Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

This book is about how machine learning and data science will transform the finance industry. It will examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). It is ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, and delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development.

Format: Paperback / softback
Length: 428 pages
Publication date: 05 January 2021
Publisher: O'Reilly Media, Inc, USA

The finance industry is set to undergo a significant transformation over the coming decades, driven by the convergence of machine learning and data science. This practical guide aims to equip analysts, traders, researchers, and developers with the skills necessary to build machine learning algorithms that are crucial to the industry's success.

The book will delve into various aspects of machine learning, including supervised, unsupervised, and reinforcement learning, as well as natural language processing (NLP). It will provide a comprehensive examination of ML concepts and over 20 case studies covering supervised learning, unsupervised learning, and reinforcement learning. These case studies will showcase real-life problems faced by practitioners and offer scientifically sound solutions supported by code and examples.

The book is designed for professionals working at hedge funds, investment and retail banks, and fintech firms. It will cover topics such as portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. Readers will gain insights into the practical applications of machine learning and its potential to enhance financial performance and decision-making processes.

The book will be organized into chapters, each covering a specific aspect of machine learning and finance. The first chapter will provide an introduction to machine learning and its applications in finance. It will discuss the basic principles of supervised learning, unsupervised learning, and reinforcement learning, as well as the importance of NLP in the field.

The second chapter will focus on supervised learning, which involves training algorithms to make predictions based on labeled data. It will cover regression-based models for trading strategies, derivative pricing, and portfolio management. The chapter will also introduce dimensionality reduction techniques, which are used to reduce the complexity of data and improve the accuracy of predictions.

The third chapter will explore supervised learning classification-based models, which are used for credit default risk prediction, fraud detection, and trading strategies. It will cover classification algorithms such as logistic regression, decision trees, and neural networks, as well as ensemble methods and cross-validation techniques.

The fourth chapter will delve into dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction. It will showcase algorithms and clustering techniques that are used to find similar objects and identify patterns in financial data.

The fifth chapter will focus on reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management. It will cover reinforcement learning algorithms such as Q-learning and policy gradients, as well as techniques for dealing with complex environments and optimizing performance.

The sixth chapter will explore NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations. It will cover topics such as tokenization, stemming, and part-of-speech tagging, as well as text classification and sentiment analysis.

The final chapter will provide a summary of the key concepts covered in the book and offer insights into the future of machine learning and finance. It will discuss the challenges and opportunities that lie ahead for practitioners and researchers in the field.

In conclusion, this practical guide aims to equip professionals with the skills necessary to build machine learning algorithms that are crucial to the finance industry's transformation. It will provide a comprehensive examination of ML concepts and over 20 case studies, covering supervised, unsupervised, and reinforcement learning, as well as NLP. The book is designed for professionals working at hedge funds, investment and retail banks, and fintech firms and will cover topics such as portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. With its comprehensive coverage and practical insights, this book is an essential resource for anyone looking to stay ahead of the curve in the rapidly evolving world of machine learning and finance.

Weight: 672g
Dimension: 178 x 233 x 24 (mm)
ISBN-13: 9781492073055