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
General
Imprint: |
O'Reilly Media
|
Country of origin: |
United States |
Release date: |
November 2020 |
Authors: |
Hariom Tatsat
• Sahil Puri
• Brad Lookabaugh
|
Dimensions: |
233 x 178 x 24mm (L x W x T) |
Format: |
Paperback
|
Pages: |
428 |
ISBN-13: |
978-1-4920-7305-5 |
Categories: |
Books
|
LSN: |
1-4920-7305-9 |
Barcode: |
9781492073055 |
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