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Enregistrement W2771177222 · doi:10.55016/ojs/sppp.v10i1.42920

Why Banning Embedded Sales Commissions Is a Public Policy Issue

2017· article· en· W2771177222 sur OpenAlexaffabout
Henri-Paul Rousseau

Notice bibliographique

RevueThe School of Public Policy Publications · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueICT Impact and Policies
Établissements canadiensCapital Power (Canada)
Organismes subventionnairesnon disponible
Mots-clésBusinessPublic administrationLaw and economicsPublic relationsPolitical scienceEconomics

Résumé

récupéré en direct d'OpenAlex

Regulatory authorities have consulted on the option of banning embedded sales commissions for Canadian financial advisors. Such an action would create more problems than it would solve. It would have serious ramifications for Canadians’ access to financial advice and raise issues of choice, industry concentration and price transparency for clients seeking advice on investments and retirement. Financial advisors have much greater knowledge of investments than their clients, who rightly expect value from their advisors’ services. Advisors may also face conflicts of interest when they make recommendations about a financial product whose manufacturer might be paying the advisor for selling its products. Banning sales commissions from the manufacturers and having the client pay the advisor directly instead brings its own problems. This is because financial advice is a good with peculiar characteristics. Firstly, financial advice has three fundamental components – the alpha, beta and gamma factors. Together, they define the roles financial advisors play: (alpha) asset or portfolio manager, (beta) asset allocator (rebalancing a client’s portfolio), and (gamma) coach with regard to savings discipline and financial planning. Financial advice has value thanks to the interplay between the three factors. Studies of the issue which have focused on one factor at a time, usually the alpha, produce results that are skewed; however, when studies measure all three factors, the evidence shows that financial advice has significant value, greater than the usual cost charged to clients. Secondly, financial advice is an “experience good”, meaning that clients don’t know ahead of time how good financial advice is until they see how it works out. Assessing the value of financial advice may take many years. Since they can’t immediately measure what they’re paying for, clients with modest incomes or wealth are usually willing only to pay low fees, or not pay at all up front. This means that banning embedded commissions would lead to a reduction in demand for advice from modest-income households. The U.K. provides an example which should not be followed. Regulators there have banned embedded commissions, forcing clients to pay directly for financial advice. The result is that modest-income clients have decided not to seek financial advice, even though that decision will likely negatively affect their portfolios. The dangers of this “advice gap” are being downplayed by those who believe that robo-advisors and banks can fill the need instead. In fact, robo-advisors and banks are mostly not equipped to step into the gamma role of coaching their clients. A ban would also mean less choice in the market for a service that needs to be competitive and innovative to serve the broad spectrum of clients’ circumstances, risk appetites and needs. In addition, smaller and independent product manufacturers and distributors would be squeezed out, creating a market concentration in the hands of the bigger players. Pricing transparency might very well be another victim of a ban as a market with significant disparities in fee levels is created. In crafting their policies, regulatory authorities should bear in mind that people need to have wide access to financial advice and to have an opportunity to become more financially literate. Keeping the market for financial services and products competitive, innovative and transparent is the path to continued success. A ban on embedded sales commissions would severely hamper these goals.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,027
score de la tête « metaresearch » (Gemma)0,096
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,672
Score d'incertitude au seuil0,660

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0270,096
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0120,015
Communication savante0,0150,014
Science ouverte0,0050,004
Intégrité de la recherche0,0280,023
Charge utile insuffisante (le modèle a refusé de juger)0,0340,005

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.

Tête enseignante Opus0,054
Tête enseignante GPT0,341
Écart entre enseignants0,288 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission2
Résumé présentoui

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