**Portfolio Probe » R language**, and kindly contributed to R-bloggers)

The R/Finance conference happened in Chicago at the end of April. If, like me, you weren’t there, you can still benefit from it because slides from many of the talks are now online.

Here is a quick synopsis (in chronological order) of some of the talks I found most interesting.

### Michael Kane

Michael Kane and colleagues show an analysis of flash crash data, and of the circuit breakers that were introduced in response to the flash crash. They seem to think that the curcuit breakers will not be effective in stopping a repeat. That perhaps their installation was more effective politically than practically.

### Nicholas Switanek

This looks at the speed of impact of news based on how readable it is. The talk looks like it was quite interesting, however the slides seem to end rather abruptly.

### Kris Kumar

Kris Kumar shows some analysis of carry trades. And wants to add a particular bit of spice to them.

### Joe Rothermich

This is about measuring market sentiment. I’m really sorry not to have been at this talk — how could I not have known about the danceability index. The theory is that unfettered dancing is a sign of irrational exuberance.

### Robert Gramacy

The talk is about dealing with missing values. It would probably be a good project to think about how to apply these ideas to the way missing values are dealt with in the functions in the `BurStFin` package that estimate variance matrices via Ledoit-Wolf shrinkage and a statistical factor model. My personal preference would be for someone other than me to do it.

By the way, you can get the package via the R command:

`install.packages("BurStFin", repos=
"http://www.burns-stat.com/R")`

### Doug Martin

This talk starts with risk fractions as done in Portfolio Probe (with variance), and then moves on to other ideas of risk.

### John Bollinger

John gives a history lesson in computational finance.

There was one slide that caught my attention. I was ready to jump on him for being wrong, but via a private conversation I now understand what he was saying, agree with him, and think it is a good insight.

The statement was: “Volatility is not mean-reverting”.

Well, everyone knows that volatility **is** mean-reverting — if volatility is high, it is likely to go down; if volatility is low, at some point it will go up. And we have a sense of what “low” and “high” mean.

John’s point is that people interpret that to mean that volatility is drawn to some equilibrium spot. That’s our natural image of mean reversion. But volatility overshoots the mean — it is almost always substantially below the mean or way above it. Volatility is more an aimless wanderer that often retraces its steps.

### Guy Yollin

Guy asks if market cap weighted indices are as useful as they might be. He strolls into the low volatility arena that we’ve discussed a few times before.

## That’s not all, folks

If you are the technical sort, there is a lot of geeky stuff in addition to the above. (Many, I’m sure, think that should read “**even more **geeky stuff”.) Again, the place to go to is: http://www.rinfinance.com/agenda/

**leave a comment**for the author, please follow the link and comment on their blog:

**Portfolio Probe » R language**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...