Bookflare net SAS for finance

SAS for Finance

Forecasting and data analysis techniques with real-world examples to build powerful financial
models


Harish Gulati

BIRMINGHAM - MUMBAI



SAS for Finance
Copyright © 2018 Packt Publishing
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First published: M ay 2018
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Published by Packt Publishing Ltd.
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ISBN 978-1-78862-456-5
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Contributors


About the author
Harish Gulati is a consultant, analyst, modeler, and trainer based in London. He has 15 years'
financial, consulting, and project management experience with leading banks, management
consultancies, and media hubs. He enjoys demystifying his complex line of work in his spare time.
This has led to him being an author and orator at analytical forums. He has also co-authored Role of a
Data Analyst, published by the British Chartered Institute of IT (BCS). He has an MBA in
brand communications and a degree in psychology and statistics.


About the reviewer
Rashmi Gupta is an entrepreneur and consultant for established media and financial brands in the
field of marketing and digital analytics. She is currently the director of Agile Fintech Partners.
Artificial intelligence is a subject area that interests her, and she is currently building her expertise in
the area.


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Table of Contents
Title Page
Copyright and Credits
SAS for Finance
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Disclaimer


1.

Time Series Modeling in the Financial Industry
Time series illustration

The importance of time series
Forecasting across industries
Characteristics of time series data
Seasonality
Trend
Outliers and rare events
Disruptions
Challenges in data
Influencer variables
Definition changes
Granularity required
Legacy issues
System differences
Source constraints
Vendor changes
Archiving policy
Good versus bad forecasts
Use of time series in the financial industry
Predicting stock prices and making portfolio decisions
Adhering to Basel norms
Demand planning
Inflation forecasting
Managing customer journeys and maintaining loyalty
Summary
References


2.

Forecasting Stock Prices and Portfolio Decisions using Time Series

Portfolio forecasting
A portfolio demands decisions
Forecasting process
Visualization of time series data
Business case study
Data collection and transformation
Model selection and fitting
Part A – Fit statistics
Part B - Diagnostic plots
Part C - Residual plots
Dealing with multicollinearity
Role of autocorrelation
Scoring based on PROC REG
ARIMA
Validation of models
Model implementation
Recap of key terms
Summary


3.

Credit Risk Management
Risk types
Basel norms
Credit risk key metrics
Exposure at default
Probability of default
Loss given default
Expected loss

Aspects of credit risk management
Basel and regulatory authority guidelines
Governance
Validation
Data
PD model build
Genmod procedure
Proc logistic
Proc Genmod probit
Summary


4.

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
What is inflation?
Reasons for inflation
Inflation outcome and the Philips curve
Winners and losers

Business case for forecasting inflation
Data-gathering exercise
Modeling methodology
Multivariate regression model
Forward selection model
Backward selection
Maximize R
Univariate model
Summary


6.

Managing Customer Loyalty Using Time Series Data
Advantages of survival modeling
Key aspects of survival analysis
Data structure
Business problem
Data preparation and exploration
Non-parametric procedure analysis
Survival curve for groups
Survival curve and covariates
Parametric procedure analysis
Semi-parametric procedure analysis
Summary


7.

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
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Preface
SAS is the world's largest privately held software business that offers an integrated suite of software
solutions to manage data, produce reports, and build statistical models.


Who this book is for
The book introduces statistical models in the finance industry in a simplified manner. It has realworld examples supported by data and code that reproduces the models. The chapters explain the
relevance of the models to business problems, and the discussions about the diagnostics explains how
the models can be implemented. The book uses various graphical illustrations, rather than having a
focus on equations, to help the reader understand complex models. The book is designed to be a quick
introduction to various modeling techniques by explaining their key concepts.
The intended reader is someone aspiring to work in the financial industry, or one of the many
financial industry professionals who want to explore its various facets. The reader could also be a
student curious to know how theoretical knowledge is applied in the industry, or a finance

professional who wants to up-skill and move on to another role. The book's audience may also
include any individual who works as a data analyst, data scientist, data architect, data engineer,
analytics and insights professional, business analyst, or someone who integrates the outputs of models
in business strategy but isn't aware of how problems are solved.


What this book covers
, Time Series Modeling in the Financial Industry, introduces time series modeling, and
discusses its importance, the characteristics and challenges of data, and explains its use in the
financial industry. The chapter also discusses the way forecasting is used across industries and what
is meant by a good or bad forecast.
Chapter 1

, Forecasting Stock Prices and Portfolio Decisions using Time Series, discusses the
concept of portfolio forecasting and the decisions involved in managing portfolios. After exploring
the forecasting process and the visualization of time series data, the chapter discusses modeling
techniques and explains how to select the most suitable one based on real-world modeling examples.
Chapter 2

, Credit Risk Management, provides context regarding the highly regulated nature of the
industry. Basel norms and key terms such as PD, LGD, EAD, and EL are discussed. A PD model
build methodology is briefly discussed.
Chapter 3

, Budget and Demand Forecasting, helps create an understanding of the Markov model and
showcases how to build a model. The chapter goes on to compare the Markov model forecast with
ARIMA-generated forecasts. It also explains how Markov Chain Monte Carlo can be used for data
imputation.
Chapter 4

, Inflation Forecasting for Financial Planning, defines inflation, explores the reasons for
inflation, and discusses its outcomes using the theory of the Phillips curve. The chapter also shows
how to leverage various procedures for data quality checks. Univariate and multivariate modeling
techniques are used for forecasting and a comparison of the results.
Chapter 5

, Managing Customer Loyalty using Time Series Data, introduces survival modeling, data
preparation techniques, and various methodologies, including parametric and semi-parametric
methods. It does this in the context of solving a business problem related to customer loyalty.
Chapter 6

, Transforming Time Series – Market Basket and Clustering, provides multiple business
examples while discussing the background and methodology of these techniques.
Chapter 7


To get the most out of this book
Basic knowledge of undergraduate-level mathematics is necessary. However, no advanced
mathematical degree is required to decipher how the financial industry uses time series modeling to
solve problems. Functional knowledge of SAS is desirable but isn't mandatory.
SAS University Edition is free software that is used throughout the book. Download details can be
found at />

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