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The Third This paper was submitted (dual submission) to the 3rd Annual Entrepreneurial Finance and Innovation Conference on June 10th-11th in Boston, MA, is supported by the Kauffman Foundation (EFIC) conference 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). It was rejected from both.
The Boehmer reference has a typo - the second author is Mus'''u'''meci. AlsoThere are seperate lit reviews for related topics, such as:*[[Information Asymmetry Measures]]*[[Information Asymmetry in para 2, p.19, I think it was McKinley that "suggestAcquisitions Lit Review]]*[[edThe Winners Curse]] a method that combines both cross-sectional and time-series information..."
Other pointsThe key papers found were:*There were a few other typos.[[Masulis Nahata (2011) - Venture Capital Conflicts Of Interest]]*Also the GX paper gets very little mention [[Gompers Xuan (2008) - I thought we had a whole subsection devoted to them...Bridge Building In Venture Capital Backed Acquisitions]]*I was surprised that we didn't have a year fixed[[Gompers Xuan (2006) -effect variable in the main analysis The Role Of Venture Capitalists In The Acquisition Of Private Companies]]*[[Benson Ziedonis (though we have ''Boom'', which is more interesting2010)- Corporate Venture Capital And The Returns To Acquiring Portfolio Companies]]
The paper requires a complete rebuild of all the results, Comparing our CARs with those from the data updated to literature (some values are inferred - see the end of 2011review 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. We should also consider several extensions to the paper8% 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, detailed in a later section.961 490 8693 1261 489
===Main Data===
Acquisitions (from SDC):*Events from 1980-2011 Note that meet using the following criteriasubtraction model our univariate results are:**Acquirer is publicly traded on the AMEX0.95% (all), Nasdaq or NYSE**Target is privately-held prior to acquisition 0.98% (note: new restriction Non- target was not an LBOVC)**Acquisition is for 100and 0.59% of the firm**Acqisition is complete before end of January 2012(VC).
Subsequent restriction: Drop acquisitions where market value of assets is negative or very small compared with the TV.==Latest Version==
Venture Capital (from VentureXpert)The latest version of the paper is:*Portfolio companies that received VC from 1975[[:Image:Brander Egan (2012) -2011Investor Expectations and the Role of Venture Capitalists in Acquisitions. Must not be LBOs.*LBOs from 1975 to 2011 to ensure that they are not in doc |Brander Egan (2012) - Investor Expectations and the control group Role of privately-held non-VC backed firms)Venture Capitalists in Acquisitions.doc]]
Returns (from CRSP)Note:*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 version was submitted to the same period*Stock returns for 7 days beginning 3 days before both the announcement EFIC and ending 3 days afterRCFS.
NoteThe current reference is: an observation must have 50 days *Brander, James A., and Edward J. Egan (2012), "Investor Expectations and the Role of continuous trading Venture Capitalists in Acquisitions: Bargaining and the estimation windowWinner’s Curse", Working paper, March 2012, and be traded under review for inclusion in the event window, to be includedThird Entrepreneurial Finance and Innovation Conference and a special issue of Review of Corporate Finance Studies.
Accounting Data (From COMPUSTAT):*Various accounting variables for our acquirers, drawn for the year of the acquisition, and the lagged year for total assets.==Submission Details==
===Supplementary Data===This paper was '''submitted''' under the dual-submission process to both the '''3rd EFIC and the RCFS''' on March 7th.
We need to rebuild the industry classification to update it to include NAICS2007 The Third Entrepreneurial Finance and Innovation Conference (EFIC) on June 10th- this has largely been done 11th in another of my papersBoston, but that work was for firms with patentsMA, is supported by the Kauffman Foundation and it is possible that some codes are still missingthe Society for financial studies. Conference papers will be considered for inclusion in a special issue of the Review of Corporate Finance Studies (RCFS).
To determine the information asymmetry ranking of sectors we will need (either for 1 year or across the entire year range)The conference details are here:http://sites.kauffman.org/efic/overview.cfm
CRSP: *idiosyncratic volatility Authors will be notified if their paper has been selected by the end of stock returnsApril. The program committee includes: requires returns Thomas Hellmann, Adam Jaffe, Bill Kerr, Josh Lerner, David Robinson, Morten Sorenson, Bob Strom, 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..others.)*NAIC
COMPUSTAT:*intangible assets*total assets*Tobin's Q: Market value/book value of assets*NAIC ==Rebuilding the Paper=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 The paper required a complete rebuild of all TVs the results, with the data updated to the end of 2011. We can also consider several extensions to the paper, detailed in 2011 dollarsa later section.
