Value Investing in Brazil: A Novel Application of Benjamin Graham’s Criteria to Generating Abnormal Returns
Bibliographic record
Abstract
Objective: This study aimed to adapt Benjamin Graham’s criteria to the Brazilian stock market, using a ranking strategy to build winning portfolios that offer abnormal returns. Method: We have collected data from all companies traded on the stock exchange in Brazil between the 4th quarter of 1998 and the 2nd quarter of 2020. Graham’s criteria were adapted using each indicator’s quarterly median and sector-wise. We employed the Greenblatt (2006) ranking strategy in the portfolio construction. Results: We employed the five-factor asset pricing model to analyze the abnormal returns of the portfolios. Our findings indicate that portfolios formed with the adapted criteria consistently outperformed the market average. Notably, the portfolios with 10, 20, and 30 assets demonstrated superior returns compared to the Ibovespa, IBrX 100, and LFTs, with the 10-asset portfolio generating the highest Alpha. Contributions: This research advances the literature on value investing in emerging markets by adapting Benjamin Graham’s criteria to the Brazilian context using quarterly sector medians and a ranking strategy. The study demonstrates the potential for generating abnormal returns, outperforming benchmarks such as the Ibovespa and IBrX 100. It underscores the importance of periodic adjustments and sector-specific adaptations, providing valuable insights for investors applying fundamental analysis in emerging markets. These contributions bridge traditional value investing principles with the unique dynamics of emerging markets, aiding in more informed portfolio management decisions.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".