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.

Connect to the database with:

psql -h 128.32.252.201 -U ed_egan Acqs

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..."

Other points:

There were a few other typos.
Also the GX paper gets very little mention - I thought we had a whole subsection devoted to them...
I was surprised that we didn't have a year fixed-effect variable in the main analysis (though we have Boom, which is more interesting)

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):

Acquisition is completed indicator (as a check)
Acquisition percentage (as a check)
Target Name
Acquirer Name
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
Acquirer 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, and all LBOs seperately):

VC (binary variable)
VE industry classification (to use as a reference set to update the industry classification)
LBO (binary variable) to exclude these from the control group
If we add one or more extension (see below), then we'll need a fully flushed out VE database build including portcos, rounds, deals, funds, firms, and possibly executives.

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.
The cummulative return [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.

For the tables we need ARs for 2,3 and 7 day, where 2 day is days 0 & 1, and others are symmetric.

For robustness we need ARs for 5,9, and 11 days.

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: [math]\frac{Total\;Liabilities}{Total\;Assets}[/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.

At present I have the 2006 NBER patent data loaded up in a database. I could add in patents and citations up to 2006 with a day or two of work. I am working on the 2011 update to the NBER patent data (see: http://www.nber.com/~edegan/w/index.php) but this will NOT be done before the March 7th deadline.

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.

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.

Proposed Rebuild Order

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.
Update the industry classification, using the old one, my new one, and VentureXpert as a reference set.
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)?
Rebuild the "Information Asymmetry (IA) by Industry" data.

Time Estimates

My time estimates are going to be wild for three reasons: It is just really hard to estimate some of these things (the time goes into the things that you don't anticipate being a problem but are); some of my skills are rusty, and on the flip-side I now have some serious hardware to throw at this; and I'm currently recovering from some health problems. However, my best ballpark is:

1 day
2 days
1/2 day
1 day
1/2 day
1 day + 2 days to get everything together into a dataset for analysis
2 days
1 day
3 days
2 days
3 days

By the end of step 6, which I think will take 8 days, I should have a the original data rebuilt and analyzed again. To get steps 7-9, which would give us two good extensions to the data, would add another 6 days. The patent data extension (if wanted) would add another 2, and then the rebuild of IA data is guesstimated at another 3.

There are 16 calendar days between now and March 7th (excluding the 7th). I am going to lose 2 to a course that I'm taking, and 1 to health-care. That leaves 13, which is one short of the 8+6 for the 2 extensions. I will probably also need one or two days off (I just can't keep working 7 day weeks), but nevertheless, it looks like I should be able to complete the basic rebuild in time, and perhaps (if things go well) add an extension or two.

Rebuild Notes

Thoughts for discussion

The experience variables (# Previous Acqs) are generated using the primary data, and will be truncated by the start of the dataset. We should probably consider year fixed effects to mitigate any induced bias.

Downloading the Acquisitions

Basic Criteria:

US Targets
Announced: 1/1/1980 to 12/31/2011
Target Nation: US
Acquiror Nation: US
Target Status: Private (V)
Acquirer Status: Public (P)
Percentage of Shares Owned after Transaction: 100 to 100 (will exclude those with missing data)

The completed deal flag is in Deal Status - this will be restricted to 'C' in the processing.

New Flags in SDC (downloaded for exclusions):

Bankruptcy Flag
Failed bank Flag
Leveraged Buyout Flag
Reverse LBO Flag
Spinoff Flag
Splitoff Flag
Target is a Leveraged Buyout Firm
And many others. These will be reviewed and excluded.

Founding year/Age of the Target was not available in the data. It is in VE for VC-backed only.

The street address is multiline and problematic if included. This can be drawn seperately if needed. We have the City, Zip and State, which is sufficient to get a Google Maps lookup. Likewise 'Competing Offer Flag (Y/N)', also known as COMPETE and Competing Bidder, is a multiline - with each presumably corresponding to a different bidder identity. It was excluded.

The NormalizeFixedWidth.pl script uses the spacing in the header to determine the column breaks. The EquityValue column has two spaces in front of its name that screws this. Both EquityValue and EnterpriseValue needed to be imported as varchar(10), as they have the code 'np' in some observations.

The NormalizeFixedWidth.pl script was modified so that it only drops commas in numbers and not those in names etc.

Processing the Acquisitions

A large number of 'new' flags are now available in SDC. Most of them have no bite on our data. But I have excluded the following:

Cases where the target was bankrupt or distressed as indicated by: TargetBankrupt, TargetBankInsolvent, Liquidation, Restructuring.
Cases where the form wasn't genuinely privately-held as indicated by: OpenMarketPurchases, GovOwnedInvolvement, JointVenture, Privatization (which capture government sales).
Cases where there was a share recap going on concurrently with the acquisition: Recap
Targets that had LBO involvement (more will likely be removed in the next phase of matching to LBO targets): LBO, SecondaryBuyoutFlag, ReverseTakeOver (used for LBO'd firms doing a reverse take over), IPOFlag (likewise).
Firms where the deal began as a rumor (so the information leakage is problematic): DealBeganAsRumor

All together, these constraints reduce us from 41,572 to 40,306 acquisitions. Constraints that had no bite included: Spinoff, TwoStepSpinOff, and Splitoff. Further restricting the data to completed transactions, and those with valid codes for when the transaction value was amended, reduces the data to 40,035.

Acquiror and Target names were keyed into unique names. The first acquisition of several was taken. 33 records had multiple entries, the correct one of which could not be determined. These were discarded.

Retrieving CUSIPs

The acquisitions data lists 6 digit CUSIPs, we need the 'correct' (the right issue for the right period) 8 or 9 digit CUSIP with which to search CRSP and COMPUSTAT. A full list of all CUSIPs was retrieved from COMPUSTAT for the period Jan 1978 - Jan 2012 using the annual data.

