SAS for Finance

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

Harish Gulati

• Publisher: Packt Publishing
• Year: 2018
• ISBN:
  9781788622486
• Edition: 1
• Topic: Informatik
• Subtopic: Datenverarbeitung

Leverage the analytical power of SAS to perform financial analysis efficiently

Key Features

• Leverage the power of SAS to analyze financial data with ease
• Find hidden patterns in your data, predict future trends, and optimize risk management
• Learn why leading banks and financial institutions rely on SAS for financial analysis

Book Description

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.

What you will learn

• Understand time series data and its relevance in the financial industry
• Build a time series forecasting model in SAS using advanced modeling theories
• Develop models in SAS and infer using regression and Markov chains
• Forecast inflation by building an econometric model in SAS for your financial planning
• Manage customer loyalty by creating a survival model in SAS using various groupings
• Understand similarity analysis and clustering in SAS using time series data

Who this book is for

Financial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.

Budget and Demand Forecasting

Budget and demand forecasting are important aspects of any finance team. Budget forecasting is the outcome, and demand forecasting is one of its components. Contrary to popular belief, the finance team cannot make demand forecasting decisions without consulting various other teams. The marketing team may need to be involved to figure out the impact of a brand-new commercial series on potential sales in the next 12 months. This needs to be an input in demand forecasting to calculate revenue. The risk management team needs to be consulted to calculate the impact of risk modeling on capital provision requirements. This needs to be an input in budget estimation. These examples are more apt for a mid-range or larger organization. New customers joining and churn in an organization are aspects that also impact small organizations. This would impact budget planning for a small organization.

Hindsight is a luxury that the budget-forecasting process doesn't have. Hence, demand forecasting becomes an important component of the budgeting process. There are various qualitative and quantitative ways in which demand forecasting can be done. These could range from the de-briefing of sales staff, updating a model on a spreadsheet with basic assumptions, conducting a primary research survey, advanced statistical modeling, and so on.

Some of the topics that we will cover in the chapter include:

• Understanding the Markov model and exploring its use
• Forecasting using a Markov model
• Comparing Markov model forecasts with ARIMA generated forecasts
• Showcasing the use of the Markov model Monte Carlo method for data imputation

The need for the Markov model

Given the range of models we are discussing in this book, is there a need to discuss Markov models? When we speak about forecasting, one of the main inputs is the historical information. This could be in the form of a time series. However, Markov models don't need historical information to be able to forecast. When we build a Markov model, we are interested in the state (value/behavior/phenomenon) of a subject at the present time. We are also interested in the states that the subject can get transitioned to and the transition probabilities involved. A textbook definition of the Markov model would be a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. To understand the terms better, let's look at the states that a car being driven may experience:

Transition state flow

P(Stationary|Stationary)=0.3

P(In motion|Stationary)=0.4

P(Braking|Stationary)=0.3

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

Total Probability of transition from Stationary State = 1

The possible transition states are as follows:

Original state	Transitioned state
Stationary	Stationary
Stationary	In motion
Stationary	Braking
In motion	In motion
In motion	Stationary
In motion	Braking
Braking	Braking
Braking	Stationary
Braking	In motion

Figure 4.1: Inter-state transition probability of a car

While consider...