Financial Analytics with R

Building a Laptop Laboratory for Data Science

Inbunden, Engelska, 2016

Av Mark J. Bennett, Dirk L. Hugen, Mark J. (University of Chicago) Bennett, Dirk L. (University of Iowa) Hugen, Mark J Bennett, Dirk L Hugen

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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.

Produktinformation

Utgivningsdatum2016-10-06
Mått180 x 254 x 22 mm
Vikt920 g
FormatInbunden
SpråkEngelska
Antal sidor392
FörlagCambridge University Press
ISBN9781107150751

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Mark J. Bennett is a senior data scientist with a major investment bank and a lecturer in the University of Chicago's Master's program in analytics. He has held software positions at Argonne National Laboratory, Unisys Corporation, AT&T Bell Laboratories, Northrop Grumman, and XR Trading Securities. Dirk L. Hugen is a graduate student in the Department of Statistics and Actuarial Science at the University of Iowa. He previously worked as a signal processing engineer.

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.