Comparison of Three Different Companies in the US. Retail Industry Based on Beta (β-risk) Analysis and Financial Statement Analysis: Costco vs. Walmart vs. Target
Bibliographic record
Abstract
The purpose of this paper is to analyze the capital structure, business risk (levered vs. unlevered beta β), and financial statements in the U.S. grocery retailers to determine how their unique capital structure, risks, and financial characteristics explain the differences in their performance and investment returns. We chose three U.S. grocery stores: Costco, Walmart, and Target, each with a unique business structure. We conducted a detailed beta (β) analysis, both leveraged and unleveraged, as well as a dedicate financial statement analysis focused on ratio analysis. Liquidity, profitability, and solvency abilities were examined to determine if they depend on each retailer’s specific capital structure, risks, and characteristics, and how they would affect investors’ investment decisions. Our results reveal that the capital structure, or the level of financial leverage, and size of market capitalization play key roles in determining a company’s levered and unlevered beta (β). In addition, we found that Target has the highest investment return but exhibits substantially weaker sales and has a high liquidity risk. Costco exhibits significant low margins in both gross and operating, but has a lower level of debt and a greater ability to generate free cash flow. Walmart has the largest market capitalization, the lowest business risk (β) and return, but the large amount of debts it currently holds limits its free cash flow and ability to grow rapidly. Above all, it is advisable to invest in Costco rather than Target and Walmart under today’s unprecedented (post) Covid-19 pandemic context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".