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Old VC Acquisitions Paper (view source)
Revision as of 16:15, 2 September 2016
, 16:15, 2 September 2016Created page with "This page details the work rebuilding Brander Egan (2007) - The Role of VCs in Acquisitions in to a new paper for our submission to the RCFS special issue and associated c..."
This page details the work rebuilding [[Brander Egan (2007) - The Role of VCs in Acquisitions]] in to a new paper for our submission to the RCFS special issue and associated conference.
Connect to the Postgres database (inside the Berkeley network only) with:
psql -h 128.32.204.203 -U ed_egan Acqs
==Current Status==
This paper was submitted (dual submission) to the 3rd Annual Entrepreneurial Finance and Innovation Conference (EFIC) conference and the Review of Corporate Finance Studies (RCFS). It was rejected from both.
The key points from the reviewers were:
*We should explain how we get a different result from Masulis and Nahata (2011), who find a +3% return premium to VC-backed acquisitions
*We should use our continous measures of IA (i.e., look to find a monotonic effect). And consider within-industry acquisitions to see whether this mitigates IA problems.
*To back up the winner's curse we should consider the number of competing bidders.
*Possibly we should consider long rum performance and attempt to explain why the acquirers buy VC-backed firms.
*Focus less on the univariate results.
The obvious possibilities for this paper are to:
*Focus more on the IA.
*Do a supply-side VC analysis (i.e., include reputations, the possibility of grandstanding, etc.)
The immediate 'to do' is:
*Read the Masulis and Nahata (2011) paper carefully.
*Do a proper literature review again!
===Lit Review===
The main [[VC Acquisitions Lit Review]] page details searches for papers related to the intersection of venture capital, acquisitions and explaining abnormal returns.
There are seperate lit reviews for related topics, such as:
*[[Information Asymmetry Measures]]
*[[Information Asymmetry in Acquisitions Lit Review]]
*[[The Winners Curse]]
The key papers found were:
*[[Masulis Nahata (2011) - Venture Capital Conflicts Of Interest]]
*[[Gompers Xuan (2008) - Bridge Building In Venture Capital Backed Acquisitions]]
*[[Gompers Xuan (2006) - The Role Of Venture Capitalists In The Acquisition Of Private Companies]]
*[[Benson Ziedonis (2010) - Corporate Venture Capital And The Returns To Acquiring Portfolio Companies]]
====Comparing Results====
Comparing our CARs with those from the literature (some values are inferred - see the review pages)
Our Paper M&N'11 G&X'06 G&X'08 B&Z'10
Data Range 80-'10 91-06 90-01 92-06 80-03
Model Mkt Sub Mkt Mkt Mkt
Univariate
Private Target 0.7% 4.8% 1.5% NA NA
VC 0.3% 6.3% 0.6% 0.7% 0.7%
Non-VC 0.8% 3.4% 1.6% NA NA
With Controls
VC indicator 0.5% 2.7% 0.3%
Target Ind. Q 4.8%
Related 0.5%
R2 1.6% 4.3% 1.6%
N 22,961 490 8693 1261 489
Note that using the subtraction model our univariate results are: 0.95% (all), 0.98% (Non-VC) and 0.59% (VC).
==Latest Version==
The latest version of the paper is:
*[[:Image:Brander Egan (2012) - Investor Expectations and the Role of Venture Capitalists in Acquisitions.doc |Brander Egan (2012) - Investor Expectations and the Role of Venture Capitalists in Acquisitions.doc]]
Note: This version was submitted to the both the EFIC and RCFS.
The current reference is:
*Brander, James A., and Edward J. Egan (2012), "Investor Expectations and the Role of Venture Capitalists in Acquisitions: Bargaining and the Winner’s Curse", Working paper, March 2012, under review for inclusion in the Third Entrepreneurial Finance and Innovation Conference and a special issue of Review of Corporate Finance Studies.
==Submission Details==
This paper was '''submitted''' under the dual-submission process to both the '''3rd EFIC and the RCFS''' on March 7th.
The Third Entrepreneurial Finance and Innovation Conference (EFIC) 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 (RCFS).
The conference details are here: http://sites.kauffman.org/efic/overview.cfm
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.
==Rebuilding the Paper==
The paper required a complete rebuild of all the results, with the data updated to the end of 2011. We can also consider several extensions to the paper, detailed in a later section.
===Dataset Processing Notes===
#The acquisitions data was retrieved from SDC (see below) and imported into Postgres. There were 41,572 records.
#The flag variables were reviewed for variation - some had no bite (e.g. Spinoff, TwoStepSpinOff, and Splitoff) and were ignored. Others led to data being discarded as flag exclusions.
#All variables were checked for coding, range, dispersion, etc.
#Restriction were placed on the data (Completion, flags, exclude LBOs) etc. This reduced the data to 40,035 observations
#Certain variables were reprocessed, e.g. Percentage Shares, NAIC codes, etc. (see below)
#Acquiror and Target names were keyed to account for repetitions etc.
#Duplicate acquisition data (same event) was eliminated
#Multiple acquisition of the same target (i.e. a target is acquired, spun-off and acquired again, etc) were eliminated.
#CUSIPs were processed into 6, 8, and 9 digit variables, by searching COMPUSTAT annual data (Jan 1978 - Jan 2012) using the 6 digit CUSIP and then finding the correct 9 digit CUSIP for a particular issue-year. Note that a 9 digit CUSIP is a 6 digit Issuer Number, a 2 digit Issue Number, and a check digit. CRSP uses 8 digit Cusips. There were 27,401 acquisitions with 7,348 valid CUSIPs.
#CRSP data was retrieved and processed (see below). After processing we had data for 23,802 observations.
#COMPUSTAT data was retrieved and processed (see below)
#VC PortCo data was retrieved and processed. PortCos were flagged and portco data added for appropriate observations.
#LBO data was retrieved and processed. 164 observations were discarded.
#Acquisition Histories were calculated as number of past acquisitions for each acquirer: Total, VC only, Non-VC only
#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)
#Percentage variables were multiplied by 100 to get nice coefficients
#Every observation was assigned a unique observation number (obsno)
#Compound variables such as ''horiz'',''vert'', and ''cong'' were calculated.
Variable check notes:
*1,514 had estimated announce dates. These were flagged.
*443 had their transaction value amended. These were flagged.
*41,473 had a deal code of 'C' for completed. These were kept.
*The number of bidders was always disclosed and 2 in 54 cases and 3 in 2 cases.
*Number of considerations offered and sought varied from 1 to 8
*State codes were USPS official standards: https://www.usps.com/send/official-abbreviations.htm
*There was data from 35 stock exchanges. 32,177 observations recorded Amex, Nasdaq or NYSE.
*25 acquirors were LBO firms and 3 targets were LBOs These were excluded.
*All acquirors and targets had 6 digit NAIC codes, though some were truncated e.g. 517000 and others invalid. COMPUSTAT NAIC codes were used when SDC NAIC codes failed when these were recorded in WRDS.
Flag Exclusions:
*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
Processing of variables:
*The original announce date was determined as min{announcedate, announcedateorg}. Those where annoucedate \ne announcedateorg were flagged.
*Percentage stock, cash, other and unknown were reprocessed to include data from the ConsidStruct field, which tags Stock Only, Cash Only, etc.
*State codes were reprocessed to numerics using the lookup table below
*IT, BT (Biotech), HT (Hightech) and NAIC1, NAIC2, NAIC3, Indu1, Indu2, Indu3 variables were created using the lookup tables below (see the variable descriptions for more info). Note that the IT, BT and HT variables were coding using aggregate codes whereever possible (i.e. 517110, etc, all appear in IT and cover the 517 code entirely, so the 517 block would be coded as IT even if SDC recorded the code as 517000.
===Acquisitions Data===
====SDC Search Criteria====
SDC search 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)
====SDC Variables====
The following variables were pulled:
YEARANN, YEAREFF, DA, DE, DATEANNORIG_DAYS, PCTACQ, PCTOWN, DAE, DATEEFFEXP, DUNCON, DAO, VALAMEND, VEST, STATC, VAL, ENTVAL, EQVAL, BIDCOUNT, CONSID_STRUCTURE, CONSID_STRUCT_DESC, CURRC, COUNT_CONSIDO, COUNT_CONSIDS, A_POSTMERGE_OWN_PCT, PCTOWN, PCT_STK, PCT_CASH, PCT_OTHER, PCT_UNKNOWN, AN, ANL, ANATC, ANAICP, AIN, ACU, ASTC, ASTIC, AIP, AUP, AEXCH, ACITY, AZIP, ALBOFIRM, TN, TIN, TCU, TLBOFIRM, TNL, TNATC, TNAICP, TSTC, STIC, TCITY, TZIP, IASS, COMEQ, BV, TASS, SALES, TASS, TLIA, RND, BNKRUPT, TWOSTEPSPIN, CHA, DBT_RESTRUCT, DUTCH, PRIVATIZATION, FBNK, RECAP, GOV_OWN_INVOLV_YN, JV, RESTR, LBO, LIQ, MOE, OMKT, IPO, REVERSE, RUM, SBO, SPIN, SPLIT
This provided (of particular note):
*Target Name
*Acquirer Name
*Transaction Value
*Payment Method
*Acquisition announcement date
*Payment method (cash/stock/mix)
*PC of stock in the deal
*No. of bidders
*Acquirer CUSIP
*Target NAIC
*Acquirer NAIC
*Age (of target)
*Sales (of target)
*Leverage (of target)
*Intangible Assets (of target)
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.
