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SAS for Finance

You're reading from   SAS for Finance Forecasting and data analysis techniques with real-world examples to build powerful financial models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788624565
Length 306 pages
Edition 1st Edition
Tools
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Author (1):
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 Gulati Gulati
Author Profile Icon Gulati
Gulati
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Table of Contents (13) Chapters Close

Title Page
Packt Upsell
Contributors
Preface
1. Time Series Modeling in the Financial Industry FREE CHAPTER 2. Forecasting Stock Prices and Portfolio Decisions using Time Series 3. Credit Risk Management 4. Budget and Demand Forecasting 5. Inflation Forecasting for Financial Planning 6. Managing Customer Loyalty Using Time Series Data 7. Transforming Time Series – Market Basket and Clustering 1. Other Books You May Enjoy Index

Appendix 1. Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Big Data Analytics with SAS David Pope

ISBN: 978-1-78829-090-6

  • Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting.
  • Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis.
  • Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages.
  • Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS
  • Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems.

IBM SPSS Modeler Essentials Jesus Salcedo, Keith McCormick

ISBN: 978-1-78829-111-8

  • Understand the basics of data mining and familiarize yourself with Modeler’s visual programming interface
  • Import data into Modeler and learn how to properly declare metadata
  • Obtain summary statistics and audit the quality of your data
  • Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields
  • Assess simple relationships using various statistical and graphing techniques
  • Get an overview of the different types of models available in Modeler
  • Build a decision tree model and assess its results
  • Score new data and export predictions
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