R / Finance 2014: Packaged Takeaways

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by Joseph Rickert

I was very happy to have been able to attend R / Finance 2014 which wrapped up a couple of weeks ago. In general, the talks were at a very high level of play, some dealing with brand new ideas and many presented at a significant level of technical or mathematical sophistication. Fortunately, most of the slides from the presentations are quite detailed and available at the conference site. Collectively, these presentations provide a view of the boundaries of the conceptual space imagined by the leaders in quantitative finance. Some of this space covers infrastructure issues involving ideas for pushing the limits of R (Some Performance Improvements for the R Engine) or building a new infrasturcture (New Ideas for Large Network Analysis) or (Building Simple Data Caches) for example. Others are involved with new computational tools (Solving Cone Constrained Convex Programs) or attempt to push the limits on getting some actionable insight from the mathematical abstrations: (Portfolio Inference withthei One Wierd Trick) or (Twinkle twinkle litle STAR: Smooth Transition AR Models in R) for example.

But while the talks may be illuminating, the real take-aways from the conference are the R packages. These tools embody the work of the thought leaders in the field of computational finance and are the means for anyone sufficiently motivated to understand this cutting edge work.  By my count, 20 of the 44 tutorials and talks given at the conference were based on a particular R package. Some of the packages listed in the following table are well-established and others are work-in-progress sitting out on R-Forge or GitHub, providing opportunities for the interested to get involved.

R Finance 2014 Talk

Package

Description

Introduction to data.table

  data.table

Extension of the data frame

An Example-Driven Hands-on introduction to Rcpp

  Rcpp

Functions to facilitate integrating R with C++

Portfolio Optimization: Utility, Computation, Equities Applications

  fPortfolio

Environment for reaching Financial Engineering and Computational Finance

Re-Evaluation of the Low Risk Anomaly via Matching

  cem

Implementation of the Coarsened Exact matching Algorithm

BCP Stability Analytics: New Directions in Tactical Asset Management

  bcp

Bayesian Analysis of Change Point Problems

On the Persistence of Cointegration in Pairs Trading

  egcm

Engle-Granger Cointegration Models

Tests for Robust Versus Least Squares Factor Model Fits

  robust

robust methods

The R Package cccp: Solving Cone Constrained Convex Programs

  cccp

Solver for convex problems for cone constraints

Twinkle, twinkle little STAR: Smooth Transition AR Models in R

  Twinkle

Modeling smooth transition models

Asset Allocaton with Higher Order Moments and Factor Models

  DEoptim /   PortfolioAnalytics

Global optimization by differential evolution / Numerical methods for portfolio optimization

Event Studies in R

  eventstudies

Event study and extreme event analysis

An R package on Credit Default Swaps

  CDS

Provides tools for pricing credit default swaps

New Ideas for Large Network Analysis, Implemented in R

  IRL

Implicitly restarted Lanczos methods for R

Package “Intermediate and Long Memory Time Series

  ILMTS

 

Simulate & Detect Intermediate and Long Memory Processes / in development

Stochvol: Dealing with Stochastic Volatility in Time Series

  stochvol

Efficient Bayesian Inference for Stochastic Volatility (SV) Models

Divide and Recombine for the Analysis of Large Complex Data with R

  RHIPE

Package for using R with Hadoop

gpusvcalibration: Fast Stochastic Volatility Model Calibration using GPUs

  gpusvcalibration

Fast calibration of stochastic volatility models for option pricing models

The FlexBayes Package

  FlexBayes

Provides an MCMC engine for the class of hierarchical feneralized linear models and connections to WinBUGS and OpenBUGS

Building Simple Redis Data Caches

  RcppReddis

Rcpp bindings for Redis that connects R to the Redis key/value store

Package pbo: Probability of Backtest Overfitting

  pbo

Uses Combinatorial Symmetric Cross Validation to implement performance tests.

 

Many of these packages / projects also have supplementary material that is worth chasing down. Be sure to take a look at Alexios Ghalanos recent post that provides an accessible introduction to his stellar keynote address.

Twinkle

Many thanks to the organizers of the conference who, once again, did a superb job, and to the many professionals attending who graciously attempted to explain their ideas to a dilletante. My impression was that most of the attendies thoroughly enjoyed themselves and that the general sentiment was expressed by the last slide of Stephen Rush's presentation:

Are_you_not_entertained_

 

 

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