revisiting the dumb money effect-rushing into stellar performing funds
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Revisiting the Dumb Money Effect:
Rushing Into Stellar Performing Funds
Joseph Esposito
University at Albany School of Business Working Paper
Financial Analysis Honors Program
Fall 2012
Abstract
Previous research shows that there is a difference in the mutual fund purchasing decisions
of individual and institutional investors. Using a similar methodology to Frazzini and Lammont
(2008), the current study extends this to the present using fund flows and excess returns. The
results indicate that on a month over month basis, retail investors react more strongly to changes
in excess returns than institutional investors.
Keywords: behavioral finance, mutual funds, institutional investors, retail investors, fund flow, excess returns
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I. Introduction
There is a documented difference between the investing behavior of retail and
institutional investors. This difference is intuitive: institutional investors have extensive time
and resources that bolster investment decisions, while retail investors may lack these advantages.
As previous studies have demonstrated, retail investors have shown to be “dumb” investors,
achieving underperforming returns compared to institutional investors. This study investigates
this contrast between the two types of investors, measuring the difference of the sensitivity of
changes in mutual fund flows in relation to excess returns.
This pattern may stem from the herding effect seen in the 1980s. Amid a wild bull
market and lucrative expansion of the mutual fund industry, retail investors rushed into high
performing funds with retirement and pension funds. Later, studies would show that there is a
relation between the returns of funds and the subsequent flows, along with the distinction of
flows and returns between individual and institutional investors. The current study examines this
relation and the extent to which individual investors’ and institutional investors’ investment and
redemption behavior differ in response to mutual funds’ recent performance.
II. Literature Review
The inspiration for the current paper comes from Leibowitz and Hammond’s (2004) work
on individual and institutional investor allocation patterns. Using data on university endowments
and individual defined-contribution pensions (Hammond was the Chief Investment Strategist of
TIAA-CREF), the two looked at the difference in reallocation strategies after major market
moves. The data taken from 1992 to 2003 reflected the differences in initial allocation between
the two types of investors—endowments invested much more heavily in equities than individual
investors. In the analysis, they identify four types of investor types that would alter the final
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allocation: re-balancer, holder, valuator, and shifter. Re-balancers reallocate in response to
major market movements in order to stabilize the asset allocation across time. Holders do the
opposite, they pursue a buy and hold strategy and do not reallocate regardless of market
conditions. Like rebalancers, valuators reallocate but only when they perceive a favorable
reward for assumed risk. Shifters reallocate in response to changes unrelated to market
conditions (e.g. a change in risk tolerance). The data shows that endowments reallocate in
response to major market movements while individuals follow more of a buy and hold strategy.
Figure 1: Actual vs. Projected Equity Allocations-Equities and Bonds Only
Within the 11 year time period, the correction following the bull market of the late 1990s is an
excellent example of a major market movement. From 1999 to 2002, individual investors
allowed their equity allocation to fluctuate from 68% to 49%. This 19-point difference is
remarkably similar to the market value change of the average portfolio during that time (negative
22% with an initial equity/fixed income allocation of 68/32%). Individual investors held their
allocations while institutional investors rebalanced accordingly. Further analysis shows that
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there is little difference in final allocation (in the short-run) between rebalancers and holders
after the market returns to pre-correction levels. Using the allocation strategies listed above,
Leibowitz and Hammond propose a model of allocation that can be used for investment
decisions amid a changing investment landscape. However, the short time horizon of individual
investors renders this model as effective only for institutional investors.
Goetzmann, Massa and Rouwenhorst (1999) identified different behavioral influences in
mutual fund flows. They assumed the broad asset classes that various mutual funds represent,
rather than specific underlying assets within each fund, drove the purchase of mutual funds.
Using a sample of nearly 1,000 U.S. mutual fund flows in a year and a half time period (January
1998-July 1999), they focus on Net Asset Value (NAV) and Net Asset Value Per Share
(NAVPS) when testing for correlations between 50 different types of asset classes. They find a
negative correlation between cash and equity flows, which they claim could result from changes
in allocation between the two. One of the most important findings of the study (along with the
most statistically significant finding) is the negative correlation found between stocks and bonds.
Other factors could have contributed to this negative correlation (e.g. the need to transfer money
from a money market account to a different account to purchase equities), but the negative
correlation between precious metals (especially gold) and equities supports the belief that
investors shifted their allocation based on future expected equity performance. One surprising
finding in the study was the lack of correlation between municipal bond funds and other bond
funds, which suggests that municipals are not used as substitutes for bonds or as safe havens for
equities.