Downloading the VC PortCos

The following criteria was applied:

Moneytree deals (i.e. VC only)
Company Nation: US
Round date: 1/1/1975 to 1/1/2012

Total of 30364 records.

Note that the coverage of VE before 1980 is problematic.

Selected fields:

Name
Nation
State
Founding Date
Total $Inv
No Rounds
Address info (various fields)
Date first inv
Date last inv

Check flags:

Moneytree
Venture Related

Implicit Price Deflators

Accounting vars were converted to 2011 real values using the official BEA implicit GDP price deflator index: http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=13&ViewSeries=NO&Java=no&Request3Place=N&3Place=N&FromView=YES&Freq=Year&FirstYear=1978&LastYear=2010&3Place=N&Update=Update&JavaBox=no)

To Do

Log accounting vars.
Acq is material (Can't find defn)?

Also - throw out :

Market Value of acquiror is negative
Mkt Value 'very small' relative to TV

Variables

The following is quick description of the variables in the Version 1 dataset, in order.

Acquisition Specific Variables

Note: All variables ending "11" are amounts in 2011 dollars

acqno: The acquisition number (an index)
dateann: Date announced
dateannisest: Whether the date announced is estimated
yearann: Year announced
boom
yearcomp: Year completed
dateeff: Date Effective
tv: Transaction Value
tv11
enterpriseval
enterpriseval11
equityval
equityval11
valueamended: Whether the Value was amended
valueamendedupdown: Whether the amendment was Up (1) or Down (0)
valueest: Whether the transaction value is flagged as estimated
factor: The real dollar adjustment factor for the year of the acquisition
factor_m1: The real dollar adjustment factor for the previous year
mergerofequals: Whether SDC flags this as a merger of equals
nobidders
challenged: Whether the deal was challenged (an SDC flag)
noconsidoffer: Number of considerations offered
noconsidsought: Number of considerations sought
pccash: percentage of cash in the deal
pcother: percentage of other considerations (not cash/stock) in the deal
pcstock: percentage of stock in the deal
pcunknown
horiz
vert
cong

Estimation and Event Window Variables

Note: for return variables, _m indicates minus and _p indicates plus days, so that r_m1 is the return on the stock at day minus 1, where 0 is the announcement day or the first trading day following the announcement if the exchange was closed when the announcement was made.

alpha: The constant from the estimation regression
beta: The coefficient on the market return from the estimation regression
prc: The stock price 30 days prior to the announcement
rmse: The RMSE from the estimation regression

Returns for the stock (single period buy and hold, including dividends):

r_0
r_m1
r_m2
r_m3
r_m4
r_m5
r_p1
r_p2
r_p3
r_p4
r_p5

The corresponding market returns on the Value-Weighted Amex-Nasdaq-NYSE composite (including dividends):

m_0
m_m1
m_m2
m_m3
m_m4
m_m5
m_p1
m_p2
m_p3
m_p4
m_p5

Abnormal returns calculated using the market model:

arm_0
arm_m1
arm_m2
arm_m3
arm_m4
arm_m5
arm_p1
arm_p2
arm_p3
arm_p4
arm_p5

Abnormal returns calculated using the subtraction method:

ars_0
ars_m1
ars_m2
ars_m3
ars_m4
ars_m5
ars_p1
ars_p2
ars_p3
ars_p4
ars_p5

The three day cummulative abnormal return (market model):

carm_3

Acquiror Specific Variables

Note: Again "11" indicated values in 2011 dollar, but _m1 indicates the previous year for the annual accounting variables.

aname: The acquiror name
astate: A numeric code for the acquiror state (there is a lookup table)

Whether the acquiror is an IT or Biotech firm (binary):

ait
abt

Acquiror NAIC codes and Indu codes. 1 indicates 1 digit, 2 is 2 digit, and 3 is 3 digit. The indu variables have IT and BT taken out an recoded as 10&11 for 1 digit, 100&101 for 2 digit, and 1000 & 1001 for 3 digit.

anaic
anaic1
anaic2
anaic3
aindu1
aindu2
aindu3

The count of previous acquisitions occuring strictly before the announcement:

noprevacqs
noprevacqsnonvc
noprevacqsvc

Various acquiror accounting variables, for the year of the acquisition and lagged:

assets
assets11
asset11_m1
asset_m1
intangibles
intangibles11
intangibles11_m1
intangibles_m1
leverage
leverage11
leverage11_m1
leverage_m1
liabilities
liabilities11
liabilities11_m1
liabilities_m1
mktvalue
mktvalue11
mktvalue11_m1
mktvalue_m1
mv: Market value calculated using the shares outstanding and the price 30 days before the announcement.
mv11
sales
sales11
sales11_m1
sales_m1
sharesout: the number of shares outstanding
sharesout11: a fake variable to construct mv11
sharesout11_m1
sharesout_m1

Target Specific Variables

tname: Target name
tstate: A numeric code of state (same lookup as acquiror)

The IT, Biotech and NAIC/Indu variables, constructed the same as for the acquiror:

targetit as tit
targetbt as tbt
tindu1
tindu2
tindu3
tnaic
tnaic1
tnaic2
tnaic3

The VC variables:

vc: A binary variable - 1=VC backed, 0 otherwise
firstinvdate
lastinvdate
norounds
totalinvested
totalinvested11
vccontafteracq: Whether VC investment appears to continue after the acquisition is supposed to have completed
foundingdate

Target Accounting Variables

targetcommonequity
targetcommonequity11
targetintangibles
targetintangibles11
targetnetsales
targetnetsales11
targetrandd: R&D
targetrandd11
targettotalassets
targettotalassets11
targettotalliabilities
targettotalliabilities11