Investment value of recommendations in the Italian stock exchange
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Financial analysts’ research activity seems to be important for investors in their investment decisions. Understanding if financial analysts’ reports can influence the market and the degree of reliability of their forecasts has been a theme lively debated in the academic literature but also in the press, mainly because of recent financial scandals. The main objective of the paper is to calculate the investment value of financial analysts’ recommendations on companies listed in the Italian Stock Exchange and to verify the possibility of profiting from relying on the average consensus of recommendations. We have enclosed in the analysis all the 16,634 reports issued between the 1st January 1999 and the 23rd July 2004 and available on the website of the Italian Stock Exchange, constructing a unique database for Italy. After classifying companies by quarter, five portfolios are formed based on analysts’ average consensus to calculate the excess returns of each portfolio in each quarter. Our results suggest that analysts’ recommendations have indeed investment value, even if investors should carefully consider neutral recommendations that can be considered as negative ones. These results, furthermore, give some interesting regulatory suggestions for a policy maker that wants to ensure transparency in the markets
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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 it