Gruber (1996) notes the growth of actively managed mutual funds (CAGR of 22% in the
10 years leading up to the study) in spite of inferior performance relative to index funds. Using
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the risk-adjusted returns of 227 actively managed equity mutual funds between January 1985 and
December 1994, Gruber calculates the cash flow alphas for each fund. The results showed that
the returns of newly invested money in the funds are statistically significantly higher than the
average return for all investors within a particular fund. More specifically, the weighted average
performance of funds with high net inflows is positive, adjusting for risk and other factors.
Therefore in this study, the funds that received the higher initial inflows yielded higher returns.
This effect was later coined as the “smart money effect.” These funds that have larger net flows
outperform less popular peers. Therefore, money tends to flow into funds that will outperform in
the months to come.
Zheng (1999) uses mutual fund flows to examine the purchasing and selling decisions of
investors. The major question in her study is whether the vast amount of information on mutual
funds that is accessible to investors can allow them to predict mutual fund performance. Can
investors predict which funds will outperform in the near future? To avoid biased comparisons
across time, portfolio holdings from the previous period were used as a benchmark. 1,826 open-
ended mutual fund data from December 1961 through December 1993 were used in this study,
including defunct funds. Jumping off of Gruber’s building blocks, Zheng expands the data set to
cover all equity funds between 1970 and 1993. The results in Zheng’s paper support the smart
money effect, which is consistent with the evidence found in Gruber’s study. Funds that receive
positive net flows perform much better (on a risk adjusted basis) than funds with negative flows.
Zheng did not find convincing evidence that the smart money effect was caused by investors
pursuing outperforming funds. Instead, she found that funds that receive positive versus
negative money flows outperform. Also, in contrast to Goetzmann’s study, Zheng argues that
the findings support her belief that the smart money effect is due to fund-specific information
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and therefore can be implemented into a strategy to capture abnormal returns (the “information
effect”). Another finding in Zheng’s study is the difference in fund flows between small and
large fund sizes. She concludes that this may have a two-fold effect: (a) funds with more money
under management have the ability to pay higher compensation to the best possible managers
and (b) investors may feel more comfortable investing in a fund that many people invest (and
trust) in. An important distinction between Zheng and Gruber’s work and future studies is the
time period—this smart money effect seems to be present in the short-run.
At the time of Jegadeesh and Titman’s (1993) publication, previous studies regarding
stock price momentum were still being criticized. The argument for stock price momentum was
as follows: individuals and thus stock prices overreact to information. Therefore, buying past
losers and selling past winners should achieve high returns (the contrarian strategy). Critics
claim that the results found in the previous studies were influenced by the high systematic risk in
the portfolios. In contrast to these studies that focused on long-term horizons, Jegadeesh and
Titman chose a short-term horizon to study stock price momentum. Using stocks from the 1965
to 1989 time period, Jegadeesh and Titman show that buying those with high positive returns in
the previous six months and selling those with negative returns produces a profitable trading
strategy in the short-term (3 to 12 month time horizon). Although these results were significant,
this study did not make any concrete conclusions regarding the investor behavior that could have
led to this momentum effect.
Building on Gruber and Zheng’s findings, Sapp and Tiwari (2004) examine whether the
smart money effect is related to stock return momentum introduced by Jegadeesh and Titman
(1993). Sapp and Tiwari’s test uses equity mutual funds with a variety of investment strategies
and objectives over the period of 1970 to 2000 from the CRSP Survivor-Bias Free U.S. Mutual
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Fund Database. From the onset, they believed that a large part of the “smart-money effect” is
related to the high inflow of funds to outperforming mutual funds. They believed that as funds
perform well, they would continue to do well as investors hop on the bandwagon to achieve
higher returns. Therefore, the initial investors would benefit from the momentum of later
investors, driving up returns. Like previous studies (Gruber, Zheng), they create two new-money
portfolios at the beginning of each quarter (one has positive net cash flows, and the other
negative cash flows). But unlike those studies, Sapp and Tiwari create a momentum factor to
determine the relation between the smart money effect and stock return momentum. When they
control for the momentum effect using the benchmarks, the smart money effect disappears
(reaffirming the momentum phenomenon documented by Jegadeesh and Titman). Additional
information outside of the study leads them to conclude that investors are chasing winning funds
to achieve superior performance.
