Budget and demand forecasting are important aspects of any finance team. Budget forecasting is the outcome, and demand forecasting is one of its components. Contrary to popular belief, the finance team cannot make demand forecasting decisions without consulting various other teams. The marketing team may need to be involved to figure out the impact of a brand-new commercial series on potential sales in the next 12 months. This needs to be an input in demand forecasting to calculate revenue. The risk management team needs to be consulted to calculate the impact of risk modeling on capital provision requirements. This needs to be an input in budget estimation. These examples are more apt for a mid-range or larger organization. New customers joining and churn in an organization are aspects that also impact small organizations. This would impact budget...
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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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