[R] How to Describe R to Finance People

Tamas Papp tpapp at axelero.hu
Fri Jun 4 16:44:58 CEST 2004

On Fri, Jun 04, 2004 at 11:50:32PM +1200, Ko-Kang Kevin Wang wrote:

> Although it has a slightly higher learning curve than SPSS-like
> program, it gets easier to use once one is familiar with it.  One of
> the main advantage it has over SPSS-like software is that you do not
> need to explicitly create dummy variables.  You only need to specify
> your dependent variable and independent variables and R will fit it
> (and create dummy variables automatically) for you.

I think that even if the above is an advantage when compared to SPSS,
it is more of a minor, convenient feature than one of the major
advantages of R.

Even though I majored in finance, at the moment I consider myself to
be more of a macroeconomist than a "finance person".  I wrote a lot of
my finance calculations in R (without using Rmetrics, as this was two
years ago and I did not know about Rmetrics then), including
derivatives pricing, binomial trees and term structure models.

I would emphasize the following:

1. R is free, both in the sense of "gratis" and "libre".  The latter
is more important in this context, as "finance people" can usually
afford the price of software, but IMO free software often means better
quality (this applies to R for sure).

2. The programming language is really friendly and convenient to work
with.  In finance, you often need to hack together special solutions
for problems that are not conventional (especially in term structure
models, but I think that the same applies to bi- and trinomial models
and their ilk).  As an R newbie, it took me an afternoon to implement
a basic toolkit for the former, which I could use for interesting

3. Advanced graphing packages.  This is quite important, visualization
is often the key in finance models -- sometimes one doesn't notice
that the model is wrong until one sees a graph (eg a term structure
model with unexplained "breaks" in the interest rate curve).

4. Interface to databases, eg Oracle and MySQL.  Building certain
types of models requires access to huge amounts of data (eg credit
scoring systems).

5. Tons of statistical functions.  For example, R has all the tools
one needs to build very sophisticated credit scoring systems (note
that banks for often pay thousands of dollars for commercial versions
of software used for this purpose, I am baffled about this).

Hope this helps,


Tamás K. Papp
E-mail: tpapp at axelero.hu
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