In addition to the “smart money” effects found by Gruber (1996) and Zheng (1999), other
studies have shown that there is a “dumb money effect.” More specifically, the mutual fund
purchasing decisions of individual retail investors produce underperforming returns, and can be
used as an anti-investment strategy (the “dumb money” effect). This study (Frazzini and
Lammont 2008) intends to capture the long-term trends of wealth creation or destruction, while
previous findings (e.g. Zheng, Gruber) focused on short-term effects. In general, if individual
investors have a high positive sentiment for a stock, there will be a rush of inflows pushing the
price up higher. Frazzini and Lamont investigate this phenomenon, using mutual fund flows as a
gauge of individual investor sentiment. If there is a strong, statistically significant inflow into a
fund, there must be a positive individual investor sentiment in the market. Therefore, they
Revisiting the Dumb Money Effect: Rushing Into Stellar Performing Funds University at Albany School of Business Working Paper
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hypothesized that if this is the case, astute investors savvy to this pattern should be able to
predict winning stocks by analyzing the fund inflows.
Frazzini and Lamont looked at flows and stock returns during the 1980 to 2003 period
from the CRSP Mutual Fund Database on a quarterly basis. They then measured the percent of
outstanding shares for each stock within the study that were owned by the mutual fund sector.
This is then adjusted to reflect the disproportionate flows into the different funds, the effect of
different portfolio weightings across funds and the difference in purchase price. This variable
(“FLOW”) is an indicator of the portfolios of funds experiencing large inflows. Three-year
flows were used as the baseline time horizon to measure the long-term changes in wealth for
individual investors. They tested to control for size, value and price momentum (like Sapp and
Tiwari) of the different funds. In contrast to smart money, the dumb money effect describes the
relation between the flows and the shares of a stock purchased by these high and low flow funds.
After aggregating all of the data, the analysis showed that the stocks that individual
(retail) investors choose have low future returns (the dumb money effect). These
underperforming effects on returns are compounded by the costs of periodically switching
mutual funds. Contributing factors to the diminished returns of individual investors include the
tendency to jump on new issuances, overweight growth stocks and poorly pick security/mutual
funds. In addition, they conclude that the dumb money effect is related to the “value effect.”
The value effect is when money flows out of mutual funds that own purely or mostly value
stocks and into funds that are growth oriented.
This dumb money effect could have an impact on management’s allocation decisions that
may hurt the performance of the fund going forward. Solomon shows that when mutual funds
decide to change investment strategies, untimely fund flows could have an adverse effect on
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future returns. As dumb money flows into a fund, the new flows are used to reweight the entire
portfolio. The low future returns expected in the dumb money effect will cause the portfolio to
depreciate. In response to this depreciation, fund managers change the investment thesis of the
portfolio to match other funds with high inflows (the adverse investor pressure hypothesis).
The work of Keswani and Stolin is of special importance to this study, as it accomplishes
the task of the current paper but using data from the U.K. In contrast to previous work by
Gruber, Zheng, and Sapp/Tiwari, Keswani and Stolin (2008) use monthly fund inflows and
outflows instead of quarterly data. Also, they add a new element to the study: they investigate
the difference in inflows and outflows between institutional and individual investors. The
methodology has a major difference as well: the above studies aggregate the money flows to
funds, relying on factors like NAVPS and fund returns, while Keswani and Stolin have access to
exact net flows for U.K. mutual funds. The study discusses the U.K. mutual fund industry’s
major differences from its larger counterpart in the U.S.
One major difference is in the U.K. there are explicit rules for classifications of mutual
funds into objectives and strategies, while in the U.S. this is something investors and rating
agencies must decipher for themselves (if the mutual fund itself does not volunteer this
information). Secondly, the U.K. system has a more simplified tax structure for mutual funds
compared to the U.S. additional taxes on distributed net capital gains from the mutual fund.
Manually fine-tuning for a survivorship bias and funds with fewer than ten months of data
(among other adjustments), they use 3,456 U.K. equity funds from 1992 - 2000 time period.
They find that there is statistically significant evidence of a smart money effect in the
U.K., shown by the performance difference between positive and negative net flow funds, driven
by fund purchases. These results do not differ between individual and institutional investors.
