Companies Identified as Having the Greatest Returns to Capital in Emerging Markets During a Worldwide Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic of the year 2020 resulted in high unemployment, business closings, property loss and decimation of individual wealth, disruption of global supply chains, and illness and deaths everywhere, but most intensely in countries classified as emerging markets. However, during this year, cash flow from investors in established markets to emerging markets has been of immense magnitude. While many companies, in emerging markets, reported very high risk-adjusted rates of return, many others reported so low rates during this period. This study aims to establish a unique profile of risk-return characteristics of the companies in emerging markets that have constantly reported the highest risk-adjusted returns to total capital during the pandemic. The statistical results of our study suggest that such unique profile can be used as a tool to forecast which companies, in such markets and during such disturbances in the future, will maintain high returns to capital providing an invaluable tool for investors, investment counselors and financial researchers tasked to determine firm’s intrinsic value in such an environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".