FE515 Introduction to R

From Hanlon Financial Systems Lab Web Encyclopedia
Jump to: navigation, search



Course Catalog Description

Introduction

This course is designed for graduate students. Starting from 2018 fall semester, this course is extended to 2 hours each week.

Upon completion the students will gain an understanding of the programming syntax and should be able to use R in any future courses.

Campus Fall Spring Summer
On Campus X X
Web Campus

Instructors

Professor Email Office
Ziwen Ye
zye2@stevens.edu Altorfer 301






Course Resources

Textbook

Lecture Notes and Code
  • The art of R programming: a tour of statistical software design. Norman Matloff, First Edition, 2011. ISBN-10: 1593273843, ISBN-13: 978-1593273842
  • An Introduction to Analysis of Financial Data with R. Ruey Tsay, First Edition, 2012. ISBN-10: 0470890819, ISBN-13: 978-0470890813
  • Introduction to the Practice of Statistics. David S. Moore, George P. McCabe, Bruce A. Craig, Eighth Edition, 2014. ISBN-13: 978-1464158933, ISBN-10: 1464158932

Additional References

CRAN: http://www.wikibooks.org

R-help Info: https://stat.ethz.ch/mailman/listinfo/r-help

R-help Archive: http://r.789695.n4.nabble.com

Quick R: http://www.statmethods.net



Grading

Grading Policies

The plan is to schedule 5 assignments for this semester. The assignments will due exactly before the next class. All LATE SUBMISSION will be punished unless you send me an email BEFORE DUE and get approved. If your submission passes the due for less than 24 hours, your highest score will be 67%; between 24 and 48 hours, your highest score will be 33%; after 48 hours this assignment will be graded as 0. If the assignments I give out is more than 5, the lowest grade will be dropped in final grading calculation.

For this course, all students will have the midterm and final exams. Both exams are 2 hours length and will be held during the class. As a coding class, we only test the coding skill from students. Therefore, both exams will be open book. Students can use any materials during exams (such as notes, Google search engine and etc.) to help them answer all questions. However, any communication tools (such as Skype, email and etc.) and tutoring websites are NOT allowed.

If students have any concern or questions regarding to the teaching contents and homework, they are encouraged to seek help from the instructor. Discussing homework with classmates are prohibited for this course. All code and reports must be written by yourself. Copying solutions from sources other than your brain is strictly forbidden. This kind of behavior will be considered as academic dishonesty/misconduct and will be dealt with according to the Stevens honor board policy.

Grade distribution

Assignments – 30%

Midterm – 30%

Final – 40%

Bonus – TBD (Bonus includes but not limited to attendance and bonus questions)


Lecture Outline

Topic Reading
Week 1 R basics(1)

Data structures & Loops

Week 2 Labor Day, No Classes
Week 3 R basics(2)

Self-defined functions

”apply” functions

Week 4 R basics(3)

Generating random variables

Discreet distribution & Sampling

Week 5 Date and time objects

Simple return and compounded return

Plots

Week 6 Download data through R:

Bloomberg API, Yahoo API (Equity and option)

Week 7 Yahoo API (advanced)

Basic statistics

Week 8 Linear regression models

Stepwise selection & goodness criteria

Week 9 Midterm
Week 10 T-test and ANOVA
Week 11 Newton’s method and gradient descent
Week 12 Volatility

GBM and BS Model

Week 13 GGplot
Week 14 Rmarkdown and LaTeX