The Market for Financial Adviser Misconduct

79 Pages·2017·0.79 MB·English

NBER WORKING PAPER SERIES
THE MARKET FOR FINANCIAL ADVISER MISCONDUCT
Mark Egan
Gregor Matvos
Amit Seru
Working Paper 22050
http://www.nber.org/papers/w22050
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
February 2016, Revised September 2017
We thank Sumit Agarwal, Ulf Axelson, Jonathan Berk, Jörn Boehnke, Douglas Diamond, Steve
Dimmock, Alexander Dyck, Michael Fishman, Mark Flannery, Will Gerken, Erik Hurst, Anil
Kashyap, Brigitte Madrian, Robert MacDonald, Lasse Pedersen, Jonathan Sokobin, Amir Sufi,
Vikrant Vig, Rob Vishny, Luigi Zingales, and the seminar participants at the Becker Friedman
Institute Industrial Organization of the Financial Sector Conference, the NBER Corporate
Finance, NBER Summer Institute, NBER Household Finance, NBER Risk of Financial
Institutions, CSEF-EIEF-SITE Conference on Finance and Labor, Mitsui Michigan Conference,
LBS Summer Symposium, Society for Economic Dynamics Meetings, the University of
California Berkeley, Boston College, Columbia University, the University of Chicago, Harvard
Business School, London School of Economics, London Business School, the University of
North Carolina, the Massachusetts Institute of Technology, the University of Minnesota, New
York FED, New York University, FINRA, Oxford University, SEC DERA, SEC Enforcement,
Stanford University, University of Virginia, Wharton, and Yale. The views expressed herein are
those of the authors and do not necessarily reflect the views of the National Bureau of Economic
Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2016 by Mark Egan, Gregor Matvos, and Amit Seru. All rights reserved. Short sections of text,
not to exceed two paragraphs, may be quoted without explicit permission provided that full
credit, including © notice, is given to the source.
The Market for Financial Adviser Misconduct
Mark Egan, Gregor Matvos, and Amit Seru
NBER Working Paper No. 22050
February 2016, Revised September 2017
JEL No. D14,D18,G24,G28
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 of the finance and insurance
sector. We provide the first large-scale study that documents the economy-wide extent of
misconduct among financial advisers and the associated labor market consequences of
misconduct. Seven percent of advisers have misconduct records, and this share reaches more than
15% at some of the largest advisory firms. Roughly one third of advisers with misconduct are
repeat offenders. Prior offenders are five times as likely to engage in new misconduct as the
average financial adviser. Firms discipline misconduct: approximately half of financial advisers
lose their jobs after misconduct. The labor market partially undoes firm-level discipline by
rehiring such advisers. Firms that hire these advisers also have higher rates of prior misconduct
themselves, suggesting “matching on misconduct.” These firms are less desirable and offer lower
compensation. We argue that heterogeneity in consumer sophistication could explain the
prevalence and persistence of misconduct at such firms. Misconduct is concentrated at firms with
retail customers and in counties with low education, elderly populations, and high incomes. Our
findings are consistent with some firms “specializing” in misconduct and catering to
unsophisticated consumers, while others use their clean reputation to attract sophisticated
consumers.
Mark Egan Amit Seru
Harvard Business School Stanford Graduate School of Business
Baker Library 365 Stanford University
Boston, MA 02163 655 Knight Way
[email protected] ¸˛Stanford, CA 94305
and NBER
Gregor Matvos [email protected]
McCombs School of Business
University of Texas at Austin
2110 Speedway, Stop B6600
CBA 6.724
Austin, TX 78712
and NBER
[email protected]
1 Introduction
American households rely on (cid:28)nancial advisers for (cid:28)nancial planning and transaction services. Over 650,000
registered (cid:28)nancial advisers in the United States help manage over $30 trillion of investible assets, and
represent approximately 10% of total employment of the (cid:28)nance and insurance sector (NAICS 52; Coen
1
2015). As of 2010, 56% of all American households sought advice from a (cid:28)nancial professional (Survey of
ConsumerFinances,2010). Despitetheirprevalenceandimportance,(cid:28)nancialadvisersareoftenperceivedas
dishonest and consistently rank among the least trustworthy professionals (e.g., Edelman Trust Barometer
2015, Prior 2015, Zingales 2015). This perception has been largely shaped by highly publicized scandals
that have rocked the industry over the past decade. While it is clear that egregious fraud does occur in
the (cid:28)nancial industry, the extent of misconduct in the industry as a whole has not been systematically
documented. Moreover, given that every industry may have some bad apples, it is important to know how
the (cid:28)nancial industry deals with misconduct. In this paper we attempt to provide the (cid:28)rst large-scale study
that documents the economy-wide extent of misconduct among (cid:28)nancial advisers and (cid:28)nancial advisory
(cid:28)rms. We examine the labor market consequences of misconduct for (cid:28)nancial advisers and study adviser
allocation across (cid:28)rms following misconduct. Lastly, we provide an explanation that is consistent with the
facts we document.
