Factors Influencing Muslim Investors to Invest in Sharia Stocks during the Covid-19 Pandemic
Bibliographic record
Abstract
There had been a notable increase in the number of capital market investors in 2020 and 2021 as compared to previous years. In 2020, the number of investors increased by 56.21% from that of the previous year in 2019, and in 2021 it increased to 92.99% as compared to the number in 2020. Such considerable increase was surprising despite the outbreak of Covid pandemic throughout those years, which had an impact on the downturn of community's economy, as seen from an increase in the amount of unemployment by 7.07%, and a poverty rate of 9.8%. Likewise, Indonesia experienced negative economic growth in the quarter II and III of 2020. This study aims to find the factors that influence investors in making investment decisions amidst pandemic conditions that took place in 2020 and 2021 with the variables of Financial Literacy, Influencer’s Influence, Social Media Influence, social environment, and religiosity. It was obvious that these 5 factors had an influence on investment decisions, with the Financial Literacy factor as the factor that had the greatest influence and significance with a regression coefficient of 0.721.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".