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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

Predicting the trend of a security

In the preceding example, we played the role of a trader who followed the portfolio allocation set by the treasurer. Assuming that our job is to follow the securities required by the treasurer, the profit and loss of the trader hinges on how can we profit from buying low and selling high. We took the daily pricing history of securities as the data to build our model. In the following section, we will demonstrate how to predict the trend before making buy decisions for assets.

Solution

There are two major processes—one on model development and another on model backtesting. Both processes include a total of eight steps for real-time deployment, which we will not include here. However, it is very similar to model backtesting. The following diagram illustrates the flow of the process:

Loading, converting, and storing data

In this step, we will load the data, convert...

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