Revisiting the Dumb Money Effect: Rushing Into Stellar Performing Funds University at Albany School of Business Working Paper
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Repeating the control for small versus large fund size as seen in Zheng’s study, they found no
major discrepancies in the smart money effect between fund sizes. They determine that the
difference in findings is not country-specific, but directly relatable to the data samples that were
taken (monthly in the U.K. vs. quarterly in the U.S.). Keswani and Stolin reexamined Sapp and
Tiwari’s study using monthly instead of quarterly flows in the time period after 1991, compared
to the initial time period of 1970 -1990. The findings reflect a smart money effect even when
controlling for a momentum factor. They repeated the study with quarterly flows after 1991 and
achieved the same results. Therefore, Sapp and Tiwari’s initial findings seem to be heavily
biased by the time period and duration fund flows. A caveat of this paper, as well as Gruber’s, is
that smart money cannot account for all of this effect. Keswani and Stolin show this by
comparing the effect net-of-charges for new money with money passively invested over the same
time horizon. Regardless, within the universe of actively managed funds this smart money
phenomenon seems to persist.
Why isolate the differences in flows between institutional and individual investors? Like
Zheng initially pointed out, a clearer picture of return patterns of individual investors could
produce a profitable trading strategy. On a more general note, an understanding of the
investment strategies of major contributors in the market could help recognize inefficiencies and
opportunities. Gibson and Safieddine (2003) identify a major role that institutional investors
play as price setters. These institutional investors consist of mutual funds, pension funds,
insurance companies, and other large organizations that spend billions of dollars per year on
investment research. These behemoths are characterized as informed investors in contrast to
individual investors that are more reactive to the moves of others. Using data from 1980 - 1994,
they create various types of portfolios and isolate quarterly institutional ownership and stock
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return. The benchmark-adjusted return is calculated by subtracting the CRSP value-weighted
index return for each portfolio. Gibson and Safieddine find that (with the exception of small-cap
stocks), institutional investors’ initial trades lead stock prices higher. Increases (Decreases) in
institutional ownership are significantly correlated with positive (negative) returns. The two
controlled for potential confounding variables, including the momentum effect from quarter-to-
quarter. Focusing only on institutional investors, Gibson and Safieddine fail to capture the
behavioral implications from the perspective of an individual investor.
A large number of previous research shows that in contrast to fundamental assumptions
in the field of economics, investors do not act rationally. Instead of diminishing systematic risk
by diversifying portfolios, many hold overweight positions. Individual investors are more likely
to trade actively (and thus, at a higher cost) while taken on more speculative investments rather
those that are grounded in fundamentals. With this in mind, it is not surprising that many
individual investors hand over this burden to mutual funds, especially as individual investors lick
their wounds from the harsh investment climate of the past decade.
Barber and Odean (2011) discusses the different factors that individual investors
encounter when making investment decisions, along with the patterns that cause them to invest
in unconventional ways (compared to fundamental standards of institutions). One factor is the
limited amount of time that investors have to research investment decisions. It is well
documented that when picking stocks, investors focus on stocks that are well publicized or first
catch their attention. This causes individual investors to miss important information that could
erroneously lead them to believe that they have found an attractively valued stock. Barber also
notes that individual investors are more likely to trade S&P indexed stocks that have recently
been written about in a newspaper. This insufficient time allowance allocated towards
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investment research may affect individuals that invest in mutual funds rather than stocks. If they
don’t have time to investigate important information such as the underlying securities within
funds or the manager’s background and experience, they may rely on funds that have
outperformed in the past. This could cause individual investors to rush into funds that have had
stellar performance of late, especially in the most recent year.
A number of different psychological factors could cause individual investors or
institutional investors to rush into stellar performing funds. Institutional investors that are
constantly exposed to the financial markets could be affected by an availability bias (Pompian
2012). If an institutional investor is following a fund for an extended period of time and
recognizes its superior performance, this may act as a mental shortcut. The next time a mutual
fund investment decision is made, the institutional investor could erroneously estimate that the
probability of future returns is higher due to the earlier exposure to this information. The easier
it is for the investor to recall the fund’s outperformance, the more likely he or she is to have an
availability bias. Both sets of investors (especially those that are less disciplined) can be
influenced by emotional biases like the loss-aversion bias. This theory states that investors
would much rather prefer to avoid losses rather than gaining positive returns. So, it may be
much more comforting to invest in a fund that has a convincing track record of avoiding losses.
