In our illustration, the business problem could be defined as the need to understand the probability of a machine being operational across its lifespan. Did all the machines fail at some point in the period of evaluation? No. The data spans 20 years and we have 55% of the instances when the machines haven't stopped functioning in the given time span. So how can we find the survival rate or hazard rate of machines when they haven't stopped functioning? Well, we could use the data of the machines that did stop functioning. We could maybe look at the common characteristics (such as the manufacturer, functional area the machine has been deployed for, the location of the plant where it has been installed, servicing history, and so on) of the failed and the functioning machines and extrapolate the knowledge gained from analyzing failed machines...
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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