Metaheuristics for Financial Investment Strategies: Applications Survey

Machine learning has been widely used as part of financial markets investment strategies, whether for forecasting the financial assets exchange rate, managing market volatility, or solving different classification problems that help with decision-making. Building an investment strategy using a scien...

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Bibliographic Details
Published in2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) pp. 1 - 6
Main Authors Bousbaa, Zineb, Bencharef, Omar
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.07.2023
Subjects
Buildings
Exchange Rate forecasting
Exchange rates
Financial Markets
Investment strategies
Machine learning
Mataheuristics
Mechatronics
Metaheuristics
optimization techniques
Prediction algorithms
Surveys
Times Series
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Summary:Machine learning has been widely used as part of financial markets investment strategies, whether for forecasting the financial assets exchange rate, managing market volatility, or solving different classification problems that help with decision-making. Building an investment strategy using a scientific approach requires a massive amount of data, good computational power, and some expertise in the finance industry. Machine learning applications to the financial field, such as price exchange rate prediction, market pattern recognition, or other trading strategy tasks, are considered optimization problems. As they require an efficient algorithm dedicated to finding a global optimum, they can be solved using metaheuristics. In this survey, we study how metaheuristic optimization techniques contribute to building a robust learning model dedicated to financial investment strategy applications.
DOI:10.1109/ICECCME57830.2023.10253170