The Impact of Investors’ Sentiment on the Equity Market: Evidence from Ghanaian Stock Market
Bibliographic record
Abstract
Investor’s Sentiment plays a major role in choosing which stocks we invest. Investors’ sentiment can be defined as investors’ attitude and opinion towards investing in the Stocks. The aim of this research is to analyse the individual investor’s sentiment and also to analyse the influence of Market Specific Factors on investors’ sentiment. The investor’s attitude towards investing is influenced by rumours, intuition, herd behaviour among investors and media coverage of the stock. 100 investors in Ghana were chosen for the study. These investors were administered a Structured Schedule, containing pre-validated scales to measure the investor sentiment. Once the constructs were found to be both reliable and valid, the impact of Herd Behaviour, Internet Led Access to Information and Trading, Macro Economic Factors, Risk and Cost Factors, Performance Factors and Confidence Level of Institutional Investors, Best Game in Town Factors were tested by using the Bootstrapping method. Few Market Specific Factors had a significant impact on the investors’ sentiment in Ghana.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".