Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

By Kai Keng Ang, Hiok Chai Quek. 2006

130 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Financial market prediction system with Evolino neural network and Delphi method (2013, methodology): “Ang and Quek (2006) proposed a model, which synergizes the price difference forecast method with a forecast bottleneck-free trading decision model.”
A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading (2011, background): “In recent years, there has been an increasing emphasis on neuro-fuzzy systems in financial engineering (Trippi & Turban, 1993) and stock price forecasting (Thammano, 1999; Wang, 2003; Ang & Quek, 2006).”
Applications of neuro fuzzy systems: A brief review and future outline (2014, background): “[61] K.K. Ang, C. Quek, Stock trading using rspop: a novel rough set-based neuro-fuzzy approach, IEEE Transactions on Neural Networks 17 (5) (2006) 1301–1315.”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “The traditional rough set strength of rule set management was combined with neuro-fuzzy systems by [6] for time series prediction based on actual stock market data.”
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2017, background): “In the context of the stock data learning, various feature representation strategies have been proposed from multiple views [20]–[22].”
Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification (2020, background): “Because of the fine properties of neurofuzzy models, different variants have been proposed until now and applied to diverse fields including nonlinear system identification and control [3]–[8], time series prediction [9], [10], regression [11], [12] and classification [13]–[19].”
Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach (2010, background): “…regression, neural networks, fuzzy systems, support vector machines and evolutionary computations) for stock price modeling and prediction (Ang & Quek, 2006; Saad, Prokhorov, & Wunsch, 1998), forecasting of ﬁnancial variables (Noh et al., 1994; Refenes, Burgess, & Bentz, 1997), equity…”