SAS for Finance: Forecasting and data analysis techniques with real-world examples to build powerful financial models

by Packt Publishing

Overview

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.

Product Details

ISBN-13:	9781788622486
Publisher:	Packt Publishing
Publication date:	05/30/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	306
File size:	6 MB

About the Author

Harish Gulati is a consultant, analyst, modeler, and trainer based in London. He has 15 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. He has also co-authored Role of a Data Analyst, published by British Chartered Institute of IT (BCS). He has an MBA in brand communications and a degree in psychology and statistics.

Table of Contents

Table of Contents

1. Introduction to Time Series modelling in financial industry
2. Forecasting stock prices and portfolio decisions using Time Series data (Stocks Forecasting)
3. Build Probability of Default (PD) model to adhere to BASEL norms (Risk Management)
4. Revenue forecasting to manage budgets/operational strategies (Budget and Demand Forecasting)
5. Inflation forecasting for financial planning (Econometric Modelling)
6. Manage customer loyalty using Time Series data (Customer Loyalty)
7. Pattern Discovery in Product Purchases (Segmentation)