Bibliographic record
Abstract
This paper examines the performance of actively managed portfolios constructed using target price recommendations provided by analysts. We propose two methods for constructing portfolios based on Bloomberg’s 12-month target price consensus, which serves as a signal to buy or sell assets. Using a sample of 50 European stocks over a 19-year period (from 1 April 2004 to 31 March 2023), we compare the performance of target-price-based portfolios to traditional alternatives, such as a naïve homogeneous portfolio and the Eurostoxx 50 index, as well as to passive portfolios based on average recommendations. We also look into the mean-variance efficiency of these portfolios and find that all exhibit similar levels of efficiency, which are well below the performance of the theoretical tangent portfolios. Our results indicate that target-price-based portfolios show performance very close to that of the naïve homogeneous portfolio. Even the passive “average” target price portfolios, which require previous knowledge of targets for the entire investment period, are unable to outperform the naïve portfolio. Our main findings are based on a 15-year investment horizon but are robust when considering smaller maturities and out-of-sample data. We also investigate the impact of rebalancing on portfolio performance and find that it does pay off in the long run (over an 8-year investment period), but the frequency of rebalancing matters. Rebalancing only once a year is as detrimental to performance as not rebalancing at all. However, it is unclear whether the transaction costs associated with frequent rebalancing would offset any relative outperformance. Overall, our study contributes to the literature on portfolio management and market efficiency by demonstrating the potential benefits and limitations of using target price recommendations to construct portfolios, highlighting the importance of carefully considering rebalancing strategies to achieve optimal performance.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".