| Autor: |
Arif, Erman, Suherman, Suherman, Widodo, Aris Puji |
| Předmět: |
|
| Zdroj: |
Mathematical Modelling of Engineering Problems; Nov2024, Vol. 11 Issue 11, p2929-2943, 15p |
| Abstrakt: |
This study examines the application of machine learning methods to improve the accuracy of stock price prediction by integrating technical analysis. In this study, we evaluate machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Neural Networks, and Logistic Regression in the context of stock price prediction by utilizing technical indicators such as Moving Average, Relative Strength Index (RSI), and MACD. We use historical stock price datasets from the Indonesian capital market over a five-year period to train and test the models. The results show that the integration of technical analysis with machine learning methods can significantly improve prediction accuracy compared to using technical analysis or machine learning separately. The Neural Networks model performed the best in terms of prediction accuracy, with an improvement of 15% compared to the traditional method. These findings have important implications for investors and financial professionals in data-driven decision making. This study contributes to the development of more effective stock price prediction methods by combining analytical and technological approaches. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
