A company that decides to invest all of its money in fixed deposits, rather than in government bonds, will have more decisions to make. At some point in time, the cash flow of the company will increase or decrease. If it increases, there might be further investment decisions, and a need to decide between various investment choices. Again, the easiest choice would be to go for the option that offers the highest rate of return. But what about putting all of your eggs in one basket? Shouldn't the company think about the need to diversify? Is the option of fixed deposits as attractive as last time? Interest rates might have moved, or the fixed term on offer could be different. And what about using the funds to expand, rather than investing? Suppose that this tranche of funds available for investment is expected to be invested for a longer time period...
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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