Stock Price Prediction in the Financial Market Using Machine Learning Models
This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices ar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/1/3 |
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| author | Diogo M. Teixeira Ramiro S. Barbosa |
| author_facet | Diogo M. Teixeira Ramiro S. Barbosa |
| author_sort | Diogo M. Teixeira |
| collection | DOAJ |
| description | This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. The results show that while each model possesses distinct characteristics, selecting the most efficient approach heavily depends on the specific data and forecasting objectives. The complexity of advanced models such as XGBoost and GRU is reflected in their overall performance, suggesting that they can be particularly effective at capturing patterns and making accurate predictions in more complex time series, such as stock prices. |
| format | Article |
| id | doaj-art-c0b5f2caaca540c88656d901cc3bb402 |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-c0b5f2caaca540c88656d901cc3bb4022025-01-24T13:27:45ZengMDPI AGComputation2079-31972024-12-01131310.3390/computation13010003Stock Price Prediction in the Financial Market Using Machine Learning ModelsDiogo M. Teixeira0Ramiro S. Barbosa1Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, PortugalDepartment of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, PortugalThis paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential and simple averages, and various global indices are analyzed to be used as inputs for machine learning models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and XGBoost. The results show that while each model possesses distinct characteristics, selecting the most efficient approach heavily depends on the specific data and forecasting objectives. The complexity of advanced models such as XGBoost and GRU is reflected in their overall performance, suggesting that they can be particularly effective at capturing patterns and making accurate predictions in more complex time series, such as stock prices.https://www.mdpi.com/2079-3197/13/1/3stock market predictionLSTMCNNGRUXGBoosttime series |
| spellingShingle | Diogo M. Teixeira Ramiro S. Barbosa Stock Price Prediction in the Financial Market Using Machine Learning Models Computation stock market prediction LSTM CNN GRU XGBoost time series |
| title | Stock Price Prediction in the Financial Market Using Machine Learning Models |
| title_full | Stock Price Prediction in the Financial Market Using Machine Learning Models |
| title_fullStr | Stock Price Prediction in the Financial Market Using Machine Learning Models |
| title_full_unstemmed | Stock Price Prediction in the Financial Market Using Machine Learning Models |
| title_short | Stock Price Prediction in the Financial Market Using Machine Learning Models |
| title_sort | stock price prediction in the financial market using machine learning models |
| topic | stock market prediction LSTM CNN GRU XGBoost time series |
| url | https://www.mdpi.com/2079-3197/13/1/3 |
| work_keys_str_mv | AT diogomteixeira stockpricepredictioninthefinancialmarketusingmachinelearningmodels AT ramirosbarbosa stockpricepredictioninthefinancialmarketusingmachinelearningmodels |