Prospects of Trade Management System (TMS) in Nepalese Stock Market: Evidence from Structural Equation Modelling
Bibliographic record
Abstract
This paper attempts to get opinions from the TMS users about its performance along with its main barriers in Nepalese stock market. Signaling theory and explanatory research design are adopted to seek causal relationship amongst online stock traders in Kathmandu valley. Following convenient sampling technique, 316 TMS users were interviewed with structured questionnaire; where data collection is done by using Kobo Toolbox. Structural Equation Modelling (SEM) is used to see the prospects of Trade Management System amongst TMS users using SPSS and SPSS AMOS software. Findings indicate that volume signals and online trading are significant causal relationship to investment behavior of TMS users in Kathmandu Valley which support Signaling Theory. Therefore, online trading would emerge as a more reliable investment platform and will provide large-scale investment possibilities in Nepalese stock market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".