In addition, the investment decisions of institutional investors could cause an
“information cascade” that flows down to individual investors. Cleary and Atkinson (2012)
describe the information cascade as the flow of information from those who act first to those who
follow the crowd. If many individual investors are basing their investment decisions on
institutional investors, this would impact their seemingly heightened trading activity. In contrast
to “dumb” investors trading on any information in the 24-hour news cycle, the more disciplined
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institutional investor has a stronger sell discipline and thus, a lower sensitivity to changes in
stock price or new information.
The current study reflects many of the themes and methodologies used in previous works.
For example, like some of the aforementioned studies, institutional and individual share classes
are matched up and compared. Excess returns and fund flows are used as a measure to compare
the two classes. The alternative hypothesis is as follows:
HA: There is a difference in the relation between excess returns and subsequent
fund flows for institutional v. individual investors.
III. Methodology and Data
Using data from Morningstar Direct, the study uses monthly U.S. mutual fund flows
across a range of objectives and allocations. While these funds are located in the U.S., the
underlying assets do not necessarily represent investments stationed on U.S. exchanges. The
data include defunct funds to eliminate any survivorship bias, which is a common practice of
previous studies. To build on the work of Frazzini and Lammont, the study uses data from
January 2003-October 2012. The fund flows are implied flows compiled by Morningstar, unlike
the exact flows that are available in the U.K. (as used in Keswani and Stolin’s study). This
information is the most accurate information that is available for U.S. mutual funds. Excess
returns are used for two main reasons: (a) absolute returns would be influenced by major market
movements unrelated to the individual fund performance and (b) institutional investors will base
many of their buying and selling decisions on the performance relative to a given benchmark.
The fund flows of the current period (t=1) are compared with the excess returns from the period
before (t-1) to determine how excess returns (dependent variable) affect fund flows (independent
variable).
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Figure 2 shows the breakdown of each screening variable and the corresponding number
of funds remaining at each point. In order to weed out confounding variables, index, life cycle,
socially conscious, non-diversified, enhanced index, Sharia compliant and funds of funds are
initially ruled out. All of these types of funds are unique in their nature and may have abnormal
fund flows than a typical U.S. mutual fund. Next, each fund has to have a primary prospectus
benchmark to be included going forward in order to justify using excess returns in relation to the
fund’s primary prospectus benchmark.
The funds that had empty excess returns or fund flows are removed to avoid including
funds with no data. At this point, in some fund families there are multiple institutional and retail
share classes with the same primary prospectus benchmark and investment objective that would
unfairly weight larger fund families against their smaller or defunct counterparts. Therefore,
within each fund family of institutional and retail share classes, the share class with the largest
(positive or negative) aggregate fund flow is used to represent the entire fund. Then, each
institutional share class is matched up with its retail pair. The share classes that did not have a
counterpart are screened out at this point, leaving a total of 5,770 share classes (and 2,885 pairs
of retail and institutional share classes). The fund flows and excess returns of the 2,885 pairs of
retail and institutional fund classes are used as a representative sample to examine the difference
in investment and redemption behavior between the two types of investors.
Matching up corresponding share classes is not a simple task, for most of the names of
institutional differed from the retail share classes they are matched up with. In order to get
around this problem, a “unique ID” consisting of pieces of management history, management
name, fund names, Morningstar category, and other fund-defining characteristics are
concatenated.
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Figure 2: Data Screening
Total Number of Funds Screening Variable
48,999 Starting Funds
43,452 No Funds of Funds
42,023 No Index Funds
41,876 No Life Cycle Funds
41,224 No Socially Conscious Funds
32,875 No Non-Diversified Funds
32,525 No Enhanced Index or
Sharia Compliant Funds
25,431 No Primary Prospectus
Benchmark Listed
19,429 Empty Flows or Excess Returns
9,443 Largest Institutional and
Retail Share Class
5,770 No Matching Institutional or
Retail Share Class
--- ---
2,885 Pairs of Institutional and
Retail Share Classes
These fund-specific identifiers create a singular identifying category, to avoid pairing together
funds with different managers, fund history, or investment objectives. In addition to matching
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up the share classes, the unique ID differentiates the multitude of retail and institutional share
classes within a fund family that would otherwise appear to be identical.
