1. 1 | P a g e
“Smart Money Effect” and Potential Time Lag
Modeling in 2007-2010 and 2012-2015
Cornell University
ORIE 5370 Project 1
Shabai Chen (sc999)
Ruiqi Zhang (rz268)
Qi Zhou (qz256)
March, 2016

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I. Introduction:
1.1 Literature Review and Incentives:
The “smart money effect” hypothesis of Gruber (1996)i
and Zheng (1999)ii
suggests that
investors have selection ability. There are some fund managers and some individual investors
can detect those high-skilled fund managers and send their money to skilled managers. Gruber
(1996) and Zheng (1999) show that the short term performance of funds with inflows (which
equally means with positive net cash flows) is significantly better than those with outflows(or
negative net cash flows), suggesting that mutual fund investors have selection ability. And in
Travis Sapp and Ashish Tiwari’s research “Does Stock Return Momentum Explain the “Smart
Money” Effect?”iii
, they take momentum into account and try to answer the question: whether
the smart money effect is really due to fund-specific information as suggested by Gruber(1996)
and Zheng(1999), or whether it can be explained by exposure to momentum. Momentum is an
important common factor in explaining stock returns, as explored by Carhart(1997)iv
. Carhart
shows that the previously documented evidence of persistence in mutual fund performance is
not robust to the momentum factor.
Combining the above two interesting topics, we want to examine “smart money effect with
momentum factor” on recent data and particularly we would like to make a comparison
between the financial crisis period and the recent period. Our guess is that during the financial
crisis period, due to the nature of the unstable and risky market and the fact that people make
irrational investment decisions during such turbulence, momentum would have a limited
influence on the alpha, which should be smaller and even turn negative using the three-factor
Fama-French Modelv
compared with both the prior and post crisis period.
In addition, in Travis Sapp and Ashish Tiwari’s research they made the implicit assumption
that there is a potential one time-period (one month in their research) lag between the time that
investors make investment decisions and either invest in or divest from the mutual funds and
the actual time that corresponding funds’ returns show actual differences. In this paper we
would like to further challenge this assumption by including two different models regarding
the time-lag.
1.2 Modeling and Results
To address the above two questions we have in mind, we conduct our research based on
data of mutual funds within a cross-sectional regression framework. We use the complete
universe of diversified U.S. equity mutual funds for the period of 2007-2010 and for the period
of 2012-2015 in CRSP Survivor-Bias Free U.S Mutual Fund Database.
We begin by exploring the smart money effect by explicitly controlling for momentum.
After excluding momentum, if the investors are indeed able to select high-skilled mutual fund
managers, then the new cash flows to those outperformance funds should continue to earn
positive returns anyway. Using the similar methodology as Gruber(1996) and Zheng(1999)
did, we construct two new portfolios of funds with different net cash flows, named “Positive
Net Cash Flow Portfolio(PNCF)” and “Negative Net Cash Flow Portfolio(NNCF)”. Then, we
run the regression of PNCF and NNCF respectively on the three-factor Fama-French model
which are Market Excess Return(MExR), small minus big(SMB) and high minus low(HML).
Secondly, we include the momentum factor and run the regressions again. To be noticed, we