To study misconduct by (cid:28)nancial advisers, we construct a panel database of all (cid:28)nancial advisers (about
1.2 million) registered in the United States from 2005 to 2015, representing approximately 10% of total
employment of the (cid:28)nance and insurance sector. The data set contains the employment history of each
adviser. We observe all customer disputes, disciplinary events, and (cid:28)nancial matters reported by FINRA
from advisers’ disclosure statements during that period. The disciplinary events include civil, criminal,
and regulatory events, and disclosed investigations, which FINRA classi(cid:28)es into twenty-three disclosure
categories. Because disclosures are not always indicative of wrongdoing, we conservatively isolate six of the
twenty-threecategoriesasmisconductincludingregulatoryo(cid:27)enses,criminalo(cid:27)enses,andcustomerdisputes
that were resolved in favor of the customer.
Inthe(cid:28)rstpartofthepaper,wedocumenttheextentof(cid:28)nancialmisconductamong(cid:28)nancialadvisersand
(cid:28)nancial advisory (cid:28)rms. We (cid:28)nd that (cid:28)nancial adviser misconduct is broader than a few heavily publicized
scandals. One in thirteen (cid:28)nancial advisers have a misconduct-related disclosure on their record. Adviser
misconduct results in substantial costs; the median settlement paid to consumers is $40,000, and the mean
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is $550,000. These settlements have cost the (cid:28)nancial industry almost half a billion dollars per year.
Relativetomisconductfrequency,misconductistooconcentratedamongadviserstobedrivenbyrandom
1We will use the term (cid:16)(cid:28)nancial adviser(cid:17) throughout the paper to refer to representatives registered with the Financial
IndustryRegulatoryAuthority(FINRA).FINRAisthelargestself-regulatoryorganizationthatisauthorizedbyCongresswith
protecting investors in the U.S. Our de(cid:28)nition, similar to FINRA’s, includes all brokers and the set of investment advisers
on BrokerCheck who are also registered as brokers. FINRA reports that the term (cid:16)(cid:28)nancial advisor is a generic term that
typicallyreferstoabroker(ortousethetechnicalterm,aregisteredrepresentative)(cid:17). [http://www.(cid:28)nra.org/investors/brokers
andhttp://www.(cid:28)nra.org/investors/investment-advisers].
2Wecalculatethetotalcosttotheindustryasthesumofallsettlementsgrantedperyearinourdata.
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mistakes. Approximatelyone-quarterofadviserswithmisconductrecordsarerepeato(cid:27)enders. Pasto(cid:27)enders
are (cid:28)ve times more likely to engage in misconduct than the average adviser, even compared with other
advisers in the same (cid:28)rm, at the same location, and at the same point in time. The large presence of repeat
o(cid:27)enderssuggeststhatconsumerscouldavoidasubstantialamountofmisconductbyavoidingadviserswith
misconduct records. Furthermore, this result implies that neither market forces nor regulators fully prevent
such advisers from providing services in the future.
