PREDICTING COMPANY PERFORMANCE BY DISCRIMINANT ANALYSIS
Bibliographic record
Abstract
This paper aims at evaluating the performance through discriminant analysis of 20 companies traded on the Bucharest Stock Exchange (BVB). As these companies are similar in terms of business profile (manufacturing industry), we choose ten financial indicators that relate to stock value (PRICE, BETA, ALPHA, etc.) and book value (Debt / Equity, ROA and ROE) to assess and classify the companies as good or bad. For a sustainable characterization the average value of the financial indicators is estimated between the first quarter of 2005 and third quarter of 2013. The initial grouping is made according to return on assets (ROA) and splits the sample into 10 “good and 10 “bad companies. We find that discriminant analysis correctly validates the classification of firms by ROA criterion in 90% of cases (18 of 20 companies). Moreover, our analysis establishes that ROA is of first importance in evaluating company performance as suggested by the F test-statistic and Wilks'Lambda coefficient.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 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 | 0.001 |
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".