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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.Read more
- 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
Reviews & endorsements
"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 UniversitySee more reviews
'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)
09th Dec 2018 by Adiari1
I had to withhold a 5 star because I had to do a lot of googling to get access to the resources to use the book. This, the authors could have done by just a simple line of instruction on a page titled Instructions and Additional resources for the book.See all reviews
12th Feb 2019 by Yueda
I really enjoyed reading the book, and I would be rather delighted to give an overview of my thoughts on this book. This book is a perfect book for someone who has limited exposure to finance or zero knowledge in finance, but who is capable in at least applied mathematics or some algorithms. Further, the book gave an immense demonstration of how analytics can be applied to the finance world in a short but concise way with symbolic examples and codes abreast. This is a book more than just getting shaped for a finance-related job. It intrigues me to study more on each topic. Something about me I am a recent master graduate in Economics at The University of Warwick. My dissertation is about Game Theory ( proof-based). I do enjoy studying pure math in my leisure. Courses taken related to this book are Time Series Analysis (To GARCH and more in detail), Stochastic Calculus (with some measure), Statistical learning (Hastie book) and all their prerequisites (Linear Math, Differential Equations, Real Analysis etc.). However, I have never taken finance modules. This book made quick linking to most hot topics in finance. Strong Recommendation!
Review was not posted due to profanity×
- Date Published: November 2016
- format: Hardback
- isbn: 9781107150751
- length: 392 pages
- dimensions: 254 x 180 x 22 mm
- weight: 0.92kg
- contains: 60 b/w illus. 100 colour illus. 40 exercises
- availability: In stock
Table of Contents
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
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An Interview with Dirk Hugen, co-author of 'Financial Analytics with R'
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