The data represents all asset classes and a variety of investment objectives. In a perfect
world, each major type of U.S. asset class would be equally weighted according to its underlying
weighting in the U.S. mutual fund universe. But, this is unlikely after screening for a variety of
other factors (see Figure 2). As shown in Figure 3, the lion’s share of mutual funds has an
underlying asset class of U.S stock funds (47.5%). Some types of mutual funds are
underrepresented, such as commodities (0.1%), alternative assets (1.7%) and balanced funds
(5.9%). Although this imbalance may seem like a factor that would bias U.S. stock and taxable
bond funds, these funds should be more heavily weighted in the first place. These two
fundamental asset classes represent the most basic and common types of investments available to
individual and institutional investors.
Each pair of retail and institutional share classes holds data of excess returns and fund
flows for a 117-month period (January 2003 to October 2012). The fund flows and excess
returns are aggregated on a month-by-month basis and separated by retail and institutional share
class. For example, as shown in Figure 4, all of the institutional fund flows in November 2005
were combined to form one data point for that time period. This information on fund flows
would later be regressed on institutional excess returns for one period before (October 2005).
This cross-sectional study will compare the regressions of fund flows on excess returns for two
different data sets: retail and institutional funds. Each regression uses data from the aggregate
fund flows and average excess returns from the 117-month period, spanning from January 2003
to October 2012.
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Figure 3: Breakdown of Asset Classes
Alternative
Balanced
Commodities
InternationalStock
MunicipalBond
SectorStock
TaxableBond
U.S.Stock
Alternative 1.7%
Balanced 5.9%
Commodities 0.1%
International Stock 17.3%
Municipal Bond 5.6%
Sector Stock 2.8%
Taxable Bond 19.0%
U.S. Stock 47.5%
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Figure 4: Aggregate Institutional Fund Data
V. Results
In order to compare the regressions of institutional and retail funds, a Chow test is used.
Shown in Figure 5, this test enables the researcher to determine if the independent variables
(flows) have a different effect on the dependent variable.
Figure 5: Chow Test
In other words, are the slopes for the regressions of institutional and retail share classes different
and at what statistical significance level? As stated above, the data in Figure 3 are used in each
regression calculation. To compare the slopes of each regression, the Chow test uses the sum of
squared error for retail, institutional and combined regressions. This “combined” variable was
Year Month Flows (t=n) ER (t=n‐1)
2005‐10 11,205,231,333 ‐1.18
2005‐11 5,750,766,504 1.06
2005‐12 3,744,376,185 ‐0.15
2006‐01 5,665,352,752 2.20
2006‐02 8,347,527,586 2.22
2006‐03 4,087,778,095 0.49
2006‐04 5,960,142,055 ‐1.93
2006‐05 3,378,456,985 0.54
2006‐06 2,520,951,104 3.91
2006‐07 2,164,717,939 1.51
2006‐08 2,918,728,632 4.11
2006‐09 2,002,813,581 2.24
2006‐10 3,732,458,872 ‐0.10
2006‐11 3,430,144,203 ‐0.12
2006‐12 5,593,648,022 5.52
2007‐01 7,615,356,053 8.29
2007‐02 8,502,351,753 ‐2.86
2007‐03 5,426,718,471 3.04
Institutional Funds
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created prior to the regression by combining the fund flows and excess returns for retail and
institutional share classes. Each of the 117 months contains a Chow test to measure the
difference in slopes between the regressions. An F-statistic determines the statistical
significance of each Chow test under the month’s degrees of freedom. Figure 6 shows the
statistical significance of the Chow tests at the 90%, 95% and 99% confidence levels. Even at
the 99% confidence level, 85% of the 117 months were statistically significant. In other words,
in 100 out of 117 months the slope of retail and institutional regressions were significantly
different from each other.
The average and median p-values demonstrate the extent to this significance. Nine
outliers cause the distribution of p-values to be positively skewed, making the average p-value
materially higher than the median. These outliers each had negative Chow tests, driven by heavy
fund flows in each respective month. Many of these months occurred amid times of financial
turmoil and panic, which would explain an underlying flow factor. For instance, two of these
months occurred in the third and fourth quarters of 2008.
Figure 6: Results
The degrees of freedom of the data points are noteworthy, due to its importance in the
Chow test calculation. As shown in Figure 4, the denominator is divided by degrees of freedom.
