VC Acquisitions Paper

This page details the work rebuilding Brander Egan (2007) - The Role of VCs in Acquisitions for our submission to the RCFS special issue and associated conference.

Submission Details

The Third Entrepreneurial Finance and Innovation Conference on June 10th-11th in Boston, MA, is supported by the Kauffman Foundation and the Society for financial studies. Conference papers will be considered for inclusion in a special issue of the Review of Corporate Finance Studies.

The conference details are here: http://sites.kauffman.org/efic/overview.cfm

The deadline for submission is March 7th, 2012, though earlier submission is encouraged. Authors will be notified if their paper has been selected by the end of April.

The program committee includes: Thomas Hellmann, Adam Jaffe, Bill Kerr, Josh Lerner, David Robinson, Morten Sorenson, Bob Strom, and others.

Errors in the existing version

The Dierkins 1991 reference is missing:

@article{dierkens1991information,
  title={Information asymmetry and equity issues},
  author={Dierkens, N.},
  journal={Journal of Financial and Quantitative Analysis},
  volume={26},
  number={2},
  pages={181--199},
  year={1991},
  publisher={Cambridge Univ Press}
}

The Boehmer reference has a typo - the second author is Musumeci. Also, in para 2, p.19, I think it was McKinley that "suggest[ed] a method that combines both cross-sectional and time-series information..."

There were a few other typos.

Rebuilding the Paper

The paper requires a complete rebuild of all the results, with the data updated to the end of 2011. We should also consider several extensions to the paper, detailed in a later section.

Main Data

Acquisitions (from SDC):

• Events from 1980-2011 that meet the following criteria:
  • Acquirer is publicly traded on the AMEX, Nasdaq or NYSE
  • Target is privately-held prior to acquisition (note: new restriction - target was not an LBO)
  • Acquisition is for 100% of the firm
  • Acqisition is complete before end of January 2012

Subsequent restriction: Drop acquisitions where market value of assets is negative or very small compared with the TV.

Venture Capital (from VentureXpert):

• Portfolio companies that received VC from 1975-2011. Must not be LBOs.
• LBOs from 1975 to 2011 to ensure that they are not in the control group of privately-held non-VC backed firms)

Returns (from CRSP):

• Stock returns for 1 year (250 Calendar days) for the acquirer, ending 30 days before the announcement. This will be the estimation window.
• Market returns for the same period
• Stock returns for 7 days beginning 3 days before the announcement and ending 3 days after

Note: an observation must have 50 days of continuous trading in the estimation window, and be traded in the event window, to be included.

Accounting Data (From COMPUSTAT):

• Various accounting variables for our acquirers, drawn for the year of the acquisition, and the lagged year for total assets.

Supplementary Data

We need to rebuild the industry classification to update it to include NAICS2007 - this has largely been done in another of my papers, but that work was for firms with patents, and it is possible that some codes are still missing.

To determine the information asymmetry ranking of sectors we will need (either for 1 year or across the entire year range):

CRSP:

• idiosyncratic volatility of stock returns: requires returns and mkt returns
• relative trading volume (this appears to be called TURNOVER, as opposed to absolute volume which is VOLUME. The measure is relative to the exchange's trading volume I think...)
• NAIC

COMPUSTAT:

• intangible assets
• total assets
• Tobin's Q: Market value/book value of assets
• NAIC

Raw Variables

From SDC (for all acquisitions in the sample):

• Transaction Value
• Payment Method
• Acquisition announcement date
• Acquisition announcement year
• Total assets of acquirer (if available)
• Payment method (cash/stock/mix)
• PC of stock in the deal
• No. of bidders
• CUSIP (for join to COMPUSTAT)
• Target NAIC
• Acquirer NAIC (if available)
• Age (of target)
• Sales (of target)
• Leverage (of target)
• Intangible Assets (of target)

Notes: convert all TVs in 2011 dollars.

