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The Third Entrepreneurial Finance and Innovation Conference on June 10thComparing our CARs with those from the literature (some values are inferred -11th in Boston, MA, is supported by see the Kauffman Foundation and the Society for financial studiesreview 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. Conference papers will be considered for inclusion in a special issue of the Review of Corporate Finance Studies3% 1. 6% N 22,961 490 8693 1261 489
The conference details are here: http://sites.kauffman.org/efic/overview.cfm
The deadline for submission is '''March 7thNote that using the subtraction model our univariate results are: 0.95% (all), 2012''', though earlier submission is encouraged0.98% (Non-VC) and 0. Authors will be notified if their paper has been selected by the end of April59% (VC).
The program committee includes: Thomas Hellmann, Adam Jaffe, Bill Kerr, Josh Lerner, David Robinson, Morten Sorenson, Bob Strom, and others.==Latest Version==
==Errors 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 existing version==Role of Venture Capitalists in Acquisitions.doc]]
The Dierkins 1991 reference is missingNote: @article{dierkens1991information, title={Information asymmetry This version was submitted to the both the EFIC and equity issues}, author={Dierkens, NRCFS.}, journal={Journal of Financial and Quantitative Analysis}, volume={26}, number={2}, pages={181--199}, year={1991}, publisher={Cambridge Univ Press} }
Other points:*There were a few other typos.*Also the GX paper gets very little mention - I thought we had a whole subsection devoted to them...*I was surprised that we didn't have a year fixed-effect variable in the main analysis (though we have ''Boom'', which is more interesting)==Submission Details==
==Rebuilding This paper was '''submitted''' under the Paper==dual-submission process to both the '''3rd EFIC and the RCFS''' on March 7th.
===Main Data===The conference details are here: http://sites.kauffman.org/efic/overview.cfm
Acquisitions (from SDC):*Events from 1980-2011 that meet Authors will be notified if their paper has been selected by the following criteriaend of April. The program committee includes:**Acquirer is publicly traded on the AMEXThomas Hellmann, Adam Jaffe, Bill Kerr, Josh Lerner, David Robinson, Morten Sorenson, Bob Strom, Nasdaq or NYSE**Target is privately-held prior to acquisition (note: new restriction - target was not an LBO)**Acquisition is for 100% of the firm**Acqisition is complete before end of January 2012and others.
Subsequent restriction: Drop acquisitions where market value ==Rebuilding the Paper== The paper required a complete rebuild of assets is negative or very small compared all the results, with the TVdata updated to the end of 2011. We can also consider several extensions to the paper, detailed in a later section.
Venture Capital (from VentureXpert):*Portfolio companies that received VC from 1975-2011. Must not be LBOs.*LBOs from 1975 to 2011 to ensure that they are not in the control group of privately-held non-VC backed firms)===Dataset Processing Notes===
Returns #The acquisitions data was retrieved from SDC (from CRSPsee below):and imported into Postgres. There were 41,572 records.*Stock returns #The flag variables were reviewed for 1 year variation - some had no bite (250 Calendar dayse.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 acquirerdata (Completion, flags, ending 30 days before the announcementexclude LBOs) etc. This will be reduced the estimation windowdata to 40,035 observations#Certain variables were reprocessed, e.g. Percentage Shares, NAIC codes, etc.(see below)*Market returns #Acquiror and Target names were keyed to account for repetitions etc.#Duplicate acquisition data (same event) was eliminated#Multiple acquisition of the same periodtarget (i.e. a target is acquired, spun-off and acquired again, etc) were eliminated.*Stock returns #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 days beginning 3 days before ,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 announcement 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 ending 3 days after''cong'' were calculated.
NoteVariable check notes: an observation must have 50 days *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 continuous trading 'C' for completed. These were kept.*The number of bidders was always disclosed and 2 in the estimation window54 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 be traded others invalid. COMPUSTAT NAIC codes were used when SDC NAIC codes failed when these were recorded in the event window, to be includedWRDS.
Accounting Data 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 (From COMPUSTATwhich capture government sales).*Cases where there was a share recap going on concurrently with the acquisition:Recap*Various accounting variables for our acquirers, drawn for Targets that had LBO involvement (more will likely be removed in the year next phase of the acquisitionmatching to LBO targets): LBO, SecondaryBuyoutFlag, and the lagged year ReverseTakeOver (used for total assetsLBO'd firms doing a reverse take over), IPOFlag (likewise).*Firms where the deal began as a rumor (so the information leakage is problematic): DealBeganAsRumor
===Supplementary Data===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.
We need to rebuild the industry classification to update it to include NAICS2007 - this has largely been done in another of my papers, but that work was for firms with patents, and it is possible that some codes are still missing.===Acquisitions Data===
To determine the information asymmetry ranking ====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 sectors we Shares Owned after Transaction: 100 to 100 (will need (either for 1 year or across the entire year rangeexclude those with missing data):
CRSP: *idiosyncratic volatility of stock returns: requires returns and mkt returns*relative trading volume (this appears to be called TURNOVER, as opposed to absolute volume which is VOLUME. The measure is relative to the exchange's trading volume I think...)*NAIC====SDC Variables====
COMPUSTATThe following variables were pulled:*intangible assets*total assets*Tobin's Q: Market value/book value of assets*NAIC
===Raw Variables===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
From SDC This provided (for all acquisitions in the sampleof particular note):*Acquisition is completed indicator (as a check)*Acquisition percentage (as a check)
*Acquisition announcement year
*Total assets of acquirer (if available)
From COMPUSTAT ===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 both all acquirers 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 createdannounceday-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 universe residual from the mkt model regression. Then calc: <math>\sigma_{\epsilon}={( \mathbb{E}(\epsilon - \mathbb{E} \epsilon))}^{\frac{1}{2}}</math>**RMSE of firmsthe 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:*Total Assets *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. (in Note that Cusip, NAIC, datayear, fiscal year and 1 fiscal year laggedend were included in the download. NAIC was used to supplement SDC NAICs.) Data included:*Total Assets*Market Value (SHROUT*Price at start of event window)
From VentureExpert (all VC backed firms, and all LBOs seperately)COMPUSTAT:*VC (binary variable)intangible assets*VE industry classification (to use as a reference set to update the industry classification)total assets*LBO (binary variable) to exclude these from the control groupTobin's Q: Market value/book value of assets*If we add one or more extension (see below), then we'll need a fully flushed out VE database build including portcos, rounds, deals, funds, firms, and possibly executives.NAIC
Returns:*<math> AR_i = R_i- (\hat{\alpha_i} + \hat{\beta_i}R_m) </math>*<math> AR^S_i = R_i - R_m </math>*Let <math>\epsilon</math> be the residual from the mkt model regression. Then calc: <math>\sigma_{\epsilon}={( \mathbb{E}(\epsilon - \mathbb{E} \epsilon))}^{\frac{1}{2}}</math>*RMSE The following is quick description of the Mkt Model: <math>RMSE={( \mathbb{E}(X- \mathbb{E} X))}^{\frac{1}{2}}</math> - this is variables in the ereturn list in STATA and will be used for the Patell Standard Errors.*The cummulative return <math>CAR_i = \sum_t AR_i</math>*Check that the Boehmer standard errors are the cross-sectional ones generated by OLS.*Check the specification of the McKinley standard errorsVersion 3 dataset.
For the tables we need ARs for 2,3 and 7 day, where 2 day is days 0 & 1, and others ===Acquisition Specific Variables===Note: All variables ending "11" are symmetric.amounts in 2011 dollars
For robustness we need ARs *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 5,9, and 11 days.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
SDC===Estimation and Event Window Variables===Note:*No of past acquisitions for each acquirer: Totalreturn variables, VC only_m indicates minus and _p indicates plus days, Non-VC only*Target so that r_m1 is VC/Non-VC*Acq the return on the stock at day minus 1, where 0 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 acquirersthe announcement day or the first trading day following the announcement if the exchange was closed when the announcement was made.
Dataset level calculations*alpha: The constant from the estimation regression*beta:The coefficient on the market return from the estimation regression*Boomprc: <math>1990\le year \le 1999</math>The stock price 30 days prior to the announcement*Leveragermse: <math>\frac{Total\;Liabilities}{Total\;Assets}</math>The RMSE from the estimation regression
==Extending Returns for the paper==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
Coming back to it, The corresponding market returns on the paper looks a little thin Value-Weighted Amex-Nasdaq-NYSE composite (though clearly the data is a monster alreadyincluding dividends). 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:*m_0*m_m1*m_m2*m_m3*m_m4*m_m5*m_p1*m_p2*m_p3*m_p4*m_p5
(NoteAbnormal returns calculated using the market model: The defacto standard method of determining the lead investor is to see which (if any) investor was present from the first round.)*arm_0*arm_m1*arm_m2*arm_m3*arm_m4*arm_m5*arm_p1*arm_p2*arm_p3*arm_p4*arm_p5
===Using Patents===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
Patents might act to certify their patent-holders in the face of information asymmetries The three day cummulative abnormal return (see, for example, Hsu and Ziedonis, 2007market model). 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.:*carm_3
At present I have the 2006 NBER patent data loaded up in a database. I could add ===Acquiror Specific Variables===Note: Again "11" indicated values in patents and citations up to 2006 with a day or two of work. I am working on the 2011 update to dollar, but _m1 indicates the NBER patent data (see: http://www.nber.com/~edegan/w/index.php) but this will NOT be done before previous year for the March 7th deadlineannual accounting variables.
===VC Reputations===*aname: The acquiror name*astate: A numeric code for the acquiror state (there is a lookup table)
We argue, explicitly, that VCs use their reputations to certify thier firms. We can calculate Whether 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, acquiror is an IT or the average number of funds raised across all investors Biotech firm (again perhaps with a weightingbinary). :*ait*abt
===VC Information Asymmetries===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
Implicit in our argument is that VCs mitigate The count of previous acquisitions occuring strictly before 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.announcement:*noprevacqs*noprevacqsnonvc*noprevacqsvc
====Distances====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
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. ===Target Specific Variables===
I can get 2,500 requests per IP address *tname: Target name*tstate: A numeric code of state (I can run 3+ concurrently from Berkeleysame lookup as acquiror) from the Google Maps api, with responses including driving distances and estimated driving times.
====Active Monitoring====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
I can also determine whether the lead The VC variables:*vc: A binary variable - 1=VC backed, 0 otherwise*firstinvdate*lastinvdate*norounds*totalinvested*totalinvested11*vccontafteracq: Whether VC has a board seat at the portfolio company at the time of investment appears to continue after 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.is supposed to have completed*foundingdate
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.===Additional Variables===
My order is therefore:#Download, clean, and process We use the SDC data so that it can be joined to CRSP US Postal Service (and the other data sourcesUSPS)#Download the CRSP data for the estimation and event windows. Set the estimation windows running. Build the event window code while they runOfficial State Codes, 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 measuresfound at: https://www.#Calculate the distances for all VCs to all acquired targetsusps. 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 citationscom/send/official-received (not corrected for truncation)?#Rebuild the "Information Asymmetry (IA) by Industry" dataabbreviations.htm
===Time Estimates=== 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
My time estimates are going to be wild for three reasons: It is just really hard to estimate some ==Classifiction of these things (the time goes into the things that you don't anticipate being a problem but are); some of my skills are rustyIT, BT 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 daysHT==
By the end of step 6, which I think will take 8 days, I should have a the original data rebuilt ===Information 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 Communications Technology (if wantedIT) 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 The following is one short our definition 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.IT:
==Rebuild Notes== 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
#The experience variables (# Previous Acqs) are generated using the primary data, and will be truncated by the start following is our definition of the dataset. We should probably consider year fixed effects to mitigate any induced bias.Biotech:
===Downloading the Acquisitions=== 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
Basic Criteria:*US Targets*Announced: 1/1/1980 to 12/31/2011*Target Nation: US*Acquiror Nation: US*Target Status: Private ===High Tech (V)*Acquirer Status: Public (P)*Percentage of Shares Owned after Transaction: 100 to 100 (will exclude those with missing dataHT)===
New Flags in SDC 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 (downloaded 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 exclusionsResidential, 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 *Bankruptcy Flag 3364 both 336411 336411 Aircraft Manufacturing *Failed bank Flag 3364 both 336412 336412 Aircraft Engine and Engine Parts Manufacturing 3364 both 336413 336413 Other Aircraft Parts and Auxiliary Equipment Manufacturing *Leveraged Buyout Flag 3364 both 336414 336414 Guided Missile and Space Vehicle Manufacturing *Reverse LBO Flag 3364 both 336415 336415 Guided Missile and Space Vehicle Propulsion Unit and Propulsion Unit Parts Manufacturing *Spinoff Flag 3364 both 336419 336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing *Splitoff Flag 3369 both 336991 336991 Motorcycle, Bicycle, and Parts Manufacturing *Target is a Leveraged Buyout Firm 3369 both 336992 336992 Military Armored Vehicle, Tank, and Tank Component Manufacturing *And many others. These will be reviewed 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 excluded.Maintenance
Founding year/Age of the Target was not available in the data. It is in VE for VC-backed only.===Other HT Definitions===
The NormalizeFixedWidth.pl script uses ==Extending 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.paper==
The NormalizeFixedWidthOnce this 'draft' is complete we can consider some extentions.pl script was modified so that it only drops commas in numbers and not those in names etcI am currently working on the VC Reputations data.
A large 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 'new' flags are now available previous acquisitions and/or IPOs conducted by VCs present in SDC. Most the last round of them have no bite on our datainvestment into the firm prior to the acquisition announcement. But I have excluded the following:*Cases where the target was bankrupt The number of previous acquisitions and/or distressed as indicated IPOs conducted bythe lead VC (Note: TargetBankrupt, TargetBankInsolvent, Liquidation, Restructuring.*Cases where The defacto standard method of determining the form wasn't genuinely privately-held as indicated by: OpenMarketPurchases, GovOwnedInvolvement, JointVenture, Privatization lead investor is to see which (which capture government salesif any)investor was present from the first round in every round until the last.)*Cases where there was a share recap going on concurrently with Likewise for the average or dollar-invested weighted average of all investors in the acquisition: Recapport co.*Targets that had LBO involvement (more will likely be removed in the next phase of matching to LBO targets): LBOLast round, SecondaryBuyoutFlaglead investor, ReverseTakeOver (used for LBO'd firms doing a reverse take over)or average number of previous funds raised by investors, IPOFlag or their fund size or total cummulative firm size (likewisei.e. summed across all funds)at the announcement.*Firms where Whether the deal began as VCs will raise a rumor next fund (so though this could actually be endogenuous with the information leakage is problematicCAR): DealBeganAsRumor
All together, these constraints reduce us from 41,572 to 40,306 acquisitions. Constraints that had no bite included: Spinoff, TwoStepSpinOff, and Splitoff. Further restricting the data to completed transactions, and those with valid codes for when the transaction value was amended, reduces the data to 40,035.===Outside Options===
Acquiror and Target names were keyed Outside options affect bargaining. A VC that is near to the end of its fund when it made it's last investment into unique names. The first acquisition the portfolio company (either in terms of several was taken. 33 records had multiple entriesdates or dollars), and particularly one that won't raise a next fund, will be unable to continue financing the portco without the correct one of acquisition and therefore has no good outside option with which to bargain. We could not be determined. These were discardedcalculate 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.
The acquisitions data lists 6 digit CUSIPs, we need It might be the 'correct' (case that some VCs specialize in providing bargaining skills. We could test this hypothesis by:*Creating fixed-effect variables for the right issue for presence of each repeat VC in a portfolio company*Regressing these fixed-effects on the right period) 8 or 9 digit CUSIP with which to search CRSP CARs and COMPUSTATsorting the coefficient into quartiles/deciles etc. A full list of all CUSIPs was retrieved from COMPUSTAT for *Testing the period Jan 1978 - Jan 2012 using hypothesis that firms in the annual datatop decile are more likely than expected to appear in a last round of financing.
===Downloading 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 PortCos===is likely to be informed about their porfolio firm.
The following criteria was applied:*Moneytree deals (i.e. VC only)*Company Nation: US*Round date: 1/1/1975 to 1/1/2012====Distances====
Total We can use the road or great-circle distance from the lead investor to the portfolio company as a measure of 30364 recordsthe 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.
Note that I can get 2,500 requests per IP address (I can run 3+ concurrently from Berkeley) from the coverage of VE before 1980 is problematicGoogle Maps api, with responses including driving distances and estimated driving times.
Selected fields:*Name*Nation*Founding Date*Total $Inv*No Rounds*Address info (various fields)*Date first inv*Date last inv====Active Monitoring====
Check flags:*Moneytree*Venture RelatedI 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.
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== 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====
The Boehmer current reference has a typo - the second author is Mus'''u'''meci:*Brander, James A., and Edward J. AlsoEgan (2012), "Investor Expectations and the Role of Venture Capitalists in para 2Acquisitions: Bargaining and the Winner’s Curse", p.19Working paper, March 2012, I think it was McKinley that "suggest[ed] under review for inclusion in the Third Entrepreneurial Finance and Innovation Conference and a method that combines both cross-sectional and time-series informationspecial issue of Review of Corporate Finance Studies..."
The paper requires a complete rebuild of all the resultsThird Entrepreneurial Finance and Innovation Conference (EFIC) on June 10th-11th in Boston, MA, with is supported by the data updated to Kauffman Foundation and the end of 2011Society for financial studies. We should also consider several extensions to the paper, detailed Conference papers will be considered for inclusion in a later sectionspecial issue of the Review of Corporate Finance Studies (RCFS).
*Target Name
*Acquirer Name
*Payment Method
*Acquisition announcement date
*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)
*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: convert all TVs *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 2011 dollarsnames etc.
*Sales
*Leverage variables (Revenue, Variable Cost, Op Income, Net Income, Total Liabilities, Stockholder's equity)
*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
===Calculated variables=Variables==
==Rebuild Plan=Target Accounting Variables===*targetcommonequity*targetcommonequity11*targetintangibles*targetintangibles11*targetnetsales*targetnetsales11*targetrandd: R&D*targetrandd11*targettotalassets*targettotalassets11*targettotalliabilities*targettotalliabilities11
The regressions for 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 estimation window will have a runPaytas-time that might be considerable; even given Berglund definition of HT*aht_hecker: Uses the Hecker definition of HT*aht_pb_notitbt: Uses the hardware that I have put together at BerkeleyPaytas-Berglund definition of HT, I suspect that this will take at least 24hrs but removes IT and BT*aht_hecker_notitbt: Uses the Hecker definition of compute timeHT, but removes IT and BT*tht: The same as above but on the target's NAIC code. THIS IS THE ONE YOU WANT FIRST. I therefore plan on doing this very early *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 setting it running0 if the patent data will be inherently truncated.
===Proposed Rebuild Order=State Codes==
===Thoughts for discussionBiotech (BT)===
The completed deal flag following is in ''Deal Status'' our definition of other (i.e. Not IT/BT) High- this will be restricted to 'C' in the processing.tech:
The street address is multiline and problematic if includedreferences 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. This can be drawn seperately if neededgov/opub/mlr/2005/07/art6full. We have the Citypdf *Paytas, Zip Jerry and StateBerglund, which is sufficient to get a Google Maps lookup. Likewise 'Competing Offer Flag Dan (Y/N2004)', also known as COMPETE "Technology Industries and Competing BidderOccupations for NAICS Industry Data", is a multiline - with each presumably corresponding to a different bidder identity. It was excludedCarnegie Mellon University, Center for Economic Development and State Science & Technology Institute.
===Processing the AcquisitionsVC Reputations===
===Retrieving CUSIPsBargaining Superstars===
===VC Information Asymmetries===
