7,612
edits
Changes
Jump to navigation
Jump to search
There's an early draft, without tables The working paper is called "CEO CompensationJB2Compensation Full (Last Version).docx". It's in the dropbox (Dropbox\coauthoredprojects\ResearchWithJim\ExecCompAcqs) . This produces the pdf below, which is also available on SSRN: :Brander, James A. and Egan, Edward and Endl, Sophie, Comparing CEO Compensation Effects of Public and in the main folder Private Acquisitions (E:\McNair\Projects\CompAcqs\January 15, 2021). There's also a CEOAvailable at SSRN: https://ssrn.log file that details results run on the old dataset, for information onlycom/abstract=3766568 <pdf>File:Brander_Egan_Endl_(2019)_-_Comparing_CEO_Compensation_Effects_of_Public_and_Private_Acquisitions.pdf</pdf>
*Spec and build summary statistics and regression tables==Old Notes==
mktvalue mkvalt- market value
CEO Compensation and Returns to Public and Private Acquisitions: Testing the Shareholder Value Hypothesis (view source)
Revision as of 15:10, 20 February 2021
, 15:10, 20 February 2021→New Measures
{{AcademicPaper
|Has title=Executive CEO Compensation and Returns to Public and Private Acquisitions: Testing the Shareholder Value Hypothesis
|Has author=Ed Egan, Jim Brander,
|Has paper status=In developmentWorking paper
}}
==DraftWorking Paper==
==Database, dataset and scripts==
MasterV1-1.txt
There's also a CEO.log file that details results run on the old dataset, for information only. ==Current Notes== The paper was submitted to the [https://www.mdpi.com/journal/jrfm Journal of Risk and Financial Management (JRFM)], an open-access journal with fast turn-around times on January 17th, 2021. On February 8th, 2021, we received an R&R with a 10-day revision window. This section provides notes on the feasibility of the R&R. A key issue concerns requests for us to update the data, which:*Isn't a live dataset on my current [[Research Computing Hardware]]. *Requires Cumulative Abnormal Returns (CARs) that were previously calculated as a part of the [[Winner's Curse in Acquisitions (Academic Paper)]] that was accepted for publication in 2017.**Uses both public and private acquisitions data, that were loaded as a part of [[VCDB20]] but may need more variables.*Needs ExecComp, Compustat, and CRSP data from WRDS (I have access to all until March). I didn't notice any requests from the reviewers to handle multiple acquisitions on the same day, which doesn't appear to have been fixed in the previous version. However, I can see we are aggregating acquisitions over the year, so this is somewhat moot in the averages and totals. ===Reviewer Comments=== Some of Reviewer 3s comments are problematic:*Another major problem with this empirical work is due to the problem of endogeneity reported by the authors. How was this problem resolved? The methodology used by the authors does not solve this problem! Why didn't the authors think about using the latest '''dynamic panel data models'''? There are some models who have emerged to precisely solve the problems that this sample presents. This should be explored by the authors.**Fixed effect panel data should address issue of unobserved heterogeneity.*On the other hand, the authors need to present to us in the tables the overall results of the tests to the models used. For example, they refer us to the Hausman Test but do not show us the results of this test! Wald tests, Test F, Fixed Effects Test F and the respective R-Square value for fixed effects regression and random effects regression are missing.**Just generate test stats!*Where is the correlation matrix? It must be presented and its coefficients analysed.