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===Dataset Processing Notes===
From VentureExpert #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 (all VC backed firmse.g. Spinoff, TwoStepSpinOff, and all 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 seperately):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.*VC #Duplicate acquisition data (binary variablesame event)was eliminated*VE industry classification #Multiple acquisition of the same target (to use as i.e. a reference set to update the industry classificationtarget is acquired, spun-off and acquired again, etc)were eliminated.*LBO #CUSIPs were processed into 6, 8, and 9 digit variables, by searching COMPUSTAT annual data (binary variableJan 1978 - Jan 2012) to exclude these from using the 6 digit CUSIP and then finding the control groupcorrect 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.*If we add one or more extension #CRSP data was retrieved and processed (see below), then . After processing we'll need a fully flushed out VE database build including portcoshad data for 23, rounds802 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, dealsVC only, fundsNon-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'', firms''vert'', and possibly executives''cong'' were calculated.
===Calculated variables===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.
ReturnsFlag Exclusions:*<math> AR_i = R_i- (\hat{\alpha_i} + \hat{\beta_i}R_m) </math>Cases where the target was bankrupt or distressed as indicated by: TargetBankrupt, TargetBankInsolvent, Liquidation, Restructuring.*<math> AR^S_i = R_i Cases where the form wasn't genuinely privately- R_m </math>*Let <math>\epsilon</math> be the residual from the mkt model regression. Then calcheld as indicated by: <math>\sigma_{\epsilon}={OpenMarketPurchases, GovOwnedInvolvement, JointVenture, Privatization ( \mathbb{E}(\epsilon - \mathbb{E} \epsilonwhich capture government sales))}^{\frac{1}{2}}</math>.*RMSE of Cases where there was a share recap going on concurrently with the Mkt Modelacquisition: <math>RMSE={Recap*Targets that had LBO involvement ( \mathbb{E}(X- \mathbb{E} X))}^{\frac{1}{2}}</math> - this is more will likely be removed in the ereturn list in STATA and will be next phase of matching to LBO targets): LBO, SecondaryBuyoutFlag, ReverseTakeOver (used for the Patell Standard ErrorsLBO'd firms doing a reverse take over), IPOFlag (likewise).*The cummulative return <math>CAR_i = \sum_t AR_i</math>*Check that Firms where the Boehmer standard errors are deal began as a rumor (so 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 information leakage is days 0 & 1, and others are symmetric.problematic): DealBeganAsRumor
For robustness we need ARs 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 5more info). Note that the IT, BT and HT variables were coding using aggregate codes whereever possible (i.e. 517110,9etc, all appear in IT and 11 dayscover the 517 code entirely, so the 517 block would be coded as IT even if SDC recorded the code as 517000.
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.===Acquisitions Data===
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.) SDC Search Criteria===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 SDC 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 Criteriacriteria:
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.===CRSP Data===
The NormalizeFixedWidth.pl script uses the spacing in the header to determine the column breaksDaily return data was downloaded using 8 digit CUSIPs from CRSP. The EquityValue column has two spaces in front of its name that screws this. Both EquityValue and EnterpriseValue needed to be imported as varcharfollowing variables were retrieved from 1/1/1978-1/1/2012 (10the latest month available), as they have the code 'np' in some observations.:*Cusip*Date*prc*ret*vwretd
A large number of 'new' flags are now available in SDC. Most From COMPUSTAT we drew accounting variables for all of them have no bite on our Cusips, then extracted data. But I have excluded for the following:*Cases where announcement years and the target was bankrupt or distressed as indicated by: TargetBankruptlagged announcement years. (Note that Cusip, TargetBankInsolventNAIC, Liquidationdatayear, Restructuring.*Cases where fiscal year and fiscal year end were included in the form wasn't genuinely privately-held as indicated by: OpenMarketPurchases, GovOwnedInvolvement, JointVenture, Privatization (which capture government sales)download.*Cases where there NAIC 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 used to LBO targets): LBO, SecondaryBuyoutFlag, ReverseTakeOver (used for LBO'd firms doing a reverse take over), IPOFlag (likewise)supplement SDC NAICs.*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 Data 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.*Total Assets*Market Value*Sales*Total Liabilities*Intangible Assets*Shares Outstanding
Acquiror and Target names were keyed into unique names. The first acquisition of several Note that leverage was taken. 33 records had multiple entries, the correct one of which could not be determined. These were discarded. calculated as:<math>Leverage=\frac{Total\;Liabilities}{Total\;Assets}</math>
===Retrieving CUSIPs===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''.
