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Record W7062142727

Three Essays on the Effect of External Business Environment on Corporate Investment

2017· article· en· W7062142727 on OpenAlexaboutno aff

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersUniversity of Cincinnati
KeywordsQuarter (Canadian coin)PensionVolatility (finance)Corporate governanceInvestment (military)Stock marketPortfolioStock (firearms)Investment styleStock exchange
DOInot available

Abstract

fetched live from OpenAlex

This dissertation consists of three related essays on corporate investment.In Essay I, I examine how the characteristics of the country in which firms operate affect firm's "fourth quarter effect".In Essay II, I study how firm's local risk preference influence their acquiring decisions.And in Essay III, I investigate the relation between country laws and institutions and corporate pension underfunding, as well as the effect of pension underfunding on corporate investment and valuation.Essay I: It is documented that companies and government agencies in the United States invest more in the fourth fiscal quarter without having higher investment opportunities.While previous studies focus on the agency conflicts and information asymmetry within organizations, we are motivated by Scharfstein and Stein's (2000) two-tiered agency model, and examine how firms' external business environment affects the "fourth quarter effect".We implement our study in a sample of 41 countries, and observe similar seasonality in firm investment as documented in the U.S. market.More importantly, using country characteristics, we find that firms from countries with better investor rights and protection, and more developed financial markets show less severe over-investment in the fourth fiscal quarter.Essay II: This essay studies how managers' risk aversion, captured by the local religious beliefs prevailing in the locations of their firms, could affect firms' merger and acquisition activities.We find that firms headquartered in areas with higher Catholics-to-Protestants (CP) ratio, reflective of stronger gambling preferences, tend to conduct more acquisitions.Moreover, these acquisitions are more likely to be international and conglomerate, and more likely to involve targets with high stock return volatility and R&D spending.The acquisitions are also associated with value destruction from the perspective of the acquiring firm's shareholders and the effect is magnified in an environment of weaker corporate governance.When we interact CP ratio with executive's career risk, we find that local gambling preference significantly promotes managerial risk-taking in mergers and acquisitions.iii Essay III: Financial crises, low interest rate, and extended life expectancy cast doubt on whether corporate defined benefit (DB) pensions are sufficiently funded to cover their liabilities.We study the effect of pension underfunding on corporate investment using international data involving 29 countries at heterogeneous economic development stages and different regulatory environments.We document that corporate pensions are significantly underfunded in most countries of our sample in the period of 2001 to 2015.Pension assets are on average less than 50% of pension liabilities among firms that provide DB pension plans.To the extent of pension underfunding effect on corporate investment, we find that the (inverse) relationship is subject to variation in country characteristics.Specifically, firm's investment is more severely constrained by pension underfunding in countries with stronger labor union power and less developed financial market.Finally, while substantial pension underfunding has a negative impact on firm value, our results show that offering more DB pension plans do not reduce firm value.Overall, our findings suggest that DB pension plans could still play a useful role in creating firm value.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.012
GPT teacher head0.204
Teacher spread0.191 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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