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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
Toc

Table of Contents (14) Chapters close

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

Network building and analysis using Neo4j

As sell-side analysts, besides finding out the primary impact of news on the company, we should also find out the secondary effect of any news. In our example, we will find out the suppliers, customers, and competitors of any news on the stocks.

We can do this using three approaches:

  • By means of direct disclosure, such as annual reports
  • By means of secondary sources (media reporting)
  • By means of industry inferences (for example, raw materials industries, such as oil industries, provide the output for transportation industries)

In this book, we use direct disclosure from the company to illustrate the point.

We are playing the role of equity researchers for the company stock, and one of our key roles is to understand the relevant parties' connections to the company. We seek to find out the related parties of the company—Duke Energy—by reading the company...

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