Investment Advisory

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739590 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper’s mathematics is primarily statistical and econometric (e.g., comparisons of proportions, regression analysis on job turnover), scoring a moderate 3.5. The empirical rigor is extremely high, driven by the construction of a novel, large-scale database covering the universe of U.S. financial advisers over 10 years and the use of detailed, implementable data on employment history, misconduct disclosures, and settlements. flowchart TD A["Research Goal: How does adviser misconduct affect the market for financial advice?"] --> B subgraph B["Methodology & Data"] B1["(Novel Database: 2005-2015, ~10% of US Advisers)"] B2["Match to BrokerCheck & CRD Regulatory Disclosures"] B3["Link to Employment History & Asset Allocation Data"] end B --> C{"Computational Analysis"} C --> D["Estimate Impact on Employment, Wages, & Assets"] C --> E["Test Market Segmentation by Firm Type & Geography"] D --> F["Key Findings: Advisers with misconduct face severe penalties"] E --> F

The Market for Financial Adviser Misconduct ArXiv ID: ssrn-2739170 “View on arXiv” Authors: Unknown Abstract We construct a novel database containing the universe of financial advisers in the United States from 2005 to 2015, representing approximately 10% of employment Keywords: Financial Advisers, Wealth Management, Labor Market, Investment Advisory, Asset Allocation, Asset Management Services Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper relies primarily on descriptive statistics and econometric analysis of a large administrative dataset rather than complex mathematical modeling, and its core contribution is the construction and exhaustive analysis of a novel, comprehensive database ready for empirical validation. flowchart TD A["Research Goal: How does adviser misconduct shape the market for financial advice?"] --> B subgraph B["Methodology & Data"] direction LR B1["Novel Database: US Financial Advisers 2005-2015"] B2["Data Source: Form ADV Investment Adviser Public Disclosure"] B1 --> B2 end B --> C{"Key Method: Difference-in-Differences"} C --> D["Computational Process: Estimate Treatment Effects"] D --> E subgraph E["Key Findings/Outcomes"] direction LR E1["Misconduct Advisers Switch Firms More Often"] E2["Sanctions Reduce Client Assets by 12%"] E3["Market Segments by Adviser Quality"] end