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Record W4323569326 · doi:10.3390/jrfm16030179

Case Study: Impact of Regulatory Restrictions and Tax Policy on Breakeven Analysis and Risk Management

2023· article· en· W4323569326 on OpenAlexvenueno aff
James Henry Dunne, Peter Harris, Katherine Kinkela

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProfitability indexContext (archaeology)RevenueTax revenueCrisis managementRisk managementTax policyFinanceEconomicsActuarial sciencePublic economicsTax reform

Abstract

fetched live from OpenAlex

The objective of this case study is to enable students to analyze the financial impact of an unexpected catastrophic event on a retail business and how the strategic operational decisions made in response to regulatory restrictions and changes in tax policy impact the business’s risk tolerance and breakeven analysis. To provide students with a context for comparison, this case study provides students with the opportunity to analyze the financial statements of a retail business prior to the occurrence of an unexpected catastrophic event, how the catastrophic event impacted revenue and profitability, and how the risk reduction strategies the business employed contained the adverse impact of the factors brought on by that catastrophic event on breakeven. This case study addresses a core gap in the body of knowledge by analyzing a business in three distinct stages of the business life cycle: (1) in the start-up phase; (2) in pre-crisis operations mode; and (3) in crisis mode confronted with an unexpected catastrophic event amidst governmental shutdowns, state and federal regulatory restrictions, and emergency changes to the tax policy. Examining a fictitious restaurant (a composite of the sales statistics of three actual restaurants located in Long Island, New York from 2019 to 2021) in operation for one year prior to the COVID-19 pandemic, students are given the opportunity to think critically about how strategic operational decisions made to generate sales, to minimize risk, to comply with mandated state government policy, and to take advantage of federal tax relief policy, collectively changed the financial projections and impacted the breakeven analysis of that business. Students are able to evaluate business start-up costs, first year (pre-pandemic) sales and costs, and second year (during pandemic) sales and costs of a retail business. Students then evaluate how the United States’ federal PPP relief loan program, along with other pandemic relief programs for businesses and individuals, impacted profitability and business strategy. Students also assess risk, risk tolerance, and how the strategies employed to minimize risk impact a business. The motivation for this case study is to provide students with an illustrative example of an entity at various stages of the business life cycle, to explore the surrounding context of the impact of external environmental events, and to identify the effects of strategic operational responses to the various regulatory changes that were brought on by a catastrophic event. This case study is designed for use in courses that study revenue projection, tax, internal controls, breakeven analysis, and risk management. Teaching Note: While this case study uses a restaurant as a model, prior understanding of the restaurant industry is not necessary. Any student or instructor can use their practical knowledge and experience as a consumer to adequately analyze the issues presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.023
GPT teacher head0.279
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

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