We (cid:28)nd large di(cid:27)erences in misconduct across (cid:28)nancial advisory (cid:28)rms. Some (cid:28)rms employ substan-
tially more advisers with records of misconduct than others. More than one in seven (cid:28)nancial advisers at
Oppenheimer & Co., Wells Fargo Advisors Financial Network, and First Allied Securities have a record of
misconduct. At USAA Financial Advisors on the other hand, the ratio is roughly one in thirty-six. We (cid:28)nd
thatadvisersworkingfor(cid:28)rmswhoseexecutivesando(cid:30)cershaverecordsofmisconductaremorethantwice
as likely to engage in misconduct. Di(cid:27)erences across (cid:28)rms are persistent and survive after conditioning on
a (cid:28)rm’s business model, such as whether advisers are client facing or not, (cid:28)rm structure, and regulatory su-
pervision. Therefore, (cid:28)rms and advisers with clean records coexist with (cid:28)rms and advisers that persistently
engage in misconduct.
Afterdocumentingbasicdi(cid:27)erencesintheprevalenceofmisconductacross(cid:28)nancialadvisersand(cid:28)nancial
advisory(cid:28)rms,weexplorethelabormarketconsequencesof(cid:28)nancialadvisermisconduct. Whatpunishment
should we expect for misconduct? One benchmark is extreme punishment of misconduct at the (cid:28)rm and
industrylevels. Firms,wantingtoprotecttheirreputationforhonestdealing,would(cid:28)readviserswhoengage
inmisconduct. Other(cid:28)rmswouldhavethesamereputationconcernsandwouldnothiresuchadvisers. Then
adviserswouldbepurgedfromtheindustryimmediatelyfollowingmisconduct, andonlyadviserswithclean
records would survive in equilibrium. The alternative benchmark is extreme tolerance of misconduct. Firms
would not (cid:28)re advisers who engage in misconduct, and employees with misconduct would not be penalized
when looking for new jobs. Of course, we expect reality to fall somewhere between these benchmarks. We
usethepanelstructureofourdatatoinvestigatehow(cid:28)rmspunishmisconductandhowadvisers’misconduct
records a(cid:27)ect their employment dynamics. We then show that di(cid:27)erences between (cid:28)rms play an important
role in how the market for misconduct operates.
Thesubstantialpresenceofrepeato(cid:27)endersinthepoolof(cid:28)nancialadvisersimpliesthatmisconductdoes
not automatically result in removal of an adviser from the industry. Therefore, it is perhaps surprising that
(cid:28)rms are quite strict in disciplining employees’ misconduct. Almost half of (cid:28)nancial advisers who engage in
misconduct in a given year do not keep their jobs into the subsequent year. The job turnover rate among
adviserswithrecentmisconductisroughly31percentagepoints(pp)higherthanthejobturnoverrateamong
adviserswithoutrecentmisconduct(19%). Wecon(cid:28)rmourresultsdonotarisebecauseofdi(cid:27)erencesbetween
(cid:28)rms, regulations, customer bases, or labor market conditions by comparing employees from the same (cid:28)rm,
in the same county, and at the same time. Firms do not discipline randomly, but seem to deliberately assess
the severity of misconduct before making a termination decision. We (cid:28)nd that larger monetary damages
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from misconduct result in a higher job separation probability.
If individual (cid:28)rms are strict in disciplining bad employees, why are there so many repeat o(cid:27)enders in
the population of (cid:28)nancial advisers? We (cid:28)nd that 44% of advisers who lost their jobs after misconduct (cid:28)nd
employmentintheindustrywithinayear. Thehiringofemployeeswithmisconductrecordsundoessomeof
thedisciplinepracticedby(cid:28)rms. However,reemploymentdoesnotimplythatdisciplinerelatedtomisconduct
is completely absent at the industry level. Even accounting for reemployment, advisers experience elevated
probabilitiesofindustry exitfollowingmisconduct. Theyexperiencelongergapsbetweenemploymentspells
in the industry. Conditional on (cid:28)nding new employment, they move to (cid:28)rms with lower compensation and
that are less desirable, as measured by (cid:16)followers to a (cid:28)rm(cid:17) on a social networking website for professionals.
Again, we (cid:28)nd these patterns even when we compare advisers with misconduct to other employees from the
same (cid:28)rm, at the same location, and at the same point in time.
In the last part of the paper we provide a potential interpretation that is consistent with these facts.