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go through such processes on the datasets from 2007-2010 and 2012-2015 based on two
different time-lag models described above.
Our results show that while during the 2007-2010 financial crisis period PNCF yields a
slightly higher alpha than the NNCF with both alphas falling in the negative range, the data of
2012-2015 yields some surprisingly irregular results with PNCF yielding smaller alpha than
the NNCF. Nevertheless, regression results of both the datasets prove that momentum could
largely explain the smart money effect which leads to the increment in alpha. In addition, for
the second question regarding the time lag model we seek to solve, we find that the 1-month
Lag Model has a relative advantage over the No Lag Model.
II. Data and Methodology
2.1 Data Sample Selecting and Raw Data Processing :
To obtain data of the mutual funds during both 2007-2010 financial crisis time period and
recent prior-crisis time period, we use the data from CRSP Survivor-Bias Free US Mutual Fund
Database’s Return and Fama Model section of two specific periods, year 2007 to 2010, and
year 2012-2015. This provides data of the variables including Fund Identifier Number, Net
Asset as of the Month End, Total Return as of Month End, Merger or Liquidation Identifier,
the Three-Factor Fama-French Model variables (Excess Return on Market, Risk-free Return,
Small-Minus-Big Return and High-Minus-Low Return) and Momentum Factor at a monthly
basis.
We begin data processing by eliminating the funds that might have irregular composition
or factors that may influence the returns, including sector funds, specialized funds, balanced
funds and international funds. Through sorting the Fund Identifier Number using another
section of the dataset, CRSP Mutual Funds-Summary section, we successfully exclude those
funds through sorting the Lipper classification codes of funds and hence creating a new list of
Fund Identifiers that we are able to use in picking suitable funds for our model. We then
exclude the funds with irregular fund size (error code -99 or blank) and blank rate of return
which would reduce the accuracy of the model. In addition, we also get rid of the funds which
experience merges within the past one month period to prevent the change of total net asset of
the fund from being influenced by the merger or acquisition, which is a significant assumption
for our below calculation of the total cash flow.
Please check the table 1 in appendix for the summary statistics regarding the dataset for
2007-2010 and 2012-2016, each of which contains data of 48 months. For 07-10, we have
5,138 different funds included with 195,443 samples in total, while for 12-15 we have 5,254
different funds with 229,336 samples in total. The average net assets for 07-10 and 12-15 time
periods are 463.11 and 554.98 million respectively, and the corresponding average monthly
returns are 0.25% and 0.46%. In addition, regarding the net cash flows as defined below in the
next section in two different models, we have 3.46 and 3.52 million for 07-10, and 2.06 and
2.07 million for 12-15. For detailed report on features of the dataset, please check the table.

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2.2 Grouping of the New Portfolios:
We then follow the approach of the literature by forming two portfolios based on the net
cash flow (NCF) experienced by the fund. NCF in the current month is calculated using the
Total Next Assent (TNA) by the end of this month minus the TNA by the end of last month
times the net return (1+r). To be noticed, as we have eliminated the funds that experience
merges or acquisitions during the past month, the cash flow is essentially not influence by any
factor other than the additional new net investment the fund either gained or lost during the
past month.
𝑁𝐶𝐹𝑖,𝑡=𝑇𝑁𝐴𝑖,𝑡- 𝑇𝑁𝐴𝑖,𝑡−1×(1+𝑟𝑖,𝑡)
We then group all the funds into two portfolios, either the portfolio with positive net cash
flow or negative net cash flow based on this formula.
Starting here, we conduct two separate tests on two different models. As mentioned in the
introduction, we doubt whether there exists such a lagging effect for the smart money effect,
which is reflected by time interval between the appearances of new NCF and corresponding
influence on portfolio returns. Therefore, we would explore this problem by developing two
different models, the No Lag Model and the 1-month Lag Model.
For the first model ‘No Lag Model’, it assumes that the smart money effect takes place
simultaneously during the month when the investment or divestment occurs, and hence in the
tth month, we rebalance based on NCFi,t, with the implicit assumption is that investors make
investment decisions based on the past information in (t-1)th month and would influence the
corresponding returns in tth month.
In contrast, the second model ‘1-month Lag Model’ which is also the model used by Sapp
and Tiwari assumes that the smart money effect exhibits a one-period lag right after the new
cash flow appears, hence in tth month we rebalance the portfolio based on NCFi,t-1, implying
that that investors make investment decisions based on the past information in (t-2)th month
and would influence the corresponding returns in tth month.
-15
-10
-5
0
5
10
PortfolioReturns(in%)
Month/Year
2007-2010 1-Month Portfolio Returns
-15
-10
-5
0
5
10
PortfolioReturns(in%)
Month/Year
2007-2010 No-lag Portfolio Returns

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For each dataset (2007-2010 &2012-2015) we have four years’ time period which is
divided into 48 months. Consequently for No Lag Model, at the beginning of each month
starting the second month, we would rebalance portfolios based on the NCF in the current
month and hence we would rebalance 47 times in total. Similarly, for the 1-month Lag Model,
we would start rebalancing at the beginning of the third month based on the NCF in the past
month and hence would in total rebalance 46 times. In addition, we considered two difference
weighted approaches here for the two portfolios, an equally weighted approach which divides
the total investment equally into all qualified funds, and a TNA-weighted approach which
allocates the total investment into the qualified funds based on the relative percentage of the
TNA of the fund in the total asset values of all funds in the portfolios. The results proved to
be generally the same after being tested on some randomly selected samples and hence we
would only report the final statistics for the equally-weighted approach here for simplicity.
One thing to be noticed here is that we implicitly assume a 100% reinvestment ratio in the
funds of the dividend received for all investors.
-4
-2
0
2
4
PortfolioReturns(in%)
Month/ Year
2012-2015 1-Month Portfolio Returns
-4
-2
0
2
4
PortfolioReturns(in%)
Month/ Year
2012-2015 No-lag Portfolio Returns
-15
-10
-5
0
5
10
2007-2010 1-Month Portfolio Retruns (Positive/Negative NCF)
Positve NCF Negative NCF
-15
-5
5
2007-2010 No-lag Portfolio Returns (Positive/Negative NCF)
Positive Negative

