We have looked at two analysis methodologies in this chapter. MBA analysis and segmentation. Both use datasets related to time series, but they transform the data and don't consider the time element explicitly as part of the analysis. In the MBA analysis, we saw that sequence was a sort of proxy for time series. We generated insights by focusing on business problems that were the subject of modeling. MBA lacks the statistical depth and rigor that clustering has. However, neither are strictly statistically-driven analysis scenarios. With MBA, we showcased how it makes intuitive sense to evaluate the association between the products and services offered by a bank and leverage the information. In segmentation via clustering, we showcased how the number of clusters generated differed from the preferred statistical information available to us from two different methodologies...
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Book Overview & Buying
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Table Of Contents
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.
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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