Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

By Wei Shen, Xiaopeng Guo, Chao Wu, Desheng Dash Wu. 2011

397 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Walid Saad
2. Merouane Debbah
3. Mingzhe Chen
4. Georgios Sermpinis
5. Changchuan Yin
6. Christian L. Dunis
7. Sara Ahmed
8. Alaa F. Sheta

In Electrical and Electronic Engineering

1. Walid Saad
2. Merouane Debbah
3. Mingzhe Chen
4. Changchuan Yin
5. Medhi Bennis
6. Ursula Challita
7. Pooyan Nayyeri
8. Hamed Jabani

In Management Science and Operations Research

1. Walid Saad
2. Merouane Debbah
3. Pooyan Nayyeri
4. Hamed Jabani
5. Efstratios F. Georgopoulos
6. Georgios Sermpinis
7. Andreas S. Karathanasopoulos
8. Changchuan Yin

Citations in the corpus

• Deep Learning for Stock Market Prediction (2020, methodology): “eloping the model performance. The final results demonstrated that the proposed method was capable of dealing with the stock market fluctuations with suitable prediction accuracy. Shen, Guo, Wu et al [23] introduced the Artificial Fish Swarm Algorithm (AFSA) for training radial basis function neural”
• Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization (2013, methodology): “Section 5 gives the empirical results of all the models considered and investigates the possibility of improving their performance with the introduction of a sophisticated trading strategy while section 6 provides some concluding remarks.”
• Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies (2017, methodology): “With respect to this model Shen et al. (2011) note that it exhibits the typical limitations of artificial intelligence models and discuss some of the optimisation approaches that have been adopted in practice to counter this.”
• Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “RBF networks have been applied across many applications [149]– [153] including function approximation, stock prediction, classification, and face recognition.”
• A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, methodology): “Variations of ANNs can be used to perform classification, pattern recognition and predictive tasks [15], [19], [20], [22], [30].”