Short description:

Moving well beyond simply speeding up computation, this book tackles AI for Finance from a range of perspectives including business, technology, research, and students. Covering aspects like algorithms, big data, and machine learning, this book answers these and many other questions.

Long description:

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.

Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.

To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. Trading depth for readability, AI for Finance will help readers decide whether to invest more time into the subject.

?This important book is an unusually topical attempt to introduce readers to the relationship between the technical analysis of financial market prices and the automated implementation of its findings. The book will be of considerable interest to those who wish to know about this relationship in an eminently readable form: both professional financial market analysts and those considering future employment in the field.? --Michael Dempster, ?Professor Emeritus in the Statistical Laboratory at the University of Cambridge

?AI is an important part of finance today. Students who want to join the finance industry should read this book. The trained eyes will also find a lot of insights in the book. I cannot think of any other book that teaches computational finance at a beginner's level but at the same time is useful to practitioners.? --Amadeo Alentorn, PhD, Head of Systematic Equities at Jupiter Asset Management

"AI for Finance is an excellent primer for experts and newcomers seeking to unlock the potential of AI. The book combines deep thinking with a bird?s eye view of the whole field - the ideal text to get inspired and apply AI. A big thank you to Edward Tsang, a pioneer of AI and quantitative finance, for making the concepts and usage of AI easily accessible to academics and practitioners." --Richard Olsen, Founder and CEO of Lykke, co-founder of OANDA, and pioneer in high frequency finance and fintech

?Without a doubt, AI symbolizes the future of finance and, in this important book, Professor Tsang provides an excellent account of its mechanics, concepts and strategies. Books featuring AI in finance are rare so practitioners and students would do well to read it to gain focus and valuable insights into this fast-evolving technology. Congratulations to Professor Tsang for providing a readable and engaging work in a complex technology that will appeal to all levels of readers!? --Dr David Norman, Founder of the TTC Institute

"The use of AI/ML in the financial industry is now more than a hype. In financial institutions there are numerous active transformation programs to introduce AI/ML enabled products in areas such as risk, trading and advanced analytics. In this book, Edward, one of the early adopters of AI in finance, has provided an insightful guide for both finance practitioners and academics. I can see this book becoming a major reference in real-world applied AI in finance. Directional Change (Chapter 6) should be of particular interest to data scientists in finance, as how one collects data determines what one can reason about." -- Dr Ali Rais Shaghaghi, Lead Data Scientist at NatWest Group.

Table of Contents:

1. AI-Finance Synergy, 2. Machine Learning Knows No Boundaries?, 3.Machine Learning in Finance, 4. Modelling, Simulation and Machine Learning, 5. Portfolio Optimization, 6. Financial Data: Beyond Time Series, 7. Over the Horizon