Hands-on data analysis in R for finance

By:

Collard, Jean-Francois

Material type: Text

TextPublication details: CRC Press New York 2023Description: xx, 393 pISBN:

9781032340975

Subject(s):

DDC classification:

006.3 COL

Summary: The subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Finance & Accounting	006.3 COL (Browse shelf(Opens below))	1	Available		005620

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1. Your Working Environment

2. Reading Data in R

3. Financial Data

4. Introduction to R

5. Functions

6. Data Transformation

7. Merging Data Sets

8. Graphing Using Ggplot

9. Returns and Returns-based Statistics

10. Portfolios

11. Modeling Returns and Simulations

12. Linear and Polynomial Regression

13. Fixed Income

14. Principal Component Analysis

15. Options

16. Value at Risk

17. Time Series Analysis

18. Machine Learning

19. Presenting the Results of Your Analyses

20. Appendix: Main Packages Seen in this Book

The subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference.

Assumes no prior knowledge of R
The content has been tested in actual university classes
Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more
Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet

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