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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.
Friday, April 11, 2025 Monday, January 1, 1

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