Other Corporate Information Sources Usage: Evidence from Jordanian Individual Investors
Bibliographic record
Abstract
This study aims to provide insight into of the extent of usage of other corporate financial information sources by Jordanian individual investors in taking their investment decisions in Amman Stock Exchange (ASE) in comparison with annual corporate reports. The study also aims to identify the main reasons for using sources of information other than corporate annual reports. The result of study revealed that corporate annual report was the most used sources of information. This followed by published daily share price, newspapers and magazines, corporate web sites, advice of friend, tips and rumours, stockbrokers’ advice and discussion with company staff respectively. These results indicated that Jordanian individual investors put more emphasis on the usage of the written sources than verbal sources. The results also indicated that Jordanian individual investors start to give more attention to the usage of electronic sources as the corporate web sites ranked forth. In respect to reasons that encouraged investors to use sources of information other than corporate annual reports, the results indicated that the first three reasons include; easier to get information, containing new information and giving up-to-date information. These reasons form the features of the written sources which were indicated as the most used sources of information.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".