Return Saham, Likuiditas Saham, EPS, dan PER Sebelum dan Sesudah Melakukan Stock Split pada Perusahaan yang Terdaftar di Bursa Efek Indonesia
Bibliographic record
Abstract
Kenaikan harga yang terlalu tinggi, akan menyebabkan permintaan terhadap pembelian saham tersebut mengalami penurunan dan pada akhirnya dapat meyebabkan harga saham tersebut menjadi tidak fluktuatif lagi. Untuk \nmenghindari kondisi tersebut, maka yang dilakukan oleh perusahaan adalah menurunkan harga saham pada kisaran harga yang menarik minat investor untuk membeli yaitu melalui pemecahan saham (stock split). Penelitian mengenai stock \nsplit sudah sering dilakukan sebelumnya, seperti penelitian terdahulu yang dilakukan oleh Naomi Stephanie Saurake dan Tanti Irawati Muclis (2012) yang berjudul Stock Split Peformance Analysis Before and After Stock Split on Basic \nIndustry and Chemical in Indonesia mengemukakan bahwa stock split berpengaruh signifikan terhadap return saham dan likuiditas saham sedangkan EPS dan PER tidak berpengaruh atas peristiwa stock split. \nPenelitian ini menunjukan bahwa penulis memaparkan hipotesis pertama dan kedua terdapat pengaruh yang signifikan terhadap rata-rata return saham dan likuiditas sebelum dan sesudah stock split. Hipotisis ketiga dan keempat menunjukan tidak ada pengaruh yang signifikan rata-rata EPS dan PER sebelum dan sesudah stock split.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".