*Finally, there is another problem in the work of these authors and the main objective of the paper and the fact that the authors want to understand the difference between so-called public and private acquisitions. At the beginning of the development of the empirical work the authors must demonstrate to us that in fact the two subsamples are different in terms of the variables that will be studied. Thus, differences tests should be performed and reported to these two subsamples, only by so that it will make sense to perform all the empirical work later. ===Build Notes=== I started a new database called [[wrds]] for the major pulls. ===Ed To Do=== *Institutional investors measures*Profitability: profit and net income*MA activity 2016 to 2020*Take a look at the dynamic panel Note see also: [[Governance Measures]] ====New Measures==== From Thomson-Reuters 13F filings (joined using CUSIP8 and year, where available):*iisharestot -- Total number of shares held by institutional investors*iisharestop5 -- Number of shares held by the five largest institutional investors*iitop5prop -- Proportion of shares held by the five largest institutional investors compared to all institutional investors*iicount -- Number institutional investors*blocktot -- Number of blockholders (institutional investors with >= 5% of shares) From COMPUSTAT Fundamentals Annual (joined using CUSIP8 and year, where available):*gp -- gross profit*ni -- net income*gpr -- gross profit ratio*npr -- net profit ratio*csho -- number of shares outstanding (in millions)*revt -- revenue (note that this replaces the old revt variable)*roa -- return on assets (ebitda/totalassets) *leverage -- total liabilities/total assets*totalassets -- same as the old at variable but from a new pull*interlock -- 1/0 CEO is on compensation committee*mktcap -- Market Capitalization calculated as closing price (prcc_c) x common shares outstanding (csho) Thomson-Reuters 13F filings in conjunction with COMPUSTAT Fundamentals Annual (joined using CUSIP8 and year, where both available):*iifractot -- fraction of shares held by institutional investors*iifractop5 -- fraction of shares held by top 5 institutional investors ====Old Governance Measures==== The following governance measures are listed under firm variables below: agovgoldenparachute - 0/1 for the CEO has a golden parachute (many missing) agovlimitabilityactbywritconsent - 0/1 whether governance has limited shareholders ability to act by written consent (many missing) agovmajvoterequirement - 0/1 for whether board members are elected on majority or plurality, which matters for uncontested seats (many missing) agovresignrequiredonmajorityvote - 0/1 "Indicates that a director is required to submit his/her resignation upon failing to receive support from a majority of votes cast (which, typically, the board may chose to accept or reject)." (many missing) I tried each of them in a FE panel regression of the type found in table 3, like: areg tdc116l car5totpub car5totpriv agovresignrequiredonmajorityvote i.year, absorb(ceofirmid) cluster(ceofirmid) In each case, the sample dropped to ~2k (from 41k), and there was no significance for ANY variable. =====Reference Papers and their data===== Hartzell Starks (2000) - Institutional Investors and Executive Compensation:*we obtain institutional equity holdings for each year between December 1991 and December 1996 from the CDA Spectrum database. CDA Spectrum derives these holdings from institutional investors’ 13-f filings. (Institutional investors with more than $100 million in equities must report their equity ownership to the SEC in quarterly 13-f filings)*Measures: We measure total institutional ownership as the fraction of shares outstanding owned by institutions. This measure should primarily capture indirect institutional monitoring, although it may also reflect some direct monitoring. Our second measure of institutional investor influence, the concentration of institutional ownership, is designed to capture the direct institutional monitoring. It is calculated as the proportion of the institutional investor ownership accounted for by the top five institutional investors in the firm. David Kochhar Levitas (1998) - The effect of institutional investors on the level and mix of CEO compensation:*Money Market Directory, Moody's Bank and Finance Manual, Nelson's Directory of Investment Managers*Percent of ownership (of II) by type*Blockholders (shareholders with greater than 5%) provide better governance (indicator variable) Bebchuk Cohen Hirst (2017) - The Agency Problems of Institutional Investors:*References data from FactSet Ownership database Lewellen (2011) - Institutional investors and the limits of arbitrage:*The CDA/Spectrum database is compiled from institutions’ 13F filings with the Securities and Exchange Commission (SEC).