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Please check the table 2 in the appendix for descriptive statistics for the returns including
the new cash flow based on the two models. Note that from the summary we see that for 2007-
2010, the PNCF portfolio with an average return of 0.26% outperforms the NNCF portfolio
with an average return of 0.15% under the no lag mode but it outperformed by the NNCF
portfolio (0.17% vs 0.21%) under the 1-month lag model. In contrast, under both no lag and
1-month lag models, the 2012-2015 PNCF portfolio fail to outperform the NNCF portfolio
(0.35% vs 0.59% and 0.40% vs 0.41%). Please check the tables for the detailed information
and statistics regarding the returns.
Based on such two different models and returns of the datasets of the two time periods, we
then conduct the performance regression analysis.
III. Performance Evaluation:
3.1 Evaluation Models:
We evaluate the performance of the positive and negative cash flow portfolios using a
three-factor Fama-French Model fundamentally. In addition, to check whether momentum is
the main factor for the smart money effect as claimed by Travis Sapp and Ashish Tiwari’s
research, we also conduct a regression research based on the four-factor model as in Carhart
(1997), which includes a momentum factor and is superior to both the CAPM and the Fama-
French three-factor model in explaining the cross-sectional variation in fund returns. The
benchmarking model is given specifically by:
𝑟𝑝,𝑡 = 𝛼 𝑝 + 𝛽1,𝑝 ∙ 𝑅 𝑝,𝑡
𝑚𝑘𝑡
+ 𝛽2,𝑝 ∙ 𝑅 𝑝,𝑡
𝑆𝑀𝐵
+ 𝛽3,𝑝 ∙ 𝑅 𝑝,𝑡
𝐻𝑀𝐿
+ 𝛽4,𝑝 ∙ 𝑅 𝑝,𝑡
𝑀𝑜𝑚
+ 𝜖 𝑝,𝑡
-5
0
5
2012-2015 1-Month Portfolio Returns(Positive/ Negative NCF)
Postive NCF Negative NCF
-5
0
5
2012-2015 No-lag Portfolio Returns (Positive/ Neative NCF)
Positive NCF Negative NCF

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Here, 𝑟𝑝,𝑡 is the average monthly excess return on a portfolio of funds over one month T-
bill return; 𝑅 𝑝,𝑡
𝑚𝑘𝑡
is the excess return on a value-weighted market portfolio; 𝑅 𝑝,𝑡
𝑆𝑀𝐵
, 𝑅 𝑝,𝑡
𝐻𝑀𝐿
,
𝑅 𝑝,𝑡
𝑀𝑜𝑚
are returns on zero-investment factor-mimicking portfolios for size, book-to-market,
and 1-year momentum in stock returns. We test for fund selection ability on the part of
investors by examining the differences between the alphas of the positive and the negative cash
flow portfolios. In order to provide a comparison to previous studies that haven’t incorporated
a momentum factor in the performance benchmark, we also report the portfolio alphas based
on the Fama-French three-factor model that excludes the momentum factor.
We begin our analysis by examining whether investors are able to earn superior returns
based on their investment decision. In total we conduct eight regressions using Matlab based
on the data we processed as described in the previous section. We conduct both the three-factor
and four-factor regressions on the positive cash flow and negative cash flow portfolios of 2007-
2010 and 2012-2015 using the No-Lag Model. Then we replicate the whole process using the
1-month Lag Model again.
3.2 Evaluation Results Analysis:
As shown in the table above, all the results are significant except for the two NNCF
portfolios of 2012-2015 using No-Lag Model, with adjusted R-square of 0.691 and 0.690 for
three-factor and four-factor model respectively. Most of the alphas in the table are negative
(shown in parentheses) and we only have four positive alphas which are all in No-Lag models
for PNCF portfolios of 2007-2010 and NNCF portfolios of 2012-2015.
The PNCF portfolio of 2007-2010 for the four-factor 1-month model has a statistically
significant alpha of -4.2 basis points (bps) per month, which is lower than the significant alpha
of three-factor 1-Month model (-3.2bps) . The alpha of the NNCF portfolio for the four-factor
1-Month model is a significant -8.6 bps, lower than the significant alpha of three-factor No-
lag model (-5.9bps). For the period of 2007-2010, the PNCF portfolio for the four-factor No-
lag model has a statistically significant alpha of 3.7bps, lower than the significant alpha of
three-factor No-lag model (5.8bps). For NNCF portfolio, the 2007-2010 four-factor No-lag

