Bennett, Mark J.

Financial analytics with R: building a laptop laboratory for data science - Cambridge Cambridge University Press 2016 - xvi, 377 p.

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

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.


R Computer Program Language

Finance - Mathematical Models - Data Processing
Finance - Databases

332.0285513 / B3F4

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