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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

Backtesting


The word "backtesting" refers to calculating the results of a trading strategy on a historical dataset. In our case, we will use the same dataset because of which we will overestimate the effectiveness, as our statistical models were optimized on exactly the same data. In the real life, we might go for a different time period or a different group of equities (or both) to measure efficiency more objectively.

No matter how we got the best performers separated, testing the investment idea follows the same logic. You translate the result into rules, pick the firms (normally from a different sample) that fulfill the requirements and place them into one cluster, and then create another cluster to contain all the other companies. Finally, compare the mean and/or median performance of the two groups.

To test the selection rules of the decision tree, we have to create a subset of firms that fulfil the requirements of having a cash ratio above 1.6, fixed asset ratio exceeding 12.3 percent...

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