Abstract
Forecasting the stock market has always been one of the challenges for stock market participants to make more profit. Among the problems of stock price forecasting, we can mention its dynamic nature, complexity and its dependence on factors such as the governing system of countries, emotions, economic conditions, inflation, and so on. In recent years, many studies have been conducted to predict the capital stock market using traditional techniques, machine learning algorithms and deep learning. The lower our forecast stock error, the More we can reduce investment risk and increase profitability. In this paper, we present a machine learning (ML) approach called support vector machine (SVM) that can be taught using existing data. SVM extracts knowledge between data and ultimately uses this knowledge to predict new stock data. We have also aimed to select the best SVM method parameters using the particle swarm optimization (PSO) algorithm to prevent over-fitting and improve forecast accuracy. Finally, we compare our proposed method (SVM-PSO) with several other methods, including support vector machine, artificial neural network (ANN) and LSTM. The results show that the proposed algorithm works better than other methods and in all cases, its forecast accuracy is above 90%.
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Bazrkar, M.J., Hosseini, S. Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm. Comput Econ 62, 165–186 (2023). https://doi.org/10.1007/s10614-022-10273-3
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DOI: https://doi.org/10.1007/s10614-022-10273-3