Stock prediction based on financial correlation

In this paper, we propose a neuro-genetic stock prediction system based on financial correlation between companies. A number of input variables are produced from the relatively highly correlated companies. The genetic algorithm selects a set of informative input features among them for a recurrent neural network. It showed notable improvement over not only the buy-and-hold strategy but also the recurrent neural network using only the input variables from the target company. Categories and Subject Descriptors J.1 [Computer Applications]: Administrative Data Processing—Financial General Terms Experimentation Keywords Stock prediction, financial network, cross-correlation, feedforward neural network

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Yung-Keun Kwon, Sung-Soon Choi, Byung Ro Moon
• Keywords: GECCO 2005, Input Variables, Neural Network, Recurrent Neural Network