Literature DB >> 27872888

Newly listed firms' M&A activities data and their VC-backing data.

Ting Cao1.   

Abstract

This article contains hand collected and matched data on the VC investment situation in newly listed firms in SME and ChiNext board from 2009 to 2012 and the corresponding M&A activities data undertaken by these newly listed firms as acquirers in three years following initial public offering. Mentioned data are related to the research article "Heterogeneous Venture Capital, M&A Activity, and Market Response" (W. Li, T. Cao, Z. Feng, 2016) [1].

Entities:  

Year:  2016        PMID: 27872888      PMCID: PMC5109247          DOI: 10.1016/j.dib.2016.11.015

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications Table Value of the data Hand collected data from public firms’ IPO prospectus supplements the missing data in existing PEDATA database as for VC׳s investment. Incorporate the firm׳s VC investment data with their M&A activities data. The data can be useful for other researchers investigating VC׳s influence on firm׳s M&A activities.

Data

The panel data consist of 781 listed firms ranging from the first year following IPO to the third year following IPO totaling 2267 observations. Definition of variables involved in the data and the basic statistic description of the data are shown in Table 1, Table 2 respectively.
Table 1

Definition of variables.

Variable nameData typeDefinition
MABinary variableMA is short for merger and acquisition. It measures whether firm i undertakes M&A as an acquirer in year t. Dummy=1 if firm i in year t undertakes M&A as an acquirer, otherwise 0.
MAnumberCount variableMAnumber measures the number of M&A activities undertaken by firm i in year t.
VCBinary variableVC is short for venture capital. Dummy =1 if VC holds firm i׳s share at the time of IPO.
VCshareContinuous variableVC׳s pre-IPO investment share.
PeriodWeighted average continuous variableWeighted average of VCs’ investment periods before IPO backing firm i, measured in month.
Table 2

Statistical description of variables.

VariableNumber of observationsMeanMinMaxStd.
MA22670.23380.00001.00000.4233
MAnumber22670.31670.00008.00000.6828
VC22670.49360.00001.00000.5001
VCshare22676.63090.000050.00009.2453
Period226714.76600.0000117.000019.4613

Experimental design, materials and methods

We first obtain VC’ investment in newly public firms listed on ChiNext board or SME board from 2009 to 2012 from PEDATA database, including VC׳s total investment amount, first entry time, shareholding at IPO data.Given the VC institution criteria may vary across different database, to make our VC institution list more convincing, we cross check the VC investment data using WIND database to make sure every VC institution we include in our research sample appears in both databases [1].As for the missing data for each VC observation, we hand collected them from the listed firm׳s IPO prospectus.After the VC investment data was ready, we filtered M&A activities data for each newly public firms listed on ChiNext board or SME board from 2009 to 2012 from CSMAR database. Then we matched it with prepared each sample firm׳s VC investment data.
Subject areaCorporate finance and governance
More specific subject areaVenture capital investment time, newly listed firms’ M&As occurrence and frequencies
Type of dataTable, Excel file
How data was acquiredCSMAR database
PEDATA database
WIND database
Prospectus of newly listed firms
Data formatFiltered
Experimental factorsFinancial firms (Industry code J) are eliminated from the samples, because the regulations are different in the financial industry.
The M&A sample is selected using following criteria: the acquirer company undergoes an IPO from 2009 to 2012 on the ChiNext or SME board; M&A types include asset acquisition and merger. The acquisition subject is only limited to stock when it refers to an asset acquisition.
Observations without complete financial information are deleted.
Experimental featuresObtain information from public database and hand-collected data.
Data source locationChina
Data accessibilityData is within this article
  1 in total

1.  Survey data on factors affecting negotiation of professional fees between Estate Valuers and their clients when the mortgage is financed by bank loan: A case study of mortgage valuations in Ikeja, Lagos State, Nigeria.

Authors:  Chukwuemeka O Iroham; Hilary I Okagbue; Olalekan A Ogunkoya; James D Owolabi
Journal:  Data Brief       Date:  2017-05-01
  1 in total

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