Fundamental, Technical and Sentiment Analysis for Algorithmic Trading with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Christodoulaki:2023:SSCI,

• author = "Eva Christodoulaki and Michael Kampouridis",
• booktitle = "2023 IEEE Symposium Series on Computational Intelligence (SSCI)",
• title = "Fundamental, Technical and Sentiment Analysis for Algorithmic Trading with Genetic Programming",
• year = "2023",
• pages = "83--89",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Support vector machines, Measurement, Sentiment analysis, Profitability, Companies, Algorithmic Trading",
• ISSN = "2472-8322",
• DOI = "doi:10.1109/SSCI52147.2023.10372070",
• abstract = "Algorithmic trading is a topic with major developments in the last years. Investors rely mostly on indicators derived from fundamental (FA) or technical analysis (TA), while sentiment analysis (SA) has also received attention in the last decade. This has led to great financial advantages with algorithms being the main tool to create pre-programmed trading strategies. Although the three analysis types have been mainly considered individually, their combination has not been studied as much. Given the ability of each individual analysis type in identifying profitable trading strategies, we are motivated to investigate if we can increase the profitability of such strategies by combining their indicators. we propose a novel Genetic Programming (GP) algorithm that combines the three analysis types and we showcase the advantages of their combination in terms of three financial metrics, namely Sharpe ratio, rate of return and risk. We conduct experiments on 30 companies and based on the results, the combination of the three analysis types statistically and significantly outperforms their individual results, as well as their pairwise combinations. More specifically, the proposed GP algorithm has the highest mean and median values for Sharpe ratio and rate of return, and the lowest (best) mean value for risk. Moreover, we benchmark our GP algorithm against multilayer perceptron and support vector machine, and show that it statistically outperforms both algorithms in terms of Sharpe ratio and risk.",
• notes = "Also known as \cite{10372070}",

}

Genetic Programming entries for Eva Christodoulaki Michael Kampouridis

Citations