Other Notes:
*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.
===CRSP Data===
Daily return data was downloaded using 8 digit CUSIPs from CRSP. The following variables were retrieved from 1/1/1978-1/1/2012 (the latest month available):
*Cusip
*Date
*prc
*ret
*vwretd
The data was processed:
#Announcedays were coded to the current or next following trading day.
#Trading days were indexed from the announcement day (day 0) for all announcement-cusip pairs.
#A refined estimation set beginning 280 and ending 30 days before the acquisition was extracted for each announceday-cusip pair
#Cusips with multiple announcements on the same day had these announcements flagged and a unique announceday-cusip pair index (acqno) was created
announceday-cusip pair observation were included in an estimation regression provided that there were 50 continuous trading days ending at day -30.
*The parameters. errors and statistics from the regression, particularly <math>\hat{\alpha_i},\hat{\beta_i}</math>, were estimated for each announceday-cusip pair in the following regression:
<math>R_i = \hat{\alpha_i} + \hat{\beta_i}R_m + \epsilon</math>
*Days from -5 to +5, to allow for an 11 day window, were extracted into an event window and processed to produce:
**<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.
*Then other variables were calculated or included:
**The cummulative return <math>CAR_i = \sum_t AR_i</math>
**The price 30 days before the acquisition was recorded for the market value calculation
===COMPUSTAT Data===
From COMPUSTAT we drew accounting variables for all of our Cusips, then extracted data for the announcement years and the lagged announcement years. (Note that Cusip, NAIC, datayear, fiscal year and fiscal year end were included in the download. NAIC was used to supplement SDC NAICs.)
Data included:
*Total Assets
*Market Value
*Sales
*Total Liabilities
*Intangible Assets
*Shares Outstanding
Note that leverage was calculated as:
<math>Leverage=\frac{Total\;Liabilities}{Total\;Assets}</math>
Variables were translated to 2011 dollars and marked ''varname11'', lagged (minus one year) variables were recorded as ''varname_m1''. In STATA log variables were created as ''varnamel''.
===The VC PortCos===
The following criteria was applied to the SDC search:
*Moneytree deals (i.e. VC only)
*Company Nation: US
*Round date: 1/1/1975 to 1/1/2012
A basic variable set was downloaded including:
*PortCo Name
*Nation
*State
*Location
*Address
*Total VC Invested
*Date of First Inv
*Date of Last Inv
*Date of Founding
*No Rounds
Check flags:
*Moneytree
*Venture Related
The data was reprocessed, specifically:
*Unique PortCo Names were determined using Names, States and Location data to determine unique portcos.
*Duplicate records were eliminated
*Discontinuous (multiple) records (pertaining to the financing history of a single firm) were assembled into single records
*PortCos were matched to Acquisition Targets using name based matching, checking state and location information.
*In a small number of cases VC appears to continue after the acquisition. This is almost surely an error in VE, but these obserations are flagged.
Note: The coverage of VE before 1980 is problematic, so we will discard acquisition records before 1985 in STATA before the analysis.
===Removing additional LBOs===
A set of LBO portcos were downloaded from SDC using the flags LBO=yes, PWCMoneytree=No, StdUSVentureDisbursement=No
The LBOs were matched against the acquisition targets and removed (LBO initial investment dates were checked).
===Processing NAIC Codes===
While SDC provide 6 digit NAIC codes for all acquirers, some of these NAIC codes are invalid (proprietary to SDC). These were replaced with COMPUSTAT NAIC codes whenever available. The SDC NAIC codes found were:
SDCnaic | SDCindustry
------------+------------------------------------------------
BBBBBA | Miscellaneous Retail Trade
BBBBBA | Business Services
BBBBBA | Advertising Services
BBBBBA | Prepackaged Software
BBBBBB | Business Services
BCCCCA | Investment & Commodity Firms,Dealers,Exchanges
BCCCCD | Investment & Commodity Firms,Dealers,Exchanges
BCCCCD | Business Services
BCCCCD | Social Services
BCCCCE | Investment & Commodity Firms,Dealers,Exchanges
Details of the IT, BT, and HT codes are below.
An acquisition was classified as:
*''Vert'' if ''acquirornaic6''=''targetnaic6''
*''Horiz'' if ''acquirornaic6''=''targetnaic6'' AND ''acquirornaic5''!=''targetnaic5''
*''Cong'' if !''Vert'' AND !''Horiz''
*''Related'' if ''Vert'' OR ''Horiz''
===Patent Data===
NBER patent data with assignee names from 1975-2006 was used to add patent counts to the data. Only patents filed before the annoucement date were included. Assignee names were matched to target names by name matching software, with matches validated by hand. A patent count and 'has patents' variable (''patents'') were generated and a flag was added to recorded that have their acqusition announcement before 2006. Targets acquired after 2006 have their patent applications up to and including 2006 recorded, though these numbers will as their true counts are right-truncated. Likewise, a target may have existed and made patent applications prior to 1975, resulting in left-truncation. Therefore year fixed-effects are warranted.
====Patents and Information Asymmetries====
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.
Note: 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.
===Analyis Calculations and Notes===
The following is performed on the dataset before analysis:
*Observations were dropped if yearann<1985, to give 5 years of VC data before the announcement
*asize = market value + lagged liabilities
*rsize = tv/asize
*log variables were calculated as log(1+var)
*The following aliases were created:
**tit -> it
**tbt -> bt
**tht -> ht (?)
**yearann -> year
*Interaction effect variables were created
*Year x anaic2 (2 digit acquiror naic) fixed effect indicators were created
*CARM variables (Market Model CAR) were created for the 7 day window for the figures
*Vscore variables were created for the significance tests on CARS using the RMSE from the estimation window: <math>vscore = \left| \frac{carm}{\left( \frac{rmse}{\sqrt{n}} \right)} \right|</math>. This was done on a per group basis, using the variable names xgroupvar, where group=it or itvc or null.
*Year range variables from 1 to 6 were created for years 1985-1989, ... , 2005-2009, 2010-2011.
*In the regression analysis we clustered standard errors on acqno (the cusip-announceday pair that could have multiple acquisitions, marked with ''sameday''=1), using STATA's vce(cluster ''clustvar'') documented here: http://www.stata.com/support/faqs/stat/robust_ref.html
Notes:
#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.
#In the previous version of the paper we threw out cases when the mkt value of the acquirer was 'very small' relative to TV.
#Boom is defined as: <math>1990\le year \le 1999</math>
#The Boehmer standard errors are the cross-sectional ones generated by OLS. Clustering them isn't part of the specification, but clearly should be done.
==Supplementary Data==
To determine the information asymmetry ranking of sectors again we will need (either for 1 year or across the entire year range 1985-2011):
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 should be relative to the exchange's trading volume)
*NAIC
COMPUSTAT:
*intangible assets
*total assets
*Tobin's Q: Market value/book value of assets
*NAIC
==Variables==
The following is quick description of the variables in the Version 3 dataset.
===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
===Additional Variables===
The following variables have now been added to the data:
*aht: Uses our HT definition that does not included IT or BT, on the acquiror's NAIC code
*aht_pb: Uses the Paytas-Berglund definition of HT
*aht_hecker: Uses the Hecker definition of HT
*aht_pb_notitbt: Uses the Paytas-Berglund definition of HT, but removes IT and BT
*aht_hecker_notitbt: Uses the Hecker definition of HT, but removes IT and BT
*tht: The same as above but on the target's NAIC code. THIS IS THE ONE YOU WANT FIRST.
*tht_pb
*tht_hecker
*tht_pb_notitbt
*tht_hecker_notitbt
*patents: A binary variable indicating whether the firm has patent (1) or not (0) applications filed up to an including the year of the announcement of the acquisition
*patentcount: The count of the above patents
*patentdata: Takes the value 1 if the announcement year equal to or less than 2006, so the firm can have all of its patents recorded (from 1975 forward), and 0 if the patent data will be inherently truncated.
