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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 2. Forecasting Stock Prices and Portfolio Decisions using Time Series FREE CHAPTER 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

Assumptions for MBA

Like most analysis, insights generated should be used after a conceptual understanding of how the analysis has been generated. The sequence plays an important role in the analysis. If, for instance, we conclude that customers with a personal current account are more likely to open a savings account, we are implying a sequence. This is easier to infer in a two-product set size. However, for a three-product set size, the insight statement might be misleading. When a customer has a personal current account and savings account, the customer is likely to take out insurance. In this instance, the right-hand side (RHS) of the rule is the savings account. The left-hand side (LHS) rule is a combination of the two products. If we assume that a personal current account is the first condition to adhere to in the rule, we could be wrong. Some procedures/macros (pre-written...

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