ANALISIS FUNDAMENTAL SAHAM DI SEKTOR ASURANSI ( Studi Kasus Di Bursa Efek Indonesia 2014-2017)
Bibliographic record
Abstract
This study aims to determine the prospect of growth and fair value of a stock by using fundamental analysis with EPS method (Earning Per Share and PER (Price To Earning Ratio) in determining an investment.The research method used is by using descriptive method with qualitative approach from the Report Annual Finance in 7 companies in the insurance sector. The result of this research is that the insurance sector experienced a fairly slow growth but still produce earnings.Experienced rankings of 7 companies in the insurance sector in 2018 quarter was ASRM of 283.95 medium the lowest PER level is ASDM of 5.15x. But if viewed from the market capitalization and the number of shares in circulation then the fair value is ASDM with the result then if you have to choose then that will be selected is a low PER with the number of shares outstanding smaller . The Per To Earning Ratio The ratio to PBV (Price Book Value) results in a decision to buy or sell stock prices. Keywords: Fundamental Analysis, PER and PBV
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".