Why are some (cid:28)rms willing to hire advisers who were (cid:28)red following misconduct? If (cid:28)rms had identical
tolerance toward misconduct, such rehiring would not take place. We (cid:28)nd that advisers with misconduct
switch to (cid:28)rms that employ more advisers with past misconduct records. These results suggest that there
is matching between advisers and (cid:28)rms on the dimension of misconduct. We (cid:28)nd further evidence of such
matchingwhenexaminingthecompositionofnewhiresacross(cid:28)rms. The(cid:28)rmsthathiremoreadviserswith
misconduct records are also less likely to (cid:28)re advisers for new misconduct. This inclination should make
these (cid:28)rms especially attractive to advisers who might engage in future misconduct. Thus the matching
between (cid:28)rms and advisers on misconduct partially undermines the disciplining mechanism in the industry,
lessening the punishment for misconduct in the market for (cid:28)nancial advisers.
Thedisciplinaryrecordsof(cid:28)nancialadvisersarepublicrecord. Therefore,onemightaskwhycompetition
amongadvisersandreputationdoesnotdriveoutbadadvisersand(cid:28)rms. Onepotentialreasonisthatsome
3
customers may not be very sophisticated. Such customers do not know either that such disclosures even
exist, or how to interpret them. If there are di(cid:27)erences in consumer sophistication, then the market can be
segmented. Some (cid:28)rms (cid:16)specialize(cid:17) in misconduct and attract unsophisticated customers, and others cater
to more sophisticated customers and specialize in honesty, in the spirit of Stahl (1989) and Carlin (2009).
To shed more light on this mechanism, we collect additional data on (cid:28)nancial advisory (cid:28)rms’ customer
basefromtheSecuritiesandExchangeCommission(SEC)FormADV.Retailinvestors,whoarenothighnet
4
worth individuals, are generally considered less sophisticated. We (cid:28)nd that misconduct is more common
among (cid:28)rms that advise retail investors. The geographic distribution of advisory (cid:28)rms is also consistent
with market segmentation along the lines of investor sophistication. We document substantial geographic
di(cid:27)erences in (cid:28)nancial misconduct. In many counties in Florida and California, roughly one in (cid:28)ve (cid:28)nancial
3For other examples of work on consumer sophistication and household (cid:28)nancial decisions, see, for example, Gabaix and
Laibson 2006; Hastings and Tejeda-Ashton 2008; Carlin and Manso 2011; Lusardi and Mitchell, 2011; Duarte and Hastings
2012.
4This de(cid:28)nition is also used for regulatory purposes. The Investment Company Act of 1940 considers high net worth
individualstobemoresophisticatedthansmallerretailinvestors,allowingthemsubstantiallymorelatitudeintheirinvestments.
4
advisers have engaged in misconduct in the past. Misconduct is more common in wealthy, elderly, and
less educated counties. The latter two categories have generally been associated with low levels of (cid:28)nancial
sophistication (Gurun et al. 2015). Rates of misconduct are 19% higher, on average, in regions with the
most vulnerable populations; those counties that rank below the national averages in terms of household
5
incomes and college education rates. Misconduct among these vulnerable populations may be particularly
costly, as these populations likely have the highest marginal propensity to consume. These results, while
not conclusive, suggest that misconduct may be targeted at customers who are potentially less (cid:28)nancially
sophisticated.
We conduct several tests to ensure the patterns we document are robust. First, we examine alterna-
tive classi(cid:28)cations when constructing our measures of misconduct. In particular, the facts we uncover are
qualitatively similar when we use a (cid:16)severe(cid:17) measure of misconduct. To measure (cid:16)severe(cid:17) misconduct, we
restrict our de(cid:28)nition of misconduct disclosures to include only disclosures that are de(cid:28)nitive cases of ad-
viser dishonesty, such as fraud and unauthorized activity. Moreover, we also experiment with alternative
speci(cid:28)cations and (cid:28)nd similar results. When studying recidivism and labor market outcomes of advisers
followingmisconduct,wecompare(cid:28)nancialadviserswithina(cid:28)rm,inthesamecounty,andinthesameyear.
