Machine learning technology has reshaped the way modern industries are running their affairs. In the financial services industry, AI provides powerful tools that are helping streamline banking services in process automation, fraud detection and prevention, algorithmic trading, underwriting and credit scoring, customer retention, and more.
There are numerous AI applications in the pipeline and significant gains can be realized in the future. For instance, thanks to AI, the FinTech industry is estimated to save over $1 trillion by 2030. Overall, the global AI in the Fintech market is estimated to reach USD 26.67 billion by 2026 from USD 7.91 billion in 2020.
However, most financial institutions are still only beginning to implement AI and ML technologies. For professionals in the industry, it’s a call towards learning to build machine learning algorithms crucial to the fintech industry.
If you are a student, financial analyst, trader, researcher, or developer who is wondering where to get started in building AI-based finance applications, this guide comprises the best and highly recommended books in AI for financial professionals, from theory to practice. Read on.
If you want to know more about ML applications in finance, this is one of the books you need on your reading list and it won’t be long before you agree with other readers that the authors Matthew F. Dixon, Igor Halperin, and Paul Bilokon have done a great job!
Like the title says, Machine Learning in Finance: From Theory to Practice provides readers with comprehensive ML techniques in Finance. It’s therefore a handy guide for students and practitioners looking to learn about AI in the finance industry. The authors discuss models that are relevant to finance covering theory and applications in supervised learning, unsupervised, and reinforcement learning especially the use of Inverse Reinforcement Learning in trading, investment, and wealth management.
The most important and helpful bit is that the concepts are accompanied by well-designed Python notebooks presenting examples that you can put into practice.
Another impressive thing about this book is that it covers a range of foundational concepts in maths and statistics. Anyone interested in the current state of machine learning models in finance has much to learn from this book. You can get it here.
2. Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis
You’ll find this book insightful and worthwhile to an extent of finding it difficult to put it down. The author Eryk Lewinson guides you through solving common financial challenges using Python libraries including SciPy, NumPy, and pandas with a lot of examples to get you started.
With every chapter, you get a starting point where you can easily customize the code to fit your financial problem statement. There are many libraries out there. Having a copy of this book shows which library is best suited for what problem saving you a lot of research time. And you don’t have to read it through one time, work as a good reference book for a specific library you could be working on.
It’s a must-have python for finance book that I highly recommend. Even when you have little to no Python knowledge, you can be sure it’ll help you learn python concepts. You’ll find the recipes extremely helpful as they have crucial info to jump into when you are looking for info about which library to use for a particular application.
For anyone who wants to understand modern investment management, this book is a must-read. Students, practitioners, and technologists working on solutions for the investment company will find it an exciting read. Also, finance professionals already familiar with statistical data analysis techniques and who want a technical roadmap to join the machine learning wave will find the book very helpful.
The author, Marcos López de Prado, perfectly blends the cutting-edge technology developments in AI/ ML with important life lessons learned from years of experience in the financial industry.
He explains how portfolio managers use machine learning to develop, test, and implement investment strategies in real-world scenarios. Additionally, he explains why standard ML algorithms fail when applied to financial problems and provides practical solutions to specific challenges faced by financial analysts.
Simply put, it’s loaded with lessons and techniques covering both the theoretical and applied knowledge for anyone interested in or looking to deploy and succeed with ML techniques in finance. Get yours today!
4. Artificial Intelligence for Finance Executives: From Industry Trends and Case studies to Algorithms and Concepts
Alexis Besse runs a Fintech company that applies machine learning and data analytics to future-proof financial companies’ operations. He is also an adviser to banks, investment managers, and Fintech companies.
In this book, he targets finance executives in organizations by showing you how to build a successful data-driven enterprise. To do this, he goes past the myths and misconceptions to explore AI in the financial sector at a deep level. He gets you started by looking into what really is AI in finance, how you can develop concrete use cases from idea to production, the challenges you may face while developing AI technologies and how to go about them.
This book is as real as it can get when a successful data-driven organization is what you want to build. It spells out answers to many questions that are usually ignored with a vision for the future. Get yours today.
