Journal ArticleDOI
Forecasting stock indices with back propagation neural network
TLDR
A new approach to forecasting the stock prices via the Wavelet De-noising-based Back Propagation (WDBP) neural network is proposed and an effective algorithm for predicting theStock prices is developed.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.read more
Citations
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Journal ArticleDOI
Deep learning networks for stock market analysis and prediction
TL;DR: A systematic analysis of the use of deep learning networks for stock market analysis and prediction using five-minute intraday data from the Korean KOSPI stock market as input data to examine the effects of three unsupervised feature extraction methods.
Journal ArticleDOI
Computational Intelligence and Financial Markets
Rodolfo C. Cavalcante,Rodrigo C. Brasileiro,Victor L. F. Souza,Jarley P. Nobrega,Adriano L. I. Oliveira +4 more
TL;DR: An overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others, are given.
Journal ArticleDOI
Review: Cloud computing service composition: A systematic literature review
TL;DR: By dividing the research into four main groups based on the problem-solving approaches and identifying the investigated quality of service parameters, intended objectives, and developing environments, beneficial results and statistics are obtained that can contribute to future research.
Journal ArticleDOI
Recurrent neural network and a hybrid model for prediction of stock returns
TL;DR: A novel hybrid model is proposed for prediction of stocks returns which is hybrid of two linear models and a non-linear model which outperforms recurrent neural network.
Journal ArticleDOI
NSE Stock Market Prediction Using Deep-Learning Models
TL;DR: Four types of deep learning architectures are used i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available for day-wise closing price of two different stock markets.
References
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