1st Edition
by Hariom Tatsat (Author), Sahil Puri (Author), Brad Lookabaugh (Author)
Over the next few decades,
machine learning and data science will transform the finance industry.
With this practical book, analysts, traders, researchers, and developers
will learn how to build machine learning algorithms crucial to the
industry. You'll examine ML concepts and over 20 case studies in
supervised, unsupervised, and reinforcement learning, along with natural
language processing (NLP).
Ideal for professionals working at
hedge funds, investment and retail banks, and fintech firms, this book
also delves deep into portfolio management, algorithmic trading,
derivative pricing, fraud detection, asset price prediction, sentiment
analysis, and chatbot development. You'll explore real-life problems
faced by practitioners and learn scientifically sound solutions
supported by code and examples.
This book covers:
- Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
- Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
- Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
- Algorithms
and clustering techniques for finding similar objects, with case
studies in trading strategies and portfolio management
- Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
- NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