From COMPUSTAT (for both all acquirers and for the universe of firms):

• Total Assets (in year and 1 year lagged)
• Market Value (SHROUT*Price at start of event window)
• Sales
• Leverage variables (Revenue, Variable Cost, Op Income, Net Income, Total Liabilities, Stockholder's equity)
• Intangible Assets
• NAIC

From VentureExpert (all VC backed firms):

• VC (binary variable)

Calculated variables

Returns:

• [math] AR_i = R_i- (\hat{\alpha_i} + \hat{\beta_i}R_m [/math]
• [math] AR^S_i = R_i - R_m [/math]
• Let [math]\epsilon[/math] be the residual from the mkt model regression. Then calc: [math]\sigma_{\epsilon}={( \mathbb{E}(\epsilon - \mathbb{E} \epsilon))}^{\frac{1}{2}}[/math]
• RMSE of the Mkt Model: [math]RMSE={( \mathbb{E}(X- \mathbb{E} X))}^{\frac{1}{2}}[/math] - this is in the ereturn list in STATA and will be used for the Patell Standard Errors.
• [math] CAR_i = \sum_t AR_i[/math]
• Check that the Boehmer standard errors are the cross-sectional ones generated by OLS.
• Check the specification of the McKinley standard errors.

SDC:

• No of past acquisitions for each acquirer: Total, VC only, Non-VC only
• Target is VC/Non-VC
• Acq is Horizontal (same 6 digit), Vertical (same 2 digit/ITBT), Conglomerate (other), and Related (not cong.)
• 3dg NAIC for controls
• IT/BT/HT and 1dg-NAIC, 2dg-NAIC, other classification. Applied to targets and acquirers.

Dataset level calculations:

• Boom: [math]1990\le year \le 1999[/math]
• Leverage:
  • Finanial leverage is [math]\frac{Op.\;Income}{Net\;Income}[/math]
  • Operating leverage is [math]\frac{Revenue - Variable\;Cost}{Op.\;Income}[/math]
  • I think we used: [math]\frac{Total\;Liabilities}{Equity}[/math]

Extending the paper

Coming back to it, the paper looks a little thin (though clearly the data is a monster already). I think it would benefit from a couple of extensions, particularly the inclusion of something that resembles an instrument. I have the following ideas, which might be feasible in the time we have:

(Note: The defacto standard method of determining the lead investor is to see which (if any) investor was present from the first round.)

Using Patents

Patents might act to certify their patent-holders in the face of information asymmetries (see, for example, Hsu and Ziedonis, 2007). Thus firms with acquirers of targets with patents might value the certification of a venture capitalist less than when they consider targets without patents. Likewise, on average about 2/3rds of all patent citations are added by examiners (Alcacer and Gittelman, 2006 and Cotropia et al., 2010). Thus citation counts might represent the search costs associated with finding information about patents. That is, patents with more citations are the ones that are easiest to find, and so mitigate information asymmetries the most successfully.

VC Reputations

We argue, explicitly, that VCs use their reputations to certify thier firms. We can calculate the defacto standard measures of reputation - the number of IPOs and the total number of successful exits, and use these to instrument our effects. This could be done for either the lead investor, or the most successful investor, or a weighted average of all investors (weighting by the number of rounds they participated in, or the proportional dollar value they may have provided). Likewise we can calculate the number of funds the lead investor had successfully raised at the time of the exit, or the average number of funds raised across all investors (again perhaps with a weighting).

VC Information Asymmetries

Implicit in our argument is that VCs mitigate the information asymmetries between themselves and their portfolio firms effectively. We can refine this argument to consider the degree to which a VC is likely to be informed about their porfolio firm.

Distances

We can use the road or great-circle distance from the lead investor to the portfolio company as a measure of the information acquisition cost. We could also create a cruder but likely more meaningful version of this by creating a binary variable to see whether the lead investor was within a 20-minute drive of the portfolio company (this is the so called '20 minute rule' - discussed as important for monitoring in Tian, 2006). Alternatively we could consider the nearest investor, or the average of the nearest investors across all rounds, etc.

I can get 2,500 requests per IP address (I can run 3+ concurrently from Berkeley) from the Google Maps api, with responses including driving distances and estimated driving times.

Active Monitoring

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.