# Customer Momentum

Mykola Pinchuk Simon Business School, University of Rochester. Email:
<EMAIL_ADDRESS>
I would like to thank Yixin Chen, Bill Schwert, Giulio Trigilia, Shuaiyu Chen,
David Swanson, Zhao Jin, Pingle Wang and Robert Mann for helpful comments.

(08 December 2018)

ABSTRACT

This paper examines customer momentum, defined as a positive relationship
between a firm’s returns and past returns of its customers. I confirm previous
evidence (Cohen and Frazzini 2008) that customer momentum is both
statistically and economically significant. Long-short equally-weighted
(value-weighted) decile portfolio generates a monthly return of 122 (106)
basis points and a t-statistic above 4 (2.8) with respect to Fama-French
factor models. The paper reports that customer momentum neither explains nor
is explained by price momentum and earnings momentum. Customer momentum is
partially driven by the lead-lag relationship between small and large stocks.
I find that in the post-discovery sample, customer momentum has a smaller
magnitude and loses statistical significance. The results are consistent with
the hypothesis that after its discovery, customer momentum decreased due to
exploitation by investors.

##  1 Introduction

The supply chain link is arguably the most important measure of the
relationship between the firms. The distress of the main customer is bad news
for the firm. In the world of costless information production and unlimited
investor attention all value-relevant news, including information on the main
customers, is immediately incorporated into the stock price. The costs of
collecting this information in a world, where most analysts specialize in
separate stocks or industries, suggest that the diffusion of such information
need not be immediate.

####

According to the hypothesis of slow information diffusion, firms with high
recent returns of their customers are more likely to have high abnormal
returns. Cohen and Frazzini (2008) document a positive relationship between
the contemporaneous return of the firm and past returns of its customers. They
refer to this effect as customer momentum and interpret it by slow information
diffusion. This paper has two main objectives. The first goal is to replicate
the results in Cohen and Frazzini (2008) and verify them in the post-discovery
sample. The second objective is to explore the interaction between different
types of momentum effects in order to gain insight into whether they capture
different phenomena.

####

This paper examines the stock returns of the firms, linked by customer
relationships. Consistent with the past evidence, it finds return
predictability by past returns of the customers. Long-short equally-weighted
(value-weighted) portfolio, formed on past returns of customers, has a monthly
return of 122 (106) basis points, suggesting a large magnitude of customer
momentum. These returns do not decrease significantly after controlling for
traditional asset pricing factors. To further test whether customer momentum
is captured by factor models, I perform Fama-MacBeth regressions and factor-
spanning tests. In both cases factor models can not explain customer momentum,
implying that customer momentum is another asset pricing anomaly.

####

While presented results are consistent with limited attention, there may be
liquidity or risk-based explanations of customer momentum. To gain insight
into the possible interpretation of this effect as a risk premium, I examine
the relationship of customer momentum with macroeconomic variables. Customer
momentum has a very low correlation with consumption growth, GDP growth, and
different proxies for labor income. While these relationships are not a formal
test of risk-based explanation, they do not suggest that customer momentum
captures risk under consumption-based asset pricing models.

####

In the second part of the paper, I examine the relationship between customer
momentum, price momentum, and earnings momentum. Returns of customers and
suppliers exhibit a significantly positive contemporaneous correlation.
Therefore, customer momentum can be driven by the combination of the price
momentum of customers and the contemporaneous correlation between the returns
of customers and suppliers. Previous research (Chordia and Shivakumar 2006,
Novy-Marx 2015) suggests that earnings momentum captures price momentum. I
confirm these findings and report that after controlling for earnings
momentum, the effect of price momentum loses significance and reverses the
sign. Customer momentum, defined by the returns of the customer portfolio
within the most recent month, does not have explanatory power for price
momentum or earnings momentum. Customer momentum is not explained by the other
types of momentum, suggesting that it reflects a different effect. When
customer momentum is measured as past returns of the customer portfolio for
more than 1 recent month, it is captured by both price momentum and earnings
momentum. These results suggest that the customer momentum should be measured
using 1-1 lag since otherwise, it is a noisy proxy for price momentum.

####

By using the data after 2004, this paper provides an out-of-sample analysis of
the customer momentum effect, discovered by Cohen and Frazzini on 1981-2004
sample. Given the recent surge in the number of documented asset pricing
anomalies, researchers expressed concerns that these anomalies are just
statistical artifacts (Harvey, Liu and Zhu 2016). Alternatively, the anomalies
could have been real at the time of publication but disappeared due to trading
activity after the publication (Schwert 2003, Pontiff and MacLean 2015).
Customer momentum has the same pattern and smaller magnitude in the recent
sample. Returns of value-weighted long-short portfolios, formed on customer
momentum, are not significantly different from zero. The customer momentum
factor has a negative return during 2005-2018. These findings suggest that
customer momentum probably is not a statistical artifact, but becomes less
significant due to post-publication exploitation by investors.

####

Lead-lag relationship, defined as cross-correlations between returns of stocks
at different lags (Lo and MacKinlay 1990), can be an alternative explanation
of customer momentum. Since the sample consists of firms and customers, which
account for more than 10% of total sales, the mean customer is much larger
than the mean supplier. Therefore, there should exist some correlation between
past returns of large customers and current returns of small suppliers. After
restricting the sample to the links with the smallest ratio of customer-to-
supplier size, the magnitude of customer momentum decreases by a factor of
2-4. After adding an interaction term between past returns of customers and
their relative size to Fama-MacBeth regressions, customer momentum reverses
the sign. Overall, the results suggest that customer momentum is at least
partially driven by the lead-lag relationship between the returns of large and
small stocks.