Therefore,theconclusionsfromthisanalysisarenottheresultof(cid:28)rmdi(cid:27)erences,includingdi(cid:27)erentbusiness
models. In our baseline labor market analysis, the (cid:16)control(cid:17) group comprises advisers who were employed at
the same (cid:28)rm, in the same location, at the same time,and who also switched jobs. One might be concerned
that this (cid:16)control(cid:17) group selects on advisers who switch jobs and therefore does not accurately represent
the average adviser at the (cid:28)rm. To address this concern, we examine outcomes of advisers from dissolved
(cid:28)rms. In such (cid:28)rms, all advisers, independent of past misconduct, are forced to (cid:28)nd new employment. The
results mirror those from our baseline speci(cid:28)cation qualitatively as well as quantitatively. Finally, we (cid:28)nd
ourfactsfor(cid:28)nancialadvisersregisteredwithFINRA,thoseregisteredasinvestmentadviserswiththeSEC,
and those dually registered with both the SEC and FINRA. Unlike those (cid:28)nancial advisers solely registered
with FINRA, advisers registered as investment advisers are held to a (cid:28)duciary standard. Although other
research, such as Egan (2016), has shown that holding all (cid:28)nancial advisers to a (cid:28)duciary standard could
improve investment outcomes, doing so may not be adequate in dealing with misconduct.
Our paper relates to the literature on fraud and misconduct in (cid:28)nance. The economics literature on
misconduct dates back to the seminal work of Becker (1968) on crime and punishment. More recently, there
has been a growing literature on misconduct among (cid:28)nancial advisers. Qureshi and Sokobin (2015) examine
the characteristics of those (cid:28)nancial advisers who cause investor harm and the predictability of investor
harm. Dimmocketal. (2015)studythetransmissionofbrokeragefraudthroughpeer(career)networksand
(cid:28)ndthatfraudiscontagiousacross(cid:28)rms. Thisconclusionisconsistentwithour(cid:28)ndingthattheincidenceof
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fraud varies systematically across (cid:28)rms. Previous research has documented misconduct in other industries.
5Over the period 2009-2013, the average incidence of misconduct in counties below both the median level of household
incomeandcollegeeducationrateswas1.07%perannum. Conversely,theaverageincidenceofmisconductinallothercounties
aboveboththemedianlevelofhouseholdincomeandcollegeeducationsrateswas0.90%perannum.
6There is also a related literature which has argued that (cid:28)nancial advisers steer clients towards worse (cid:28)nancial products
5
For example, Piskorski et al. (2013) and Gri(cid:30)n and Maturana (2014) document evidence of misconduct in
themortgageindustry,andnumerouspapershavedocumentedsimilarevidenceofcorporatefraudincluding:
Povel et al. (2007), Dyck et al. (2010; 2014), Wang et al. (2010), Khanna et al. (2015), and Parsons et al.
(2015).
Our paper also relates to the broad literature on how labor markets punish corporate misconduct (Fama
1980,FamaandJensen1983). Previousworkshowsthatdirectorsloseboardseatsiftheir(cid:28)rmsrestatetheir
earnings (Srinivasan, 2005), are involved in class action lawsuits (Helland 2006), engage in (cid:28)nancial fraud
(FichandShivdasani2007), orareinvolvedinproxycontests(FosandTsoutsoura2014). CEOsfacesimilar
careerpunishmentsiftheir(cid:28)rmsengagein(cid:28)nancialmisconduct(e.g.,Agrawal,Ja(cid:27)e,andKarpo(cid:27)1999). For
example, Karpo(cid:27) et al. (2008) (cid:28)nd that CEOs who lose their jobs following regulatory enforcement actions
also do worse in the labor market in the future.
A recent literature documents the importance of (cid:28)nancial advisers and other intermediaries in shaping
7
the investment decisions of households. Trust and consumer sophistication are believed to be critically
important in these markets (see, Gennaioli, Shleifer and Vishny 2015; Guiso, Sapienza, and Zingales 2008;
and Garleanu and Pedersen 2016). We build on this literature by documenting the roles of consumer
sophistication and misconduct, both of which of which have important implications for trust (Gurun et al.
