105 St. George Street Telephone: (416) 946 5647 E-mail: susan.christoffersen@rotman.utoronto.ca 

Toronto, Ontario, Canada M5S 3E6 Facsimile: (416) 971 3048 Web site: www.rotman.utoronto.ca/schristoffersen

Vice-Dean, Undergraduate and Pre-experience Programs 

September 10, 2015 

Filed Electronically: e-ORI@dol.gov

Office of Regulations and Interpretations 

Employee Benefits Security Administration 

Attn: Conflict of Interest Rule, Room N-5655 

U.S. Department of Labor 

200 Constitution Avenue NW, Washington, DC 20210 

RE: RIN 1210-AB32: Regulatory Impact Analysis, Definition of the Term “Fiduciary”: Conflict 

of Interest Rule – Retirement Investment Advice 

To Whom It May Concern: 

We would like to respond to recent comments by the Investment Company Institute (ICI) 

on our paper “What do consumers’ fund flows maximize? Evidence from their brokers’ 

incentives”, Journal of Finance 68(2013), 201-235. Our paper has been cited extensively as 

it relates to the Department of Labor’s proposed Conflict of Interest Rule. In light of this, our 

paper has come under close scrutiny by stakeholders on both sides of the debate. In the 

ICI’s recent letter (dated July 21, 2015) to the Office of Regulations and Interpretations at 

the US Department of Labor, the ICI makes several incorrect claims about the results and 

interpretation of our paper, so we would like to make clear that our results are correctly 

calculated and interpreted. 

The four concerns regarding our paper that are identified by the ICI in Section II of their 

July 21, 2015 letter addressed to the US Department of Labor are the following: 

1. Our regression analysis is not asset-weighted 

2. The data sample stops in 2009 and the market has changed since this period of 

time 

3. Both front load and excess loads are included in the regression explaining flows so 

we should aggregate the effects of front load and excess loads when evaluating 

the effect of broker compensation on flows. 

4. When we interpret the economic meaning of the coefficient in the return 

regression in Table V, we should use 0% rather than 2.3% as currently used in the 

paper. 
2 

The first two concerns are common to many other papers in the literature while the last 

two are specific to the results of our paper. Our discussion below clarifies that the claims 

made by ICI are incorrect. Regarding the first point, our methodology accounts and 

adjusts for the variation in funds’ assets. Regarding the second, the statistics the ICI chose 

for its letter are misleading, as is apparent in statistics from the ICI’s own website that show 

that investments subject to loads have grown significantly. Lastly, with regards to the latter 

two points, both are wrong. 

Comment 1. Importance of asset-weighting 

All the regressions in the paper use robust standard errors which control for the 

heteroscedasticity in variance often associated with funds of different size. This implies that 

for each observation the variance estimate is allowed to vary in proportion to the 

independent predictors of the regression (which include log asset size) so effectively the 

variances in the regression are asset-weighted. We also cluster standard errors by each 

fund so standard errors are allowed to vary fund-by-fund. Lastly, log of asset size is 

included as a control variable in all the regressions. Thus, the variation in fund assets is 

addressed thoroughly in our analysis. 

Comment 2. Sample period and changes in the industry 

The ICI argues that the industry has changed significantly from the end of our sample 

period in 2009. To illustrate this they show a drastic increase in the number of funds with 

no-load shareclasses in 2010 compared to load share classes. Figure 1 in their letter shows 

that the number of funds without no-load shareclasses declines from 49% to 10% (so by 

extension those with no-load shareclasses increase from 51% to 90%) between 2000 and 

2010. This information is however misleading since it is not the number of shareclasses that 

matters but the dollars in these shareclasses. 

The dollars in Load vs. No-Load shareclasses are reported in Table 5.10 in the ICI Factbook 

2015, which shows the following (in Billions of $US): 

2005 2009 2010 2011 2012 2013 2014 

Load 2313 2185 2352 2176 2362 2658 2634 

Front Load 1728 1750 1882 1751 1893 2148 2116 

No-load (Retail) 2404 2666 3069 2991 3469 4144 4625 

Load +Retail Noload 4717 4851 5421 5167 5831 6802 7259 

The table shows that the dollars in load and no-load retail funds have both increased over 

time and since 2009 (the end of our sample). The growth in assets held in the no-load 

channel has outstripped that in the load channel, but the load channel remains a very 

significant percent of the retail market in terms of assets (~36% in 2014). Within the load 

channel, approximately 75 to 80% of the load funds are front-end load which is where the 

analysis of our study focuses. So the count of shareclasses offered by the ICI misrepresents 

the reality that investments in load funds have grown and remain a significant portion of 

the retail market. 
3 

Also, it is important to note that the no-load channel includes brokers who offer advice 

and receive payments. Brokers in the no-load channel are paid 12b-1 fees and receive 

revenue-sharing. Our paper discusses revenue-sharing arrangements as they affect flows 

and future performance, so our results are relevant to this part of the market. 

