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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
Languages
Tools
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis 2. Factor Models FREE CHAPTER 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

Collecting data


Building the required database could be one of the biggest challenges. Here, we do not only need dividend-adjusted price quotes but also financial statements data. Chapter 4, Big Data – Advanced Analytics described how to access some of the open data sources, but those rarely offer you all the required information in a package.

Another option might be to use professional financial data providers as a source. These platforms allow you to create tailor-made tables that can be exported to Microsoft Excel. For the sake of this chapter, we used a Bloomberg terminal. As a first step, we exported the data to Microsoft Excel.

Spreadsheets may be an excellent tool to build a database of data collected from different sources. No matter how you got your data ready on a spreadsheet, you need to notice that due to the changing output formats (xls, xlsx, xlsm, xlsb) and the advanced formatting features (for example, merging cells), this is not the best form to feed R with your data. Instead...

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