PENGARUH PROFITABILITAS DAN KEBIJAKAN DIVIDEN TERHADAP HARGA SAHAM DI SEKTOR PERBANKAN BURSA EFEK INDONESIA DIMASA PANDEMI COVID-19
Bibliographic record
Abstract
This study was conducted to determine the effect of profitability and dividend policy on stock prices during the Covid-19 pandemic separately in 2020 and 2021. This type of research uses a quantitative approach. The type of data in this study is secondary data obtained from the financial statements of the second quarter - fourth quarter 2020 and first quarter - third quarter 2021. The population in this study are companies listed on theIndonesia Stock Exchange. The sampling technique used is purposive sampling where the sample obtained in the study is 40 banking companies listed on the Indonesia Stock Exchange. This research method uses multiple linear regression analysis. This study examines separately between 2020 and 2021 to see the effect of dividend policy and ROE when the ups and downs of Covid-19 cases affect the JCI. The results show that profitability has a positive effect on stock prices, while dividend policy has no effect on stock prices during the Covid-19 pandemic. While there are two phenomena between 2020 and 2021, the high and low profitability still illustrates the company’s prospects that can attract investors so that it can affect stock prices. The results of this study can be a reference for investors to minimize investment risk before investing in stocks. Keywords: stock prices, profitability, dividend policy, Covid-19.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".