by Joseph Rickert

R/Finance 2016 is less than a month away and, as always, I am very much looking forward to it. In past years, I have elaborated on what puts it among my favorite conferences even though I am not a finance guy. R/Finance is small, single track and intense with almost no fluff. And scattered among the esoterica of finance and trading there has, so far, always been a rich mix of mathematics, time series applications, R programming, stimulating conversation and attitude. When it comes down to it, it’s the people, the organizers and participants who make a conference. Looking over the agenda for this year, I am sure that once again, for two days at least, Chicago will be the center of the R world.

This year, however, I am going to be ready for R/Finance. I am going to do my homework. If I had done a little prep last year I would have had a copy of Arthur Koestler’s The Sleepwalkers in my bag. So when Emanuel Derman went deep philosophy I could have gone through that looking-glass with him.

So what’s on the line up this year? Rishi Narang will lead off for the keynote speakers with a talk provocatively entitled “Rage Against the Machine Learning”. There is not much online for and industry outsider to latch onto, but it probably wouldn’t hurt to have a look at one of his three books on quantitative trading.

Tarek Eldin will deliver the second keynote entitled ‘Random Pricing Errors and Systematic Returns: The Flaw in Fundamental Prices” My guess is that this online paper might provide some relevant preparatory reading.

Frank Diebold’s keynote is entitled “Estimating Global Bank Network Connectedness”. I think it’s a safe bet that his recent paper with Mert Demirer, Laura Liu and Kami Yilmaz will indeed be relevant.

Batting cleanup for the keynote speakers will be none other than the R Inferno himself, who vaguely and possibly misleadingly suggests that preparation for his talk, “Some Linguistics of Quantitative Finance” might begin with Yucatan.

For preparation on more solid ground, I am going to look into the R packages explicitly called out in the agenda. Of course, there will be Rcpp. Chicago is Eddelbuettel country and no doubt much of the conversation over coffee will revolve around high performance computing. But, even R users who are not particularly interested in writing high performance code themselves ought to know something about this package. With a reverse table listing hundreds of packages it is becoming the foundation for much of R.

In addition to Dirk’s tutorial on Rcpp and RcppArmadillo, Matt Dziubinski will talk about getting the most out of Rcpp in practice and Jason foster will talk about using RcppParallel for multi-asset principal component regression. Look here for some older talks by Matt.

Robert McDonald will describe the derivmkts package which contains functions that support his book Derivatives Markets.

Eran Raviv will talk about combining multiple forecasts using R’s ForecastCombinations package.

Kjell Konis will describe how to compare Fitted Factor Models with his fit.models package.

Steven Pav will speak of madness, package for multivariate automatic differentiation. There is a very nice vignette that describes the mathematics of madness.

Qiang Kou will talk about deep learning in R using the MxNet package which makes use of GPUs.

Mario Annau will talk about the h5 package, an S4 interface to the HDF5 storage format.

Robert Krzyzanowski will describe the Syberia development framework for R.

Dirk Eddelbuettel will revisit the Rblapi package for connecting R to Bloomberg.

Michael Kane will talk about a new package he is writing glmnetlib which is intended to be a low-level library for Regularized Regression.

Matt Brigida use a Shiny implementation to talk about Community Finance Teaching Resources.

When I registered for the conference I saw that the preconference tutorial by Harte and Weylandt on modern Bayesian tools for time series analysis is going to use STAN. So, I need to add rstan to the list.

In his tutorial on leveraging the Azure cloud from R, Doug Service will show how to use the foreach package in the Azure environment.

And then, for some serious preparation it might be helpful to take a look at the math underlying some of the presentations. For example, Klaus Spanderen will talk about calibrating Heston Local Stochastic Volatility Models. Sida Yang will discuss using Latent Dirichlet Allocation to discover distributions underlying financial news topics and Pedro Alexander will discuss portfolio selection with support vector regression.

All that I have listed won't even cover half of what will be presented at the conference, however, I hope some of it will be helpful in preparing for R/Finance. But, most importantly, don’t forget to register! Unfortunately, this year many, if not most, of the people who would like to go to the useR! conference will not be able to attend. Don’t get locked out of R/Finance too!