==State Codes==
We use the US Postal Service (USPS) Official State Codes, found at: https://www.usps.com/send/official-abbreviations.htm
OfficialCode NumericCode State
AK 1 ALASKA
AL 2 ALABAMA
AR 3 ARKANSAS
AS 4 AMERICAN SAMOA
AZ 5 ARIZONA
CA 6 CALIFORNIA
CO 7 COLORADO
CT 8 CONNECTICUT
DC 9 DISTRICT OF COLUMBIA
DE 10 DELAWARE
FL 11 FLORIDA
FM 12 FEDERATED STATES OF MICRONESIA
GA 13 GEORGIA
GU 14 GUAM GU
HI 15 HAWAII
IA 16 IOWA
ID 17 IDAHO
IL 18 ILLINOIS
IN 19 INDIANA
KS 20 KANSAS
KY 21 KENTUCKY
LA 22 LOUISIANA
MA 23 MASSACHUSETTS
MD 24 MARYLAND
ME 25 MAINE
MH 26 MARSHALL ISLANDS
MI 27 MICHIGAN
MN 28 MINNESOTA
MO 29 MISSOURI
MP 30 NORTHERN MARIANA ISLANDS
MS 31 MISSISSIPPI
MT 32 MONTANA
NC 33 NORTH CAROLINA
ND 34 NORTH DAKOTA
NE 35 NEBRASKA
NH 36 NEW HAMPSHIRE
NJ 37 NEW JERSEY
NM 38 NEW MEXICO
NV 39 NEVADA
NY 40 NEW YORK
OH 41 OHIO
OK 42 OKLAHOMA
OR 43 OREGON
PA 44 PENNSYLVANIA
PR 45 PUERTO RICO
PW 46 PALAU
RI 47 RHODE ISLAND
SC 48 SOUTH CAROLINA
SD 49 SOUTH DAKOTA
TN 50 TENNESSEE
TX 51 TEXAS
UT 52 UTAH
VA 53 VIRGINIA
VI 54 VIRGIN ISLANDS
VT 55 VERMONT
WA 56 WASHINGTON
WI 57 WISCONSIN
WV 58 WEST VIRGINIA
WY 59 WYOMING
99 UNKNOWN
==Classifiction of IT, BT and HT==
===Information and Communications Technology (IT)===
The following is our definition of IT:
333295 both 333295 Semiconductor Machinery Manufacturing
3341 both 334111 Electronic Computer Manufacturing
3341 both 334112 Computer Storage Device Manufacturing
3341 both 334113 Computer Terminal Manufacturing
3341 both 334119 Other Computer Peripheral Equipment Manufacturing
3342 both 334210 Telephone Apparatus Manufacturing
3342 both 334220 Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing
3342 both 334290 Other Communications Equipment Manufacturing
334413 both 334413 Semiconductor and Related Device Manufacturing
334611 both 334611 Software Reproducing
334613 both 334613 Magnetic and Optical Recording Media Manufacturing
33592 both 335921 Fiber Optic Cable Manufacturing
33592 both 335929 Other Communication and Energy Wire Manufacturing
42343 both 423430 Computer and Computer Peripheral Equipment and Software Merchant Wholesalers
42511 both 425110 Business to Business Electronic Markets
44312 both 443120 Computer and Software Stores
4541 both 454111 Electronic Shopping
4541 both 454112 Electronic Auctions
4541 both 454113 Mail-Order Houses
5112 both 511210 Software Publishers
516 2002 516110 Internet Publishing and Broadcasting
517 both 517110 Wired Telecommunications Carriers
517 both 517210 Wireless Telecommunications Carriers (except Satellite)
517 2002 517211 Paging
517 2002 517310 Telecommunications Resellers
517 both 517410 Satellite Telecommunications
517 2002 517510 Cable and Other Program Distribution
517 2002 517910 Other Telecommunications
517 2007 517911 Telecommunications Resellers
517 2007 517919 All Other Telecommunications
518 2002 518111 Internet Service Providers
518 2002 518112 Web Search Portals
518 both 518210 Data Processing, Hosting, and Related Services
51913 2007 519130 Internet Publishing and Broadcasting and Web Search Portals
51919 both 519190 All Other Information Services
5415 both 541511 Custom Computer Programming Services
5415 both 541512 Computer Systems Design Services
5415 both 541513 Computer Facilities Management Services
5415 both 541519 Other Computer Related Services
61142 both 611420 Computer Training
811212 both 811212 Computer and Office Machine Repair and Maintenance
811213 both 811213 Communication Equipment Repair and Maintenance
===Biotech (BT)===
The following is our definition of Biotech:
3254 both 325411 Medicinal and Botanical Manufacturing
3254 both 325412 Pharmaceutical Preparation Manufacturing
3254 both 325413 In-Vitro Diagnostic Substance Manufacturing
3254 both 325414 Biological Product (except Diagnostic) Manufacturing
334510 both 334510 Electromedical and Electrotherapeutic Apparatus Manufacturing
334516 both 334516 Analytical Laboratory Instrument Manufacturing
334517 both 334517 Irradiation Apparatus Manufacturing
339112 both 339112 Surgical and Medical Instrument Manufacturing
339113 both 339113 Surgical Appliance and Supplies Manufacturing
54138 both 541380 Testing Laboratories
541711 2007 541711 Research and Development in Biotechnology
6215 both 621511 Medical Laboratories
6215 both 621512 Diagnostic Imaging Centers
===High Tech (HT)===
The following is our definition of other (i.e. Not IT/BT) High-tech:
211 both 211111 211111 Crude Petroleum and Natural Gas Extraction
211 both 211112 211112 Natural Gas Liquid Extraction
2211 both 221111 221111 Hydroelectric Power Generation
2211 both 221112 221112 Fossil Fuel Electric Power Generation
2211 both 221113 221113 Nuclear Electric Power Generation
2211 both 221119 221119 Other Electric Power Generation
2211 both 221121 221121 Electric Bulk Power Transmission and Control
2211 both 221122 221122 Electric Power Distribution
324 both 324110 324110 Petroleum Refineries
324 both 324121 324121 Asphalt Paving Mixture and Block Manufacturing
324 both 324122 324122 Asphalt Shingle and Coating Materials Manufacturing
324 both 324191 324191 Petroleum Lubricating Oil and Grease Manufacturing
324 both 324199 324199 All Other Petroleum and Coal Products Manufacturing
3251 both 325110 325110 Petrochemical Manufacturing
3251 both 325120 325120 Industrial Gas Manufacturing
3251 both 325131 325131 Inorganic Dye and Pigment Manufacturing
3251 both 325132 325132 Synthetic Organic Dye and Pigment Manufacturing
3251 both 325181 325181 Alkalies and Chlorine Manufacturing
3251 both 325182 325182 Carbon Black Manufacturing
3251 both 325188 325188 All Other Basic Inorganic Chemical Manufacturing
3251 both 325191 325191 Gum and Wood Chemical Manufacturing
3251 both 325192 325192 Cyclic Crude and Intermediate Manufacturing
3251 both 325193 325193 Ethyl Alcohol Manufacturing
3251 both 325199 325199 All Other Basic Organic Chemical Manufacturing
3252 both 325211 325211 Plastics Material and Resin Manufacturing
3252 both 325212 325212 Synthetic Rubber Manufacturing
3252 both 325221 325221 Cellulosic Organic Fiber Manufacturing
3252 both 325222 325222 Noncellulosic Organic Fiber Manufacturing
3253 both 325311 325311 Nitrogenous Fertilizer Manufacturing
3253 both 325312 325312 Phosphatic Fertilizer Manufacturing
3253 both 325314 325314 Fertilizer (Mixing Only) Manufacturing
3253 both 325320 325320 Pesticide and Other Agricultural Chemical Manufacturing
3255 both 325510 325510 Paint and Coating Manufacturing
3255 both 325520 325520 Adhesive Manufacturing
3255 both 325910 325910 Printing Ink Manufacturing
3259 both 325920 325920 Explosives Manufacturing
3259 both 325991 325991 Custom Compounding of Purchased Resins
3259 both 325992 325992 Photographic Film, Paper, Plate, and Chemical Manufacturing
3259 both 325998 325998 All Other Miscellaneous Chemical Product and Preparation Manufacturing
33321 both 333210 333210 Sawmill and Woodworking Machinery Manufacturing
33322 both 333220 333220 Plastics and Rubber Industry Machinery Manufacturing
333291 both 333291 333291 Paper Industry Machinery Manufacturing
333292 both 333292 333292 Textile Machinery Manufacturing
333293 both 333293 333293 Printing Machinery and Equipment Manufacturing
333294 both 333294 333294 Food Product Machinery Manufacturing
333298 both 333298 333298 All Other Industrial Machinery Manufacturing
3333 both 333311 333311 Automatic Vending Machine Manufacturing
3333 both 333312 333312 Commercial Laundry, Drycleaning, and Pressing Machine Manufacturing
3333 both 333313 333313 Office Machinery Manufacturing
3333 both 333314 333314 Optical Instrument and Lens Manufacturing
3333 both 333315 333315 Photographic and Photocopying Equipment Manufacturing
3333 both 333319 333319 Other Commercial and Service Industry Machinery Manufacturing
3336 both 333611 333611 Turbine and Turbine Generator Set Units Manufacturing
3336 both 333612 333612 Speed Changer, Industrial High-Speed Drive, and Gear Manufacturing
3336 both 333613 333613 Mechanical Power Transmission Equipment Manufacturing
3336 both 333618 333618 Other Engine Equipment Manufacturing
3339 both 333911 333911 Pump and Pumping Equipment Manufacturing
3339 both 333912 333912 Air and Gas Compressor Manufacturing
3339 both 333913 333913 Measuring and Dispensing Pump Manufacturing