2017). Our (cid:28)ndings suggest that a natural policy response to lowering misconduct is an increase in market
transparency and in policies targeting unsophisticated consumers. In doing so, our paper connects to the
literature that has evaluated various policy responses in regulating consumer (cid:28)nancial products (Campbell
2006; Campbell et al. 2011; Agarwal et al. 2009 and Agarwal et al. 2014).
2 Data and Descriptive Statistics
We construct a novel data set containing all (cid:28)nancial advisers in the United States from 2005 to 2015. We
collect the data from Financial Industry Regulatory Authority’s (FINRA) BrokerCheck database. FINRA
is the largest self-regulatory organization tasked by Congress with ensuring that the securities industry
operates fairly and honestly. The data includes all (cid:28)nancial advisers registered with FINRA. Throughout
the paper we refer to a (cid:28)nancial adviser as any individual who is registered with FINRA but are careful to
make distinctions about additional registrations or quali(cid:28)cations a (cid:28)nancial adviser may hold, such as being
aregisteredinvestmentadviserorageneralsecuritiesprincipal. Brokers(orstockbrokers)areregisteredwith
FINRA and the SEC and are de(cid:28)ned in the Securities and Exchange Act 1934 as (cid:16)any person engaged in
the business of e(cid:27)ecting transactions in securities for the account of other.(cid:17) An investment adviser provides
(cid:28)nancial advice rather than transaction services. Although both are often considered (cid:16)(cid:28)nancial advisers,(cid:17)
without engaging in misconduct (e.g., Bergstresser, Chalmers, and Tufano, 2009; Mullainathan, Noeth, and Schoar, 2012;
Christo(cid:27)ersen,EvansandMusto2013;ChalmersandReuter,2015;Egan2016).
7Forexample,Anagol,Cole,andSarkar(2013)intheinsuranceindustry;Gurun,MatvosandSeru(2015)inthemortgage
industry; Hastings, Horta(cid:231)su and Syverson (2015) in the fund industry; and Barwick, Pathak and Wong (2015) in the real
estateindustry.
6
brokers and investment advisers di(cid:27)er in terms of their registration, duties, and legal requirements. Two of
the main di(cid:27)erences are that brokers are regulated by FINRA and are held to a suitability standard while
investment advisers are regulated by the SEC and are held to a (cid:28)duciary standard. Roughly 84% of active
SECregisteredinvestmentadvisersarealsoduallyregisteredwithFINRAasbrokers. Thus,theBrokerCheck
data includes all brokers and the vast majority of investment advisers. Throughout the paper, we will use
terminologyconsistentwithFINRAandrefertobothinvestmentadvisersandbrokersas(cid:16)(cid:28)nancialadvisers.(cid:17)
We present results for the two groups separately in Section 6.
For each adviser, the data set includes the adviser’s employment history, quali(cid:28)cations, and disclosure
information. In total, the data set contains 1.2 million (cid:28)nancial advisers and includes roughly 8 million
adviser year observations over the period. We also collect information on the universe of (cid:28)nancial advisory
(cid:28)rms from the BrokerCheck database. We supplement our FINRA data set with additional (cid:28)rm-level data.
For a small subset of the (cid:28)rms, we observe (cid:28)rm assets, revenues, and compensation structure data from a
private industry survey. We acquire data on the popularity of a (cid:28)rm using CVs in the database of a leading
social networking website for professionals. We hand-match the names of the (cid:28)rms to the FINRA data.
We also utilize county-level data from the 2010 Census and the 2010-2013 American Community Survey to
obtain country-level employment and demographic information. Lastly, we collect data on (cid:28)rms’ customer
bases and fee structures from Form ADV (cid:28)lings, which investment advisory (cid:28)rms (cid:28)le with the SEC. We
match this data to BrokerCheck data exactly, using the unique numerical identi(cid:28)er, CRD#.
2.1 Financial Adviser-Level Summary Statistics
The data set contains a monthly panel of all registered advisers from 2005 to 2015. This panel includes
644,277 currently registered advisers and 638,528 previously registered advisers who have since left the
industry. For each of the 1.2 million advisers in the data set, we observe the following information:
• The adviser’s registrations, licenses, and industry exams he or she has passed.
• The adviser’s employment history in the (cid:28)nancial services industry. For many advisers we observe
employment history dating back substantially further than the past ten years.
