Abstract
Abstract Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task filled with challenges. However, in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock prices using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function neural network, general regression neural network, support vector machine regression (SVMR) and least squares support vector machine regression. We apply the five models to make price predictions for three individual stocks, namely, Bank of China, Vanke A and Guizhou Maotai. Adopting mean square error and average absolute percentage error as criteria, we find that BP neural network consistently and robustly outperforms the other four models. Then some theoretical and practical implications have been discussed.
Highlights
Price prediction in equity markets is of great practical and theoretical interest
Researchers often use the fact of whether or not the price can be forecast as a tool to check market efficiency
To the best of our knowledge, there is a dearth in the literature of studies that are focused on comparing the effectiveness of the above-mentioned five algorithms reviewed in this paper. We present this comparative view by using data to compare the performance of these five neural networks, namely back propagation neural networks (BPNN), radial basis function neural network (RBFNN), general regression neural network (GRNN), Support Vector Machine (SVM) and LS-support vector machine regression (SVMR), in predicting stock price
Summary
Price prediction in equity markets is of great practical and theoretical interest. Relatively accurate prediction brings maximum profit to investors. Researchers often use the fact of whether or not the price can be forecast as a tool to check market efficiency. They invent, apply or adjust different models to improve the predictive power. Finding a good method to forecast stock price more accurately will be a topic forever in both the academic field and the financial industry. Equity price prediction is regarded as a challenging task in the financial time series prediction doi:10.2478/amns.2021.2.00144
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