Analisis Pangaruh Asset Growth, Total Asset Turnover, Firm Size, Operating Leverage, Dan Financial Leverage Terhadap Beta Saham (StudiKasusPerusahaan Finansial yang terdaftar di Bursa Efek Indonesia Periode 2013-2016)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk memperolah bukti secara empiris pengaruh, Asset Growth, Total Asset Turnover, Firm Size, Operating Leverage , dan Financial Leverage terhadap Beta Saham (Studi Kasus Perusahaan Finansial yang terdaftar di Bursa Efek Indonesia Periode 2013-2016).Berdasarkan purposive sampling diperoleh 28 pada Perusahaan Finansial di Bursa Efek Indonesia (BEI) sebagai sampel dalam penelitian ini dan periode pengamatan dari tahun 2013 sampai tahun 2016. Dalam pemecahan masalah peneliti memakai uji asumsi klasik dan uji hipotesis dengan analisa regresi berganda. Hasil perhitungan, pengujian, dan pembahasan membuktikan bahwa Asset Growth, Total Asset Turnover, Operating Leverage, dan Financial Leverage berpengaruh positif dan Signifikan terhadap Beta Saham,sedangkan Firm Size berpengaruh negatif dan Signifikan terhadap Beta Saham. Kata kunci :Beta Saham, Asset Growth, Total Asset Turnover, Firm Size, Operating Leverage , dan Financial Leverage
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".