If you are curious about exploring present-day and future applications of Machine Learning in Finance from a practical perspective, this book is highly recommended and may be what you need. Machine Learning and Data Science Blueprints for Finance is another great book that covers virtually everything you need to know about machine learning applications in finance.
One of the reasons that will make you love this book is the fact that it comprises code examples and useful case studies. That means that this book will provide you with a great deal of theory and practical tools for learning and solving all kinds of problems in the field. Since it focuses on a hands-on approach, you’ll have the option to try out the codes on your own.
By leveraging the codes of the presented case studies and associated GitHub repo, you’ll be able to implement some of those problem statements you have been thinking about related to the finance industry. Get your copy and take that deep dive from theory to the practice of AI/ML in finance.
6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
This listicle would be incomplete without this highly recommended book. The author Aurélien Géron, a machine learning consultant and trainer assume you have no or very little knowledge about Machine Learning. He provides readers with ML concepts and tools they need to implement programs with the capability of learning from data. You’ll be pleased that the book covers a large number of techniques, from the simplest to some of the deep learning techniques.
The book also comprises workouts in each chapter to help you apply and get grounded in what you learn. The code is well written and also accessed on Github providing a hands-on approach to learning by doing.
If you are looking to start building predictive finance models in Python, this book will provide you with the knowledge, grounding, and tools for solving many kinds of problems including financial problems. Check it out here.
As you probably know, Python is a top programming language that has been recently adopted by financial institutions including the largest investment banks and hedge funds who are using it to develop essential finance systems.
Python for Finance helps you get started with the Python programming language, guiding you through Python libraries and tools for building financial applications.
Yves Hilpisch the author uses practical examples throughout the entire book to demonstrate financial frameworks based on a case study to help every reader master data-driven finance algorithms.
8. Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python
Mastering Python for Finance will take your financial skills in AI/ML to the next level by helping you master cutting-edge mathematical and statistical financial applications.
The author guides you through concepts including an outline of Financial Analysis with Python, Linearity, and Nonlinearity in Finance, Numerical approaches for Pricing Options, Modeling Interest Rates and Derivatives, Time Series Statistical Analysis, Interactive Financial Analytics with VIX, creating an Algorithmic Trading Platform. Backtesting System Implementation, Machine Learning for Finance, Deep Learning for Finance, and more.
You’ll also be able to set up your Jupyter notebook to execute codes throughout the book. In the end, you’ll have explored forward-thinking advanced financial models and perform efficient data analysis in the field using libraries such as TensorFlow, Numpy, Keras, SciPy, and sklearn, all these and much more. Please get the book here.
9. Build and Deploy Algorithmic Trading Systems and Strategies using Python and Advanced Data Analysis
As the name suggests, Algorithmic Trading starts with an introduction to algorithmic trading and how to set up the environment you need to code presented in the book.
The author then takes you through understanding algorithmic trading systems and strategies. You then move on to design, and build practical trading strategies using real-world market data as well as perform real-world trading strategies analysis.
As you come to the end of the book, you’ll be able to create a trading bot from scratch using the knowledge you have gained from previous strategies. Get a copy and learn and build algorithmic trading systems using Python.
Algorithms have become pervasive in the financial markets. They have spurred trading algorithms and their usage will no doubt continue to expand. It’s what makes this book a must-read for traders, portfolio managers, investment brokers, and other finance professionals.
Written by Jeffrey M Bacidore, Algorithmic Trading is a guide delivering a practical introduction to modern execution algorithms. It provides readers with well-written details on how trading algorithms work in real-world practice, how they should be used, and how to evaluate their performance. Jeffrey exceptionally combines theory and years of practical experience to building and using algorithms in a number of different contexts.
Specifically, the book covers VWAP, TWAP, POV, and Implementation Shortfall algorithms. It also details multi-order algorithms including Pairs Trading and Portfolio Trading algorithms, performance measurements such as trading benchmarks, and more. You can get the book here.
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