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The Wall Street Stampede: Exit as Governance with Interacting Blockholders, 1994-2011
Creator
Dasgupta, A, London School of Economics
Zachariadis, K, Queen Mary University of London
Study number / PID
856716 (UKDA)
10.5255/UKDA-SN-856716 (DOI)
Data access
Restricted
Series
Not available
Abstract
The asset management industry's growth has led to firms often having multiple institutional blockholders, which has a significant impact on corporate governance through exit strategies. A model has been proposed to illuminate this dynamic, emphasizing the role of open-ended institutional investors like mutual funds in enhancing corporate governance when informed blockholders exit. This model introduces a novel perspective on the influence of mutual funds in shaping corporate governance. Empirical evidence consistent with this framework is presented by examining mutual fund trading around exits by activist hedge funds.
The empirical analysis centers on a dataset of 399 firms that underwent activist campaigns ending in exit between 1994 and 2011. Findings support the model's predictions, even after adjusting for unobservable firm-level variations and broader economic conditions. Following exits by activist hedge funds, mutual funds driven by fund flows significantly reduce their holdings in the target company compared to other institutional investors. This divergence in trading behavior is more pronounced when activists exit at a financial loss, campaigns show no clear success, or market reactions suggest liquidity concerns weren't the main driver.
To conduct this analysis, data on activist campaigns is merged with institutional holdings information from the Thomson Reuters 13F database and the Morningstar Open-end Mutual Fund portfolio holdings dataset. The activist campaign data is based on Schedule 13D filings and aligns with the data collection methods of previous studies. Trading behavior of other blockholders is tracked using quarterly 13F filings, a requirement for institutional investors managing $100 million or more. The S34 dataset (13F filings) from Thomson Reuters is combined with the Morningstar Open End Mutual Funds database to classify mutual funds throughout the 1994–2013 period.In the research programme outlined in this grant proposal, we shall...
Terminology used is generally based on DDI controlled vocabularies: Time Method, Analysis Unit, Sampling Procedure and Mode of Collection, available at CESSDA Vocabulary Service.
Methodology
Data collection period
30/09/2019 - 29/09/2023
Country
United Kingdom, United States
Time dimension
Not available
Analysis unit
Organization
Time unit
Other
Universe
Not available
Sampling procedure
Not available
Kind of data
Numeric
Text
Data collection mode
The study combines data from activist hedge fund campaigns with information on institutional holdings in target companies, sourcing data from the Thomson Reuters 13F database and the Morningstar Open-end Mutual Fund portfolio holdings dataset. The dataset on activist campaigns, spanning from 1994 to 2011, is provided by Alon Brav and primarily relies on Schedule 13D filings.Schedule 13D filings, mandated under Section 13(d) of the 1934 Exchange Act, require investors to report to the SEC within 10 days of acquiring more than 5% of any class of securities in a publicly traded company when they intend to influence its management or operations. These filings include essential details such as the filing date, ownership changes, purchase costs, and the filer's stated intentions.The study also tracks the trading behavior of other blockholders using quarterly 13F filings. In the United States, institutional investors managing $100 million or more are obligated to disclose their stock holdings via Form 13F submissions to the SEC. The research utilizes the S34 dataset, comprising 13F filings, compiled by Thomson Reuters, and integrates it with the Morningstar Open End Mutual Funds database. All funds listed in the Morningstar Open End Mutual Funds database during the 1994–2013 period are classified as mutual funds for the purposes of the study.
Funding information
Grant number
ES/S016686/1
Access
Publisher
UK Data Service
Publication year
2023
Terms of data access
The Data Collection is available for download to users registered with the UK Data Service.