A Hybrid Neurogenetic Approach for Stock Forecasting

By Yung-Keun Kwon, B. Moon. 2007

200 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research, Evolutionary Computation, Time-Series & Forecasting

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Citations in the corpus

Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, background): “Kwon et al. [19] proposed a recurrent neural network solution with genetic algorithms to predict stock values.”
A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data (2017, background): “Recently, many studies suggested using Neural Networks in financial trading systems [24]–[27].”
Financial Time-Series Data Analysis Using Deep Convolutional Neural Networks (2016, unknown): “[12] also used a hybrid model, the combination of recurrent neural network and genetic algorithm, to predict for NASDAQ, other 36 companies stock prices and textual feature data, and got more favorable results than traditional financial method.”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “E-mail address: leesj@mail.ee.nsysu.edu.tw (S.-J. Lee).”
Support vector regression with chaos-based firefly algorithm for stock market price forecasting (2013, background): “…of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia c Islamic Azad University, Tafresh Branch, Young Researchers Club, Tafresh, Iran d School of Information Systems, Curtin University, Perth, WA, Australia a r t i c l e i n f o”
A multiple-kernel support vector regression approach for stock market price forecasting (2011, unknown): “…autoregressive conditional heteroskedasticity (GARCH) (Bollerslev, 1986), artificial neural networks (ANN) (Hansen & Nelson, 1997; Kim & Han, 2008; Kwon & Moon, 2007; Qi & Zhang, 2008; Zhang & Zhou, 2004), fuzzy neural networks (FNN) (Chang & Liu, 2008; Oh, Pedrycz, & Park, 2006; Zarandi, Rezaee,…”
Computationally efficient FLANN-based intelligent stock price prediction system (2009, methodology): “The return from trading based on this model is shown to be better than buy-and-hold strategy and the random walk model [7]-[8].”
CARTMAP: a neural network method for automated feature selection in financial time series forecasting (2012, unknown): “, [26]).”
Stock prediction using deep learning (2016, methodology): “The literature includes Genetic Algorithm to optimise a RNN for stock forecasting [13].”