Notice bibliographique
Résumé
Financial analysts play an intermediary role in financial markets, resulting in two steps for information to be fully absorbed into the stock price: analysts’ reaction to information, and investors’ reaction to analysts’ recommendations. Thus any observed inefficiency in stock pricing could result from two possibilities: analysts failed to fully incorporate the market information into their stock analysis, or the information released in the analysts’ report is not fully believed by investors. The documented optimism of financial analysts may suggest the possibility of the later case. To test the accuracy of analysts from another perspective, we follow a market microstructure model and use intraday market data to estimate the probability of an information event, the probability of good or bad news, and the rates that different traders arrive at the market. By comparing those estimates based on days with and without recommendation changes, we find inconsistent results with regard to a difference in the probability of an information event. For some stocks, we do observe an increase in the likelihood of news on days when analysts change their recommendations, but this is not the case for most stocks. However, even though they are inaccurate most of the time, uninformed investors usually believe financial analysts. Furthermore, it seems that uninformed investors disbelieve analyst recommendation changes at those instances when analysts are most accurate. Because of this, we hypothesise that market makers might suspect that orders in the opposite direction of an analyst’s recommendation change are more likely to come from informed traders. This is consistent with the intuition that most traders are uninformed and will simply follow the advice of a perceived expert, and therefore those that don’t follow that advice may be more likely to have special information of their own. We check whether there are any differences in the probability of information-based trading (PIN) and for the conditional probability of information-based trading conditioned on sell (PIN|sell) and buy (PIN|buy) between days with and without recommendation changes. We did not find any significant difference, indicating that although we may observe a higher arrival rate of informed traders on recommendation change days, the probabilities of information-based trading do not change substantially. More informed traders seem to come to the market merely because the higher arrival rate of uninformed traders on recommendation days gives them a good opportunity to camouflage their behaviour. And the specialists likely would not have to change their behaviour on those days by increasing or shifting bid-ask spreads since the increased costs from the higher volume of informed trading are balanced by increased profits from the higher volume of uninformed trading. Furthermore, regression of the probabilities of informed trading (conditional or unconditional) on firm size, trading volume, and volatility of daily return shows nothing significant, so we weren’t able to identify influential factors that affect informed trading or explain differences in informed trading between firms.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,206 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».