All sessions will take place in Central Daylight Time!
POSTER PRESENTATIONS:
Poster presentations are available to view from May 18 through May 23 via ACCI’s (unlisted) YouTube Channel Poster Playlist sent out with the Zoom meeting room links to all attendees.
Poster are listed in this schedule on the last day at 6 PM so they appear at the end. Please scroll to the end to find the poster numbers, titles, descriptions, and presenters.
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Researchers have increasingly relied on regulatory data to analyze financial advisor misconduct. While these data are promising for the purposes of better understanding how regulators can best protect consumers, little is known about how data limitations may systematically bias the results of such analyses. Using a dataset of FINRA-licensed individuals that was enriched to include variables not present in the publicly-available FINRA data, a series of binary logistic regressions are used to illustrate how unobserved differences may bias misconduct analyses. When using CFP® status as the sole predictor of misconduct, CFP® professionals are found to be 86% percent more likely to have engaged in culpable advisory-related misconduct compared to non-CFP® professionals. However, we present evidence that this relationship is spurious. After controlling for other factors and using the enriched data to limit the analysis to only those solely working as financial advisors, CFP® professionals are found to be 20% less likely to have engaged in culpable advisory-related misconduct. Because job classifications are generally not available in the standard SEC and FINRA datasets, these findings illustrate how the inability to control for job classifications (or other unobserved differences) may bias misconduct analyses relying solely on regulatory data.
Author(s): Derek Tharp, Steven Lee, Jeffrey Camarda, Pieter de Jong