Prediction of S&P 500 and DJIA stock indices using Particle Swarm Optimization technique

By R. Majhi, Ganapati Panda, G. Sahoo, A. Panda, Arvind Choubey. 2008

45 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Sara Ahmed
2. Alaa F. Sheta
3. Hossam Faris
4. Desheng Dash Wu
5. Anthony Grace

In Management Science and Operations Research

1. Sara Ahmed
2. Alaa F. Sheta
3. Hossam Faris
4. Desheng Dash Wu
5. Weiwei Jiang
6. Anthony Grace
7. Will Serrano

Citations in the corpus

• Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, background): “These techniques include artificial neural networks (ANN) [21, 22], fuzzy logic (FL) [23], support vectormachines (SVM) [24, 25], particle swarm optimization (PSO) [26], and many others.”
• Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm (2011, background): “Forecasting accuracy of RBF algorithms optimized by AFSA, GA and PSO improved to some extent and were very close to each other.”
• A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, methodology): “It is important to develop such a relationships between variables in many cases such as predicting stock market [13], [14], [30], [31].”