Financial Analytics with R: Building a Laptop Laboratory for Data Science

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.. 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. Explore the analytical fringes of investments and risk management. Build a hands-on laboratory and run many simulations. 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.

. 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. He previously worked as a signal proc

Comprehensive, concise, well written, and useful. Wish computing books are of this quality. Well-written, well-structured, and meticulously edited book. This is one of those rare books you don't mind paying premium for (though relatively more expensive) and keeping in your library. It's essentially a primer on quantitative finance and trading with practical applications and codes. Good balance and explanation of theoretical and practical content.. Highly recommend this book for those who want to know how George D Jayaratnam Highly recommend this book for those who want to know how to implement theoretical models found in academic literature with actual real world data. The R code examples are specific and with good explanations. And the book covers a wide spectrum of models from time-series analysis to Bayesian probabilities and simulation modelling.. A well-written book with tons of useful R code mobrown I'm extremely pleased with this book! A great deal of R code is presented for statistical models relevant to trading and portfolio management and everything is well organized. The explanations are easy to follow and many visualizations are included with the examples. I rarely write reviews, but was inspired to do so on this occasion since this is one of the most useful books I've bought in a while.

"A very well-written text on financial analytics, focusing on developing statistical models and using simulation to better understand financial data. Hilbe, Arizona State University . 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