####

Finally, this paper investigates the limited attention channel of customer
momentum. To measure attention to the customer-supplier link, I calculate the
normalized abnormal trading volume of the supplier during the day of the
earnings announcement of its customer. No spike in the trading volume of the
supplier during the customer’s earnings announcement can imply low attention
to the customer-supplier link. The results suggest that customer momentum
across equally-weighted portfolios is significantly stronger for low-attention
links, consistent with the limited attention hypothesis. However, no such
pattern is present in value-weighted portfolios. Therefore, the findings
provide mixed evidence on the limited attention explanation of customer
momentum.

####

One potential concern is the low external validity of the results. Low quality
of data on customer links leads to a small sample size. In the earlier years,
the sample represents 3%-10% of the total market capitalization. Slight
improvement in the quality of links leads to an increase in this fraction to
10%-16% in the late sample. Since the sample uses the data on returns of only
American companies, the firms, whose main customers are incorporated outside
the US, are excluded from the sample. Similarly, the sample excludes the
firms, whose primary customers are private firms or governmental agencies.
Finally, there are cross-industry differences in the number of customers and
the distribution of the total sales between them. All these facts can result
in bias.

##  2 Literature review

There is growing finance literature, exploring limited investor attention and
its consequences for asset pricing. Psychology (Kahneman 1973) suggests that
individuals have limited cognitive capacity and can allocate attention only
between small number of tasks. Merton (1987) was the first to suggest that
investors pay little attention to the stocks, for which the cost of
information collection overweights potential return. Hong and Stein (1999)
propose the model of slow information diffusion, heterogeneous across assets.
They assume that due to bounded rationality investors fail to extract the
information, contained in stock prices. Peng and Xiong (2006) develop the
model, in which investors with limited attention focus more on market-wide
news than on firm-specific news.

####

The literature on the limited investors’ attention is mostly empirical.
Huberman and Regev (2001) find that market had ignored important publicly
available information on the firm for 5 months and reacted only after the
information was published in news media. Hou and Moskowitz (2005) examine
firms heterogeneity in price delay and find that small segment of neglected
firms accounts for large proportion of the variation in returns. Barber and
Odean (2007) find that individual investors buy stocks during attention-
grabbing events, such as news, high abnormal trading volume or extreme one-day
returns. Hong, Torous and Valkanov (2005) report that industry returns appear
to predict market returns and conclude that this implies investor inattention.
Menzly and Ozbas (2010) find that stocks in economically related customer-
supplier industries cross-predict returns of each other.

####

Recent literature focuses on a search for the measures of investor
inattention. Hirshleifer, Lim and Teoh (2009) find that during the days when a
large fraction of firms report earnings, price reaction is smaller and post-
earnings announcement drift (PEAD) is larger. Hong, Peng and Xiong (2009)
report that stocks with large abnormal trading volume during the earnings
announcements experience larger one-day returns and smaller PEAD. Ben-Rephael,
Da and Israelsen (2017) propose new measure of attention of institutional
investors and report similar findings when investor attention is high.

####

While this article is the first paper to examine interplay between customer
momentum, price momentum and earnings momentum, there are several studies on
the relationship between price momentum and earnings momentum. Using the
results from return spreads in independent three-by-three portfolio sorts on
PEAD and price momentum, Chan, Jegadeesh and Lakonishok (1996) conclude that
earnings momentum and price momentum are two separate phenomena. Chordia nad
Shivakumar (2006) finds that while past returns and earnings surprises have
separate explanatory power for the future returns, price momentum factor is
subsumed by earnings momentum factor. Novy-Marx (2015) shows that earnings
momentum, both as a characteristic and as a factor, completely captures price
momentum.

##  3 Data and sample selection

The Regulation SFAS 131 requires all public firms to report identities of the
customers, accounting for more than 10% of total sales. This data is publicly
available from Compustat Segments. I use CRSP as a source of common stock
returns and Compustat as a source of accounting data. The sample covers the
period between January 1978 and June 2018.

####

Compustat Segments contains identities of the main customers in the form of
company names, originally reported by the firms. Unfortunately, firms do not
always report proper company name of their customers. Prior to 1997, many
firms reported names of their customers as abbreviations or initialisms. These
factors complicate forming customer-supplier links. I was deliberately
conservative in creating the customer links, which resulted in relatively
small number of matches. To be consistent with asset pricing literature, I
allow for 6-month lag between the fiscal year-ends of firms, reporting
customers, and their stock returns. After matching the firms and the
customers, I calculate equally-weighted returns of customers’ portfolio.

####

The sample consists of all firms with nonmissing customer links, present in
merged CRSP/Compustat. I require firms to have market equity data on June of
each year. Links data includes 255747 firm-month links and 4746 public firms
across 1977-2018. The merged sample includes 145489 firm-month observations
and 3266 firms. Table 1 reports sample coverage across years.

[Insert Table 1]

####

The Table 1 documents that the sample coverage varies between 1% and 16% of
the total market capitalization. The years before 2000 have especially low
sample coverage, lower than 5%. There are multiple reasons for such low
fraction of matched firms. Most firms, present in CRSP sample, do not report
data on the principal customers. The most frequently reported customers are US
government, different countries, states and industries. Large fraction of
firms, reported as principal customers, are private companies or the firms,
incorporated outside of the US.

