception and lateral position in driving simulators”, Charitha Dias et al., Elsevier,2020.

“Analysis and processing of environmental monitoring system”, Nurtai Albanbaib et al., 2020.

“Stock Market Prediction Using LSTM Recurrent Neural Network”, Adil MOGHAR et al.,2020.

“Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models”, Sidra Mehta et al., erjmt,2022.

“Stock Market Prediction using Long Short-Term Memory”, Stylianos Gavrie et al.,2022.

“NSE Stock Market Prediction Using Deep-Learning Models”, Hiransha Ma et al., Elsevier ,2020.

“Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis”, mojtaba nabipour et al.,2020.

“Stock Price Prediction Using Long Short-Term Memory”, Raghav Nandakumar et al., IRJET, 2021.

“Enhancing Stock Market Prediction Through LSTM Modeling and Analysis”, Weihai Huang et al., ICIDC 2023.

“Stock price prediction using deep learning”, J. Sirisha et al., IJCRT 2023.

“Stock Price Trend Forecasting using Long Short-Term Memory Recurrent Neural Networks”, Mahdi Ismael Omar et al., IJSRCIET 2020.

“Stock values predictions using deep learning-based hybrid models”, Konark Yadav et al., Willey, 2020. |

“Forecasting Directional Movement of Stock Prices using Deep Learning”, Deeksha Chandola et al., springer ,2021.

“Short term stock market price trend prediction using a comprehensive deep learning system”, Jingyi Shen and M. Omair Shafq, Journal of big data ,2020.

“Applying machine learning algorithms to predict the stock price trend in the stock market”, Tran Phuoc et al., humanities and social science communication,2024.

“Implementation of Long Short Term Memory and Gated Recurrent Units on grouped time series data to predict stock prices accurately”, Armin Law et al., Springer ,2022.

“SMP-DL: a novel stock market prediction approach based on deep learning for effective trend forecasting”, Warda M. Shaban et al., Neural computing and applications,2023.

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Published

30.09.2024

How to Cite

Prashant Chordiya. (2024). “Predicting Stock Market Trends Using Deep Long Short-Term Memory Networks”. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4018–4032. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8019

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

License

Creative Commons License

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