AI for Finance
- Available for pre-order on May 3, 2023. Item will ship after May 24, 2023
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How could Finance benefit from AI? How can AI techniques provide an edge? 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.
Table of Contents
1. AI-Finance Synergy.
1.1. Speed matters. 1.2. The race is on seeking, not running. 1.3. Pattern recognition. 1.4. Data mining. 1.5. Forecasting. 1.6. Concluding Summary: Synergy between AI and finance.
2. Machine Learning knows no boundaries?
2.1. AlphaGo: the success. 2.2. General AI: the rose garden. 2.3. Complication: the reality. 2.4. Combinatorial explosion, the curse of computation. 2.5. A missing ingredient in classical economics. 2.6. Neither can live while the other survives. 2.7. Summary: powerful but not magical.
3.Machine Learning in Finance.
3.1. Machine Learning for Forecasting. 3.2. Supervised learning. 3.3. Know your data. 3.4. A glimpse of game theory. 3.5. ‘Unsupervised learning’ for bargaining. 3.6. Summary: machine learning is a game-changer
4. Modelling, Simulation and machine learning
4.1. Modelling. 4.2. Modelling: imperfect but useful. 4.3. Simulation: beyond mathematical analysis. 4.4. Case study: Risk Analysis. 4.5. Adding machine learning to modelling and simulation. 4.6. Mechanism Design. 4.7. Conclusion: model-simulate-learn, a powerful combination.
5. Portfolio Optimization
5.1. Maximising profit, minimising risk. 5.2. The Markowitz Model for portfolio optimization. 5.3. Constrained optimization. 5.4. Two-objective optimization. 5.5. The reality is much more complex. 5.6. Economics vs Computer Science. 5.7. Summary.
6. Financial Data: beyond time series
6.1. What is Time exactly? 6.2. Event-based time representation. 6.3. Measuring market volatility under DC. 6.4. Two eyes are better than one. 6.5. Striking discoveries under DC. 6.6. Research in DC. 6.7. Conclusion: new representation, new frontier.
7. Over the Horizon
7.1. Algorithmic Trading drones. 7.2. High-Frequency Finance. 7.3. Blockchain. 7.4. Information extraction from news. 7.5. Finance as a hard science.
Edward Tsang is a retired professor and a freelance consultant. With a first degree in finance and a PhD in AI, he has broad interests in constraint satisfaction, optimization, AI and finance.
“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.