####

Quarterly Compustat is the source of data on earnings announcement.
Standardized unexpected earnings (SUE) are defined as the most recent year-to-
year change in earnings per share (EPS), divided by the standard deviation of
the changes in earnings during the recent eight announcements. I define SUE as
missing if there are less than 6 quarterly announcements during 2-year window.
I use Compustat item EPSPXQ for EPS and RDQ for earnings announcement dates. I
calculate announcement cumulative abnormal returns (CAR3) as stock returns
during 3-day window, centered around announcement date minus market return for
the same period. The paper uses CRSP value-weighted return (vwretd) as a proxy
for market return. If earnings are announced on non-trading day, I assume that
they are announced during the following trading day.

####

When defining momentum returns, the paper follows asset pricing literature and
denotes the return window by two numbers, corresponding to the first and the
last months of the window before the current month. Therefore, 12-2 price
momentum is the return of the firm during the last year, excluding the most
recent month.

##  4 Main Results

This section documents and discusses the main results of the paper. First, I
present the evidence on the magnitude and significance of the customer
momentum in the full sample, i.e., 1979-2018. Then I report the findings on
the customer momentum using only the sample after 2005, therefore performing
out-of-sample test of customer momentum. The last subsection discusses an
interaction between different types of momentum in order to determine whether
all types of momentum represent separate phenomena.

###  4.1 Customer momentum in full sample

In each month, I divide firms into 10 decile portfolios, based on their
customers’ return in the previous month. Due to the low number of stocks in
the early part of the sample, I form decile breakpoints using the whole
sample. Table 2 presents the excess returns of decile portfolios, formed on
customer momentum, calculated using different lags.

[Insert Table 2 here]

####

I report the results for the different number of lags to facilitate comparison
between customer momentum and price momentum. Price momentum is usually
defined as 12-2 momentum, i.e., returns during the past year, excluding the
most recent month. The reason for this calculation is to exclude short-term
reversal, since the returns have weak negative autocorrelation at 1-month lag.
Unlike price momentum, customer momentum seems to be significantly driven by
the returns in the previous month. For most of lags, the magnitude of customer
momentum increases if I calculate customer momentum over the window, which
includes the most recent month. These results are consistent with economic
motivation for customer momentum. Investor inattention, which is the most
intuitive explanation of customer momentum, seems unlikely to last for several
months. Therefore, for the remainder of this paper, unless otherwise
indicated, customer momentum will mean positive relationship between returns
of the target firm and the returns of the customers’ portfolio during the most
recent month. Panel A of Table 2 reports excess returns of equally-weighted
decile portfolios, formed on customer momentum. The results suggest that long-
short decile portfolio has monthly excess return of 1.22%, which is both
statistically and economically significant. Long-short value-weighted decile
portfolios, formed on customer momentum, generate somewhat smaller monthly
returns of 1.06%. These results highlight large magnitude of customer momentum
and suggest that it is stronger across small stocks. Returns of value-weighted
decile portfolios appear to be rather noisy, which is probably caused by
relatively small sample size.

####

In order to test whether return factors can explain returns of portfolios,
formed on customer momentum, the paper calculates abnormal returns and reports
them in Table 3. I use the following factor models:

  * •

CAPM

  * •

CAPM + Momentum

  * •

Fama-French 3

  * •

Fama-French 3 + Momentum

  * •

Fama-French 5

  * •

Fama-French 5 + Momentum

[Insert Table 3 here]

####

Table 3 clearly shows that traditional asset pricing factors can not explain
high returns of decile portfolios, formed on customer momentum. Abnormal
returns of equally-weighted long-short customer momentum portfolio are not
affected by Fama-French factors and are above 1.1% for any specification.
These results are highly statistically significant, featuring T-statistics
above 4.5. Abnormal returns of value-weighted portfolios, formed on customer
momentum, are significantly positive, varying between 0.91% and 1.04%.

[Insert Table 4 and Table 5 here]

####

Tables 4 and 5 report abnormal returns of decile portfolios, formed on
customer momentum 6-1 and 12-1 respectively. The results are very different
from Table 3. Now price momentum captures the effects of customer momentum and
renders alphas statistically insignificant. These findings suggest that
customer momentum has effect, independent from price momentum only if measured
during the most recent month. Therefore, customer momentum at lags, higher
than 1-1, seems to be noisy proxy for price momentum.

[Insert Table 6 and Figure 1 here]

####

Table 6 documents summary statistic of value-weighted long-short quintile and
decile portfolios, based on customer momentum. Long-short decile portfolio has
excess return 0.4% larger than market excess return and Sharpe Ratio of 0.54.
Unlike excess market return, long-short portfolios on customer momentum appear
to be right-skewed. Figure 1 shows performance of $1, invested in each
portfolio. Both quintile and decile long-short portfolios are uncorrelated
with market, implying zero market risk.

####

The obvious question is how to interpret customer momentum. While inattention
explanation seems more intuitive, there may be risk-based explanation of
customer momentum. In order to find whether customer momentum is related to
macroeconomic variables, I construct customer momentum factor and show its
correlations with macroeconomic variables in Table 7.

####

To construct customer momentum factor, I use exactly the same procedure as
French. I construct breakpoints for subsample of NYSE stocks and construct
customer momentum factor (CMOM) using two-by-three sorts by customer momentum
and size.

