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LIBRARY

OF THE

MASSACHUSETTS INSTITUTE
OF TECHNOLOGY



ALFRED P. SLOAN SCHOOL OF MANAGEMENT



FACTORS THAT AFFECT MUTUAL FUND GROWTH



256-67



F. B. Allderdice and D. E. Farrar



MASSACHUSETTS

INSTITUTE OF TECHNOLOGY

50 MEMORIAL DRIVE

CAMBRIDGE, MASSACHUSETTS 02139



FACTORS THAT AFFECT MUTUAL FUND GROWTH






F. B. Allderdice and D. E. Farrar




Sloan Fellow and Associate Professor of Finance, respectively, at the
Sloan School of Management, Massachusetts Institute of Technology.






WAY 01 ^'-^^^

M. I. T. UbWAKicb_



FACTORS THAT AFFECT MUTUAL FUND GROWTH



Introduction

The substantial growth of the mutual fund industry during the
last few years has attracted the attention of students of finance, eco-
nomics and public policy alike. Net assets managed by such funds have
grown from approximately $^50 million in 19'+0 to more than $38 billion
by June of 1966. During 1965 the mutual fund industry funneled some
$5.2 billion of new (primarily equity) funds into the capital markets;
more than twice the $2.3 billion in new equity raised by all non- financial
U. S. corporations during the year. Growth of the industry has not
been uniform, however, but has been concentrated among a relatively
small number of highly successful funds.

A portion of the industry's growth can be traced to apprecia-
tion in the market value of the securities under its management. Another
and far larger portion, however, represents net new investment by the
public in mutual fund shares. Reasons for the latter source of growth,
and especially for its uneven distribution among the industry's parti-
cipants, are examined in the present study.



Methodology

Broadly speaking, any of a wide variety of forces may be ex-
pected to have an impact on mutual fund growth. Some of these, such



Securities and Exchange Commission, Public Policy Implication of

Investment Company Growth, 89th Congress^ Second Session, House Report

#2337, U. S. Government Printing Office, Washington, D. C, I966.
■x-x-
Ibid .

Ibid.



as growth in gross national product, stock prices, and disposable
personal income may be expected to be largely time- related phenomena,
and to bear relatively uniformly on all members of the industry. Others,
such as fund performance, sales effort, and size may be expected to
vary widely across funds in the industry. Cross-section analyses,
rather than the analysis of economic aggregates over time, are required
to detect the latter source of variation in mutual fund growth. By
using successive cross sections over time, however, an effort also is
made to control at least roughly for important, time-dependent phenomena.

Simple correlations between net, new money inflows and any of
a broad range of potential explanatory variables can be helpful in iso-
lating those factors that, in a statistical sense, appear most closely
related to mutual fund growth. Care must be taken, however, to avoid
confusing statistical association with causality. There is, clearly,
no fool-proof guarantee that such confusion may be avoided. By simul-
taneously examining the relationship between a dependent variable (such
as net new investment in mutual fund shares) and as broad a range as
possible of potential explanatory variables (such as performance, sales
effort and general economic growth), the danger of spurious association
can, at least, be reduced. Accordingly, a least squares regression
model that simultaneously measures partial relationships between depen-
dent and independent variables is employed in the present study.

Defining:

_Y as a vector of observations on net new money, the
variable whose behavior is to be explained.



X as a matrix of observations on a set of explanatory
variables thought to affect the flow of new funds
into an investment company,
_b as a vector of structural parameters that relates
expected values of _Y to X, and

U as a vector of stochastic error terms,

the basic relationship hypothesized can be summarized as

Y = X Jb + U.

Measures of estimation efficiency can be summarized through

numerous statistics. A common measure of an equation's explanatory

2
power, R , may be interpreted as the percentage of a dependent variable's

total variation that can be explained through a given set of explanatory

variables. Similarly, each independent variable's contribution to an

equation's explanatory power often is measured by the familiar t- ratio,

t = b/a^,

where b is the variable's regression coefficient, and ct, is the coeffi-

2
cient's sample standard deviation. Measures of R greater than .10

or .20 are sufficiently large to suggest the presence of significant

(non-zero) relationships between Y and X over cross sectional samples

such as those encountered here; while t-ratios greater than 1.0 or 2.0

generally are interpreted as identifying meaningful (non-zero) partial

relationships between dependent and independent variables. Squared

multiple correlation coefficients, regression coefficients, and t-

ratios, accordingly, are reported in the analyses that follow.



