Paper Info
Title
A daily stock index predictor using feature selection based on a genetic wrapper
Abstract
In this experiment, we applied a genetic feature selection to global macroeconomics indicators, and used support vector regression to create a model for predicting the daily increase/decrease rate of Korea Composite Stock Price Index (KOSPI). Experiments were conducted by dividing the time series of 2007 to 2018 into various yearly time intervals. Overall, the mean absolute error was approximately 15% lower when the genetic algorithm-applied feature selection method was used. The feature selection improved the predictive performance by assembling discriminatory subsets of macroeconomic indicators that effectively represent the stock market trends in a given time interval.
Year
DOI
Venue
2020
10.1145/3377929.3398154
GECCO '20: Genetic and Evolutionary Computation Conference Cancún Mexico July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7127-8
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Dong-Hee Cho100.34
Seung-Hyun Moon200.68
Yong-Hyuk Kim335540.27