An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network

By R. Kuo, C. Chen, Y. Hwang. Fuzzy Sets Syst., 2001

336 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/40c238be863d8e1a38638d4b365911e9e38537a5

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, background): “For example, in the work of [18], a genetic algorithm based fuzzy neural network is trained with additional political, financial, economic factors etc. to formulate trading decisions.”
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, methodology): “[29] proposed a genetic algorithm based fuzzy neural network (GFNN) to formulate the knowledge base of fuzzy inference rules which could measure the qualitative effect on the stock market.”
Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, background): “In the processing phase of GAs, fitness information (instead of gradient information) is the only requirement (Kuo et al. 2001).”
Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis (2010, methodology): “Kuo, Chen, and Hwang (2001) uses a genetic algorithm base fuzzy neural network to measure the qualitative effects on the stock price.”
On-Demand Forecasting of Stock Prices Using a Real-Time Predictor (2003, methodology): “Lately, artificial neural networks (ANNs) have been applied to this area [1], [8], [12].”
A TSK type fuzzy rule based system for stock price prediction (2008, methodology): “Kuo, Chen, and Hwang (2001) uses a genetic algorithm base fuzzy neural network to measure the qualitative effects on the stock price.”
Oppositional biogeography-based optimization (2009, methodology): “In finance, EC has been employed for bankruptcy [84, 85, 86] and stock predictions [87, 88, 89].”
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].”
Pattern graph tracking-based stock price prediction using big data (2018, methodology): “After selecting the historical dataset, to generate the predicted stock data, we use an artificial neural network algorithm because it is the most widely used in stock price forecasts ([9, 28, 33]) and has a high predictive power as an advantage over learning by iterative adjustment.”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “A type-2 TSK rule is erived from each cluster to form a fuzzy rule base.”
Real stock trading using soft computing models (2005, background): “Kuo et al. [7] uses a genetic algorithm base fuzzy neural network to measure the qualitative effects on the stock price.”
Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation (2005, background): “Finally, their studies showed that NNs based on both quantitative and qualitative factors are far superior to the ones based only on the quantitative factors [21] [22].”
State of the art of control schemes for smart systems featuring magneto-rheological materials (2016, background): “Fuzzy-set theory was proposed in [83, 84], and has been successfully applied to a diverse range of applications, mostly in the fields of control and intelligence systems [85, 86].”