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VC Acquisitions Paper (view source)
Revision as of 20:42, 19 February 2012
, 20:42, 19 February 2012no edit summary
I can also determine whether the lead VC has a board seat at the portfolio company at the time of the acquisition, as well as the fraction of invested firms with board seats, and the total number of board sets held by VCs (or the fraction), using the identities of the executives. Though this will be particularly difficult in terms of data, I plan on doing it for another project with Toby Stuart anyway.
==Rebuild Plan==
I suggest that I leave the rebuild of the supplementary information asymmetry dataset (to show that that IT has greater information asymmetries than other sectors until the end). It is a lot of work, both in terms of assembly time and run-time to do the regressions, and we can use the existing table for the next version if need be. I suspect that this component will take me 3 days on its own.
The regressions for the estimation window will have a run-time that might be considerable; even given the hardware that I have put together at Berkeley, I suspect that this will take at least 24hrs of compute time. I therefore plan on doing this very early and setting it running.
My order is therefore:
#Download, clean, and process the SDC data so that it can be joined to CRSP (and the other data sources)
#Download the CRSP data for the estimation and event windows. Set the estimation windows running. Build the event window code while they run, and otherwise move forward.
#Download the VentureXpert data. Pull the portco data first, so we can construct the binary indicator.
#Download an LBO dataset, so we can remove these firms.
#Download the COMPUSTAT data, and join it to the SDC data. At this point we should have everything we need to get the basic analysis up and running again.
#Build out the a full database of VC investments into these portcos so we can calculate distances, monitoring through board positions and reputations. Stop short of actually doing the build of these variables.
#Download the GNI IPO data to calculate the standard reputation measures and join it up, then calculate these measures.
#Calculate the distances for all VCs to all acquired targets. Determine lead VCs if feasible and calculate the distance measures.
#Add in the NBER patent data to 2006, include the number of patents and patents weighted by citations-received (not corrected for truncation).
