Bibliographic record
Abstract
The market effi ciency theo ry assumes that all securities are correctly priced. However, \n \n \n \nmost stockbroking firms do provid e stock recommendations which aim to ' beat the \n \n \n \nmarket'. Hence, if the market is efficient, one wonders what the economic value of \n \n \n \nsecurity analysis is. \n \n \n \nThe performance of security analysts ' recommendations has been extensively studied in \n \n \n \nAustralia, Canada, United Kingdom and the United States. Most of these research \n \n \n \nstudies examine the value of stock recommendations using form of residual analysis, \n \n \n \nsuch as market adjusted returns and risk adjusted abnormal returns. \n \n \n \nThis Applied Research Project evaluates the performance of Singapore security \n \n \n \nanalysts ' recommendations. Two hundred and eighty-six buy recommendations by four \n \n \n \nstockbroking firms from January 1990 to December 1992 were selected and analysed. \n \n \n \nMarket adjusted returns and risk adjusted abnormal returns were used as performance \n \n \n \nmeasures for the recommendations. \n \n \n \nFrom our study, we found that not all stockbroking firms could add value in their \n \n \n \nrecommendations. Among the four stockbroking firms, only one firm managed to \n \n \n \noutperform the market consistently for all the investment horizons. Furthermore, by \n \n \n \ncomparing between the three strategies based solely on returns, it was observed that \n \n \n \ngenerally, the short-term strategy was the best. However, there was no strategy that \n \n \n \nwas superior to others in terms of both risk and returns.It can be inferred from this study that ranking might exist among stockbroking firms in \n \n \n \nthe industry. The ranking will, in turn, motivate the stockbroking firms to excel in their \n \n \n \nperformance. This will definitely benefit the investing community in the long-term, as \n \n \n \nthey will be able to obtain more valuable recommendations from the stockbroking \n \n \n \nfirms.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".