[Insert Table 7]

####

CMOM has significant correlations only with other asset pricing factors. It is
positively correlated with UMD and SMB and has negative correlation with HML
and RMW. Given the nature and construction of CMOM its positive correlation
with UMD is not surprising. The paper will discuss the relationship between
CMOM and the other factors in more detail in subsection 4.3. Lack or
correlation of CMOM with macroeconomic variables implies that there is no
obvious risk-based explanation in line with consumption asset pricing models.

###  4.2 Customer momentum in 2005-2018

Recent asset pricing studies showed that anomalies often vanish out-of-sample
(Pontiff and MacLean 2015, Hou, Xue and Zhang 2018). Data mining (Harvey, Liu
and Zhu, 2016) and arbitrage (Schwert 2003) were suggested as possible
explanations. Since Cohen and Frazzini (2008) discovered customer momentum
using the sample from 1981 to 2004, 2005-2018 sample can provide out-of-sample
test of customer momentum anomaly.

####

Table 6 documents excess returns of decile portfolios, formed on customer
momentum. Excess returns of equally-weighted long-short portfolio shrank from
1.5% to 0.6% and excess returns of value-weighted long-short portfolio
decreased from 1.3% to 0.6%. The former return is marginally statistically
significant, while the latter loses statistical significance.

[Insert Table 8, Table 9 and Table 10]

####

Tables 8-10 suggest that traditional factors are still unable to capture the
customer momentum. However, in post-discovery sample excess returns of long-
short portfolios are already small, so even small effects of factors on
abnormal returns cause them to lose statistical significance. None of the two
long-short portfolios delivers statistically significant alpha at 5%
significance level.

####

Since customer momentum has the same pattern in post-discovery sample and is
still not explained by traditional asset pricing factors, I hesitate to say
that it is statistical artifact. Small sample size can cause low power of the
tests above and low statistical significance. It is possible to interpret
smaller magnitude of customer momentum in post-discovery sample as a result of
trading activity of investors. If customer momentum is driven by mispricing,
then it should be arbitraged away over time. Recent findings from mutual funds
literature suggest that when evaluating mutual funds, investors care only
about alpha with respect to CAPM rather than alpha with respect to factor
models (Berk and Binsbergen 2015, Barber, Huang and Odean 2016). This evidence
implies that investors behave as if they attribute all anomalies to
mispricing. Thus even if customer momentum represents higher risk, it is more
likely that investors will load on this risk after the discovery, leading to
diminishing post-discovery customer momentum. Alternatively, if customer
momentum is driven by risk, then investors, less exposed to such risk, can
load on customer momentum to capture its risk premium. Therefore, we can
explain a decrease in customer momentum out of sample by investor activity
regardless of the interpretation of customer momentum.

###  4.3 Price Momentum, Earnings Momentum and Customer Momentum

This subsection discusses the interaction between customer momentum, price
momentum and earnings momentum. Although price momentum measures
autocorrelation of returns and customer momentum captures return
predictability across stocks, these two concepts are closely related. Since
customer and supplier are connected by supply chain link, their returns should
exhibit strong comovement. Contemporaneous correlation between the returns of
the firm and its customers’ portfolio is 0.26. This number is significantly
higher than the contemporaneous correlation between the returns of randomly
chosen stocks in CRSP, equal to 0.12.111In every month, this randomly-chosen
sample has the same number of stocks as full customer momentum sample. High
contemporaneous correlation between returns of customers and suppliers,
combined with price momentum of suppliers, can mechanically explain customer
momentum. Alternatively, customer momentum and contemporaneous correlation
between returns of customers and suppliers can explain price momentum of the
suppliers. Therefore, this subsection explores the relationship between price
momentum and customer momentum.

####

Following Novy-Marx (2015), I measure earnings momentum using standardized
unexpected earnings (SUE) and cumulative abnormal returns within 3-day window
around earnings announcements (CAR3).

[Insert Table 11]

####

Table 11 describes summary statistics of the measures of momentum. Notice that
customer momentum is measured using 1-1 lag, while price momentum is
calculated at 12-2 lag. Therefore, to facilitate comparison between price and
customer momentum, I add short-term reversal, which is measured using the same
lag as the customer momentum. Table 13 reports results of Fama-MacBeth
regressions of returns on stock characteristics, including different types of
momentum.

[Insert Table 13]

####

Coefficient on customer momentum is robust to different regression
specifications, implying that customer momentum is not explained by price
momentum, earnings momentum, size, book-to-market or profitability. The
magnitude of the coefficient suggests that 1 standard deviation increase in
customer momentum is associated with 4% larger annualized returns. Consistent
with Novy-Marx (2015), measures of earnings momentum capture price momentum,
which loses significance and reverses the sign.

####

Since cross-sectional tests can suffer from measurement error and rely upon
linear relationship between returns and explanatory variables, I perform
further analysis using factor spanning tests. I construct CMOM factor,
following the same methodology as UMD.

[Insert Table 14, Table15, Table16 and Table 17]

####

Tables 14, 15, 16 and 17 present results of factor spanning tests. When
controlling for the other factors, CMOM has alpha between 0.29% and 0.47%.
Specifications 1 and 2 show that CMOM has small loadings on Fama-French 5
factors and marginally significant negative loading on RMW factor.
Specification 4 documents that while UMD has small explanatory power for CMOM
in terms of its coefficient and $R^{2}$, it significantly decreases alpha of
CMOM. Overall the regressions, presented in Table 14 provide little evidence
that UMD can capture CMOM. While controlling for earnings momentum, measured
by CAR3, makes alphas of CMOM not statistically significant, this loss of
significance is caused by increased standard error rather than smaller
coefficient. Therefore, unlike price momentum, customer momentum is not
explained by earnings momentum.

