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...
SAS for Finance
By :
SAS for Finance
By:
Overview of this book
SAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.
SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.
This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.
By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.
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Current Title:
SAS for Finance
Table of Contents (9 chapters)
Preface
Free Chapter
Time Series Modeling in the Financial Industry
Forecasting Stock Prices and Portfolio Decisions using Time Series
Credit Risk Management
Budget and Demand Forecasting
Inflation Forecasting for Financial Planning
Managing Customer Loyalty Using Time Series Data
Transforming Time Series – Market Basket and Clustering
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