Financial Analytics with R
Building a Laptop Laboratory for Data Science

Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

Mark J. Bennett (Author), Dirk L. Hugen (Author)

9781107150751, Cambridge University Press

Hardback, published 6 October 2016

392 pages, 60 b/w illus. 100 colour illus. 40 exercises
25.4 x 18 x 2.2 cm, 0.92 kg

'The book at hand is unusual in addressing beginners, and in treating R as a general number crunching tool. … It is also one of very few books on R really written for non-statistician non-programmers. … R seems a viable programming language for STEM students to learn, and learning a programming language seems a good idea for such students. This book appears to be the best option for accomplishing that.' Robert W. Hayden, Mathematical Association of America Reviews (www.maa.org)

Are you innately curious about dynamically inter-operating financial markets? Since the crisis of 2008, there is a need for professionals with more understanding about statistics and data analysis, who can discuss the various risk metrics, particularly those involving extreme events. By providing a resource for training students and professionals in basic and sophisticated analytics, this book meets that need. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

Preface
Acknowledgements
1. Analytical thinking
2. The R language for statistical computing
3. Financial statistics
4. Financial securities
5. Dataset analytics and risk measurement
6. Time series analysis
7. The Sharpe ratio
8. Markowitz mean-variance optimization
9. Cluster analysis
10. Gauging the market sentiment
11. Simulating trading strategies
12. Data mining using fundamentals
13. Prediction using fundamentals
14. Binomial model for options
15. Black–Scholes model and option implied volatility
Appendix. Probability distributions and statistical analysis
Index.

Subject Areas: Probability & statistics [PBT], Finance [KFF]