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Hands-On Artificial Intelligence for Banking

You're reading from  Hands-On Artificial Intelligence for Banking

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781788830782
Pages 240 pages
Edition 1st Edition
Languages
Authors (2):
Jeffrey Ng Jeffrey Ng
Profile icon Jeffrey Ng
Subhash Shah Subhash Shah
Profile icon Subhash Shah
View More author details

Table of Contents (14) Chapters

Preface 1. Section 1: Quick Review of AI in the Finance Industry
2. The Importance of AI in Banking 3. Section 2: Machine Learning Algorithms and Hands-on Examples
4. Time Series Analysis 5. Using Features and Reinforcement Learning to Automate Bank Financing 6. Mechanizing Capital Market Decisions 7. Predicting the Future of Investment Bankers 8. Automated Portfolio Management Using Treynor-Black Model and ResNet 9. Sensing Market Sentiment for Algorithmic Marketing at Sell Side 10. Building Personal Wealth Advisers with Bank APIs 11. Mass Customization of Client Lifetime Wealth 12. Real-World Considerations 13. Other Books You May Enjoy

Financial concepts

In this section, we will explore various financial concepts. For an in-depth survey of the domain knowledge, you are encouraged to refer to the syllabus of the Chartered Finance Analyst (CFA).

Alpha and beta returns in the capital asset pricing model

According to the capital asset pricing model (CAPM), investment return equals the risk-free rate + alpha + beta * market return + noise (with a mean of zero). Alpha is the return earned by the superior performance of the firm or investors, while beta is the riskiness of the asset in comparison to the overall market return. Beta is high when the risk of the investment is riskier than the average market. Noise is the random movement or luck that has a long-term return of zero.

The asset management industry, especially professional investment managers, is commonly charging clients based on alpha. That explains why people pay so much attention to alpha.

Realized and unrealized investment...

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