Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists

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Academic Paper
Title Unobserved Complementarities between Entrepreneurs and VCs (Academic Paper)
Author Ed Egan, Jeremy Fox, David Hsu
RAs Amir Kazempour
Status In development
© edegan.com, 2016


Reference Papers

Jeremy's paper with David Hsu and Chenyu Yang is here:

Fox Hsu Yang (2015) - Unobserverd Heterogeneity in Matching Games with an Application to Venture Capital provides some notes.

Matlab Code

Abhijit Brahme (Work Log) contains his notes on working with the Matlab code. This needs a separate page.

Data specification

The data spec sent to Jeremy is in:

Z:\Projects\MatchingAcceleratorsToVCs

Data foundations

The database is vcdb2

This was built using:

Z:\VentureCapitalData\SDCVCData\vcdb2\ProcessData2.sql


Dataset build

Decisions:

  • Will we need synthetic matches? If so what we do we do for outcomes? Can still do dyadic and left/right pair variables.
  • Granularity of industry: To start let's use minor industry group (see below). We use a much finer grained industry definition and aggregate back up to balance out the counts somewhat later.
  • Matching to a fund or a firm: Both suffer from a right censorship problem. We can likely address this with indicator variables to condition on. For now we will take portfolio companies that received their last investment and funds that made their last investment before 2012. For now, we will work with funds, though deals are sometimes transferred across funds within a firm (i.e. from Kliener fund IV to Kliener fund V), this is probably comparatively rare (check!).
  • Determination of lead VC - see below
  • How to collapse VC rounds (date, amount, etc.): We will use only seed, early, later stage investment and insist on the presence of seed/early for inclusion. We can then have date first, investment duration (to date last), total investment.


The objective is:

  • Unit of observation - a startup-fund match.
  • Startup name and ID, fund name and ID
  • Geocoded location of startup, state of incorporation of startup, year of founding (if available).
  • Exit indicator, exit value, exit type indicator
  • alive2016 indicator, last round pre-2012 indicator
  • Total invested (all SEL, across all funds), number of rounds (all SEL, across all funds), investment duration (yrs), date first inv, year first inv, total MOOMI (Money Out Over Money In)
  • Number of funds investing
  • As averages (?) and for lead:
    • Fund ipo count, Fund M&A count, Fund investment count(calc at end), fund ipo rate, fund M&A rate, fund exit count, fund exit rate, fund ipo $, fund M&A $, fund exit $, fund fraction of MOOMI.
  • Total invested by lead, number of rounds participation by lead, stage of participation of lead, location of lead, last investment pre-2012 indicator, lead fund type indicator (corp, priv, gov, etc.), lead fund size, lead fund vintage year.
  • Indicator for transactional VC, amount of transactional VC.
  • Possibly some dyadic variables like: Distance between lead and portco, industry preference match between lead and portco, maybe stage-match (doesn't make a lot of sense when collapsing rounds) between lead and port co.

Identifying lead VCs

Possible methods:

  • Best performing participant (on exit count/value or fractional MOOMI) with tie-breaker
  • Closest participant (using great circle distance)
  • Most frequent participant with tie-breaker
  • Participant with greatest investment with tie-breaker
  • Participant in earliest round that stayed in for longest with tie-breaker

Minor Industry

Across all time and without regard to SEL vs. transaction, here's the minor industry list and counts:

        indminorgroup          | count
-------------------------------+-------
Industrial/Energy              |  2871
Internet Specific              |  8794
Biotechnology                  |  2592
Semiconductors/Other Elect.    |  2402
Other Products                 |  4891
Computer Hardware              |  2061
Computer Software and Services | 10550
Communications and Media       |  3271
Medical/Health                 |  4373
Consumer Related               |  3161