Forecasting stock indices with back propagation neural network

By Jian-Zhou Wang, Ju-Jie Wang, Zhe-George Zhang, Shu-Po Guo. 2011

395 citations recorded by the source corpus.

Abstract Stock prices as time series are non-stationary and highly-noisy due to the fact that stock markets are affected by a variety of factors. Predicting stock price or index with the noisy data directly is usually subject to large errors. In this paper, we propose a new approach to forecasting the stock prices via the Wavelet De-noising-based Back Propagation (WDBP) neural network. An effective algorithm for predicting the stock prices is developed. The monthly closing price data with the Shanghai Composite Index from January 1993 to December 2009 are used to illustrate the application of the WDBP neural network based algorithm in predicting the stock index. To show the advantage of this new approach for stock index forecast, the WDBP neural network is compared with the single Back Propagation (BP) neural network using the real data set.

Subfields: Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research, Time-Series & Forecasting

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Citations in the corpus

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Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks (2014, methodology): “Wang et al. (2011) used wavelets to transform the Shanghai Stock Exchange (SCE) prices into multiple levels of decomposition.”
An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework (2017, methodology): “Computational intelligence models on the other hand, such as neural networks [8, 5, 7], neuro-fuzzy models [9], support vector machines (SVM) [4], and genetic algorithms-based systems [1], demonstrated good performance achievements.”
Artificial neural networks applications in economics and management research: An exploratory literature review (2018, neutral): “Topic Count Representative Paper 1 92 [18] 2 106 [19] 3 103 [20] 4 78 [21] 5 146 [22] 6 137 [23] 7 110 [24]”
Predicting Stock Price Trend Using MACD Optimized by Historical Volatility (2018, background): “In [14], the authors argued that time series of stock prices are nonstationary and highly noisy.”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…based LSTM Encoder-Decoder (Bahdanau et al., 2014), DA-RNN (Qin et al., 2017) Sun et al. (2019) ARMA-GARCH-NN DNN, LSTM Zhou et al. (2019a) EMD2FNN ANN, FNN, EMD2NN, WDBPNN (Wang et al., 2011), Long-short Strategy The list of hybrid models between different deep learning models is shown in Table 8.”