Book Image

SAS for Finance

By : Harish Gulati
Book Image

SAS for Finance

By: Harish Gulati

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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Table of Contents (9 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Disclaimer
Free Chapter
1
Time Series Modeling in the Financial Industry
Time Series Modeling in the Financial Industry
Time series illustration
The importance of time series
Forecasting across industries
Characteristics of time series data
Challenges in data
Good versus bad forecasts
Use of time series in the financial industry
Summary
References
2
Forecasting Stock Prices and Portfolio Decisions using Time Series
Forecasting Stock Prices and Portfolio Decisions using Time Series
Portfolio forecasting
A portfolio demands decisions
Forecasting process
Visualization of time series data
Dealing with multicollinearity
Role of autocorrelation
Scoring based on PROC REG
Recap of key terms
Summary
3
Credit Risk Management
Credit Risk Management
Risk types
Basel norms
Credit risk key metrics
Aspects of credit risk management
PD model build
Summary
4
Budget and Demand Forecasting
Budget and Demand Forecasting
The need for the Markov model
Business problem
Markovian model approach
ARIMA model approach
Markov method for imputation
Summary
5
Inflation Forecasting for Financial Planning
Inflation Forecasting for Financial Planning
What is inflation?
Business case for forecasting inflation
Modeling methodology
Summary
6
Managing Customer Loyalty Using Time Series Data
Managing Customer Loyalty Using Time Series Data
Advantages of survival modeling
Key aspects of survival analysis
Business problem
Summary
7
Transforming Time Series – Market Basket and Clustering
Transforming Time Series – Market Basket and Clustering
Market basket analysis
Segmentation and clustering
MBA business problem
Data preparation for MBA
Assumptions for MBA
Analysis of a set size of two
A segmentation business problem
Segmentation overview
Clustering methodologies
Segmentation suitability in the current scenario
Segmentation modeling
Summary
8
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References

Please refer to the following articles:

  • Lo, A.W. and Mackinlay, A.C. A Non-Random Walk Down Wall Street 5th Ed. Princeton University Press, 2002
  • Shumway, R. H. (1988). Applied statistical time series analysis. Englewood Cliffs, NJ: Prentice Hall. ISBN0130415006.
  • Gershenfeld, N. (1999). The Nature of Mathematical Modeling. New York: Cambridge University Press. pp. 205-208. ISBN0521570956.
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