Machine learning methods applied for investment strategies

Abstract

Machine learning is increasingly becoming a key and preferred tool in the creation and optimization of investment portfolios. Its use lies in more accurate predictions, adaptive decision-making and the ability to learn from patterns how to respond to market events. In the field of finance, machine learning is applied using regression models, neural netarticles or even reward learning, where the goal is to predict asset price movements. Using neural netarticles, it is possible to identify complex patterns in historical data, while reward learning optimizes trading strategies based on feedback. We also discuss the advantages and challenges associated with the use of machine learning in investing, such as model interpretability or data quality.