Machine Learning in Financial Markets: Predictive Modeling for Trading Strategies

EasyChair Preprint no. 12340

Abstract

Machine learning techniques have gained significant traction in financial markets for predictive modeling and trading strategies. This paper explores the application of machine learning in financial markets with a focus on predictive modeling for trading strategies. The main objective of this research is to develop and test machine learning models that can accurately predict future market movements and generate profitable trading strategies based on these predictions. The paper begins by providing an overview of the current state of machine learning in financial markets, highlighting its benefits and challenges. Next, the paper discusses various machine learning techniques commonly used in financial markets, such as supervised learning, unsupervised learning, and reinforcement learning. It also covers popular algorithms, including decision trees, random forests, support vector machines, neural networks, and deep learning models. The paper then presents a case study where machine learning techniques are applied to predict stock price movements and develop trading strategies. It discusses the data preprocessing steps, feature engineering techniques, model selection process, and performance evaluation metrics used in the case study.

Keyphrases: financial markets, machine learning, predictive modeling

@Booklet{EasyChair:12340,
  author = {James Henry and Ryan Jace},
  title = {Machine Learning in Financial Markets: Predictive Modeling for Trading Strategies},
  howpublished = {EasyChair Preprint no. 12340},

  year = {EasyChair, 2024}}