It was another great year for the R/Finance conference, held earlier this month in Chicago. This is normally a fairly private affair: with attendance capped at around 300 people every year, it's a somewhat exclusive gathering of the best and brightest minds from industry and academia in financial data analysis with R. But for the first time this year (and with thanks to sponsorship from Microsoft), videos of the presentations are available for viewing by everyone. I've included the complete list (copied from the R/Finance website) below, but here are a few of my favourites:

- No-Bullshit Data Science. Szilard Pafka's keynote knocks down some of the myths and misconceptions about real-world data science practices.
- Risk Fast and Slow. A rare treat: in his keynote presentation Dave DeMers offers his perspectives on risk, financial engineering, and major financial events from his days the Prediction Company, Black Mesa and other investment houses.
- Syberia: A development framework for R (Robert Krzyzanowski). Syberia is a new operationalization framework for R scripts, applicable for any production workflow using R (not just Finance).
- yuimaGUI: A graphical user interface for the yuima package (Emanuele Guidotti). An impressive front-end to the Yuima project for solving stochastic differential equations.
- Zero-Revelation RegTech: Detecting Risk through Linguistic Analysis of Corporate Emails and News (Seoyoung Kim). A really interesting approach to looking at sentiment (and length!) of emails (here, the Enron corpus) to predict stock prices.
- New Tools for Performing Financial Analysis Within the 'Tidy' Ecosystem (Matt Dancho). This lightning talk shows how you can perform piping (%>%) operations with time series data from zoo and xts.
- The PE package: Modeling private equity in the 21st century (Thomas Harte). An intriguing peek into the mysterious world of private equity finance.
- Project and conquer (Bryan Lewis). A beautifully elegant introduction to the use of projections in Statistics, and a potentially revolutionary application in speeding up calculations with high-dimensional correlations and clusters.
- Detecting Fraud at 1 Million Transactions per Second (David Smith). My own presentation includes a demo of very high-frequency predictions from R models (about which I'll blog more shortly).

You can find an up-to-date version of the table below at the R/Finance website (click on the "Program" tab), and you can also browse the videos at Channel 9. Note that the lightning talk sessions (in orange) are bundled together in a single video, which you can find linked after the first talk in each session.

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