Sam, Jwels, and Darius are partners at a hedge fund that deals with the mining, e-commerce, and aviation sectors. Sam started the fund 10 years ago when he left his employer, the leading commodities fund in London. Vogue was set up with a handful of clients and Sam initially worked on wafer-thin margins to increase his clients and assets under management (AUM). Darius and Jwels joined later on in the first few years of the fund being set up, and brought both e-commerce and aviation expertise to Vogue. The last ten years have been exciting for Vogue. Profits have grown by double digits, apart from in 2008 and 2009. Some of their key clients lost big at that time, either with Vogue or elsewhere. This meant that Sam had to get in new partners and focus on retaining and growing his client base at the same time. Vogue had only started in 2006, and then...
SAS for Finance
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SAS for Finance
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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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