In Chapter 1, Time Series Modeling in the Financial Industry, we briefly touched upon the myth that every time we spoke about time series data and forecasting behavior. While this is true most of the time, it isn't valid on a lot of occasions. It was mentioned that time series data can be used for benchmarking, quality control, pattern recognition, and even estimating the effect of one variable on the observed values of another variable. Survival analysis focuses on estimating time to an event. In Chapter 2, Forecasting Stock Prices and Portfolio Decisions using Time Series Data, while discussing ARIMA, we saw that the model primarily depends on the time component and didn't focus much on the influencer variables. Survival analysis, just like multivariate regression, can also help emphasize the role of other influencer...
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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