3339 both 333921 333921 Elevator and Moving Stairway Manufacturing
3339 both 333922 333922 Conveyor and Conveying Equipment Manufacturing
3339 both 333923 333923 Overhead Traveling Crane, Hoist, and Monorail System Manufacturing
3339 both 333924 333924 Industrial Truck, Tractor, Trailer, and Stacker Machinery Manufacturing
3339 both 333991 333991 Power-Driven Handtool Manufacturing
3339 both 333992 333992 Welding and Soldering Equipment Manufacturing
3339 both 333993 333993 Packaging Machinery Manufacturing
3339 both 333994 333994 Industrial Process Furnace and Oven Manufacturing
3339 both 333995 333995 Fluid Power Cylinder and Actuator Manufacturing
3339 both 333996 333996 Fluid Power Pump and Motor Manufacturing
3339 both 333997 333997 Scale and Balance Manufacturing
3339 both 333999 333999 All Other Miscellaneous General Purpose Machinery Manufacturing
3343 both 334310 334310 Audio and Video Equipment Manufacturing
334411 both 334411 334411 Electron Tube Manufacturing
334412 both 334412 334412 Bare Printed Circuit Board Manufacturing
334414 both 334414 334414 Electronic Capacitor Manufacturing
334415 both 334415 334415 Electronic Resistor Manufacturing
334416 both 334416 334416 Electronic Coil, Transformer, and Other Inductor Manufacturing
334417 both 334417 334417 Electronic Connector Manufacturing
334418 both 334418 334418 Printed Circuit Assembly (Electronic Assembly) Manufacturing
334419 both 334419 334419 Other Electronic Component Manufacturing
334511 both 334511 334511 Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing
334512 both 334512 334512 Automatic Environmental Control Manufacturing for Residential, Commercial, and Appliance Use
334513 both 334513 334513 Instruments and Related Products Manufacturing for Measuring, Displaying, and Controlling Industrial Process Variables
334514 both 334514 334514 Totalizing Fluid Meter and Counting Device Manufacturing
334515 both 334515 334515 Instrument Manufacturing for Measuring and Testing Electricity and Electrical Signals
334518 both 334518 334518 Watch, Clock, and Part Manufacturing
334519 both 334519 334519 Other Measuring and Controlling Device Manufacturing
334612 both 334612 334612 Prerecorded Compact Disc (except Software), Tape, and Record Reproducing
3353 both 335311 335311 Power, Distribution, and Specialty Transformer Manufacturing
3353 both 335312 335312 Motor and Generator Manufacturing
3353 both 335313 335313 Switchgear and Switchboard Apparatus Manufacturing
3353 both 335314 335314 Relay and Industrial Control Manufacturing
33591 both 335911 335911 Storage Battery Manufacturing
33591 both 335912 335912 Primary Battery Manufacturing
33591 both 335931 Current-Carrying Wiring Device Manufacturing
33593 both 335932 Noncurrent-Carrying Wiring Device Manufacturing
33593 both 335991 Carbon and Graphite Product Manufacturing
33599 both 335999 All Other Miscellaneous Electrical Equipment and Component Manufacturing
3364 both 336411 336411 Aircraft Manufacturing
3364 both 336412 336412 Aircraft Engine and Engine Parts Manufacturing
3364 both 336413 336413 Other Aircraft Parts and Auxiliary Equipment Manufacturing
3364 both 336414 336414 Guided Missile and Space Vehicle Manufacturing
3364 both 336415 336415 Guided Missile and Space Vehicle Propulsion Unit and Propulsion Unit Parts Manufacturing
3364 both 336419 336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing
3369 both 336991 336991 Motorcycle, Bicycle, and Parts Manufacturing
3369 both 336992 336992 Military Armored Vehicle, Tank, and Tank Component Manufacturing
3369 both 336999 336999 All Other Transportation Equipment Manufacturing
42341 both 423410 423410 Photographic Equipment and Supplies Merchant Wholesalers
42342 both 423420 423420 Office Equipment Merchant Wholesalers
42344 both 423440 423440 Other Commercial Equipment Merchant Wholesalers
42345 both 423450 423450 Medical, Dental, and Hospital Equipment and Supplies Merchant Wholesalers
42346 both 423460 423460 Ophthalmic Goods Merchant Wholesalers
42349 both 423490 423490 Other Professional Equipment and Supplies Merchant Wholesalers
486 both 486110 486110 Pipeline Transportation of Crude Oil
486 both 486210 486210 Pipeline Transportation of Natural Gas
486 both 486910 486910 Pipeline Transportation of Refined Petroleum Products
486 both 486990 486990 All Other Pipeline Transportation
5232 both 523210 523210 Securities and Commodity Exchanges
54131 both 541310 541310 Architectural Services
54132 both 541320 541320 Landscape Architectural Services
54133 both 541330 541330 Engineering Services
54134 both 541340 541340 Drafting Services
54135 both 541350 541350 Building Inspection Services
54136 both 541360 541360 Geophysical Surveying and Mapping Services
54137 both 541370 541370 Surveying and Mapping (except Geophysical) Services
5416 both 541611 541611 Administrative Management and General Management Consulting Services
5416 both 541612 541612 Human Resources Consulting Services
5416 both 541613 541613 Marketing Consulting Services
5416 both 541614 541614 Process, Physical Distribution, and Logistics Consulting Services
5416 both 541618 541618 Other Management Consulting Services
5416 both 541620 541620 Environmental Consulting Services
5416 both 541690 541690 Other Scientific and Technical Consulting Services
541710 2002 541710 541710 Research and Development in the Physical, Engineering, and Life Sciences
541712 2007 541712 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
541720 both 541720 541720 Research and Development in the Social Sciences and Humanities
5612 both 561210 561210 Facilities Support Services
811211 both 811211 811211 Consumer Electronics Repair and Maintenance
811219 both 811219 811219 Other Electronic and Precision Equipment Repair and Maintenance
===Other HT Definitions===
The references for the other High-Tech (HT) definitions are:
*Hecker, Daniel E.(2005), "High-technology employment: a NAICS-based update", Monthly Labor Review (July): 57-72. http://www.bls.gov/opub/mlr/2005/07/art6full.pdf
*Paytas, Jerry and Berglund, Dan (2004), "Technology Industries and Occupations for NAICS Industry Data", Carnegie Mellon University, Center for Economic Development and State Science & Technology Institute.
==Extending the paper==
Once this 'draft' is complete we can consider some extentions. I am currently working on the VC Reputations data.
===VC Reputations===
VCs might use their reputations to certify their firms, or these variables might reflect VC experience (and potentially bargaining skill). We can calculate:
*Avg or max number of previous acquisitions and/or IPOs conducted by VCs present in the last round of investment into the firm prior to the acquisition announcement.
*The number of previous acquisitions and/or IPOs conducted by the lead VC (Note: The defacto standard method of determining the lead investor is to see which (if any) investor was present from the first round in every round until the last.)
*Likewise for the average or dollar-invested weighted average of all investors in the port co.
*Last round, lead investor, or average number of previous funds raised by investors, or their fund size or total cummulative firm size (i.e. summed across all funds) at the announcement.
*Whether the VCs will raise a next fund (though this could actually be endogenuous with the CAR)
===Outside Options===
Outside options affect bargaining. A VC that is near to the end of its fund when it made it's last investment into the portfolio company (either in terms of dates or dollars), and particularly one that won't raise a next fund, will be unable to continue financing the portco without the acquisition and therefore has no good outside option with which to bargain. We could calculate how near last round investors are to the ends of their funds (and whether they are going to raise another) and take averages etc, to proxy for the outside option.
===Bargaining Superstars===
It might be the case that some VCs specialize in providing bargaining skills. We could test this hypothesis by:
*Creating fixed-effect variables for the presence of each repeat VC in a portfolio company
*Regressing these fixed-effects on the CARs and sorting the coefficient into quartiles/deciles etc.