• Any disclosures (cid:28)led, including information about customer disputes, whether these are successful or
not, disciplinary events, and other (cid:28)nancial matters (i.e., personal bankruptcy).
Table 1a displays the average characteristics of (cid:28)nancial advisers. The average adviser in our sample has
11 years experience, de(cid:28)ned as the number of years since the adviser passed her (cid:28)rst quali(cid:28)cation exam.
Approximately half of active advisers are registered as both brokers and investment advisers. The advisers
inourdatasetaccountforroughly0.50%ofallemployedindividualsintheUnitedStatesandapproximately
10% of employment of the Finance and Insurance sector (NAICS 52). Central to our purposes, over 12% of
7
8
active(cid:28)nancialadvisers’recordscontaindisclosures. Adisclosureindicatesanysortofdispute, disciplinary
action, or other (cid:28)nancial matters concerning the adviser. Not all disclosures are indicative of fraud or
wrongdoing. We construct our measure of misconduct-related disclosures based on FINRA’s disclosure
classi(cid:28)cations in Section 3. FINRA classi(cid:28)es disclosures into 23 categories as described in the Appendix.
Table 1a reports the share of advisers who have passed any of the six most popular quali(cid:28)cation exams
9
taken by investment professionals. Most states require that a registered (cid:28)nancial representative, at a
minimum, pass the Series 63 exam, which covers state security regulations. The Series 7 exam is a general
securities exam that is required by any individual who wishes to sell and trade any type of general securities
products. TheSeries65and66examinationsqualifyindividualstooperateasinvestmentadvisers. Although
not required by all states, most investment advisers hold either a 65 or 66 examination. A Series 6 exam
quali(cid:28)es an investment adviser to sell open-end mutual funds and variable annuities. Finally, the Series 24
examquali(cid:28)esanindividualtooperateinano(cid:30)cerorsupervisorycapacityatgeneralsecurities(cid:28)rms. While
aboutone-fourthofactiveadvisersoperateinonlyonestate,10%areregisteredtooperateinall(cid:28)ftystates.
In the Online Appendix we examine the distribution of (cid:28)nancial advisers across the US and across
(cid:28)rms. Not surprisingly, given the nature and size of the regions, the New York, Los Angeles, and Chicago
metropolitan areas rank among the highest in terms of the number of (cid:28)nancial advisers. The number of
advisers per capita tends to be greater in more educated, more populous areas, and actually slightly less
wealthy areas.
2.2 Firm-Level Summary Statistics
From our adviser level data set, we observe the full adviser and branch history for the universe of (cid:28)nancial
advisory(cid:28)rmsovertheperiod2005-2015. TheFINRABrokerCheckdatabasealsocontainssummarydetails
for the set active (cid:28)rms (as of 2015) the advisers represent. Active (cid:28)rms are identi(cid:28)ed by the corresponding
CRD identi(cid:28)cation number. Firms with distinct CRD numbers can share a same parent company. For
instance, Wells Fargo operates several (cid:28)nancial services businesses under separate numbers. In particular,
Wells Fargo has several operations such as Wells Fargo Advisors Financial Network (CRD# 11025), Wells
Fargo Advisors (CRD# 19616), and Wells Fargo Securities (CRD# 126292). The di(cid:27)erent CRD numbers
re(cid:29)ect di(cid:27)erent operations and business lines. For example, Wells Fargo Advisors Financial Network is an
arm of Wells Fargo comprised of independent advisers that are a(cid:30)liated but not technically employed by
Wells Fargo (https://www.wfa(cid:28)net.com/). Wells Fargo Advisors re(cid:29)ects Wells Fargo’s in-house network of
advisers. Similarly, Morgan Stanley has several operations such as Morgan Stanley & Co. (CRD# 8209)
10
and Morgan Stanley (CRD# 149777). The active advisers in our data work for one of over 4,178 di(cid:27)erent
8As indicated by Ed Beeson at Law360.com, our share of advisers with disclosures over the 2005 to 2015 period, 12.7%,
closelymatchesthatofFINRA,12.6%,estimatedforcurrentlyregisteredadvisersinMarchof2016.
