Bibliographic record
Abstract
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.
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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 | 0.027 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".