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Financial analytics with R: building a laptop laboratory for data science

By: Bennett, Mark J.
Contributor(s): Hugen, Dirk L.
Material type: materialTypeLabelBookPublisher: Cambridge Cambridge University Press 2016Description: xvi, 377 p.ISBN: 9781107150751.Subject(s): R Computer Program Language | Finance - Mathematical Models - Data Processing | Finance - DatabasesDDC classification: 332.0285513 Summary: 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. https://www.goodreads.com/book/show/30462860-financial-analytics-with-r?from_search=true
List(s) this item appears in: Big data | VR_Data Analytics, Data Visualization and Big Data
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Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
Slot 611 (0 Floor, West Wing) Non-fiction 332.0285513 B3F4 (Browse shelf) Checked out 31/07/2019 193646

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.

https://www.goodreads.com/book/show/30462860-financial-analytics-with-r?from_search=true

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