####

CMOM has marginally significant explanatory power for UMD and slightly
decreases alpha of UMD from 0.85% to 0.79%. Consistent with the results from
Fama-MacBeth regressions, earnings momentum has dramatic effect on UMD,
rendering its alpha insignificant. When measured as SUEF, earnings momentum
has very large abnormal returns and is not explained by the other factors.
Specifications (4)-(8) in the Table 17 show that UMD has high explanatory
power for earnings momentum factor, measured by CAR3F. Controlling for UMD
increases $R^{2}$ thrice and renders alpha of CAR3F statistically
insignificant.

[Insert Figure 2]

####

Figure 2 shows the performance of $1, invested in CMOM and UMD in 1979. Both
factors have comparable performance and significantly positive correlation
before 2004. After 2004 the correlations between the factors decreased to
-0.03. Although CMOM did not suffer the crash in 2009, it performed even worse
than UMD in after-discovery sample. Since this plot raises questions whether
CMOM changed its pattern after 2004, I perform the same analysis of
interaction between the factors separately for 1979-2004 and 2005-2018. I
report the results in the following tables:

[Insert Tables 22-31]

####

Results of Fama-MacBeth regressions for the two subsamples are strikingly
different. Coefficients on all explanatory variables shrink by at least 40%.
In post-discovery sample SUE is the only statistically significant determinant
of stock returns. While small sample size can cause low power and low
statistical significance, it can not explain dramatic decrease in the absolute
value of coefficients on all the variables. Moreover, coefficients on book-to-
market, profitability and price momentum change the sign. Coefficient on
customer momentum decreases by 60% and is not statistically significant. One
standard deviation increase in customer momentum is correlated with 1.6%
higher annualized returns, suggesting small economic significance. These
results are consistent with hypothesis that all the variables, predicting
stock returns, regardless of whether they represent risk or mispricing, are
losing predictive power over time due to exploitation by investors. Both
variables, likely to correspond to risk factors (book-to-market, size) and
mispricing (measures of momentum) experience large decrease in the explanatory
power. Non-representative sample is the alternative explanation of the
results.

##  5 Further results

###  5.1 Customer momentum and lead-lag relationship between the returns

Lo and MacKinlay (1990) find that weekly returns of large stocks often lead
weekly returns of small stocks and partially explain this finding by the
asynchronous trading. Small stocks are less liquid and can have very little
trading volume for long periods, preventing their price from fully
incorporating all available information. Since large and liquid stocks are
likely to react to new information faster, we observe some cross-correlation
between returns of large and small stocks.

####

This paper uses the sample of monthly returns, so lead-lag effect should be
weaker than in Lo and MacKinlay (1990). To test this alternative explanation,
I restrict the sample to the links with low ratio of customer size to supplier
size. Then I calculate returns of long-short portfolios, formed on past
returns of customers.

[Insert Table 32]

After restricting the sample to the customers smaller than their suppliers,
equally-weighted return of quintile long-short customer momentum portfolio is
1.37%, consistent with the results in Table 5 of Cohen and Frazzini (2008).
Value-weighted returns of this quintile long-short portfolio are 1.92%. These
results can suggest that customer momentum is present across suppliers and
customers of comparable size. However, equally-weighted and value-weighted
returns of decile long-short customer momentum portfolio are -0.16% and 0.45%.
If we restrict sample to the links with the ratio of customer size to supplier
size below 2, then returns of long-short portfolios, formed on past returns of
customers, are between 0.45% and 0.99%.

####

Since the previous results are very noisy due to small subsample size (less
than 5% of the full sample), I calculate returns of long-short customer
momentum portfolios across 5 quintiles of relative customer size.

[Insert Table 33]

Table 33 reports the returns of long-short quintile portfolios, formed on
customer momentum across different quintiles of ratio of customer size to
supplier size. For both equally-weighted and value-weighted portfolios, there
is clear pattern of increasing returns of long-short portfolio with the
increase in relative size of customer. This pattern is especially strong for
equally-weighted returns, where returns of long-short customer momentum
portfolios increase from 0.2% for links with small relative customer size to
1.3% for links with large relative customer size. Returns of long-short value-
weighted portfolios increase from 0.19% to 0.73% as the relative customer size
increases. These results appear to contradict the results in Table 32. Since
the sample size for each quintile in Table 33 is 20% of the full sample
compared to 4% of the full sample after restricting the sample to the links
with the relative customer size below 1, the results from Table 33 should have
somewhat higher credibility.

[Insert Table 34]

The similar pattern emerges in the returns of long-short decile portfolios,
formed on customer momentum across quintiles of relative customer size. Table
34 shows that equally-weighted returns of long-short customer momentum
portfolios increase from 0.45% for links with small relative customer size to
1.61% for links with large relative customer size. Similarly, value-weighted
returns of long-short customer momentum portfolios increase from 0.51% for
links with small relative customer size to 1.12% for links with large relative
customer size. Therefore, the results from Tables 33 and 34 are consistent
with lead-lag relationship and suggest that customer momentum is much stronger
for small firms with large customers. Since the full sample was not large,
these subsamples are very small. Thus the results are noisy and I will
hesitate to interpret them as strong evidence for alternative explanation of
customer momentum.

