Machine Learning and Data Science Blueprints for Finance

Preface

The value of machine learning (ML) in finance is becoming more apparent each day. Machine learning is expected to become crucial to the functioning of financial markets. Analysts, portfolio managers, traders, and chief investment officers should all be familiar with ML techniques. For banks and other financial institutions striving to improve financial analysis, streamline processes, and increase security, ML is becoming the technology of choice. The use of ML in institutions is an increasing trend, and its potential for improving various systems can be observed in trading strategies, pricing, and risk management.

Although machine learning is making significant inroads across all verticals of the financial services industry, there is a gap between the ideas and the implementation of machine learning algorithms. A plethora of material is available on the web in these areas, yet very little is organized. Additionally, most of the literature is limited to trading algorithms only. Machine Learning and Data Science Blueprints for Finance fills this void and provides a machine learning toolbox customized for the financal market that allows the readers to be part of the machine learning revolution. This book is not limited to investing or trading strategies; it focuses on leveraging the art and craft of building ML-driven algorithms that are crucial in the finance industry.

Implementing machine learning models in finance is easier than commonly believed. There is also a misconception ...