A hybrid genetic-neural architecture for stock indexes forecasting

By G. Armano, M. Marchesi, A. Murru. 2005

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

A Multiple-Criteria Approach for Forecasting Stock Price Direction : Nonlinear Probability Models with Application in S & P 500 Index (2016, methodology): “) have been utilized in forecasting of financial problems [9-13].”
An artificial neural network (p, d, q) model for timeseries forecasting (2010, background): “Armano, Marchesi, and Murru (2005) presented a new hybrid approach that integrated artificial neural network with genetic algorithms (GAs) to stock market forecast.”
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, methodology): “We selected ten technical indicators as feature subsets by the review of domain experts and prior researches (Armano et al., 2005; Diler, 2003; Huang & Tsai, 2009; Kim, 2003 Index at time t is lower than that at time t À 1, direction t is ''0''.”
Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, background): “About 20% of the surveyed articles use as inputs technical analysis factors that are sometimes combined with daily or previous stock index prices, as in Armano et al. (2004) and Atsalakis and Valavanis (2006b).”
Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks (2015, methodology): “To improve predicting precision, various network architectures and learning algorithms have been developed in the literature [15] [16] [17] [18] [19].”
Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, background): “Armano et al. [1] presented a new hybrid approach that integrated artiﬁcial neural network with genetic algorithms (GAs) to stock market forecast.”
Ensemble Learning for Time Series Prediction (2008, methodology): “In [19], a combination genetic classifiers is proposed for predicting stock indexes, while in [20], a hybrid combination of neural networks and the ARIMA model is applied for time series forecasting.”
Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks (2016, unknown): “[11] proposed a hybrid model, the combination of genetic algorithm for technical indicators and neural network for other data, as a new time series prediction method.”