MétaCan
Menu
Back to cohort
Record W7028232918

Essays in Corporate Finance, Shareholder Litigation, and Politics

2023· dissertation· en· W7028232918 on OpenAlexfundno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsnot available
FundersConcordia UniversityUniversité du Québec à MontréalMcGill University
KeywordsShareholderPoliticsCorporate governanceDismissalCampaign financeSupreme courtVotingIdeology
DOInot available

Abstract

fetched live from OpenAlex

When firms seek to curry the favor of politicians, it inevitably leads to political corruption. Political spending totaled US$14.4 Billion in the 2020 US election cycle—and this total does not include dark money donations. Firms naturally never donate to politicians without wanting a return on their investment, so clearly political corruption is a multi-billion-dollar problem in the United States. Recently, a strand of literature examines political corruption in the US from a corporate finance perspective. Another recent strand of finance literature concerns the effects of political ideology on the outcomes of US securities-related shareholder litigation. This thesis aims to first combine and expand upon these two emerging strands of literature by analyzing the relationships of a comprehensive variety of US political and judicial variables with the outcomes of securities fraud and related shareholder litigation. We then extend our framework to a refined exploration of corporate governance as it relates to shareholder litigation. 
\n
\nIn the first essay, we study the relationship between a number of political and judicial variables in the United States with the outcomes of litigation for firms that have been sued by their shareholders. Consistent with our hypothesis, we find that a crucial factor in shareholder litigation dismissal has been the passage of the Citizens United v. FEC Supreme Court campaign finance ruling of 2010. Furthermore, we find evidence that political campaign contributions afford firms the requisite connections that will benefit them in current or future lawsuits. Also, we quantify the impact of the size and timing of the political campaign contributions. In addition, we confirm hypotheses that the fate of shareholder class action litigation against these firms is also affected by the political ideologies of some of the trusted authorities who write, administer, and interpret the laws pertinent to firms facing such litigation. These authorities are federal politicians and judges, who are ideally independent arbiters—but the great powers they are given appear to create agency and bias issues, respectively.\t 
\n
\nIn the second essay, we use the knowledge and framework attained from our conclusions from the first essay to examine various corporate governance variables with respect to their role in shareholder litigation outcomes in this new light—variables which can be categorized as board, executive, and firm ownership characteristics. We confirm hypotheses generally based on the principle that variables reflecting better corporate governance will tend to be associated with a higher lawsuit dismissal likelihood. This likelihood tends to increase with a firm’s board of directors who are older, more independent, less busy, and have a larger network size. Furthermore, the likelihood of litigation dismissal increases with greater analyst coverage of the firm, with a firm’s CEO who is older than the board of directors, with greater institutional ownership, and with a larger number of blockholders owning stakes in the firm. As well as finding results consistent with such hypotheses for our corporate governance variables, we also find some novel, unexpected interactions between political variables and corporate governance variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.310
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSpectrum Research Repository (Concordia University)Same topicBiomedical and Chemical ResearchFrench-language works237,207