Technical and Sentiment Analysis in Financial Forecasting with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Christodoulaki:2022:CIFEr,

• author = "Eva Christodoulaki and Michael Kampouridis and Panagiotis Kanellopoulos",
• booktitle = "2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)",
• title = "Technical and Sentiment Analysis in Financial Forecasting with Genetic Programming",
• year = "2022",
• abstract = "Financial Forecasting is a popular and thriving research area that relies on indicators derived from technical and sentiment analysis. In this paper, we investigate the advantages that sentiment analysis indicators provide, by comparing their performance to that of technical indicators, when both are used individually as features into a genetic programming algorithm focusing on the maximization of the Sharpe ratio. Moreover, while previous sentiment analysis research has focused mostly on the titles of articles, in this paper we use the text of the articles and their summaries. Our goal is to explore further on all possible sentiment features and identify which features contribute the most. We perform experiments on 26 different datasets and show that sentiment analysis produces better, and statistically significant, average results than technical analysis in terms of Sharpe ratio and risk.",
• keywords = "genetic algorithms, genetic programming, Economics, Sentiment analysis, Focusing, Forecasting, Computational intelligence, Technical Analysis, Sentiment Analysis, Financial Forecasting",
• DOI = "doi:10.1109/CIFEr52523.2022.9776186",
• ISSN = "2640-7701",
• month = may,
• notes = "Also known as \cite{9776186}",

}

Genetic Programming entries for Eva Christodoulaki Michael Kampouridis Panagiotis Kanellopoulos

Citations