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Ticknor, J. L. (2013). A Bayesian Regularized Artificial Neural Network for Stock Market Forecasting. Expert Systems with Applications, 40, 5501-5506.
https://doi.org/10.1016/j.eswa.2013.04.013

has been cited by the following article:

• TITLE: The Forecasting Model of Stock Price Based on PCA and BP Neural Network

  AUTHORS: Haoling Zhang

  KEYWORDS: BP Neutral Network, PCA, Stock Price Forecasting

  JOURNAL NAME: Journal of Financial Risk Management, Vol.7 No.4, December 5, 2018

  ABSTRACT: Based on Principal Component Analysis (PCA) and Back Propagation neural network, this paper establishes stock forecast model, and takes the Yunnan Baiyao (000538) as example, 29 indicators are selected from stocks technical analysis, and the neural network is input after dimension reduction and further confirms number of hidden layer nodes, learning rate, activation function and training function of the network in accordance with comparison and analysis of Mean Square Error (MSE) and Mean Absolute Error (MAE) in different parameter data experiments. Lastly, the model with steadiness and accuracy is obtained.