####

To further test alternative explanation of customer momentum by lead-lag
relationship, I perform Fama-MacBeth regressions, controlling for the
interaction term between past returns of customers and their relative size. If
alternative explanation is true, then including the interaction term will
render coefficient on past customer returns insignificant. Furthermore, under
alternative hypothesis the coefficient on the interaction term should be
significantly positive.

[Insert Table 35]

Table 35 reports the results of Fama-MacBeth regression of returns on the past
returns of customers’ portfolio as well as the interaction term between past
returns of customer portfolio and the relative size of average customer.
Notice that without controlling for interaction term the coefficient on past
customers’ returns is robust and varies very little across specifications.
Adding the interaction term flips the sign of the coefficient. Consistent with
lead-lag hypothesis, coefficient on the interaction term is significantly
positive. Together with the previous results, this evidence suggests that to
the significant extent customer momentum is driven by lead-lag relationship
between the returns of large and small stocks.

###  5.2 Customer momentum, constructed using earnings surprises or post-
earning abnormal returns of customers

To provide additional robustness check, the paper explores other sorting
variables, such as earnings surprises (SUE) and cumulative abnormal returns of
customers around earnings announcements (CAR3). Tables 36 and 37 report the
results:

[Insert Table 36 and Table 37]

Table 36 documents the findings on the returns of portfolios, formed on sorts
on SUE of customers’ portfolio. Excess returns of long-short portfolio from
quintile sorts are 0.27% and 0.36% for equally-weighted and value-weighted
portfolios respectively. For long-short decile portfolios the excess returns
are slightly larger and are 0.54% and 0.45%. The similar analysis for sorts on
CAR3 produces the returns of long-short portfolios below 0.06%. All in all,
these results suggest that customer momentum is present only when the sorting
variable is past return of customers’ portfolio. These results are not
surprising, since SUE and CAR3, unlike returns, are available at effectively
quarterly frequency. Since customer momentum is the strongest at 1-1 lag, SUE
and CAR3 are too slow-moving variables to fully capture customer momentum.

###  5.3 Limited attention hypothesis of customer momentum

Limited attention hypothesis explains customer momentum by low attention of
market participants to the supply chain links between the firms. Cohen and
Frazzini (2008) argue for this channel without testing it formally.

####

Short-term nature of customer momentum and investor attention implies that
monthly frequency may be too low to capture this effect. Therefore, this
section uses daily returns. In order to measure the attention to customer-
supplier link, I use abnormal trading volume of suppliers around earnings
announcements of the customers. To facilitate comparison between different
customer-supplier links, I restrict the sample to the links with the unique
customer. For each customer-supplier pair, I calculate normalized abnormal
trading volume (NAV) of the supplier during the day of earnings announcement
of its customer. I use supplier’s NAV as a measure of the attention to supply
chain link for the next 3 months or until the next earnings announcement.

[Insert Table 38]

Table 38 reports customer momentum across different window width of past
returns of the customer and the contemporaneous returns of the supplier. The
results suggest that strong customer momentum is present across all window
widths.

[Insert Table 39]

Table 39 documents the difference of customer momentum between the supply
chain links with low attention and high attention. First I form long-short
decile portfolios on the customer momentum. The results, reported in Table 39,
are the differences in the returns of these long-short portfolios within the
low-attention supply chain links and high-attention supply chain links.
Limited attention hypothesis predicts that returns of long-short portfolios,
formed on customer momentum of low-attention suppliers, will be larger than
the returns of long-short portfolios, formed on customer momentum of high-
attention suppliers. Low attention to supply chain links is likely associated
with slower information diffusion. Therefore, under limited attention
hypothesis, customer momentum of low-attention suppliers will be the stronger
at longer horizon.

####

In the case of equally-weighted portfolios, customer momentum across different
levels of attention is consistent with limited attention explanation. In
particular, customer momentum is absent at short frequencies (1-10 days), but
is economically and statistically significant at longer frequencies (20, 30
days). However, customer momentum across value-weighted portfolios does not
exhibit similar pattern. When customer momentum is measured as the
relationship between 30-days ahead returns of suppliers and returns of the
customers within the recent 1, 5, 10 or 20 days, low-attention supply chain
links exhibit customer momentum, lower or comparable to the customer momentum
of high-attention supply chain links. Thus, the findings from value-weighted
portfolios are inconsistent with limited attention hypothesis.

##  6 Conclusion

The paper reports that the firms with high returns of principal customers in
the previous month are likely to have abnormally high returns in the current
month. Equally-weighted (value-weighted) long-short decile portfolio of firms,
sorted on customer momentum, has 1.22% (1.06%) monthly returns. These returns
are not explained by Fama-French 5 factors and price momentum. Analysis of
customer momentum in the post-discovery sample reveals that it has a smaller
magnitude and is marginally statistically significant. The customer momentum
factor has negative returns after 2005. These results suggest that after the
discovery, customer momentum decreased due to exploitation by investors.

####

The paper explores the relationship between customer momentum, price momentum,
and earnings momentum and reports that earnings momentum is not explained by
the other types of momentum. Therefore, evidence suggests that customer
momentum captures a phenomenon, independent of the other types of momentum.
Further results show that customer momentum can not explain price momentum or
earnings momentum.

