Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices

Lu, Jinna; Bai, Yanping

doi:10.2991/icibet.2013.15

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…BPNN is also useful in the economic field. Lu and Bai [8] proposed a hybrid forecasting model [Wavelet Denoising-based Back Propagation (BP)], which firstly decomposed the original data into multiple layers by wavelet transform, and then established BPNN model using the low-frequency signal of each layer for predicting the Shanghai Composite Index (SCI) closing price. The radial basis function neural network (RBFNN) is a feed forward neural network with a simple structure, which has a single hidden layer.…”

Section: Introductionmentioning

confidence: 99%

Back propagation mathematical model for stock price prediction

Chen

Han

2021

Applied Mathematics and Nonlinear Sciences

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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.

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Section: Introductionmentioning

confidence: 99%

Back propagation mathematical model for stock price prediction

Chen

Han

2021

Applied Mathematics and Nonlinear Sciences

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“…RBFNN have also been used in various forecasting areas and achieve good forecasting performance, with demonstrated advantages over BPNN in some applications [8,10]. The general regression neural network (GRNN), is put forward by Specht [20], shows its effectiveness in pattern recognition [29], stock price prediction [12,16] and groundwater level prediction [18]. [16]showed the forecasting ability of GRNN in the prediction of closing stock price.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for stock price prediction

Song,

Zhou,

Han

2018

Preprint

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Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price 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 (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.

show abstract

Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices

Cited by 2 publications

References 5 publications

Back propagation mathematical model for stock price prediction

Back propagation mathematical model for stock price prediction

Neural networks for stock price prediction

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