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model has a significant alpha of -12.0, again lower than the significant alpha of 2007-2010
three-factor No-lag model(-10.4).
The PNCF portfolio of 2012-2015 four-factor 1-Month model has a statistically significant
alpha of -10.4 basis points per month, lower than the significant alpha of three-factor 1-Month
model (-6.1bps). The alpha of the NNCF portfolio for the four-factor 1-Month model is a
significant -7.9bps, lower than -7.1bps in the respective three-factor model. For the No-lag
model, the PNCF portfolio for the four-factor model has a alpha of -18.9bps which is lower
than -12.9bps. For NNCF portfolio, the 2012-2015 four-factor No-lag model has a
nonsignificant alpha of 8.4, comparing with the significant alpha of 13.7 for three-factor No-
lag model during 2012-2015.
IV. Time Period Comparison Analysis and Lag Model Evaluation:
4.1 Comparison Between 2007-2010 period and 1970-2000 period
For the 1970-2000 period, we see that from Travis Sapp and Ashish Tiwari’s research
“Does Stock Return Momentum Explain the “Smart Money” Effect?”, they do research on the
dataset from 1970 to 2000. When they perform regressions with a three-factor benchmark
model and does not include the momentum factor, the result gives a significant positive alpha,
showing the “smart money effect” which is a strategy of investing in the positive cash flow
portfolio and short-selling the negative cash flow portfolio. Then they include the stock return
momentum, and found that the smart money effect does not exist anymore, indicating that the
smart money effect arises largely from the momentum effect but not the investors’ selection
ability as assumed previously.
In our research, for the 2007-2010 financial crisis period’s results, for the 1-Month model,
when we exclude the momentum factor, we have negative alpha at -0.00032 and -0.00059 for
PNCF (positive net cash flow) and NNCF (negative net cash flow) portfolios respectively.
Then we include the momentum factor and get more negative alpha at -0.00042 and -0.00086
for PNCF and NNCF portfolios respectively. For the No-lag model, we get similar results.
By including momentum, values of alpha decrease from 0.00058 and -0.00104 to 0.00037 and
-0.00120. Hence our results yield the similar conclusions as in Travis Sapp and Ashish Tiwari’s
research but with some negative values of alpha. Note that here we could conclude that the
negative alpha is due to the extremely turbulent and risky market during the 2008 financial
crisis, which not only leads to the large drop in the overall return of the market reflected in the
independent variables, but also leads to the decrease in the possible selecting ability of the
potential rational investors who might be more careful in making their investment decisions.
4.2 Comparison Between 2007-2010 period and 2012-2015 period
The regression results for the period 2012-2015 are consistent with last part. In the 1-Month
model, when excluding the momentum factor in regressions we have alpha at the value of

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-0.00061 and -0.00071. Then we include the momentum factor in regressions, alpha changed
to -0.00104 and -0.00079 respectively. In the No-lag model, when excluding momentum factor,
values of alpha are -0.00129 and 0.00137. When running regressions with momentum factor,
alpha changed to -0.00189 and 0.00084. Again, our results consist with Travis Sapp and Ashish
Tiwari’s research and the negative alpha is due to financial crisis.
Regarding the irregularity during this period’s alpha for PNCF and NNCF, we have found
no solid explanation. Nevertheless, according to the publications on Board of Governors of the
Federal Reserve System (http://www.federalreserve.gov/publications/gpra/2013-strategic-
themes.htm), “the financial crisis of 2007-2009 has results in an enhanced approach to
supervision and regulation, which places a heightened emphasis on the health of both
individual institutions and the financial system as a whole.” The Board published a few
strategic objectives, such as strengthen the stability of the financial sector through the
development of policies, tools and standards, monitor financial markets and industry practices
and structures, monitor and supervise individual financial institutions and infrastructures. We
consider the new policies and regulations change the operational environments of mutual fund
in some way and causes an irregular performance during 2012-2015 period, but we need further
research on this topic.
4.3 Time Lag Model comparison
As mentioned earlier, one of the question we seek to solve through this research paper is
whether or not certain time lag exists between the entrance of new cash flow and the time when
the smart money effect on the returns of the funds selected by the investors takes place. Now
we compare the regression results of the two models on each of the datasets and see which
model shall be selected by the regression model and the data.
We begin by looking at the data of 2007-2010, the financial crisis period. Note that under
both the No Lag Model and the 1-month Lag Model, the alpha for the PNCF (positive net cash
flow portfolio) is always higher than the NNCF (negative net cash flow portfolio) regardless
of whether momentum is included in the regression. Secondly, the R-squared value for those
two models both appear to be extremely high and hence reflect a great degree of accuracy. In
addition, under both models, the inclusion of the momentum into the regression model drives
the alpha down, indicating that the smart money effect could be significantly attributed to the
momentum. Therefore from those three perspectives neither of the model has an obvious
advantage over the other one. Nevertheless, one thing to be noticed is that all of the alphas in
those regression results have relatively smaller scales compared with the standard error.
Consequently we would prefer to have a model that gives a lower standard error which leads
us to conclude that alpha is relatively more significant. Therefore, from the last perspective,
the No-lag model with a lower standard error is selected by the 2007-2010 datasets.
As for the 2012-2015 period, the 1-month Lag model has some significant advantages over
the No Lag model. Note that the alpha of PNCF is smaller than that of the NNCF under the No
Lag Model which also gives some relatively low R-squared value compared with those from
the 1-month Lag Model. Such results are not only irregular compared with our previous

