The effect of essential information and disposition effect on shifting decision investment
Bibliographic record
Abstract
The current pandemic era has given uncertainty to the country's economic growth and resulted in many countries experiencing a drastic decline in share prices. This condition impacts investors' perceptions of the funds that have invested in the stock market. This study investigates the effect of essential information and disposition effect on shifting decision investment with the character investor's moderation as the moderator variable. A survey was conducted on 252 investors who have invested in the Indonesian stock exchange. The Data processing used the partial least square (PLS) technique. This study indicates that essential information for investors in the pandemic era can increase the disposition effect in deciding beneficial share ownership. The essential information obtained by investors in the covid era regarding stock market movements and its internal performance in the stock market list can increase investor shifting decisions. The disposition effect can have a significant effect on shifting decision investors. Essential information related to stock price movements and its internal performance affects investors' courage to take risks and provide optimism for shifting decisions. Then the investor type does not affect the disposition effect on shifting decisions. This study contributes to the theory of financial behavior in decision making by considering psychological factors when uncertainty exists in the stock market.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".