A deeper look into value investing's future prospects
Bibliographic record
Abstract
Since value investing’s golden era, many things have changed: from the way individuals invest their savings, to the diversity of financial products available on the market, from the hierarchical and financial structure of the majority of the firms to the way how business is done, from the regulations that rule firms and the market, to the world economy in general, and finally, of course: the very philosophy behind value investing has also progressively changed. Only one thing does not seem to have changed: the performance criteria used to evaluate this investment theory. In recent years, when applied these criteria, the academia noticed value investing releveled relatively poor performance compared to what investors and the market were used to. But can these results be blindly trusted? When everything does seem to have changed, does it make sense to expect valid results applying the same criteria to completely different realities? That is what we propose ourselves to find out. In the present work we will provide an alternative framework to the one used by the academia over the years. We will expose some of the reasons that may motivate this (apparent) underperformance, and alternatives to overcome these difficulties. Our intention is clear: to evaluate if value investing has really lost its hedge, or if on the other hand, academics have just been measuring performance with outdated and unfitted criteria to the current reality.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".