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assumptions but also indicate certain levels of inaccuracy. Hence we conclude that the 2012-
2015 period selected the 1-month Lag model, contrary to the result of the 2007-2010 model.
Therefore, based on the above analysis on the two datasets, we see that although they make
contrary selections of the time lag model, the fact that result in 2012-2015 under the No Lag
model deviates extremely from the assumption and the requirement of accuracy while this
model has only minor advantage in the 2007-2010 period leads us to the conclusion that our
data prefers the 1-month Lag Model in general to the No Lag Model. Nevertheless, further
research might be conducted on a detailed and careful comparison among potential time lags
to reach a truly trustworthy conclusion.
V. Conclusion
In this paper, we explore Gruber(1996), Zheng(1999) and Travis Sapp, Ashish Tiwari’s
research, addressing topics on smart money effect and stock return momentum, including both
Fama-French three-factor and momentum factor on recent dataset during financial crisis and
post-financial crisis. Moreover, we extend our research by constructing two models, 1-Month
lag model and No-lag model. Our paper shows the smart money effect arises largely from the
momentum effect, suggesting that investors do not have ability to select skilled fund managers.
Hence our results yield the similar conclusions as in Travis Sapp and Ashish Tiwari’s research
but with some negative values of alpha. We suppose to conclude the negative alpha is due to
extremely irregular and risky financial markets during the 2008 financial crisis period.
Speaking of the two time lag models, we seek to explore whether or not certain time lag
exists. From our regression results, No-lag model with a lower standard error is selected by the
2007-2010 datasets. As for the 2012-2015 period, the 1-month lag model is selected. Overall
speaking, the 1-month lag model has slightly better performances in fitting the data, which also
corresponds with the method employed by Travis Sapp, Ashish Tiwari’s research.
As for future work, detailed and accurate explanation regarding the irregularity during the
2012-2015 time period’s regression alpha results that are contrary to our belief of the smart
money effect should be further studied. On the other hand, a more thoughtful testing on
different time lag between one month and no lag should be conducted to obtain a more accurate
result regarding this question.

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References
i Gruber, Martin J. "Another puzzle: The growth in actively managed mutual funds." The journal
of finance 51.3 (1996): 783-810.
ii Zheng, Lu. "Is money smart? A study of mutual fund investors' fund selection ability." The
Journal of Finance 54.3 (1999): 901-933.
iii Sapp, Travis, and Ashish Tiwari. "Does stock return momentum explain the “smart money”
effect?." The Journal of Finance 59.6 (2004): 2605-2622.
iv Carhart, Mark M. "On persistence in mutual fund performance." The Journal of finance 52.1
(1997): 57-82.
v Fama, Eugene F., and Kenneth R. French. "Common risk factors in the returns on stocks and
bonds." Journal of financial economics 33.1 (1993): 3-56.

