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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

Exploring the Treynor-Black model

Due to the instability of the Markowitz mean-variance model in managing problems associated with multi-asset class portfolios, the Treynor-Black model was established. Treynor-Black's model fits the modern portfolio allocation approach where there are certain portfolios that are active and others that are passive. Here, passive refers to an investment that follows the market rate of return—not to beat the market average return but to closely follow the market return.

An active portfolio refers to the portfolio of investment in which we seek to deliver an above-market average return. The lower the market return with a market risk level, the higher the portfolio. Then, we allocate the total capital to an active portfolio. So, why take more risk if the market return is good enough? The Treynor-Black model seeks to allocate more weight to the asset that delivers a higher return/risk level out of the total risk/return level of the...

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