Description
Book SynopsisAre 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, p
Trade Review'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
'There's a new source in town for those who want to learn R and it's a good, old-fashioned book called Financial Analytics with R: Building a Laptop Laboratory for Data Science … it is a one-stop-shop for everything you need to know to use R for financial analysis. The book meaningfully combines an education on R with relevant problem-solving in financial analysis. [It] is thorough and contextualized with examples from extreme financial events in recent times such as the housing crisis and the Euro crisis. The code samples are relevant - think functions to compute the Sharpe ratio or to implement Bayesian reasoning - and answer many of the questions you might have while trying them out. This is a book that will make you a better practitioner/student/analyst/entrepreneur - whatever your goals may be.' Carrie Shaw, Quandl
'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)
Table of ContentsPreface; 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.