Comment 3. Inclusion of front-end loads and excess loads in Table III 

In Table III of our paper we include both the level of front-end loads and the excess load 

paid to brokers to explain flows. This is necessary to separate the demand effects of loads 

from their incentive effects. We know from prior research that individuals respond 

negatively to high loads, so higher loads reduce inflows. This influence on flows is separate 

from the incentive effects of the portion of the load going to the broker, and until we 

published our paper in the Journal of Finance, there was no research study that 

disentangled the effect of the full load on consumer demand from the effect of the 

brokers’ share of the load on the brokers’ incentives. By including both the level of frontend loads (effect of loads on consumer demand) and the excess loads (effect of loads 

on broker incentives), we separate these two effects. 

The ICI incorrectly states in their letter that we should add together the effects of front-end 

loads on flows with the effect of excess load payments on flows. This would conflate the 

two effects of loads on flows that we separated, and thus negate one of the primary 

contributions of the paper, which is to cleanly and separately identify the impact of these 

additional payments to brokers on fund flows and performance. That is, our inclusion of 

both the overall front-end load and the excess load paid to the broker is what allows us to 

cleanly analyze the broker incentive effect. It controls for the level of front-end load 

payments to allow the apples-to-apples comparison between two funds that charge the 

same load to consumers but share different amounts of this load with brokers. 

Comment 4. Economic interpretation of Table V 

In our paper, we run a 2-stage regression where we first explain the level of loads paid to 

a broker (Table II) and then take the excess load from this regression to explain flows 

(Table III) and excess future performance looking 12 months ahead (Table V). We use this 

framework because it is important to control for the many different fund characteristics 

that influence what a broker is paid. The objective of this two stage set-up is to ensure 

there are no spurious factors which simultaneously affect both the amount the broker is 

paid and the fund flows or the amount brokers are paid and future performance. The ICI 

calls this a “complicated set of computations” but in fact it is a necessary construction to 

ensure our estimates cleanly measure the incentive effect of additional payments to the 

broker on flows, and the relation of broker payments to subsequent performance. The first 

stage regression in Table II explaining the loads paid to brokers has an 87% r-squared, 

which means it explains 87% of the variance of these payments. This is strong evidence 

that the first stage eliminates potential sources of spurious correlation and therefore allows 

a clean measure of the incentive relation between payment, flows, and performance. 

The ICI letter then goes on to misconstrue our economic interpretation of the second 

stage regression in Table V by claiming that we should use the average excess load of 

zero to scale our coefficient of 0.49. Zero is the wrong scaling measure as we are 
4 

economically trying to evaluate how a change in the load paid to the broker would 

relate to changes in the future performance of the fund. We chose 2.3% to evaluate this 

effect economically since this represents the change of moving from no payment to the 

broker to average payment to the broker (2.3% in our sample). If we multiply this change 

in broker fees (2.3%) by the coefficient (-0.49), we get the expected effect on changes in 

the future performance of the fund (-1.13%). The proposed use of scaling by zero is 

economically meaningless and implies the obvious: if brokers are not given any additional 

compensation to sell a fund, then there are no additional incentive problems as to which 

fund the broker sells an investor. The negative and significant coefficient relating excess 

loads to future performance implies that any positive changes in load payments to the 

broker (additional compensation for the broker) associate with decreased expected 

future performance realized by the investor over the following year. 

Thank you for your consideration and the opportunity to respond. 

Sincerely, 

_____________________________________________ 

Susan Christoffersen 

Vice-Dean, Undergraduate and Pre-experience Programs 

Associate Professor, Finance 

Rotman School of Management 

_____________________________________________ 

Richard Evans 

Associate Professor, Finance 

Darden School of Business