*Testing the hypothesis that firms in the top decile are more likely than expected to appear in a last round of financing.
===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.
Connect to the Postgres database (inside the Berkeley network only) with:
psql -h 128.32.204.203 -U ed_egan Acqs
==Current Status==
This paper was submitted (dual submission) to the 3rd Annual Entrepreneurial Finance and Innovation Conference (EFIC) conference and the Review of Corporate Finance Studies (RCFS). It was rejected from both.
The key points from the reviewers were:
*We should explain how we get a different result from Masulis and Nahata (2011), who find a +3% return premium to VC-backed acquisitions
*We should use our continous measures of IA (i.e., look to find a monotonic effect). And consider within-industry acquisitions to see whether this mitigates IA problems.
*To back up the winner's curse we should consider the number of competing bidders.
*Possibly we should consider long rum performance and attempt to explain why the acquirers buy VC-backed firms.
*Focus less on the univariate results.
The obvious possibilities for this paper are to:
*Focus more on the IA.
*Do a supply-side VC analysis (i.e., include reputations, the possibility of grandstanding, etc.)
The immediate 'to do' is:
*Read the Masulis and Nahata (2011) paper carefully.
*Do a proper literature review again!
===Lit Review===
The main [[VC Acquisitions Lit Review]] page details searches for papers related to the intersection of venture capital, acquisitions and explaining abnormal returns.
There are seperate lit reviews for related topics, such as:
*[[Information Asymmetry Measures]]
*[[Information Asymmetry in Acquisitions Lit Review]]
*[[The Winners Curse]]
The key papers found were:
*[[Masulis Nahata (2011) - Venture Capital Conflicts Of Interest]]
*[[Gompers Xuan (2008) - Bridge Building In Venture Capital Backed Acquisitions]]
*[[Gompers Xuan (2006) - The Role Of Venture Capitalists In The Acquisition Of Private Companies]]
*[[Benson Ziedonis (2010) - Corporate Venture Capital And The Returns To Acquiring Portfolio Companies]]
====Comparing Results====
Comparing our CARs with those from the literature (some values are inferred - see the review pages)
Our Paper M&N'11 G&X'06 G&X'08 B&Z'10
Data Range 80-'10 91-06 90-01 92-06 80-03
Model Mkt Sub Mkt Mkt Mkt
Univariate
Private Target 0.7% 4.8% 1.5% NA NA
VC 0.3% 6.3% 0.6% 0.7% 0.7%
Non-VC 0.8% 3.4% 1.6% NA NA
With Controls
VC indicator 0.5% 2.7% 0.3%
Target Ind. Q 4.8%
Related 0.5%
R2 1.6% 4.3% 1.6%
N 22,961 490 8693 1261 489
Note that using the subtraction model our univariate results are: 0.95% (all), 0.98% (Non-VC) and 0.59% (VC).
==Latest Version==
The latest version of the paper is:
*[[:Image:Brander Egan (2012) - Investor Expectations and the Role of Venture Capitalists in Acquisitions.doc |Brander Egan (2012) - Investor Expectations and the Role of Venture Capitalists in Acquisitions.doc]]
Note: This version was submitted to the both the EFIC and RCFS.
The current reference is:
*Brander, James A., and Edward J. Egan (2012), "Investor Expectations and the Role of Venture Capitalists in Acquisitions: Bargaining and the Winner’s Curse", Working paper, March 2012, under review for inclusion in the Third Entrepreneurial Finance and Innovation Conference and a special issue of Review of Corporate Finance Studies.
==Submission Details==
This paper was '''submitted''' under the dual-submission process to both the '''3rd EFIC and the RCFS''' on March 7th.
The Third Entrepreneurial Finance and Innovation Conference (EFIC) 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 (RCFS).
The conference details are here: http://sites.kauffman.org/efic/overview.cfm
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.
==Rebuilding the Paper==
The paper required a complete rebuild of all the results, with the data updated to the end of 2011. We can also consider several extensions to the paper, detailed in a later section.
===Dataset Processing Notes===
#The acquisitions data was retrieved from SDC (see below) and imported into Postgres. There were 41,572 records.
#The flag variables were reviewed for variation - some had no bite (e.g. Spinoff, TwoStepSpinOff, and Splitoff) and were ignored. Others led to data being discarded as flag exclusions.
#All variables were checked for coding, range, dispersion, etc.
#Restriction were placed on the data (Completion, flags, exclude LBOs) etc. This reduced the data to 40,035 observations
#Certain variables were reprocessed, e.g. Percentage Shares, NAIC codes, etc. (see below)
#Acquiror and Target names were keyed to account for repetitions etc.
#Duplicate acquisition data (same event) was eliminated
#Multiple acquisition of the same target (i.e. a target is acquired, spun-off and acquired again, etc) were eliminated.
#CUSIPs were processed into 6, 8, and 9 digit variables, by searching COMPUSTAT annual data (Jan 1978 - Jan 2012) using the 6 digit CUSIP and then finding the correct 9 digit CUSIP for a particular issue-year. Note that a 9 digit CUSIP is a 6 digit Issuer Number, a 2 digit Issue Number, and a check digit. CRSP uses 8 digit Cusips. There were 27,401 acquisitions with 7,348 valid CUSIPs.
#CRSP data was retrieved and processed (see below). After processing we had data for 23,802 observations.
#COMPUSTAT data was retrieved and processed (see below)
#VC PortCo data was retrieved and processed. PortCos were flagged and portco data added for appropriate observations.
#LBO data was retrieved and processed. 164 observations were discarded.
#Acquisition Histories were calculated as number of past acquisitions for each acquirer: Total, VC only, Non-VC only
#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)
#Percentage variables were multiplied by 100 to get nice coefficients
#Every observation was assigned a unique observation number (obsno)
#Compound variables such as ''horiz'',''vert'', and ''cong'' were calculated.
Variable check notes:
*1,514 had estimated announce dates. These were flagged.
*443 had their transaction value amended. These were flagged.
*41,473 had a deal code of 'C' for completed. These were kept.
*The number of bidders was always disclosed and 2 in 54 cases and 3 in 2 cases.
*Number of considerations offered and sought varied from 1 to 8
*State codes were USPS official standards: https://www.usps.com/send/official-abbreviations.htm
*There was data from 35 stock exchanges. 32,177 observations recorded Amex, Nasdaq or NYSE.
*25 acquirors were LBO firms and 3 targets were LBOs These were excluded.
*All acquirors and targets had 6 digit NAIC codes, though some were truncated e.g. 517000 and others invalid. COMPUSTAT NAIC codes were used when SDC NAIC codes failed when these were recorded in WRDS.
Flag Exclusions:
*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
Processing of variables:
*The original announce date was determined as min{announcedate, announcedateorg}. Those where annoucedate \ne announcedateorg were flagged.
*Percentage stock, cash, other and unknown were reprocessed to include data from the ConsidStruct field, which tags Stock Only, Cash Only, etc.
*State codes were reprocessed to numerics using the lookup table below
*IT, BT (Biotech), HT (Hightech) and NAIC1, NAIC2, NAIC3, Indu1, Indu2, Indu3 variables were created using the lookup tables below (see the variable descriptions for more info). Note that the IT, BT and HT variables were coding using aggregate codes whereever possible (i.e. 517110, etc, all appear in IT and cover the 517 code entirely, so the 517 block would be coded as IT even if SDC recorded the code as 517000.
===Acquisitions Data===
====SDC Search Criteria====
SDC search 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)
====SDC Variables====
The following variables were pulled:
YEARANN, YEAREFF, DA, DE, DATEANNORIG_DAYS, PCTACQ, PCTOWN, DAE, DATEEFFEXP, DUNCON, DAO, VALAMEND, VEST, STATC, VAL, ENTVAL, EQVAL, BIDCOUNT, CONSID_STRUCTURE, CONSID_STRUCT_DESC, CURRC, COUNT_CONSIDO, COUNT_CONSIDS, A_POSTMERGE_OWN_PCT, PCTOWN, PCT_STK, PCT_CASH, PCT_OTHER, PCT_UNKNOWN, AN, ANL, ANATC, ANAICP, AIN, ACU, ASTC, ASTIC, AIP, AUP, AEXCH, ACITY, AZIP, ALBOFIRM, TN, TIN, TCU, TLBOFIRM, TNL, TNATC, TNAICP, TSTC, STIC, TCITY, TZIP, IASS, COMEQ, BV, TASS, SALES, TASS, TLIA, RND, BNKRUPT, TWOSTEPSPIN, CHA, DBT_RESTRUCT, DUTCH, PRIVATIZATION, FBNK, RECAP, GOV_OWN_INVOLV_YN, JV, RESTR, LBO, LIQ, MOE, OMKT, IPO, REVERSE, RUM, SBO, SPIN, SPLIT
This provided (of particular note):
*Target Name
*Acquirer Name
*Transaction Value
*Payment Method
*Acquisition announcement date
*Payment method (cash/stock/mix)
*PC of stock in the deal
*No. of bidders
*Acquirer CUSIP
*Target NAIC
*Acquirer NAIC
*Age (of target)
*Sales (of target)
*Leverage (of target)
*Intangible Assets (of target)
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.
