Disclosure effectiveness in the financial planning industry
Bibliographic record
Abstract
Purpose Scandals in the Australian financial services industry highlight the conflicts of interest between those who provide financial advice (financial planners) and their clients. Disclosure is a potential governance tool to manage these conflicts of interest by reducing asymmetries in information. Yet, the efficacy of disclosure is questionable as scandals persist, so this paper aims to research the effectiveness of disclosure in financial planning. Design/methodology/approach This research used a qualitative approach involving the triangulation of data from parliamentary inquiries in financial services with data collected in semi-structured interviews with financial planning professionals. Findings The findings draw a clear portrayal of the disclosure requirements and illustrate how disclosure processes are onerous and complex. Starting with detangling the complex interactions between the beneficial role of disclosure in reducing information asymmetry and unethical behaviour and the detrimental effect of information overload, the authors then highlight effective disclosure techniques used by financial planners, including visualisation of material information. The study reveals that financial planners perceive their role as filtering information for clients and ensuring clients’ comprehension, due to the onerous disclosure requirements. Research limitations/implications The study is of interest to researchers, practitioners, policymakers and society as it implies that how disclosure occurs is as important as what information is disclosed. Those who wish to foster effective disclosure in the financial services industry need to consider the quantity, quality and process of disclosure. A limitation is the research focusses on financial planning practices and not client outcomes, which could be considered in future research. Originality/value The study adds to the understanding of how disclosure is used as a governance tool and how the quantity of information may impede the effectiveness of disclosure in the financial planning industry. In addition, the study identifies and elaborates on the influential factors and best practices for enhancing the disclosure effectiveness by financial planners.
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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 | 0.053 | 0.254 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".