Application of Machine Learning and Deep Learning Algorithms to the Prediction of Stock Market Trends

The stock market's inherent volatility poses ongoing challenges for stock traders, as it is subject to a multitude of circumstances that exert influence on its behavior. This research aims to mitigate the risk associated with forecasting stock market trends through the utilization of deep learn...

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Vydáno v:2023 Global Conference on Information Technologies and Communications (GCITC) s. 1 - 5
Hlavní autoři: Khunti, Shravan, Kumar, Prikshit, Rao, M. Lakshman, Muni, T Vijay, Singh, Varun Sanjeev, Subhani, Shaik Chand Mabhu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2023
Témata:
Artificial Neural Network
Data models
Deep learning
k-nearest neighbors
Long short term memory
Recurrent neural networks
RNN
stock market trend prediction
Stock markets
stock trading
Support Vector Classifier
Support vector machine classification
ISBN:9798350308143
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Shrnutí:The stock market's inherent volatility poses ongoing challenges for stock traders, as it is subject to a multitude of circumstances that exert influence on its behavior. This research aims to mitigate the risk associated with forecasting stock market trends through the utilization of deep learning and machine learning techniques. Eleven machine learning models were utilized in this study: random forest, logistic regression, xgboost, naive Bayes, K-nearest neighbors, decision tree and support vector classifier and extreme gradient boosting. Additionally, two powerful deep learning techniques: recurrent neural networks (RNN) and long short term memory (LSTM) were used. From the Tehran Stock Exchange, four market groups were chosen for the experimental estimates. Petroleum, non-metallic minerals, basic metals, and diversified financials are all part of these categories.
ISBN:9798350308143
DOI:10.1109/GCITC60406.2023.10426519