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Appendix:
Table 1
Descriptive Statistics for Mutual Fund Sample
TNA Return NCF(No Lag)NCF(1-M Lag) TNA Return NCF(No Lag) NCF(1-M Lag)
Mean 463.114 0.00254495 3.4668055 3.54235561 554.9799534 0.004607658 2.05509997 2.072875125
Standard Error 5.66104 0.00012762 0.5296316 0.55424853 6.796583884 6.40615E-05 0.32218924 0.31973338
Median 44.2 0.003685 -0.005772 -0.0105558 38.8 0.001667 -0.0010371 -0.0001875
Mode 0.1 0 0 -9E-07 0.1 0 0 0
Standard Deviation 2503.59 0.05643802 234.22954 238.379063 3544.944654 0.033413021 168.032731 166.7657098
Sample Variance 6267984 0.00318525 54863.476 56824.5776 12566632.6 0.00111643 28234.9988 27810.80196
Kurtosis 377.059 5.13638206 2411.5067 2360.19943 1031.476146 248.0134947 8293.20987 8541.384857
Skewness 16.7134 -0.68771715 16.509049 16.555642 26.83102441 3.952857644 14.2600904 14.59166419
Range 98152.2 1.211976 38312.835 38312.8348 215908.4 3.20592 56277.3172 56277.31717
Minimum 0 -0.678219 -15602.13 -15602.126 0.1 -0.900029 -27685.581 -27685.5814
Maximum 98152.2 0.533757 22710.709 22710.7092 215908.5 2.305891 28591.7358 28591.73575
2007-2010 2012-2015
Total Summary Statistics of Selected Funds

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Table 2
Descriptive Statistics for Mutual Fund Portfolio Returns
Positive Portfolio Negative Portfolio Total Portfolio Positive Portfolio Negative Portfolio Total Portfolio
Mean 0.00264347 0.001463323 0.001866894 0.001742487 0.002105294 0.001854188
Standard Error 0.004409871 0.005468511 0.004995802 0.004506254 0.005628727 0.005105596
Median 0.007612415 0.008805423 0.008165143 0.008902718 0.010008717 0.009087279
Standard Deviation 0.030232552 0.037490224 0.03424949 0.030562904 0.038175887 0.034627837
Sample Variance 0.000914007 0.001405517 0.001173028 0.000934091 0.001457398 0.001199087
Kurtosis 1.102550235 1.305891824 1.386260493 0.860779345 1.660732444 1.293453585
Skewness -0.752653358 -0.802207988 -0.79664093 -0.855765931 -0.754991715 -0.787553187
Range 0.16134146 0.199299093 0.186436036 0.154092619 0.212233395 0.186436036
Minimum -0.093926991 -0.12161364 -0.110812122 -0.093863939 -0.125823844 -0.110812122
Maximum 0.067414469 0.077685453 0.075623914 0.06022868 0.086409552 0.075623914
2007-2010 Portfolio Returns Summary
No Lag (47 Periods) 1-month Lag (46 Periods)
Positive Portfolio Negative Portfolio Total Portfolio Positive Portfolio Negative Portfolio Total Portfolio
Mean 0.003545303 0.005591001 0.004055854 0.004028811 0.004072814 0.004055854
Standard Error 0.002179305 0.00241457 0.002301114 0.002212814 0.002391351 0.002301114
Median 0.003923906 0.007094142 0.005162588 0.005022177 0.005240157 0.005162588
Standard Deviation 0.014940564 0.01655346 0.015606916 0.015008035 0.016218933 0.015606916
Sample Variance 0.00022322 0.000274017 0.000243576 0.000225241 0.000263054 0.000243576
Kurtosis -0.373864997 -0.200319152 -0.370326135 -0.224668751 -0.426548106 -0.370326135
Skewness -0.287530582 -0.625643132 -0.434641276 -0.373579446 -0.44818083 -0.434641276
Range 0.061918144 0.067129047 0.060306542 0.06040516 0.064628892 0.060306542
Minimum -0.029885022 -0.036346848 -0.032240535 -0.030915921 -0.034290663 -0.032240535
Maximum 0.032033121 0.030782199 0.028066008 0.029489239 0.030338229 0.028066008
2012-2015 Portfolio Returns Summary
No Lag (47 periods) 1-Month Lag (46 Periods)

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Table 3
Descriptive Statistics for Mutual Fund Portfolio Returns
-4
-2
0
2
4
Difference(in%)
Month/ Year
2007-2010 1-Month Portfolio Return Differences
-4
-2
0
2
4
Differences(in%)
Month/ Year
2007-2010 No-lag Portfolio Return Differences
-1
0
1
Differences(in%)
Month/ Year
2012-2015 1-Month Portfolio Return Differences

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-6
-4
-2
0
2
4
Differences(in%)
Month/ Year
2012-2015 No-lag Portfolio Return Differneces