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1 online resource (392 pages) : digital, PDF file(s).
  • 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.
  • (source: Nielsen Book Data)9781107150751 20161114
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.
(source: Nielsen Book Data)9781107150751 20161114
xii, 54 p. : ill. ; 26 cm.
Law Library (Crown)
1 online resource (1 volume) : illustrations


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