In real-world scenarios, data is never perfect. There will be anomalies that a trained eye will be able to spot. Issues with data may occur due to the way the data is being sourced and as a result of the process used to store it. Data-retrieval issues related to technology, level of understanding of the data, the audited and controlled process for extraction, archiving issues, and understanding the data requirements of the business can further impact data quality. These are just a few examples of what can go wrong while trying to ensure that a firm has the best data quality. Thankfully, help is at hand when left with missing or poor quality data. Using various statistical methods, imputation can be performed to ensure there are no missing values. Imputation of missing values is an important step prior to modeling as a lot of the statistical procedures...
SAS for Finance
By :
SAS for Finance
By:
Overview of this book
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.
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Current Title:
SAS for Finance
Table of Contents (9 chapters)
Preface
Free Chapter
Time Series Modeling in the Financial Industry
Forecasting Stock Prices and Portfolio Decisions using Time Series
Credit Risk Management
Budget and Demand Forecasting
Inflation Forecasting for Financial Planning
Managing Customer Loyalty Using Time Series Data
Transforming Time Series – Market Basket and Clustering
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