Other Notes:
*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.
===CRSP Data===
Daily return data was downloaded using 8 digit CUSIPs from CRSP. The following variables were retrieved from 1/1/1978-1/1/2012 (the latest month available):
*Cusip
*Date
*prc
*ret
*vwretd
The data was processed:
#Announcedays were coded to the current or next following trading day.
#Trading days were indexed from the announcement day (day 0) for all announcement-cusip pairs.
#A refined estimation set beginning 280 and ending 30 days before the acquisition was extracted for each announceday-cusip pair
#Cusips with multiple announcements on the same day had these announcements flagged and a unique announceday-cusip pair index (acqno) was created
announceday-cusip pair observation were included in an estimation regression provided that there were 50 continuous trading days ending at day -30.
*The parameters. errors and statistics from the regression, particularly <math>\hat{\alpha_i},\hat{\beta_i}</math>, were estimated for each announceday-cusip pair in the following regression:
<math>R_i = \hat{\alpha_i} + \hat{\beta_i}R_m + \epsilon</math>
*Days from -5 to +5, to allow for an 11 day window, were extracted into an event window and processed to produce:
**<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.
*Then other variables were calculated or included:
**The cummulative return <math>CAR_i = \sum_t AR_i</math>
**The price 30 days before the acquisition was recorded for the market value calculation
===COMPUSTAT Data===
From COMPUSTAT we drew accounting variables for all of our Cusips, then extracted data for the announcement years and the lagged announcement years. (Note that Cusip, NAIC, datayear, fiscal year and fiscal year end were included in the download. NAIC was used to supplement SDC NAICs.)
Data included:
*Total Assets
*Market Value
*Sales
*Total Liabilities
*Intangible Assets
*Shares Outstanding
Note that leverage was calculated as:
<math>Leverage=\frac{Total\;Liabilities}{Total\;Assets}</math>
Variables were translated to 2011 dollars and marked ''varname11'', lagged (minus one year) variables were recorded as ''varname_m1''. In STATA log variables were created as ''varnamel''.
===The VC PortCos===
The following criteria was applied to the SDC search:
*Moneytree deals (i.e. VC only)
*Company Nation: US
*Round date: 1/1/1975 to 1/1/2012
A basic variable set was downloaded including:
*PortCo Name
*Nation
*State
*Location
*Address
*Total VC Invested
*Date of First Inv
*Date of Last Inv
*Date of Founding
*No Rounds
Check flags:
*Moneytree
*Venture Related
The data was reprocessed, specifically:
*Unique PortCo Names were determined using Names, States and Location data to determine unique portcos.
*Duplicate records were eliminated
*Discontinuous (multiple) records (pertaining to the financing history of a single firm) were assembled into single records
*PortCos were matched to Acquisition Targets using name based matching, checking state and location information.
*In a small number of cases VC appears to continue after the acquisition. This is almost surely an error in VE, but these obserations are flagged.
Note: The coverage of VE before 1980 is problematic, so we will discard acquisition records before 1985 in STATA before the analysis.
===Removing additional LBOs===
A set of LBO portcos were downloaded from SDC using the flags LBO=yes, PWCMoneytree=No, StdUSVentureDisbursement=No
The LBOs were matched against the acquisition targets and removed (LBO initial investment dates were checked).
===Processing NAIC Codes===
While SDC provide 6 digit NAIC codes for all acquirers, some of these NAIC codes are invalid (proprietary to SDC). These were replaced with COMPUSTAT NAIC codes whenever available. The SDC NAIC codes found were:
SDCnaic | SDCindustry
------------+------------------------------------------------
BBBBBA | Miscellaneous Retail Trade
BBBBBA | Business Services
BBBBBA | Advertising Services
BBBBBA | Prepackaged Software
BBBBBB | Business Services
BCCCCA | Investment & Commodity Firms,Dealers,Exchanges
BCCCCD | Investment & Commodity Firms,Dealers,Exchanges
BCCCCD | Business Services
BCCCCD | Social Services
BCCCCE | Investment & Commodity Firms,Dealers,Exchanges
Details of the IT, BT, and HT codes are below.
An acquisition was classified as:
*''Vert'' if ''acquirornaic6''=''targetnaic6''
*''Horiz'' if ''acquirornaic6''=''targetnaic6'' AND ''acquirornaic5''!=''targetnaic5''
*''Cong'' if !''Vert'' AND !''Horiz''
*''Related'' if ''Vert'' OR ''Horiz''
===Patent Data===
NBER patent data with assignee names from 1975-2006 was used to add patent counts to the data. Only patents filed before the annoucement date were included. Assignee names were matched to target names by name matching software, with matches validated by hand. A patent count and 'has patents' variable (''patents'') were generated and a flag was added to recorded that have their acqusition announcement before 2006. Targets acquired after 2006 have their patent applications up to and including 2006 recorded, though these numbers will as their true counts are right-truncated. Likewise, a target may have existed and made patent applications prior to 1975, resulting in left-truncation. Therefore year fixed-effects are warranted.
====Patents and Information Asymmetries====
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.
Note: 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.
===Analyis Calculations and Notes===
The following is performed on the dataset before analysis:
*Observations were dropped if yearann<1985, to give 5 years of VC data before the announcement
*asize = market value + lagged liabilities
*rsize = tv/asize
*log variables were calculated as log(1+var)
*The following aliases were created:
**tit -> it
**tbt -> bt
**tht -> ht (?)
**yearann -> year
*Interaction effect variables were created
*Year x anaic2 (2 digit acquiror naic) fixed effect indicators were created
*CARM variables (Market Model CAR) were created for the 7 day window for the figures
*Vscore variables were created for the significance tests on CARS using the RMSE from the estimation window: <math>vscore = \left| \frac{carm}{\left( \frac{rmse}{\sqrt{n}} \right)} \right|</math>. This was done on a per group basis, using the variable names xgroupvar, where group=it or itvc or null.
*Year range variables from 1 to 6 were created for years 1985-1989, ... , 2005-2009, 2010-2011.
*In the regression analysis we clustered standard errors on acqno (the cusip-announceday pair that could have multiple acquisitions, marked with ''sameday''=1), using STATA's vce(cluster ''clustvar'') documented here: http://www.stata.com/support/faqs/stat/robust_ref.html
Notes:
#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.
#In the previous version of the paper we threw out cases when the mkt value of the acquirer was 'very small' relative to TV.
#Boom is defined as: <math>1990\le year \le 1999</math>
#The Boehmer standard errors are the cross-sectional ones generated by OLS. Clustering them isn't part of the specification, but clearly should be done.
==Supplementary Data==
To determine the information asymmetry ranking of sectors again we will need (either for 1 year or across the entire year range 1985-2011):
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 should be relative to the exchange's trading volume)
*NAIC
COMPUSTAT:
*intangible assets
*total assets
*Tobin's Q: Market value/book value of assets
*NAIC
==Variables==
The following is quick description of the variables in the Version 3 dataset.
===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
===Additional Variables===
The following variables have now been added to the data:
*aht: Uses our HT definition that does not included IT or BT, on the acquiror's NAIC code
*aht_pb: Uses the Paytas-Berglund definition of HT
*aht_hecker: Uses the Hecker definition of HT
*aht_pb_notitbt: Uses the Paytas-Berglund definition of HT, but removes IT and BT
*aht_hecker_notitbt: Uses the Hecker definition of HT, but removes IT and BT
*tht: The same as above but on the target's NAIC code. THIS IS THE ONE YOU WANT FIRST.
*tht_pb
*tht_hecker
*tht_pb_notitbt
*tht_hecker_notitbt
*patents: A binary variable indicating whether the firm has patent (1) or not (0) applications filed up to an including the year of the announcement of the acquisition
*patentcount: The count of the above patents
*patentdata: Takes the value 1 if the announcement year equal to or less than 2006, so the firm can have all of its patents recorded (from 1975 forward), and 0 if the patent data will be inherently truncated.
