Comparison of Stock Price Prediction Models
Keywords:
Linear Regression, Long Short-Term Memory, Stock price prediction, Stock market data, Support vector machineAbstract
Recent studies have increasingly explored how machine learning can predict future trends in different fields. One of the most critical applications is in the stock market, where accurate forecasting helps investors decide whether to invest in new businesses or sell their existing shares for profit. To achieve this, an effective prediction model must analyze past stock market data and project future trends accordingly. This article presents a stock price prediction approach that leverages historical trading patterns. The system examines how various market conditions influence stock prices over time and estimates potential profits or losses. Several machines learning techniques, including Random Forest, Support Vector Machines (SVM), and Regression algorithms, were evaluated to determine the most effective strategy. Among them, Support Vector Regression (SVR) emerged as a highly efficient method for analyzing time-series datasets. For this study, stock market data from a four-year period was collected and analyzed to predict future share prices. The results show that the SVR model with a Radial Basis Function (RBF) kernel outperforms other models, providing more accurate predictions. If essential financial parameters are effectively considered, this method can offer reliable stock market forecasts.
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