Mark J. Bennett

Financial Analytics with R (eBook, ePUB)

Building a Laptop Laboratory for Data Science

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 6. Oktober 2016
• Englisch
• ISBN-13: 9781316776094
• Artikelnr.: 52928144

Autorenporträt

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.

Inhaltsangabe

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.

Rezensionen

'A very well-written text on financial analytics, focusing on developing statistical models and using simulation to better understand financial data. R is used throughout for examples, allowing the reader to use the text and code to actively engage in the financial market. It is simply the best text on this subject that I have seen. Highly recommended.' Joseph M. Hilbe, Arizona State University