Surveying various genetic programming (GP) approaches to forecast real-time trends prices in the stock market

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Gite:2017:ieeeCC,

• author = "Balasaheb Gite and Khalid Sayed and Navin Mutha and Saurabhkumar Marpadge and Kshitij Patil",
• booktitle = "2017 Computing Conference",
• title = "Surveying various genetic programming (GP) approaches to forecast real-time trends prices in the stock market",
• year = "2017",
• pages = "131--134",
• abstract = "The share prices in the stock market are known for their extreme unpredictability and attempts to identify any familiar patterns in the prices poses a confounding problem for both fundamental & technical analysts. This article attempts to use symbolic regression capabilities of GP and a market trend indicator (RSI) to predict the price and trend of the particular stock as accurately as possible. The use of a market indicator to independently forecast the trend without any role of GP serves as a verification mechanism to the price predicted by GP for the next day to further validate the authenticity of the price of the stock in the context of the real-time stock market. Extensive testing has been done on the various evolution parameters and functions of GP to customize the GP approach as much as possible to suit the current application and optimise the results. Though obtained results can never be fully relied on by real technical analysts of the stock market, it could definitely be used as a decision making support.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/SAI.2017.8252093",
• month = jul,
• notes = "Also known as \cite{8252093}",

}

Genetic Programming entries for Balasaheb Gite Khalid Sayed Navin Mutha Saurabhkumar Marpadge Kshitij Patil

Citations