Open source allows us to do amazing things. Take me for example. I am a below average coder, and I can hack together these incredibly powerful tools to do something like below.
Some might naively say that rCharts and ggvis are competitors. However, I view them as an indicator of a reach ecosystem (taken from Rob Story) in which the “competition” and “learning” results in synergy.
I think that the recent shift in the R world to chains and pipes will become permanent. Even if not, this style of code translates well to Javascript and other languages. I thought a finance example exploring Fama/French factors with dplyr, magrittr, tidyr, and rCharts would help me learn and think through new workflows.
In a recent working paper from the prolific AQR Research Factory, the authors seek to dispel ten common myths of momentum investing. To their credit, they use the fine data publicly available from Kenneth French and use fairly simple metrics to make compelling arguments against the myths and for the momentum factor. I replicated most of the calculations in R, and then in a blend of replication, summary, and discussion on process, used rCharts, Gmisc, and slidify to create the following writeup. I hope others find it useful, and it serves a purpose much greater than a re-creation.
For another very detailed summary of the paper, see the post from Gary Antonacci of Optimal Momentum.
To make sure this gets seen by those who might not read the paper, I will copy the thanks section below.
Antti Petajisto got a lot of attention with his research on Active Share and Tracking Error in mutual fund management. While the research is fairly compelling, there is a missing discussion about a potential relationship between the two measures. The paper seems to suggest that mutual fund managers can independently and intentionally pursue Active Share and/or Tracking Error. However, some research from the PIMCO Quantitative Team argues that Active Share and Tracking Error are related and that they can easily be a byproduct of other decisions such as benchmark or fund exposure. I strongly suggest reading both sets of research to gather a full understanding of these concepts.
Table 2 is simple enough, but despite its simplicity I could not visualize in my head, so I copied the data and with a couple lines of R + rCharts got all the visualization I needed.
replicate:
