VentureXpert Data

McNair Project

VentureXpert Data
Project Information
Project Title	VentureXpert Data
Owner	Augi Liebster
Start Date	June 20, 2018
Deadline
Primary Billing
Notes
Has project status	Active
Copyright © 2016 edegan.com. All Rights Reserved.

Augi Liebster (Work Log)

Relevant Former Projects

1. Venture Capital (Data)
2. Retrieving US VC Data From SDC
3. VC Database Rebuild

Location

My scripts for SDC pulls are located in the Z drive in the location:

E:\VentureXpert Database\ScriptsForSDCExtract

My successfully pulled and normalized files are stored in the location:

E:\VentureXpert Database\ExtractedDataQ2

My script for loading data is in one big text file in the location:

E:\VentureXpert Database\vcdb3\LoadingScripts

The folder vcdb2 is there for reference to see what people before had done. ExtractedData is there because I pulled data before July 1st, and Ed asked me to repull the data.

Goal

I will be looking to redesign the VentureXpert Database in a way that is more intuitively built than the previous one. I will also update the database with current data.

Initial Stages

The first step of the project was to figure out what primary keys to use for each major table that I create. I looked at the primary keys used in the creation of the VC Database Rebuild and found primary keys that are decent. I have updated them and list them below:

1. CompanyBaseCore- coname, statecode, datefirstinv
2. IPOCore- issuer, issuedate, statecode
3. MACore- target name, target state code, announceddate
4. Geo - city, statecode, coname, datefirst, year
5. DeadDate - conname, statecode, datefirst, rounddate (tentative could still change)
6. RoundCore- conname, statecode, datefirst, rounddate
7. FirmBaseCore - firmname
8. FundBaseCore - fund name (firstinvedate doesn't work because not every row has an entry)

These are my initial listings and I will come back to update them if needed.

The second part of the initial stage has been to pull data from the SDC Platinum platform. I did it in July to ensure that I had two full quarters of data.

SDC Pull

When pulling data from SDC, it is a good idea to look for previously made rpt files that have the names of the pulls you will need to do. They have already been created and will save you a lot of work. The rpt files that I used are in the folder VentureXpertDB/ScriptsForSDCExtract. The files will come in pairs with one being saved as an ssh file and one as a rpt file. To update the dates to make them recent, go into the ssh file of the pair and change the date of last investment. When you open SDC, you will be given a variety of choices for which database to pull from. For each type of file chose the following:

1. VentureXpert - PortCo, PortCoLong, USVC, Firms, BranchOffices, Funds, Rounds, VCFirmLong
2. Mergres & Acquisition - MAs
3. Global New Issues Databases - IPOs

Help on pulling data from SDC is on the SDC Platinum (Wiki) page.

VCFund Pull Problem

When pulling the VCFund1980-Present, I encountered two problems. One, is that SDC is not able to sort through the funds that are US only with the built in filters. Two, there are multiple rpt files that specify different variables for the fund pull. I pulled from both to be safe, but in the VC Database Rebuild page there is a section on the fund pull where Ed specifies which rpt file he used to pull data from SDC. Regardless I have both saved in the ExtractedData folder. After speaking with Ed, he told me to use the VCFund1980-present.rpt file to extract the data. Had various problems extracting data including freezing of SDC program or getting error Out of Memory. Check the SDC Platinum (Wiki) page to fix these issues.

Loading Tables

When I describe errors I encountered, I will not describe them using line numbers. This is because as soon as any data is added, the line numbers will become useless. Instead I recommend that you copy the normalized file you are working with into an excel file and using the filter feature. This way you can find the line number in your specific file that is causing errors and fix it in the file itself. The line numbers that PuTTY errors display are often wrong, so I relied on excel to discover the error fastest. If my instructions are not enough for you to find the error, my advice would be to find key words in the line that PuTTY is telling you is causing errors and filter through excel.

DROP TABLE roundbase;
CREATE TABLE roundbase (
  coname varchar(255),
  rounddate date,
  updateddate date,
  foundingdate date,
  datelastinv date,
  datefirstinv date,
  investedk real,
  city varchar(100),
  description varchar(5000),
  msa varchar(100),
  msacode varchar(10),
  nationcode varchar(10),
  statecode varchar(10),
  addr1 varchar(100),
  addr2 varchar(100),
  indclass varchar(100),
  indsubgroup3 varchar(100),
  indminor varchar(100),
  url varchar(5000),
  zip varchar(10),
  stage1 varchar(100),
  stage3 varchar(100),
  rndamtdisck real,
  rndamtestk real,
  roundnum integer,
  numinvestors integer
);

\COPY roundbase FROM 'USVC1980-2018q2-Good.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--151549

The only error I encountered here was with Cardtronic Technology Inc. Here there was a problem with a mixture of quotation marks which cause errors in loading. Find this using the excel trick and remove it manually.

DROP TABLE ipos;
CREATE TABLE ipos (
  issuedate date,
  issuer varchar(255),
  statecode varchar(10), 
  principalamt money, --million
  proceedsamt money, --sum of all markets in million
  naiccode varchar(255), --primary NAIC code
  zipcode varchar(10),
  status varchar (20),
  foundeddate date
);

\COPY ipos FROM 'IPO1980-2018q2-NoFoot-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--12107

I encountered no errors while loading this data.

DROP TABLE branchoffices;
CREATE TABLE branchoffices (
  firmname varchar(255),
  bocity varchar(100),
  bostate varchar(2),
  bocountrycode varchar(2),
  bonation varchar(100),
  bozip varchar(10),
  boaddr1 varchar(100),
  boaddr2 varchar(100)

);

\COPY branchoffices FROM 'USVCFirmBranchOffices1980-2018q2-NoFoot-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--10353

I encountered no errors while loading this data.

DROP TABLE roundline;
CREATE TABLE roundline (
  coname varchar(255),
  statecode varchar(2),
  datelastinv date,
  datefirstinv date,
  rounddate date,
  disclosedamt money,
  investor varchar(255)
);

\COPY roundline FROM 'USVCRound1980-2018q2-NoFoot-normal-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--403189

I encountered no errors while loading this data.

DROP TABLE fundbase;
CREATE TABLE fundbase (
  fundname varchar(255),
  closedate date, --mm-dd-yyyy
  lastinvdate date, --mm-dd-yyyy
  firstinvdate date, --mm-dd-yyyy
  numportcos integer,
  investedk real,
  city varchar(100),
  fundyear varchar(4), --yyyy
  zip varchar(10),
  statecode varchar(2),
  fundsizem real,
  fundstage varchar(100),
  firmname varchar(255),
  dateinfoupdate date,
  invtype varchar(100),
  msacode varchar(10),
  nationcode varchar(10),
  raisestatus varchar(100),
  seqnum integer,
  targetsizefund real,
  fundtype varchar(100)
);

\COPY fundbase FROM 'VCFund1980-2018q2-NoFoot-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--29397

There is a Ukranian fund that has stray quotation marks in its name. It is called something along the lines of "VAT "ZNVKIF "Skhidno-Evropeis'lyi investytsiynyi Fond". If this does not help, you can filter in excel using Kiev as the keyword in the city column and find the line where you are getting errors. Then manually remove the commas in the actual text file. After that, the table should load correctly.

DROP TABLE firmbase;
CREATE TABLE firmbase(
  firmname varchar(255),
  foundingdate date, --mm-dd-yyyy
  datefirstinv date, --mm-dd-yyyy  
  datelastinv date, --mm-dd-yyyy
  addr1 varchar(100),
  addr2 varchar(100),
  location varchar(100),
  city varchar(100),
  zip varchar(10),
  areacode integer,
  county varchar(100),
  state varchar(2),
  nationcode varchar(10),
  nation varchar(100),
  worldregion varchar(100),
  numportcos integer,
  numrounds integer,
  investedk money,
  capitalundermgmt money,  
  invstatus varchar(100),
  msacode varchar(10),
  rolepref varchar(100),
  geogpref varchar(100),
  indpref varchar(100),
  stagepref varchar(100),
  type varchar(100)
);

\COPY firmbase FROM 'USVCFirms1980-2018q2-NoFoot-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--15899

The normalization for this file was wrong when I tried to load the data. To fix this go to the file where you have removed the footer and find the column header titled Firm Capital under Mgmt{0Mil}. Delete the {0mil} and renormalize the file. Then everything should be ok. A good way to check this is to copy and paste the normalized file into an excel sheet and see whether the entries line up with their column header correctly. The second error I found was with the Kerala Ventures firm. Here the address has the word l"opera in it. This quotation will cause errors so find the line number using excel and remove it manually. The third error is in an area code where 1-8 is written. This hyphen causes errors. Interestingly, the line number given by PuTTY was correct, and I found it in my text file and deleted it manually. These were the only errors I encountered while loading this table.

Instructions on Matching PortCos to Issuers and M&As From Ed

Get portco keys

DROP TABLE portcokeys;
CREATE TABLE portcokey AS
SELECT coname, statecode, datefirst
FROM portcocore;
--CHECK COUNT IS SAME AS portcocore OR THESE KEYS ARE VALID AND FIX THAT FIRST

Get distinct coname and put it in a file

\COPY (SELECT DISTINCT coname FROM portcokeys) TO 'DistinctConame.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV

Match that to itself

Move DistinctConame.txt to E:\McNair\Software\Scripts\Matcher\Input
Open powershell and change directory to E:\McNair\Software\Scripts\Matcher
Run the matcher in mode2:
 perl Matcher.pl -file1="DistinctConame.txt" -file2="DistinctConame.txt" -mode=2
Pick up the output file from E:\McNair\Software\Scripts\Matcher\Output (it is probably called DistinctConame.txt-DistinctConame.txt.matched) and move it to your Z drive directory

Load the matches into the dbase

DROP TABLE PortcoStd;
CREATE TABLE PortcoStd (
   conamestd  varchar(255),
   coname   varchar(255),
   norm  varchar(100),
   x1  varchar(255),
   x2  varchar(255)
);

\COPY CohortCoStd FROM 'DistinctConame.txt-DistinctConame.txt.matched' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
--YOUR COUNT

Join the Conamestd back to the portcokeys table to create your matching table

DROP TABLE portcokeysstd;
CREATE TABLE portcokeysstd AS
SELECT B.conamestd, A.*
FROM portcokey AS A
JOIN PortcoStd AS B ON A.coname=B.coname
--CHECK COUNT IS SAME AS portcokey OR YOU LOST SOME NAMES OR INFLATED THE DATA

Put that in a file for matching (conamestd is in first column by construction)

 \COPY portcokeysstd TO 'PortCoMatchInput.txt' WITH DELIMITER AS E'\t' HEADER NULL AS  CSV
 --YOUR COUNT

Now prepare to repeat that process for M&A's and IPOs:

• For M&As your keys (for now) will be targetname, statecode, dateannounced
• For IPOs your keys (for now) will be issuername, statecode, issuedate
• FIRST CLEAN EACH DATASET. The easiest way to remove duplicates (if you have lots of them) is to use an aggregate query:

DROP TABLE IPOCoreNoDups;
CREATE TABLE IPOCoreNoDups as
SELECT issuername, statecode, issuedate, max(var1) as var1, avg(var2) as var2, ...
FROM IPOCore GROUP BY issuername, statecode, issuedate ORDER BY issuername, statecode, issuedate;

Note that you need all vars to be inside aggregates and that you should choose the aggregate function sensibly by looking at the data. Generally use MAX for amounts and MIN for dates. You can also use MAX or MIN on text strings.

And now build the same stacks as before but to create Issuerkeystd and TargetKeystd (or whatever you call them). Make sure that issuerstd (and targetnamestd) is in the first column.

Now match Portcokeystd to Issuerkeystd, and match Portcokeystd to Targetkeystd

• Move the files into the input director as before
• Run the matcher script but WITHOUT mode 2:

perl Matcher.pl -file1="PortCoMatchInput.txt" -file2="IssuerMatchInput.txt"
perl Matcher.pl -file1="PortCoMatchInput.txt" -file2="TargetMatchInput.txt"

Open each of these files in excel and mark good matches with 1s and bad matches with 0s by adding columns to compare dates, states, etc, and filtering.

When you are done:

• Build a new sheet of just good matches.
• Save the excel files
• Copy each of your match sheets to a text file
• CREATE TABLE to reflect the data you are going to load (include std names and keys)
• \COPY the data (using the exact copy command above but changing the table and file names) into the table
• Celebrate!
• Next we'll deal with any firms that have an IPO and an M&A and decide which we'll keep
• And then we'll join in the chosen IPO and M&A data and move on!

Cleaning IPO and MA Data

It is important to follow Ed's direction of cleaning the data using aggregate function before putting the data into excel. This will keep you from a lot of manual checking that is unnecessary. When ready, paste the data you have into an excel file. In that excel file, I made three columns: one to check whether state codes were equivalent, one checking whether the date of first investment was 3 years before the MA or IPO, and one checking whether both of these conditions were satisfied for each company. I did this using simple if statements. This process is manual checking and filtering to see whether matches are correct or not and are thus extremely subjective and tedious. First, I went through and checked the companies that did not have equivalent state codes. If the company was one that I knew or the name was unique to the point that I did not believe the same name would appear in another state, I marked the state codes as equivalent. I did the same for the date of first investment vs MA/IPO date. Then I removed all duplicates that had the marking Warning Multiple Matches, and the data sheets were clean.

Finding Companies that Underwent IPOs and MAs

1. Load IPOClean and MAClean into the database.
2. Perform an INNER JOIN on the two tables in order to find the companies that underwent both MAs and IPOs. Do this by joining on MA.targetnamestd and IPO.issuerstd. Load this table into an excel sheet and manually find which companies you want to keep as MAs and which you want to keep as IPOs. Make sure to keep the portco primary key in this table.
3. Load the decided IPO and MA data back into the database, including the primary keys of the portcos.
4. LEFT JOIN the MA table with the IPO table. Select the companies where the IPO table are null as these are the companies that only had MAs. Do the same for IPOs. Now you have tables of companies that underwent only MAs and only IPOs.
5. Join the companies that underwent IPOs only and the chosen IPOs back to the original key using the primary key of the company which must be in both tables. Repeat this for the MA table.