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Record W6990977680

Essays in Corporate Finance

2019· article· en· W6990977680 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInsiderDividendInsider tradingProfitability indexInformation asymmetryQuarter (Canadian coin)Corporate financeCredit ratingDividend policy
DOInot available

Abstract

fetched live from OpenAlex

In this dissertation, I explore the effects of exogenous shocks on firms' and managers' behaviors. The first essay examines the effect of shareholder-initiated litigation risk on opportunistic insider trading by exploiting US states' staggered adoption of Universal Demand (UD) laws, which weakened shareholders' ability to file derivative lawsuits against corporate insiders. I find that UD laws lead to significantly more profitable insider trades, specifically insider sales. After the adoption of UD laws, insider sales on average avoid an additional loss of about 2 percent ($24,000) per month in buy-and-hold abnormal returns. The benefit of UD laws is greater for insiders of firms where information asymmetry is high and where monitoring by institutional blockholders is low. Moreover, the greater profitability of insider trading after UD laws comes from more opportunistic timing of trades. For instance, insiders engage in more profitable insider trading, both purchase and sales during pre-QEA period after UD laws. Overall, this study suggests that a decrease in shareholder-initiated litigation threat increases more serious types of insider trading in US firms. The second essay examines the ex-ante risk of credit rating change on firms' payout polices. My results suggest that firms near a credit rating change pay less dividend yields and are less likely to pay dividends compared to other firms. I find that firms that are on the border of their rating categories (POM) and on investment-speculative cutoffs (IG/SG) on average pay 0.09% and 0.20% less dividend yields respectively in the next quarter than other firms with similar underlying credit quality. These results are novel and are not obvious predictions of traditional theories of dividends. Furthermore, I find that POM firms are less likely to initiate a dividend and increase dividend yields compared to other firms. My results indicate that POM and IG/SG firms pay less dividend yields in all industries and almost every year from 1986 to 2016. Overall, my results show that firms with similar ability to pay dividends can have significantly different dividend payouts in response to their risks of rating change.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.169
Teacher spread0.155 · 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
Published2019
Admission routes1
Has abstractyes

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