Book Name: Machine Learning and Data Science Blueprints for Finance
Author: Brad Lookabaugh, Hariom Tatsat, Sahil Puri
File size: 9.8 MB
File format: ePub
Machine Learning and Data Science Blueprints for Finance Book Description:
Over the next few decades, machine learning and information science will alter the fund industry. With this practical book, analysts, traders, scientists, and developers will learn how to construct machine learning algorithms crucial to the industry.
You will examine ML concepts and above 20 case studies in supervised, unsupervised, and reinforcement learning, together with natural language processing (NLP). Perfect for professionals working at hedge funds, retail and investment banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset cost prediction, opinion analysis, and robo-advisor and chatbot development.
You will explore real-life issues faced by practitioners and find out scientifically sound solutions supported by code and examples.
This book covers
- Supervised learning regression-based models for trading approaches, derivative pricing, and portfolio management
- Supervised learning classification-based versions for credit default risk prediction, fraud detection, and trading strategies
- Dimensionality reduction methods with case studies in portfolio management, trading strategy, and yield curve construction
- Algorithms and clustering techniques for finding similar items, with case studies in trading strategies and portfolio management
- Reinforcement learning models and techniques employed for building trading approaches, derivatives hedging, and portfolio management
- NLP techniques using Python libraries like NLTK and scikit-learn for transforming text into meaningful representations