####

Limited attention and the lead-lag relationship between large and small stocks
due to non-synchronous trading (Lo and MacKinlay 1990) are the two likely
explanations of customer momentum. The paper finds mixed evidence for the
limited attention hypothesis. Customer momentum is 2-3 times larger among
customer-supplier links with large relative customer size. This implies that
customer momentum is driven by the fact that the average customer is 15 times
larger than the average supplier in my sample.

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## Appendix A: Main Results

Table 1: Sample coverage

The table reports equally-weighted and value-weighted fraction of firms in the
sample as a proportion of all firms in CRSP. Linked firms represents number of
firms, for which there is customer link and the customer has nonmissing
returns. Fourth column reports fraction of firms in the sample as a proportion
of all firms in CRSP. Fifth column report the same proportion, weighted by
market capitalization. Year Linked firms All firms Fraction of firms Fraction
of ME $1978$ $650$ $41,492$ $0.02$ $0.01$ $1979$ $1,842$ $41,086$ $0.04$
$0.02$ $1980$ $2,227$ $41,127$ $0.05$ $0.02$ $1981$ $2,314$ $42,128$ $0.05$
$0.03$ $1982$ $2,069$ $40,247$ $0.05$ $0.03$ $1983$ $2,414$ $45,018$ $0.05$
$0.03$ $1984$ $2,366$ $44,428$ $0.05$ $0.03$ $1985$ $2,586$ $44,742$ $0.06$
$0.03$ $1986$ $2,766$ $45,345$ $0.06$ $0.04$ $1987$ $2,992$ $46,486$ $0.06$
$0.04$ $1988$ $2,905$ $44,777$ $0.06$ $0.04$ $1989$ $2,564$ $43,784$ $0.06$
$0.03$ $1990$ $2,271$ $38,616$ $0.06$ $0.03$ $1991$ $2,462$ $39,513$ $0.06$
$0.03$ $1992$ $2,985$ $43,745$ $0.07$ $0.03$ $1993$ $3,550$ $49,552$ $0.07$
$0.03$ $1994$ $4,058$ $53,529$ $0.08$ $0.04$ $1995$ $4,646$ $57,190$ $0.08$
$0.04$ $1996$ $4,948$ $61,169$ $0.08$ $0.04$ $1997$ $5,047$ $63,063$ $0.08$
$0.03$ $1998$ $4,464$ $60,455$ $0.07$ $0.03$ $1999$ $3,926$ $56,776$ $0.07$
$0.03$ $2000$ $3,400$ $53,294$ $0.06$ $0.05$ $2001$ $3,947$ $46,025$ $0.09$
$0.08$ $2002$ $3,641$ $41,961$ $0.09$ $0.05$ $2003$ $4,194$ $42,272$ $0.10$
$0.07$ $2004$ $5,043$ $45,086$ $0.11$ $0.10$ $2005$ $4,831$ $44,242$ $0.11$
$0.12$ $2006$ $5,185$ $44,853$ $0.12$ $0.14$ $2007$ $5,315$ $44,033$ $0.12$
$0.16$ $2008$ $4,742$ $37,656$ $0.13$ $0.14$ $2009$ $4,307$ $32,263$ $0.13$
$0.14$ $2010$ $4,942$ $34,418$ $0.14$ $0.15$ $2011$ $4,998$ $34,060$ $0.15$
$0.15$ $2012$ $2,187$ $33,231$ $0.07$ $0.07$ $2013$ $2,675$ $34,276$ $0.08$
$0.08$ $2014$ $4,912$ $35,297$ $0.14$ $0.15$ $2015$ $4,596$ $35,148$ $0.13$
$0.14$ $2016$ $4,290$ $34,039$ $0.13$ $0.15$ $2017$ $4,321$ $33,800$ $0.13$
$0.14$ $2018$ $1,902$ $16,616$ $0.11$ $0.10$

Table 2: Excess returns of decile portfolios, formed on customer momentum at
different lags

This table reports excess returns of decile portfolios, formed on customer
momentum. In each month, firms are ranked by the past returns of the portfolio
of their customers. The window over which the past returns are calculated, is
defined by the lag. Lag is defined as (Beginning of return window as a number
of months before the current month)-(End of return window as a number of
months before the current month). L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios. Tenth
portfolio (D10) is the portfolio of the firms with the largest customers’
returns. Standard errors are calculated using Newey-West adjustment.

