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.

Related Content you might be interested in

Current Title:

Small book image
SAS for Finance
Harish Gulati
May, 2018 | 306 pages
Small book image
Hands-On SAS for Data Analysis
Harish Gulati
Sep, 2019 | 346 pages
Small book image
Building Statistical Models in Python
Paul N Adams | Stuart J Miller | Huy Hoang Nguyen
Aug, 2023 | 420 pages
Small book image
Data Analysis with IBM SPSS Statistics
Ken StehlikBarry | Anthony Babinec
Sep, 2017 | 446 pages
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
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Managing Customer Loyalty Using Time Series Data

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...

Previous Section
End of Section 1
Next Section