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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

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

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

Credit Risk Management

Credit Risk Management

Credit risk key metrics

Aspects of credit risk management

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

Inflation Forecasting for Financial Planning

Inflation Forecasting for Financial Planning

What is inflation?

Business case for forecasting inflation

Modeling methodology

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

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

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Dealing with multicollinearity

The modeler still wasn't sure that the model was robust enough. He remembered that he hadn't tested the model for any effects of multicollinearity. We spoke briefly about this phenomenon when we studied the correlation of stock prices with each of the eight independent variables proposed for the regression model. The multicollinearity test was run using the tolerance and the variance inflation factor (VIF).

The PROC REG code for multicollinearity is as follows:

PROC REG DATA=build plots(only label)=(RStudentByLeverage CooksD); 
ID date; 
MODEL stock = basket_index -- m1_money_supply_index/tol vif; 
RUN;

Figure 2.18: Partial output for multicollinearity

The tolerance is computed as 1-R². When the R² is high, the tolerance value is very low. Such low values of tolerance are indicative of multicollinearity. The VIF is derived by taking the...