revisiting the dumb money effect-rushing into stellar performing funds

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

Revisiting the Dumb Money Effect: Rushing Into Stellar Performing Funds University at Albany School of Business Working Paper

Joseph Esposito 2 | P a g e

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



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



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



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



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



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



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



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.



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



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



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



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).



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.



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



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.



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%



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



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



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



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.



VI. References

Barber, B. and Odean, T, 2011, “The Behavior of Individual Investors,” University of California

Working Paper.

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.

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revisiting the dumb money effect-rushing into stellar performing funds

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