9FINRA provides detailed descriptions of each quali(cid:28)cation exam on their website
[http://www.(cid:28)nra.org/industry/quali(cid:28)cation-exams?bc=1].
10Wedecidednottomerge(cid:28)rmswithdi(cid:27)erentCRD#sforseveralreasons. First,anymergingwouldbearbitraryandwould
re(cid:29)ectourchoiceratherthantheactual(cid:28)rmchoicesinregulatory(cid:28)ling. Second,thedi(cid:27)erentCRDnumbersfrequentlyre(cid:29)ect
di(cid:27)erentoperationsandbusinesslines,andweareinterestedinassessinghowvariousbusinesslinescorrelatewithmisconduct.
8
(cid:28)rms. Figure 1 displays the distribution of these (cid:28)rms. The average (cid:28)rm employs just over 155 advisers.
Firms range from one employee to over 30,000 advisers. For each active (cid:28)rm we observe the (cid:28)rm’s business
operations, including its size, number of businesses/operations, and referral arrangements as of 2015. We
alsoobserveregistrationinformation, suchasthenumberofstatesthe(cid:28)rmiscurrentlyregisteredinandthe
number of regulatory memberships. Finally, we observe the type of incorporation for active (cid:28)rms. We use
several of these (cid:28)rm characteristics in our analysis.
Table1bdisplaystheaverage(cid:28)rmcharacteristics. Roughlyoneinfour(cid:28)rmsisregisteredasaninvestment
advisory (cid:28)rm. Recall that just under half of (cid:28)nancial advisers are also registered as investment advisers.
Roughlyhalfof(cid:28)nancialadvisory(cid:28)rmsarea(cid:30)liatedwitha(cid:28)nancialorinvestmentinstitution. Forexample,
Wells Fargo Advisers is a(cid:30)liated with Wells Fargo Bank. Lastly, the average (cid:28)rm operates in roughly
six distinct types of business operations. Such operations could include trading various types of securities
(equities, corporate bonds, municipal bonds), underwriting corporate securities, retailing mutual funds, or
soliciting time deposits.
We use additional (cid:28)rm level data from the SEC’s Form ADV (cid:28)lings. The SEC requires investment
advisory (cid:28)rms to disclose information on the (cid:28)rm’s clientele and business practices in the Form ADV. We
match the (cid:28)rms in our FINRA data to the SEC form ADV (cid:28)lings based on the (cid:28)rm’s CRD#. Since not all
(cid:28)nancialadvisory/brokerage(cid:28)rms(cid:28)leFormADV,weonlyobservetheFormADV(cid:28)lingsfor405unique(cid:28)rms
in our data set over the period 2011-2014. The second panel of Table 1b displays the average characteristics
of these (cid:28)rms. The vast majority (86%) of (cid:28)rms report having retail clients. Most (cid:28)rms report charging
based on assets under management, a (cid:28)xed fee, and/or an hourly fee.
We also supplement our data set with additional information from a private industry survey and from
a popular social networking site. The industry survey provides details on the assets, revenue, and average
adviser payout/salary for a subset of the (cid:28)rms in our FINRA data as of 2014. We are able to manually
match 75 of the (cid:28)rms in our FINRA data set to the private industry survey based on the (cid:28)rm’s name.
Although we observe survey information for a subset of the (cid:28)rms, these (cid:28)rms are generally the largest such
that we observe average payout estimates for 20% of advisers. The average (cid:28)rm operates 23bn in assets and
generates 261mm in revenue. Lastly, we measure the popularity of each (cid:28)rm as the number of individuals
who follow a (cid:28)rm on a popular social media website as of May 2015. We are able to manually match 40%
of the (cid:28)rms in our FINRA data set to the social media website based on the (cid:28)rm name. The average (cid:28)rm
has 2,365 followers.
3 Misconduct
In this section we document the extent of misconduct in the (cid:28)nancial advisory industry. We (cid:28)rst construct
ourmeasureofmisconductbasedonthedisclosuresreportedtoFINRA.Next,weexaminethecharacteristics
of (cid:28)nancial advisers that are disciplined for misconduct. We then document the high incidence of repeat
9