*according to quarterly 13F filings compiled by Thomson Financial... though not exclusively [see the Wharton Research DataServices (WRDS) User Guide for details]. *Measures are N/A (wrong perspective) Bloom Klein (2012) - Institutional Investors and Stock Market Liquidity:*Any financial institution exercising discretionary management of investment portfolios over $100 million in qualified securities is required to report those holdings quarterly to the SEC using Form 13F. Qualified securities include stocks listed for trading in the US, among other securities. These filings, compiled quarterly by Thomson/CDA and available through Wharton Research Data Services (WRDS), are the source of the stock holdings used in this study for the period 1980 to 2010.*Two measures of institutional stock ownership – the percentage of a stock owned by institutions and the number of institutions that own the stock =====Looking inside WRDS===== Datasets:*Thomson Reuters Institutional (13f) Holdings - Type 3: Stock Holdings: https://wrds-web.wharton.upenn.edu/wrds//ds/tfn/types/s34type3/index.cfm**Keys: CUSIP (of holdings), manager number (of reporting institution), file date. Covers up 1980-2020.**Variables: Shares and their types and voting rights*Thomson Reuters Institutional (13f) Holdings - s34 Master File: https://wrds-web.wharton.upenn.edu/wrds//ds/tfn/sp34/index.cfm**See above. This is the more general dataset*Blockholders' data is reported by firm for the period 1996-2001: https://wrds-www.wharton.upenn.edu/pages/get-data/blockholders/ I used the first one. See [[wrds]] for the preprocessing. ===Restoring the old data=== A backup of the old database is available as compacqs_Fc.dump in Z:/mcnair/backups and the source files all appear to be in Z:/mcnair/compacqs (the SQL file and build notes are in E:\mcnair\Projects\CompAcqs). Other useful things:*Distance was calculated using GoDoMetar.pm which doesn't have a writeup but is in E:\mcnair\Projects\Winner's Curse\Distance*The Governance variables were added to the Winner's curse project later from ISS (formerly RiskMetrics). See E:\mcnair\Projects\Winner's Curse\Governance*The CARs appear to have been processed with E:\mcnair\Projects\Winner's Curse\Stata\EdsRegs.do, which takes Estimation5K.txt as an input. I made compacqs folders in E:\projects and /bulk/ for use, created a new '''compacqs''' database, and restore the tables from the last backup. The pg_restore threw some errors (the new server doesn't have a plpythonu extension control file) but was otherwise fine. I put the latest version of everything I could find in an old subfolder on E. The last version of the dataset produced by BuildCompAcqs.sql was 2-3, which was processed by CEOAug22.do to build CEOAugustv2.log. However, I don't see any outregs or other automated table generation and most of the regressions are missing. There's a do file in the dropbox, '''CEOOct.do''', that is identical to CEOAug22.do up until around line 100. It looks like the last version of the STATA code. However, it loads AcqCEOSep, which must have been a .dta produced by Jim. I built the following:*MasterV3-0.txt from the master table of the reloaded dbase, with the code in Revision.sql*Revision3-0.do, based on CEOOct.do, and loading MasterV3-0.txt. It produces Revision3-0.log, which currently puts us at the last build. I also copied them to the dropbox. ===Estimating a clean new build=== ====Variables from