===Downloading the VC PortCos=== The following criteria was appliedto the SDC search:
Total of 30364 records. Note that the coverage of VE before 1980 is problematic. Selected fieldsA basic variable set was downloaded including:*PortCo Name
*Address info (various fields)
*Date first inv
*Date last inv
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&Request3PlaceProcessing NAIC Codes=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 While SDC provide 6 digit NAIC codes for all acquirers, some of these NAIC codes are invalid (Can't find defnproprietary to SDC)?. These were replaced with COMPUSTAT NAIC codes whenever available. The SDC NAIC codes found were:
Also SDCnaic | SDCindustry ------------+------------- throw out ----------------------------------- 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:*Market Value 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 is negativenaic) 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.*Mkt Value 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
The references for ==State Codes== We use the other High-Tech US Postal Service (HTUSPS) definitions are:*Hecker, Daniel E.(2005), "High-technology employment: a NAICS-based update"Official State Codes, Monthly Labor Review (July)found at: 57-72. httphttps://www.blsusps.govcom/opubsend/mlr/2005/07/art6fullofficial-abbreviations.pdf htm*Paytas, Jerry and Berglund, Dan (2004), "Technology Industries and Occupations for NAICS Industry Data", Carnegie Mellon University, Center for Economic Development and OfficialCode NumericCode State Science & Technology Institute. 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
Protected "VC Acquisitions Paper" [edit=trusted:move=trusted:read=trusted]
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 128.32.252204.201 203 -U ed_egan Acqs
==Submission DetailsCurrent Status==
The conference details are herekey points from the reviewers were: http://sites*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.kauffman*Possibly we should consider long rum performance and attempt to explain why the acquirers buy VC-backed firms.org/efic/overview*Focus less on the univariate results.cfm
The deadline obvious possibilities for submission is '''March 7ththis paper are to:*Focus more on the IA.*Do a supply-side VC analysis (i.e., 2012'''include reputations, though earlier submission is encouraged. Authors will be notified if their paper has been selected by the end possibility of Aprilgrandstanding, etc.)
The program committee includesimmediate 'to do' is: Thomas Hellmann, Adam Jaffe, Bill Kerr, Josh Lerner, David Robinson, Morten Sorenson, Bob Strom, *Read the Masulis and othersNahata (2011) paper carefully.*Do a proper literature review again!
==Errors in the existing version=Lit Review===
The Dierkins 1991 reference is missing: @article{dierkens1991informationmain [[VC Acquisitions Lit Review]] page details searches for papers related to the intersection of venture capital, title={Information asymmetry acquisitions and equity issues}, author={Dierkens, Nexplaining abnormal returns.}, journal={Journal of Financial and Quantitative Analysis}, volume={26}, number={2}, pages={181--199}, year={1991}, publisher={Cambridge Univ Press} }
==Rebuilding the Paper==Comparing Results====
*US Targets
*Announced: 1/1/1980 to 12/31/2011
*Percentage of Shares Owned after Transaction: 100 to 100 (will exclude those with missing data)
====SDC Variables==== The completed deal flag is in ''Deal Status'' - this will be restricted to 'C' 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 processingdeal*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):
*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.
The NormalizeFixedWidthdata 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.pl script #A refined estimation set beginning 280 and ending 30 days before the acquisition was modified so 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 createdannounceday-cusip pair observation were included in an estimation regression provided that it only drops commas 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 numbers 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 not those 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 names etcSTATA 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
===Processing the AcquisitionsCOMPUSTAT Data===
===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.VC PortCos===
*Moneytree deals (i.e. VC only)
*Company Nation: US
*Round date: 1/1/1975 to 1/1/2012
*Nation
*State
*Founding Location*Address*Total VC Invested*Dateof First Inv*Total $Date of Last Inv*Date of Founding
*No Rounds
Check flags:
*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. ===Implicit Price DeflatorsRemoving additional LBOs=== A set of LBO portcos were downloaded from SDC using the flags LBO=yes, PWCMoneytree=No, StdUSVentureDisbursement=NoThe LBOs were matched against the acquisition targets and removed (LBO initial investment dates were checked).
==Variables==
The following is quick description of the variables in the Version 1 3 dataset, in order.
===Acquisition Specific Variables===
*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.
==NAIC CodesClassifiction of IT, BT and HT==
===Information and Communications Technology (IT)===
6215 both 621512 Diagnostic Imaging Centers
===Other 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
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.
