Reinforcement Learning in Financial Markets: A Study on Dynamic Model Weight Assignment

Code

• github.com/akashdeepo/Dynamic-Model-Weight-Assignment-for-Financial-Markets
  tf★ 1

Abstract

This study innovates in financial market forecasting by employing reinforcement learning within a diverse ensemble of models, including Random Forest Regression, LSTM networks, linear regression, and sentiment analysis. Through dynamic, performance-based weight adjustments, our approach shows marked improvement over static strategies. Real-world data testing evidences enhanced prediction accuracy, hinting at potentially more profitable trading decisions. This work underscores the untapped potential of reinforcement learning for optimal ensemble model management in the ever-changing financial landscape.

Tasks

Reproductions