==State Codes==
We use the US Postal Service (USPS) Official State Codes, found at: https://www.usps.com/send/official-abbreviations.htm
OfficialCode NumericCode State
AK 1 ALASKA
AL 2 ALABAMA
AR 3 ARKANSAS
AS 4 AMERICAN SAMOA
AZ 5 ARIZONA
CA 6 CALIFORNIA
CO 7 COLORADO
CT 8 CONNECTICUT
DC 9 DISTRICT OF COLUMBIA
DE 10 DELAWARE
FL 11 FLORIDA
FM 12 FEDERATED STATES OF MICRONESIA
GA 13 GEORGIA
GU 14 GUAM GU
HI 15 HAWAII
IA 16 IOWA
ID 17 IDAHO
IL 18 ILLINOIS
IN 19 INDIANA
KS 20 KANSAS
KY 21 KENTUCKY
LA 22 LOUISIANA
MA 23 MASSACHUSETTS
MD 24 MARYLAND
ME 25 MAINE
MH 26 MARSHALL ISLANDS
MI 27 MICHIGAN
MN 28 MINNESOTA
MO 29 MISSOURI
MP 30 NORTHERN MARIANA ISLANDS
MS 31 MISSISSIPPI
MT 32 MONTANA
NC 33 NORTH CAROLINA
ND 34 NORTH DAKOTA
NE 35 NEBRASKA
NH 36 NEW HAMPSHIRE
NJ 37 NEW JERSEY
NM 38 NEW MEXICO
NV 39 NEVADA
NY 40 NEW YORK
OH 41 OHIO
OK 42 OKLAHOMA
OR 43 OREGON
PA 44 PENNSYLVANIA
PR 45 PUERTO RICO
PW 46 PALAU
RI 47 RHODE ISLAND
SC 48 SOUTH CAROLINA
SD 49 SOUTH DAKOTA
TN 50 TENNESSEE
TX 51 TEXAS
UT 52 UTAH
VA 53 VIRGINIA
VI 54 VIRGIN ISLANDS
VT 55 VERMONT
WA 56 WASHINGTON
WI 57 WISCONSIN
WV 58 WEST VIRGINIA
WY 59 WYOMING
99 UNKNOWN
==Classifiction of IT, BT and HT==
===Information and Communications Technology (IT)===
The following is our definition of IT:
333295 both 333295 Semiconductor Machinery Manufacturing
3341 both 334111 Electronic Computer Manufacturing
3341 both 334112 Computer Storage Device Manufacturing
3341 both 334113 Computer Terminal Manufacturing
3341 both 334119 Other Computer Peripheral Equipment Manufacturing
3342 both 334210 Telephone Apparatus Manufacturing
3342 both 334220 Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing
3342 both 334290 Other Communications Equipment Manufacturing
334413 both 334413 Semiconductor and Related Device Manufacturing
334611 both 334611 Software Reproducing
334613 both 334613 Magnetic and Optical Recording Media Manufacturing
33592 both 335921 Fiber Optic Cable Manufacturing
33592 both 335929 Other Communication and Energy Wire Manufacturing
42343 both 423430 Computer and Computer Peripheral Equipment and Software Merchant Wholesalers
42511 both 425110 Business to Business Electronic Markets
44312 both 443120 Computer and Software Stores
4541 both 454111 Electronic Shopping
4541 both 454112 Electronic Auctions
4541 both 454113 Mail-Order Houses
5112 both 511210 Software Publishers
516 2002 516110 Internet Publishing and Broadcasting
517 both 517110 Wired Telecommunications Carriers
517 both 517210 Wireless Telecommunications Carriers (except Satellite)
517 2002 517211 Paging
517 2002 517310 Telecommunications Resellers
517 both 517410 Satellite Telecommunications
517 2002 517510 Cable and Other Program Distribution
517 2002 517910 Other Telecommunications
517 2007 517911 Telecommunications Resellers
517 2007 517919 All Other Telecommunications
518 2002 518111 Internet Service Providers
518 2002 518112 Web Search Portals
518 both 518210 Data Processing, Hosting, and Related Services
51913 2007 519130 Internet Publishing and Broadcasting and Web Search Portals
51919 both 519190 All Other Information Services
5415 both 541511 Custom Computer Programming Services
5415 both 541512 Computer Systems Design Services
5415 both 541513 Computer Facilities Management Services
5415 both 541519 Other Computer Related Services
61142 both 611420 Computer Training
811212 both 811212 Computer and Office Machine Repair and Maintenance
811213 both 811213 Communication Equipment Repair and Maintenance
===Biotech (BT)===
The following is our definition of Biotech:
3254 both 325411 Medicinal and Botanical Manufacturing
3254 both 325412 Pharmaceutical Preparation Manufacturing
3254 both 325413 In-Vitro Diagnostic Substance Manufacturing
3254 both 325414 Biological Product (except Diagnostic) Manufacturing
334510 both 334510 Electromedical and Electrotherapeutic Apparatus Manufacturing
334516 both 334516 Analytical Laboratory Instrument Manufacturing
334517 both 334517 Irradiation Apparatus Manufacturing
339112 both 339112 Surgical and Medical Instrument Manufacturing
339113 both 339113 Surgical Appliance and Supplies Manufacturing
54138 both 541380 Testing Laboratories
541711 2007 541711 Research and Development in Biotechnology
6215 both 621511 Medical Laboratories
6215 both 621512 Diagnostic Imaging Centers
===High Tech (HT)===
The following is our definition of other (i.e. Not IT/BT) High-tech:
211 both 211111 211111 Crude Petroleum and Natural Gas Extraction
211 both 211112 211112 Natural Gas Liquid Extraction
2211 both 221111 221111 Hydroelectric Power Generation
2211 both 221112 221112 Fossil Fuel Electric Power Generation
2211 both 221113 221113 Nuclear Electric Power Generation
2211 both 221119 221119 Other Electric Power Generation
2211 both 221121 221121 Electric Bulk Power Transmission and Control
2211 both 221122 221122 Electric Power Distribution
324 both 324110 324110 Petroleum Refineries
324 both 324121 324121 Asphalt Paving Mixture and Block Manufacturing
324 both 324122 324122 Asphalt Shingle and Coating Materials Manufacturing
324 both 324191 324191 Petroleum Lubricating Oil and Grease Manufacturing
324 both 324199 324199 All Other Petroleum and Coal Products Manufacturing
3251 both 325110 325110 Petrochemical Manufacturing
3251 both 325120 325120 Industrial Gas Manufacturing
3251 both 325131 325131 Inorganic Dye and Pigment Manufacturing
3251 both 325132 325132 Synthetic Organic Dye and Pigment Manufacturing
3251 both 325181 325181 Alkalies and Chlorine Manufacturing
3251 both 325182 325182 Carbon Black Manufacturing
3251 both 325188 325188 All Other Basic Inorganic Chemical Manufacturing
3251 both 325191 325191 Gum and Wood Chemical Manufacturing
3251 both 325192 325192 Cyclic Crude and Intermediate Manufacturing
3251 both 325193 325193 Ethyl Alcohol Manufacturing
3251 both 325199 325199 All Other Basic Organic Chemical Manufacturing
3252 both 325211 325211 Plastics Material and Resin Manufacturing
3252 both 325212 325212 Synthetic Rubber Manufacturing
3252 both 325221 325221 Cellulosic Organic Fiber Manufacturing
3252 both 325222 325222 Noncellulosic Organic Fiber Manufacturing
3253 both 325311 325311 Nitrogenous Fertilizer Manufacturing
3253 both 325312 325312 Phosphatic Fertilizer Manufacturing
3253 both 325314 325314 Fertilizer (Mixing Only) Manufacturing
3253 both 325320 325320 Pesticide and Other Agricultural Chemical Manufacturing
3255 both 325510 325510 Paint and Coating Manufacturing
3255 both 325520 325520 Adhesive Manufacturing
3255 both 325910 325910 Printing Ink Manufacturing
3259 both 325920 325920 Explosives Manufacturing
3259 both 325991 325991 Custom Compounding of Purchased Resins
3259 both 325992 325992 Photographic Film, Paper, Plate, and Chemical Manufacturing
3259 both 325998 325998 All Other Miscellaneous Chemical Product and Preparation Manufacturing
33321 both 333210 333210 Sawmill and Woodworking Machinery Manufacturing
33322 both 333220 333220 Plastics and Rubber Industry Machinery Manufacturing
333291 both 333291 333291 Paper Industry Machinery Manufacturing
333292 both 333292 333292 Textile Machinery Manufacturing
333293 both 333293 333293 Printing Machinery and Equipment Manufacturing
333294 both 333294 333294 Food Product Machinery Manufacturing
333298 both 333298 333298 All Other Industrial Machinery Manufacturing
3333 both 333311 333311 Automatic Vending Machine Manufacturing
3333 both 333312 333312 Commercial Laundry, Drycleaning, and Pressing Machine Manufacturing
3333 both 333313 333313 Office Machinery Manufacturing
3333 both 333314 333314 Optical Instrument and Lens Manufacturing
3333 both 333315 333315 Photographic and Photocopying Equipment Manufacturing
3333 both 333319 333319 Other Commercial and Service Industry Machinery Manufacturing
3336 both 333611 333611 Turbine and Turbine Generator Set Units Manufacturing
3336 both 333612 333612 Speed Changer, Industrial High-Speed Drive, and Gear Manufacturing
3336 both 333613 333613 Mechanical Power Transmission Equipment Manufacturing
3336 both 333618 333618 Other Engine Equipment Manufacturing
3339 both 333911 333911 Pump and Pumping Equipment Manufacturing
3339 both 333912 333912 Air and Gas Compressor Manufacturing
3339 both 333913 333913 Measuring and Dispensing Pump Manufacturing
3339 both 333921 333921 Elevator and Moving Stairway Manufacturing