Parameter T Value Parameter T Value Parameter T Value
Average 95,974 1.4691 55,820 0.8290 40,154 0.6401
Median 78,843 1.6000 35,708 0.6500 43,135 0.9500
79 80
68% 68%
90% 95% 99% Average 0.0057
108 108 100 Median 0.0029
92% 92% 85%% Total Months
Difference
Total Significant Months
% Total Months
Retail > Inst. Count
P‐Value (Using F‐Stat)
117
Total Months Confidence Interval
Chow Test
InstitutionalRetail
Beta Comparison
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This is the total number of institutional and retail mutual funds minus double the parameters.
With 2,885 pairs of funds, the number of degrees of freedom is daunting. Some of the fund
classes do not have information on excess returns or fund flows in some months and are left out
of the Chow test and degrees of freedom calculation. Even taking this into account, the number
of degrees of freedom is massive for all months—the average number of degrees of freedom of
the denominator is 4,016.
The beta comparison chart of Figure 5 shows the specifics of the retail and institutional
regressions. In this table, the parameter represents the regression slope of institutional and retail
share classes. In 68% of the total months, the average parameter and t-value of retail regressions
was larger than its institutional counterpart. Therefore, with a steeper slope retail classes were
more sensitive to excess returns in respect to fund flows than institutional classes in 79 out of the
total 117 months.
VI. Concluding Remarks
The statistical significance of the above findings at the 99% confidence interval allows
the null hypothesis to be rejected at a comfortable level. This suggests that there is a difference
in the relationship between excess returns and subsequent fund flows between the two types of
investment classes. In addition, it appears as if retail investors are more sensitive and prone to
investment and redemption behavior than institutional investors in the face of changes in excess
returns. This could be driven by a variety of underlying psychological factors or environmental
influences affecting both types of investors.
In a 24-hour news world, there is a headline at any time of day or night. In the face of a
grim headline, some investors may feel compelled to sell out of a position. The difference in sell
discipline may cause individual investors to be more “quick on the trigger” in comparison to
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institutional investors. It seems likely that the sophisticated investment professionals that
manage institutional accounts have a greater sell discipline than individual investors. Also, as
stated in the research earlier in this paper, the investment decisions of individual investors have
shown to reflect those of “dumb” investors.
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VI. References
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Cleary, S. and Atkinson, H, “Market Efficiency,” Level I CFA: Equity and Fixed Income (Vol. 5
No. 49), pp. 148-150.
Frazzini, A. and Lamont, O, 2008, “Dumb Money: Mutual Fund Flows and the Cross-Section of
Stock Returns,” The Journal of Financial Economics (Vol. 88), pp. 299-322.
Gibson, S. and Safieddine, A, 2003, “Does Smart Money Move Markets?,” The Journal of
Portfolio Management (Vol. 29 No. 3), pp. 66-77.
Goetzmann, W, Massa, M. and Rouwenhorst, G, 1999, “Behavioral Factors in Mutual Fund
Flows,” Yale International Center for Finance Working Paper (December 1999 No. 00-14).
Gruber, M, 1996, “Another Puzzle: The Growth in Actively Managed Mutual Funds,” The
Journal of Finance (Vol. 51 No. 3), pp. 783-810.
Hammond, P. and Leibowitz, M, 2004, “The Changing Mosaic of Investment Patterns: A New
Picture of Allocation Decisions,” The Journal of Portfolio Management (Vol. 30 No. 3), pp.
3-25.
Jegadeesh, N. and Titman, S, 1993, “Returns to Buying Winners and Selling Losers:
Implications for Stock Market Efficiency,” The Journal of Finance (Vol. 48), pp. 65-91.
Keswani, A. and Stolin, D, 2008, “Which Money is Smart? Mutual Fund Buys and Sells of
Individual and Institutional Investors,” The Journal of Finance (Vol. 63), pp. 85-118.
Pompian, M, 2012, “The Behavioral Biases of Individuals,” Level III CFA: Behavioral Finance,
Individual Investors, and Institutional Investors (Vol. 2 No. 7), pp. 51-110.
Sapp, T. and Tiwari, A, 2004, “Does Stock Return Momentum Explain the “Smart Money”
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Effect?,” The Journal of Finance (Vol. 49 No. 6), pp. 2,605-2,622.
Solomon, D, 2012, “Changing Horses Midstream: The Causes and Effects of Changes in
Investment Strategy Amongst Mutual Funds,” University of Chicago Booth School of
Business Working Paper.
Zheng, L, 1999, “Is Money Smart? A Study of Mutual Fund Investors’ Fund Selection Ability,”
The Journal of Finance (Vol. 54 No. 3), pp. 901-933.
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