Creating quality portfolios using score-based models: a systematic review
Bibliographic record
Abstract
Abstract This paper aims to find out if a score-based investment strategy could be developed using different scales. To achieve this objective several academic sources have been used and it is found that score-based investment not only outperforms the market but also protects the investors from the risks arising out of avoidable poor investments in the market. The project is a summary of bibliographic outcome of several scholars who have attempted to find out the impact of score-based investments in their respective markets. Score-based investments are typically dependent on accounting parameters and changes in these parameters signal that a firm’s performance is geared up for a change. The study has been done using a systematic literature review. Several research papers in peer-reviewed journals were referred starting from 1934 to 2021. Various equity-based scores like F-score, G score, L score and C score and debt-based scores like Z score, O score and M score are used for the construction of portfolios. It has been found that across geographies the use of score-based investing is known to give superior returns as compared to the market. Several pieces of literature provide the evidence. Developed countries like USA, UK, Australia, and Canada have a large concentration of literary sources that point to the evidence of score-based investing. At the same time, it is also pertinent to note that the performance of such techniques works relatively better in markets that are not efficient and where asymmetry in information flow is evident.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".