Financial Data Analysis and Modeling with R (AMATH 542)
From the webpage:
This course is an in-depth hands-on introduction to the R statistical programming language (www.r-project.org) for computational finance. The course will focus on R code and code writing, R packages, and R software development for statistical analysis of financial data including topics on factor models, time series analysis, and portfolio analytics.
Topics include:
- The R Language. Syntax, data types, resources, packages and history
- Graphics in R. Plotting and visualization
- Statistical analysis of returns. Fat-tailed skewed distributions, outliers, serial correlation
- Financial time series modeling. Covariance matrices, AR, VecAR
- Factor models. Linear regression, LS and robust fits, test statistics, model selection
- Multidimensional models. Principal components, clustering, classification
- Optimization methods. QP, LP, general nonlinear
- Portfolio optimization. Mean-variance optimization, out-of-sample back testing
- Bootstrap methods. Non-parametric, parametric, confidence intervals, tests
- Portfolio analytics. Performance and risk measures, style analysis
A quick summary:
QUICK FACTS
Status: Open
Start Date: 1/4/2012
End Date: 3/19/2012
Credits: 4 Credits
Learning Format: Online
Location: Web
Cost: $3,300
Particularly if your employer is paying for it, this might be a good way to pick up some R skills for financial data work. And R will be useful if you want to mine financial data for topic map purposes. Although, transparency and finance aren’t two concepts that occur together very often. In my experience, setting disclosure requirements means people can walk as close to the disclosure line as they dare.
In other words, disclosure requirements function as disclosure limits, with the really interesting stuff just on the other side of the line.