000			02569nam a22002297a 4500
999			_c3535 _d3535
005			20221114131844.0
008			221114b ||||| |||| 00| 0 eng d
020			_a9781107150751
082			_a332.0285513 _bBEN
100			_aBennett, Mark J. _94814
245			_aFinancial analytics with R: _bbuilding laptop laboratory for data science
260			_bCambridge University Press _aCambridge _c2016
300			_axvi, 377 p.
365			_aGBP _b51.99
504			_aTable of Contents 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.
520			_aAre 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. Contains an ideal blend of innovative research and practical applications Tackles relevant investor problems Provides a multi-disciplined approach, solving problems from both fundamental and non-traditional methods.
650			_aFinance--Mathematical models--Data processing _99995
650			_aR (Computer program language) _91512
650			_aFinance--Databases _99996
700			_aHugen, Dirk L. _99997
942			_2ddc _cBK