SAS for finance : forecasting and data analysis techniques with real-world examples to build powerful financial models / Harish Gulati.

Format:

Book

Author/Creator:

Gulati, Harish, author.

Language:

English

Subjects (All):

SAS (Computer file).

SAS (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Other Title:

Statistical analysis system for finance

Place of Publication:

Birmingham ; Mumbai : Packt, 2018.

System Details:

text file

Biography/History:

Gulati Harish: Harish Gulati is a consultant, analyst, modeler, and trainer based in London. He has 16 years of financial, consulting, and project management experience across leading banks, management consultancies, and media hubs. He enjoys demystifying his complex line of work in his spare time. This has led him to be an author and orator at analytical forums. His published books include SAS for Finance by Packt and 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.

Summary:

Leverage the analytical power of SAS to perform financial analysis efficiently About This Book 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 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. 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 in?ation 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 In Detail 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. Style and approach A comprehensive guide filled with use-cases will ensure that you have a very good conceptual and practical understanding of using SAS in the finance domain.

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 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

Chapter 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

Chapter 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

Chapter 4: Budget and Demand Forecasting

The need for the Markov model.

Business problem

Markovian model approach

ARIMA model approach

Markov method for imputation

Chapter 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

Chapter 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

Chapter 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

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Index.

Notes:

Includes bibliographical references.

Description based on print version record.

ISBN:

9781788622486

1788622480

OCLC:

1041187772