the Winners dataset==== The biggest hurdle to updating the data is that we pulled many of the variables from the Winner's dataset: --Builds the Acqbase table where the unit of observation is an acquirer year DROP TABLE Acqbase; CREATE TABLE Acqbase AS SELECT acusip, year, sum(car5) as car5tot, avg(car5) as car5avg, sum(car5*tpublic) as car5totpub, avg(car5*tpublic) as car5avgpub, sum(car5*(1-tpublic)) as car5totpriv, avg(car5*(1-tpublic)) as car5avgpriv, avg(sharesoutstanding) as sharesoutstanding, sum(car5*sharesoutstanding) as valctot, avg(car5*sharesoutstanding) as valcav, sum(tpublic) as numacqpub, sum(1-tpublic) as numacqpriv, count(tname) as numacq, sum(transactionvalue) as transactionvalue, sum(transactionvalue*tpublic) as transactionvaluepub, sum(transactionvalue*(1-tpublic)) as transactionvaluepriv, sum(CASE WHEN transactionvalue IS NOT NULL THEN 1::int ELSE 0::int END) as numwtv, sum(CASE WHEN transactionvalue IS NOT NULL THEN (1::int)*tpublic ELSE (0::int)*tpublic END) as numwtvpub, sum(CASE WHEN transactionvalue IS NOT NULL THEN (1::int)*(1-tpublic) ELSE (0::int)*(1-tpublic) END) as numwtvpriv, sum(CASE WHEN transactionvalue IS NULL THEN 1::int ELSE 0::int END) as numwotv, sum(CASE WHEN transactionvalue IS NULL THEN (1::int)*tpublic ELSE (0::int)*tpublic END) as numwotvpub, sum(CASE WHEN transactionvalue IS NULL THEN (1::int)*(1-tpublic) ELSE (0::int)*(1-tpublic) END) as numwotvpriv, sum(coalesce(transactionvalue,enterprisevalue,equityvalue)) as targetvalue, sum(coalesce(transactionvalue,enterprisevalue,equityvalue)*tpublic) as targetvaluepub, sum(coalesce(transactionvalue,enterprisevalue,equityvalue)*(1-tpublic)) as targetvaluepriv, min(anaic::int) as anaic, sum(tit) as numacqit, sum(tvc) as numacqvc, sum (stockswap) as numstockswap, sum (terminationfeeacquiror) as numterminationfeeacquiror, sum (terminationfeetarget) as numterminationfeetarget, sum (tbalancesheetprivate) as numtbalancesheetprivate, sum (tincomestatementprivate) as numtincomestatementprivate, avg (agovgoldenparachute) as agovgoldenparachute, avg (agovlimitabilityactbywritconsent) as agovlimitabilityactbywritconsent, avg (agovmajvoterequirement) as agovmajvoterequirement, avg (agovresignrequiredonmajorityvote) as agovresignrequiredonmajorityvote, avg (pcstock) as pcstock, avg (pccash) as pccash, avg (drivingdur) as drivingdur, avg (distance) as distance, sum (CASE WHEN pcstock=100 THEN 1::int ELSE 0::int END) as numallstock, sum (CASE WHEN pccash=100 THEN 1::int ELSE 0::int END) as numallcash FROM Winners GROUP BY acusip, year; --18341 The variables, along with their new sources and comments, are as follows:*acusip, year, tname, tpublic, anaic, transactionvalue, enterprisevalue, pcstock, pccash -- A part of the [[VCDB20]] build*tit, tvc -- Added with lookup tables based on NAICS (Est. 30 mins)*car5, sharesoutstanding -- Would need to be calculated using a GVKEY based data pulled from CRSP, some pre-processing, and a STATA script, then a load. (Est. 6hrs, assuming I can find the old code.) *equityvalue, stockswap, terminationfeeacquiror, terminationfeetarget, agovgoldenparachute, agovlimitabilityactbywritconsen, agovmajvoterequirement, agovresignrequiredonmajorityvote -- Could be pulled from SDC but I don't think they are used*tbalancesheetprivate, tincomestatementprivate -- Built from a COMPUSTAT pull (Add 30 mins)*drivingdur, distance -- Determined using a Google Maps API script. (Est 2hrs + $100 and 5hrs to run). ====Other things==== On top of this. we'd need to do the following:*The ExecComp pull (1hr)*The COMPUSTAT pull (1hr)*Rework the SQL (2 hrs) So, my best guess is: 1/2 + 6 + 1/2 + 2 + 1 + 1 + 2 = 13hrs...
===Same Day Acquisitions===
acqi - current income from acquisitions in CSTAT
acq - cost of acquisitions from CSTAT
agovgoldenparachute - 0/1 for the CEO has a golden parachute (many missing)
agovlimitabilityactbywritconsent - 0/1 whether governance has limited shareholders ability to act by written consent (many missing)
