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 real-world 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

Chapter 1, 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 2, 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 3, 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 4, 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 5, 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 6, 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 7, Transforming Time Series – Market Basket and Clustering, provides multiple business examples while discussing the background and methodology of these techniques.

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 https://www.sas.com/en_gb/software/university-edition.html.

Download the example code files

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/SAS-for-Finance. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/PacktPublishing/SASforFinance_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The variables tall and grade have different values for height and scores in a test."

A block of code is set as follows:

data matrix (drop = lhand1 lhand2); 
set stage2 (drop = id); 
if product1 ne product2; 
if product1 ne product3; 
if product2 ne product3; 
combo=compress(product1||product2||product3); 
lhand1=scan(combo,1); 
lhand2=scan(combo,2); 
lhand=compress(lhand1||"|"||lhand2); 
run;

Note

Warnings or important notes appear like this.

Note

Tips and tricks appear like this.

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Disclaimer

SAS Institute Inc. hereby grants the author permission to use screenshots of SAS output using SAS^® University Edition software. It is with the understanding that the data produced will be customized/provided by the author.

Created with SAS^® University Edition software. Copyright 2014, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. Reproduced with permission of SAS Institute Inc., Cary,NC