Forecasting Significant Stock Market Price Changes Using Machine Learning: Extra Trees Classifier Leads
被引:13
作者:
Pagliaro, Antonio
论文数: 0引用数: 0
h-index: 0
机构:
INAF IASF Palermo, Via Ugo La Malfa 153, I-90146 Palermo, Italy
Ist Nazl Fis Nucl, Sez Catania, Via Santa Sofia 64, I-95123 Catania, Italy
ICSC Ctr Nazl Ric HPC, Big Data & Quantum Comp, I-40121 Bologna, ItalyINAF IASF Palermo, Via Ugo La Malfa 153, I-90146 Palermo, Italy
Pagliaro, Antonio
[1
,2
,3
]
机构:
[1] INAF IASF Palermo, Via Ugo La Malfa 153, I-90146 Palermo, Italy
[2] Ist Nazl Fis Nucl, Sez Catania, Via Santa Sofia 64, I-95123 Catania, Italy
[3] ICSC Ctr Nazl Ric HPC, Big Data & Quantum Comp, I-40121 Bologna, Italy
Random Forest;
Extra Trees;
machine learning;
stock price forecasting;
DIRECTION;
D O I:
10.3390/electronics12214551
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
Predicting stock market fluctuations is a difficult task due to its intricate and ever-changing nature. To address this challenge, we propose an approach to minimize forecasting errors by utilizing a classification-based technique, which is a widely used set of algorithms in the field of machine learning. Our study focuses on the potential effectiveness of this approach in improving stock market predictions. Specifically, we introduce a new method to predict stock returns using an Extra Trees Classifier. Technical indicators are used as inputs to train our model while the target is the percentage difference between the closing price and the closing price after 10 trading days for 120 companies from various industries. The 10-day time frame strikes a good balance between accuracy and practicality for traders, avoiding the low accuracy of short time frames and the impracticality of longer ones. The Extra Trees Classifier algorithm is ideal for stock market predictions because of its ability to handle large data sets with a high number of input features and improve model robustness by reducing overfitting. Our results show that our Extra Trees Classifier model outperforms the more traditional Random Forest method, achieving an accuracy of 86.1%. These findings suggest that our model can effectively predict significant price changes in the stock market with high precision. Overall, our study provides valuable insights into the potential of classification-based techniques in enhancing stock market predictions.
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
de Faria, E. L.
Albuquerque, Marcelo P.
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机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
Albuquerque, Marcelo P.
Gonzalez, J. L.
论文数: 0引用数: 0
h-index: 0
机构:
Pontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
Gonzalez, J. L.
Cavalcante, J. T. P.
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h-index: 0
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
Cavalcante, J. T. P.
Albuquerque, Marcio P.
论文数: 0引用数: 0
h-index: 0
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
de Faria, E. L.
Albuquerque, Marcelo P.
论文数: 0引用数: 0
h-index: 0
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
Albuquerque, Marcelo P.
Gonzalez, J. L.
论文数: 0引用数: 0
h-index: 0
机构:
Pontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
Gonzalez, J. L.
Cavalcante, J. T. P.
论文数: 0引用数: 0
h-index: 0
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil
Cavalcante, J. T. P.
Albuquerque, Marcio P.
论文数: 0引用数: 0
h-index: 0
机构:
URCA, Ctr Brasileiro Pesquisas Fis, BR-22290180 Rio De Janeiro, BrazilPontificia Univ Catolica Rio de Janeiro, BR-22453900 Rio De Janeiro, Brazil