Chapter 12: Extended Machine Learning Approaches

In this section

• Natural Language Processing and Sentiment Analysis
  Learn how NLP methods and sentiment analysis transform unstructured text data—like news, earnings call transcripts, and social media—into actionable insights for financial decision-making.
• Reinforcement Learning in Trading Strategies
  Explore how reinforcement learning principles apply to developing trading strategies, from dynamic asset allocation to automated hedging, emphasizing key concepts like agent-environment interaction, reward maximization, and exploration-exploitation trade-offs.
• Deep Ensembles and Transfer Learning in Finance
  Explore how combining multiple deep neural networks and leveraging pre-trained models accelerate predictive accuracy, reduce uncertainty, and save costs in financial analytics.
• Automated Feature Selection and Engineering
  A deep dive into feature selection and engineering techniques within automated machine learning pipelines, emphasizing their importance for financial datasets full of noise and complex interactions.
• Vignette Exercises for Advanced ML Models
  Elevate your CFA® Level II quantitative skills through advanced machine learning vignettes integrating NLP, reinforcement learning, ensembles, and transfer learning, all framed within real investment scenarios.