Scope of the Study

This study's — indeed any study's - scope can be defined
along at least three dimensions: its units of observation (in this
case mutual funds), the time period over which observations are col-
lected, and the variables by which each observation is charaterized.

The present study employs observations on I69 mutual funds,
approximately U5^ of the funds in existence during June, I966. A
better idea of the sample's significance in terms of the industry's
overall size, however, can be gained by noting that the companies
covered received some $3.2 billion in net new investment during 1965?
more than 60'^^ of the industry's new money during the year. In terms
of total assets managed, this sample contained $30-2 billion at the
end of 1965, or 79^ of the industry's total assets. Similarly, com-
panies covered by the present sample contain 5*9 million shareholder
accounts, or 88^ of the industry's accounts. By any measure, therefore,
the sample covers a significant portion of the investment company
industry.

Data is collected on each fund over a three-year period of
time, from 1963-65. Data limitations, especially regarding portfolio
turnover, constitute the primary constraint on the sample's coverage.

Data elements by which each fund and year are characterized
include percentage increases in gross national product, total personal
income, and Standard and Poor's 5OO Stock Security Price Index, plus
9 measures specific to each fund, summarized in Table I. Most of the
variables in the Table are self-explanatory. Total net assets and
numbers of shareholders constitute rough indications of a fund's size.



- 5 -



Table I



RAW DATA



Factor Description


Symbol


Total Wet Assets


TA


Net Asset Value


NAV


Portfolio Turnover Ratio


pro


Dividends


D


Capital Gains


CG


Shareholders


s


Expense Ratio


ER


Sales Charge


SO


Fund Objective


OBJ



- 6 -

Net asset value, dividends and capital gains distributions provide
the per-share data out of which various performance measures are formed.
Expense ratio is defined as the ratio of expenses actually paid by the
fund, as a per cent of total net assets. It includes management fees,
the salaries of fund employees, office expenses and other expenses not
provided by the fund's advisor. Expense ratio, however, does not in-
clude services provided to the fund or its advisor in return for broker-
age. Sales charge is defined as the maximian load, applicable to a
fund's smallest (non- contractual plan) investors. Portfolio turnover
is calculated by dividing the lesser of sales or purchases, excluding
government securities and other short-term obligations, by the average
of total net assets over a year; and. is the same as that reported to the
Securities and Exchange Commission on Form N-IR. Fund objective is
that adopted by Arthur Weisenburger and Company.

From the raw data described above, a series of working data
elements is developed for each observation, and summarized in Table II.
Certain variables - specifically, total gain, dividend yield, capital
gains distribution, unrealized gain, expense ratio and portfolio turn-
over - are lagged by one year to more closely approximate the informa-
tion available to an investor at the time of a fund purchase decision.
A complete set of raw and working data elements is summarized for a
single fund in Tfeble III.



Securities and Exchange Commission, Form N-IR, Annual Report of Registered
Management Investment Companies under the Investment Company Act of 19^0
and the Securities and Exchange Act of 193^, January 25, 1965; item 1.25(a).

Wessman, L., Editor, Investment Companies , Arthur Weisenburger and
Company, New York, I966.



- 7 -



Table II

Working Data

Variable Symbol

Net New Investment, as per cent of

total assets NNI^

Net New Investment, per cent, lagged

one year NNI^, ,

Percentage growth in Standard

and Poor 500 Stock Index SP

Dividend Yield, lagged one year ^\-l

Capital Gain Yield, lagged one year "^"^^-l

Unrealized Gain, lagged one year ^*^t-l

Total Gain, lagged one year '^t-1

Portfolio Turnover, lagged one year PTO, _,

Sales Charge SC

Expense Ratio, lagged one year ^^t-l

Total Assets, lagged one year

(in logarithms) "^t-l

Fund Objective OBJ



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- 9 -

Some of the variables described above may require further
comment. Although it would be desirable to characterize each fund and
year by a complete spectriom of the variables that affect net new money
inflows during the year, it must be recognized that any characteriza-
tion will, in fact, be incomplete. To reduce the danger of introducing
spurious relationships between measured variables and other character-
istics that should be, but are not included in an independent variable
set, an effort is made to proxy excluded characteristics (such as the
size and efficiency of a fund's sales force), by including in each
equation a lagged value of its dependent variable. The rationale
underlying such a variable's use may be traced to a belief that factors
excluded from a given year's set of independent variables are likely
to have been operative during the preceding year, as well. Analyses
of the relationships reported below, excluding this effect, indicate

