When Prof Cox got the data, she realized that it contained details of both past and current customers. She decided to get rid of the past customers. Vogue shouldn't be so keen on spending their energy on seeing how segments have evolved over time. She remembered her early days at the company, where the simple mantra was to get almost any client no matter what their profile was. As long as they had money to invest and passed the regulatory requirement checks, Vogue was more than happy to take them on board. Yes, the trio might be interested in seeing the segmentation comparison over time, but then their immediate task as she understood it was to present the current profiling to the board and their team to draw up a marketing strategy. She ran some descriptive statistics on the data and she found that there were a few outliers. These outliers were clients...
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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Customer Reviews