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SAS for Finance

You're reading from   SAS for Finance Forecasting and data analysis techniques with real-world examples to build powerful financial models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788624565
Length 306 pages
Edition 1st Edition
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Author (1):
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Harish Gulati Harish Gulati
Author Profile Icon Harish Gulati
Harish Gulati
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Table of Contents (9) Chapters Close

Preface 1. Time Series Modeling in the Financial Industry FREE CHAPTER 2. Forecasting Stock Prices and Portfolio Decisions using Time Series 3. Credit Risk Management 4. Budget and Demand Forecasting 5. Inflation Forecasting for Financial Planning 6. Managing Customer Loyalty Using Time Series Data 7. Transforming Time Series – Market Basket and Clustering 8. Other Books You May Enjoy

Summary

In our business case, we realized that our fund manager wanted to identify clients who met the following criteria:

  • Have been with the firm for a long time
  • Are probably going to stay for a long time
  • Are at risk of churn
  • Continue to have AUM with the brokerage, and the reasons for this

Survival analysis models were run to try to understand how some of the business questions could be answered. Prior to that, we tried to gain an understanding of what survival analysis modeling is, the advantages it presents over other modeling types, and the various forms of the analysis techniques, namely parametric, semi-parametric, and non-parametric approaches. We also highlighted the differences between the frequentist and the Bayesian approach using PROC LIFEREG. The concept of Markov chains was also introduced in the chapter as part of the discussion on Cox models. To use these techniques...

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