the presence of serial correlation between observed residuals, and

*
accordingly, support such a variable's use.

A second broad class of measurement problems concerns the

quantification of variables, such as a fund's objective, that do not

lend themselves to continuous measurement. A fund either is a growth

fund or is not; or a maximum performance fund, or not; or an income

fund, or not, etc. To the ext.ent that labels possess sales appeal, an

ability to measure the appeal inherent in a fund's objective may be

desirable. Accordingly, a set of dummy variables is defined for each

of k Weinsenburger objectives: maximuiri gain, growth, growth- income.



For further discussion of this problem, see L. M. Koyck, Distributed Lags
and Investment Analysis , North Holland Publishing Company, Amsterdam, 195^;
and L. R. Klein"^ "The Estimation of Distributed Lags," Econometrica , 26,
^, 1958«



- 10 -

and income. Should a fund fall into one of these categories the vari-
able corresponding to the category assumes a value of 1.0, if not its
value is 0. Each fund, of course, falls into one and only one category.
To avoid singularity, of course, one objective (in this case the Growth-
Income- Stability amalgam; must be excluded from the set. Coefficient
estimates for included objectives, accordingly, are interpreted as
the difference between a given objective's sales appeal and that enjoyed
by a Growth- Income- Stability investment objective.

An effort also is made to reduce the enormous variation in
new money attributable only to fund size., by transforming total asset
measures to logarithms, and standardizing each equation's dependent
variable, net new investment, to a percentage of the fund's beginning-
of-year total assets.

Analyses

As indicated earlier, the study is designed to investigate
the relationship between percentage growth in investment company assets
through net new investment, and a wide variety of both general economic
conditions and specific fund characteristics. Broadly speaking, five
general categories of factors are investigated: economic conditions,
sales effort, fund performance, fund size, and fund objective. Initial
exploratory analyses eliminate percentage growth in such general economic
characteristics as gross national product and total personal income



See D. Suits, "Use of Dummy Variables in Regression Equations, " Journal
of the American Statistical Association, 52, 28, 1957.



11



from further consideration. An example of the type of finding uncovered
is summarized in Table IV, where all k68 usable observations on l69 mutual
funds are pooled in a single regression model. Four separate equations,
each employing a different measure of performance (total gain, dividend

yield, capital gains yield, and unrealized gain) are summarized. Regression

2
equations, t-ratios, R corrected for degrees of freedom, and sample size

are reported for each equation.

Each equation's statistical significance is considerable. Differ-

2
ences in R are trivial, leading to no preference for one equation, and

therefore one measure of performance, over another. Comparisons between
t-ratios for different parameters, however, highlight the overwhelming
importance of lagged net new investment as an explanatory variable; sug-
gesting the existence of both relevant, excluded, independent variables
and considerable stability in the forces that determine the distribution over
time of new money inflows between companies in the industry. The lagged
variable ' s considerable explanatory power also tends to reduce confidence
in the validity of equations (over this set of data) that do not contain
such a variable.

Portfolio turnover, fund size, expense ratio, and fund performance
also appear to be highly signficant determinants of the distribution of
net new investment between competing funds. Certain fund objectives such
as growth and grovrth- income also appear to have at least moderate investor
appeal. Market action, measured by percentage change in the Standard and



Thirty- nine missing data elements, primarily portfolio turnover, account
for the discreptancy between k68 useable observations and 3 x l69 = 507
expected data points for 169 funds over a three-year period of time.



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Online LibraryF. B. (Fitzhugh Berry) AllderdiceFactors that affect mutual fund growth → online text (page 1 of 2)