3339 both 333922 333922 Conveyor and Conveying Equipment Manufacturing
3339 both 333923 333923 Overhead Traveling Crane, Hoist, and Monorail System Manufacturing
3339 both 333924 333924 Industrial Truck, Tractor, Trailer, and Stacker Machinery Manufacturing
3339 both 333991 333991 Power-Driven Handtool Manufacturing
3339 both 333992 333992 Welding and Soldering Equipment Manufacturing
3339 both 333993 333993 Packaging Machinery Manufacturing
3339 both 333994 333994 Industrial Process Furnace and Oven Manufacturing
3339 both 333995 333995 Fluid Power Cylinder and Actuator Manufacturing
3339 both 333996 333996 Fluid Power Pump and Motor Manufacturing
3339 both 333997 333997 Scale and Balance Manufacturing
3339 both 333999 333999 All Other Miscellaneous General Purpose Machinery Manufacturing
3343 both 334310 334310 Audio and Video Equipment Manufacturing
334411 both 334411 334411 Electron Tube Manufacturing
334412 both 334412 334412 Bare Printed Circuit Board Manufacturing
334414 both 334414 334414 Electronic Capacitor Manufacturing
334415 both 334415 334415 Electronic Resistor Manufacturing
334416 both 334416 334416 Electronic Coil, Transformer, and Other Inductor Manufacturing
334417 both 334417 334417 Electronic Connector Manufacturing
334418 both 334418 334418 Printed Circuit Assembly (Electronic Assembly) Manufacturing
334419 both 334419 334419 Other Electronic Component Manufacturing
334511 both 334511 334511 Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing
334512 both 334512 334512 Automatic Environmental Control Manufacturing for Residential, Commercial, and Appliance Use
334513 both 334513 334513 Instruments and Related Products Manufacturing for Measuring, Displaying, and Controlling Industrial Process Variables
334514 both 334514 334514 Totalizing Fluid Meter and Counting Device Manufacturing
334515 both 334515 334515 Instrument Manufacturing for Measuring and Testing Electricity and Electrical Signals
334518 both 334518 334518 Watch, Clock, and Part Manufacturing
334519 both 334519 334519 Other Measuring and Controlling Device Manufacturing
334612 both 334612 334612 Prerecorded Compact Disc (except Software), Tape, and Record Reproducing
3353 both 335311 335311 Power, Distribution, and Specialty Transformer Manufacturing
3353 both 335312 335312 Motor and Generator Manufacturing
3353 both 335313 335313 Switchgear and Switchboard Apparatus Manufacturing
3353 both 335314 335314 Relay and Industrial Control Manufacturing
33591 both 335911 335911 Storage Battery Manufacturing
33591 both 335912 335912 Primary Battery Manufacturing
33591 both 335931 Current-Carrying Wiring Device Manufacturing
33593 both 335932 Noncurrent-Carrying Wiring Device Manufacturing
33593 both 335991 Carbon and Graphite Product Manufacturing
33599 both 335999 All Other Miscellaneous Electrical Equipment and Component Manufacturing
3364 both 336411 336411 Aircraft Manufacturing
3364 both 336412 336412 Aircraft Engine and Engine Parts Manufacturing
3364 both 336413 336413 Other Aircraft Parts and Auxiliary Equipment Manufacturing
3364 both 336414 336414 Guided Missile and Space Vehicle Manufacturing
3364 both 336415 336415 Guided Missile and Space Vehicle Propulsion Unit and Propulsion Unit Parts Manufacturing
3364 both 336419 336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing
3369 both 336991 336991 Motorcycle, Bicycle, and Parts Manufacturing
3369 both 336992 336992 Military Armored Vehicle, Tank, and Tank Component Manufacturing
3369 both 336999 336999 All Other Transportation Equipment Manufacturing
42341 both 423410 423410 Photographic Equipment and Supplies Merchant Wholesalers
42342 both 423420 423420 Office Equipment Merchant Wholesalers
42344 both 423440 423440 Other Commercial Equipment Merchant Wholesalers
42345 both 423450 423450 Medical, Dental, and Hospital Equipment and Supplies Merchant Wholesalers
42346 both 423460 423460 Ophthalmic Goods Merchant Wholesalers
42349 both 423490 423490 Other Professional Equipment and Supplies Merchant Wholesalers
486 both 486110 486110 Pipeline Transportation of Crude Oil
486 both 486210 486210 Pipeline Transportation of Natural Gas
486 both 486910 486910 Pipeline Transportation of Refined Petroleum Products
486 both 486990 486990 All Other Pipeline Transportation
5232 both 523210 523210 Securities and Commodity Exchanges
54131 both 541310 541310 Architectural Services
54132 both 541320 541320 Landscape Architectural Services
54133 both 541330 541330 Engineering Services
54134 both 541340 541340 Drafting Services
54135 both 541350 541350 Building Inspection Services
54136 both 541360 541360 Geophysical Surveying and Mapping Services
54137 both 541370 541370 Surveying and Mapping (except Geophysical) Services
5416 both 541611 541611 Administrative Management and General Management Consulting Services
5416 both 541612 541612 Human Resources Consulting Services
5416 both 541613 541613 Marketing Consulting Services
5416 both 541614 541614 Process, Physical Distribution, and Logistics Consulting Services
5416 both 541618 541618 Other Management Consulting Services
5416 both 541620 541620 Environmental Consulting Services
5416 both 541690 541690 Other Scientific and Technical Consulting Services
541710 2002 541710 541710 Research and Development in the Physical, Engineering, and Life Sciences
541712 2007 541712 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
541720 both 541720 541720 Research and Development in the Social Sciences and Humanities
5612 both 561210 561210 Facilities Support Services
811211 both 811211 811211 Consumer Electronics Repair and Maintenance
811219 both 811219 811219 Other Electronic and Precision Equipment Repair and Maintenance
===Other HT Definitions===
The references for the other High-Tech (HT) definitions are:
*Hecker, Daniel E.(2005), "High-technology employment: a NAICS-based update", Monthly Labor Review (July): 57-72. http://www.bls.gov/opub/mlr/2005/07/art6full.pdf
*Paytas, Jerry and Berglund, Dan (2004), "Technology Industries and Occupations for NAICS Industry Data", Carnegie Mellon University, Center for Economic Development and State Science & Technology Institute.
==Extending the paper==
Once this 'draft' is complete we can consider some extentions. I am currently working on the VC Reputations data.
===VC Reputations===
VCs might use their reputations to certify their firms, or these variables might reflect VC experience (and potentially bargaining skill). We can calculate:
*Avg or max number of previous acquisitions and/or IPOs conducted by VCs present in the last round of investment into the firm prior to the acquisition announcement.
*The number of previous acquisitions and/or IPOs conducted by the lead VC (Note: The defacto standard method of determining the lead investor is to see which (if any) investor was present from the first round in every round until the last.)
*Likewise for the average or dollar-invested weighted average of all investors in the port co.
*Last round, lead investor, or average number of previous funds raised by investors, or their fund size or total cummulative firm size (i.e. summed across all funds) at the announcement.
*Whether the VCs will raise a next fund (though this could actually be endogenuous with the CAR)
===Outside Options===
Outside options affect bargaining. A VC that is near to the end of its fund when it made it's last investment into the portfolio company (either in terms of dates or dollars), and particularly one that won't raise a next fund, will be unable to continue financing the portco without the acquisition and therefore has no good outside option with which to bargain. We could calculate how near last round investors are to the ends of their funds (and whether they are going to raise another) and take averages etc, to proxy for the outside option.
===Bargaining Superstars===
It might be the case that some VCs specialize in providing bargaining skills. We could test this hypothesis by:
*Creating fixed-effect variables for the presence of each repeat VC in a portfolio company
*Regressing these fixed-effects on the CARs and sorting the coefficient into quartiles/deciles etc.
*Testing the hypothesis that firms in the top decile are more likely than expected to appear in a last round of financing.
===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.
