Skip to content
All library documents

Why R Can Help with Financial Data Modeling

Article Quant Q&A · Author: Ascorpio
Read the original

Summary

The document surveys reasons a quantitative team might choose R for financial data modeling instead of spreadsheets or conventional statistical packages. It emphasizes reusable scripts, access to packages that implement statistical methods, flexibility for data manipulation, and the ability to handle larger datasets. It also points to visualization tools and an established community as practical strengths.

The examples are anecdotal: one contributor reports that an R workflow invoking C++ processed a large selection task much faster than their VBA attempt, while another describes R’s package ecosystem and data tools. The discussion also notes that specialized packages can connect R with brokers and that SAS offers an interface to R. These are personal assessments rather than controlled performance comparisons. Suitability depends on a user’s existing skills, the task, and the tools already in place; the document advises considering alternatives such as Python or Julia as well.

Key ideas

  • R scripts make data cleaning and modeling workflows reusable.
  • R packages provide a wide range of statistical methods and data manipulation tools.
  • R can work with large datasets and can call faster compiled code when needed.
  • R offers dedicated plotting libraries and a substantial community knowledge base.
  • Tool choice depends on user skill and task requirements, and alternatives may also fit.

Tags

StatisticsMachine learning
Full text
# What are the advantages of financial modelling in R?


# What are the advantages of financial modelling in R?












Recently I've found out that quantitative department in my company uses mostly R software for modeling in general.

What is the advantage of modeling financial data in R instead of Excel, or some statistical packages like SPSS and SAS? (Apart from the obvious merit that it is an open source software.)

## Answer by Fly_back (score 4, accepted)

https://quant.stackexchange.com/a/21023

It is very hard to answer this quiz as people might be good at different at tools. For example, if you are good at VBA, then you can achieve the same effect compared to R in most cases. The following parts are the reasons why I prefer to R based on my own situation.

- 'package'. This is the most obvious strength of R over Excel in terms of convenience. You may only need one line code to solve a problem which is impossible in Excel;

- Speed. Once I tried to select some data with 200,000 items with VBA, it takes more than half day, may be I do not have an efficient algorithm. However, it only takes less than half hour by calling C++ script in R.

- I am not sure if this could be a strength as I never tried but just read it from some materials that it is very convenient to integrate R and broker with some packages.

Hope this could help you.

## Answer by Sergey Bushmanov (score 3)

https://quant.stackexchange.com/a/21037

Some advantages of R over Excel:

- R is a scripting language, which allows to record a data manipulation script once and reuse it multiple times.

- R, as a [scripting] programming language is much more flexible than very limited Excel's GUI. In fact, R has become a de facto statistical programming environment, which delivers most recent statistical techniques.

- R can handle much bigger data sets, even out of memory with the help of extra packages.

- R is fast with the help of `data.table`, and fast and very flexible with the help of `dplyr`

- Community, literature, and existing knowledge base on internet/stackoverlflow.

- R has a superb quality, easy to learn graphing libraries like `ggplot2` and many special case others

Advantages of R over SPSS/SAS:

- Free (open source).

- Versatility of statistical methods (it should be noted here, that at least SAS, in recognition of R's speed of statistical innovation, provides interface to R)

Having said all that, think twice before investing into R as compared to other more modern scripting languages like Python (or Julia)

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.