Panel A: Equally-weighted decile portfolios |  |  |  |  |  |
---|---|---|---|---|---|---
|  |  |  |  |  |  |  |  |  |  |  |
lag |  statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
1-1 |  Mean |  0.59∗ |  0.93∗∗∗ |  1.03∗∗∗ |  1.06∗∗∗ |  1.08∗∗∗ |  0.93∗∗∗ |  1.40∗∗∗ |  1.36∗∗∗ |  1.50∗∗∗ |  1.81∗∗∗ |  1.22∗∗∗
|  T-stat |  [1.83] |  [2.94] |  [3.52] |  [3.68] |  [3.81] |  [3.41] |  [4.76] |  [4.46] |  [4.96] |  [5.26] |  [5.17]
2-1 |  Mean |  0.51 |  0.84∗∗∗ |  1.08∗∗∗ |  1.10∗∗∗ |  0.89∗∗∗ |  1.03∗∗∗ |  0.90∗∗∗ |  1.38∗∗∗ |  1.47∗∗∗ |  1.74∗∗∗ |  1.23∗∗∗
|  T-stat |  [1.53] |  [2.65] |  [3.60] |  [3.70] |  [3.19] |  [3.64] |  [3.03] |  [4.86] |  [4.87] |  [4.88] |  [4.44]
3-1 |  Mean |  0.53 |  0.69∗∗ |  0.97∗∗∗ |  1.21∗∗∗ |  0.92∗∗∗ |  0.94∗∗∗ |  1.03∗∗∗ |  1.31∗∗∗ |  1.25∗∗∗ |  1.60∗∗∗ |  1.07∗∗∗
|  T-stat |  [1.56] |  [2.20] |  [3.23] |  [4.01] |  [3.15] |  [3.31] |  [3.68] |  [4.39] |  [4.11] |  [4.54] |  [3.71]
7-1 |  Mean |  0.75∗∗ |  0.86∗∗∗ |  0.72∗∗ |  0.70∗∗ |  0.72∗∗ |  1.01∗∗∗ |  1.11∗∗∗ |  0.96∗∗∗ |  1.27∗∗∗ |  1.63∗∗∗ |  0.88∗∗∗
|  T-stat |  [2.27] |  [2.67] |  [2.42] |  [2.47] |  [2.43] |  [3.37] |  [4.00] |  [3.24] |  [4.03] |  [4.59] |  [3.38]
12-1 |  Mean |  0.58∗ |  0.33 |  0.83∗∗∗ |  0.90∗∗∗ |  0.47∗ |  0.90∗∗∗ |  0.94∗∗∗ |  0.81∗∗∗ |  1.30∗∗∗ |  1.68∗∗∗ |  1.10∗∗∗
|  T-stat |  [1.73] |  [1.03] |  [2.79] |  [3.09] |  [1.72] |  [2.93] |  [3.35] |  [2.86] |  [4.30] |  [4.73] |  [3.93]
2-2 |  Mean |  1.07∗∗∗ |  1.01∗∗∗ |  1.36∗∗∗ |  1.28∗∗∗ |  1.20∗∗∗ |  1.12∗∗∗ |  1.24∗∗∗ |  1.21∗∗∗ |  1.12∗∗∗ |  1.43∗∗∗ |  0.36
|  T-stat |  [3.13] |  [3.23] |  [4.35] |  [4.42] |  [4.20] |  [3.79] |  [4.45] |  [4.14] |  [3.67] |  [4.21] |  [1.46]
7-2 |  Mean |  0.84∗∗ |  0.74∗∗ |  0.80∗∗∗ |  1.21∗∗∗ |  1.16∗∗∗ |  0.85∗∗∗ |  0.94∗∗∗ |  1.28∗∗∗ |  1.08∗∗∗ |  1.43∗∗∗ |  0.59∗∗
|  T-stat |  [2.49] |  [2.23] |  [2.60] |  [4.18] |  [4.01] |  [2.99] |  [3.16] |  [4.08] |  [3.62] |  [4.04] |  [2.07]
12-2 |  Mean |  0.55 |  0.60∗ |  1.10∗∗∗ |  0.83∗∗∗ |  0.61∗∗ |  0.97∗∗∗ |  0.84∗∗∗ |  0.85∗∗∗ |  1.33∗∗∗ |  1.71∗∗∗ |  1.16∗∗∗
|  T-stat |  [1.64] |  [1.83] |  [3.86] |  [2.85] |  [2.19] |  [3.31] |  [2.83] |  [2.97] |  [4.45] |  [4.75] |  [3.92]

Panel B: Value-weighted decile portfolios |  |  |  |  |  |
---|---|---|---|---|---|---
|  |  |  |  |  |  |  |  |  |  |  |
lag |  statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
1-1 |  Mean |  0.20 |  0.38 |  0.54∗ |  0.95∗∗∗ |  0.52∗ |  0.78∗∗∗ |  0.69∗∗ |  0.57∗ |  0.78∗∗ |  1.26∗∗∗ |  1.06∗∗∗
|  T-stat |  [0.58] |  [1.05] |  [1.84] |  [3.23] |  [1.76] |  [2.66] |  [2.30] |  [1.84] |  [2.29] |  [3.46] |  [3.34]
2-1 |  Mean |  0.31 |  0.50 |  0.69∗∗ |  0.48 |  0.85∗∗∗ |  0.64∗∗ |  0.37 |  0.73∗∗ |  1.20∗∗∗ |  1.36∗∗∗ |  1.05∗∗∗
|  T-stat |  [0.87] |  [1.50] |  [2.19] |  [1.56] |  [2.97] |  [2.08] |  [1.20] |  [2.52] |  [3.57] |  [3.92] |  [3.23]
3-1 |  Mean |  0.29 |  0.40 |  0.73∗∗ |  0.68∗∗ |  0.45 |  0.59∗∗ |  0.56∗ |  1.02∗∗∗ |  0.71∗∗ |  1.47∗∗∗ |  1.18∗∗∗
|  T-stat |  [0.78] |  [1.18] |  [2.31] |  [2.14] |  [1.46] |  [2.02] |  [1.95] |  [3.16] |  [2.10] |  [4.11] |  [3.41]
7-1 |  Mean |  0.57 |  0.64∗ |  0.64∗∗ |  0.52∗ |  0.56∗ |  0.65∗∗ |  0.98∗∗∗ |  0.39 |  1.03∗∗∗ |  1.24∗∗∗ |  0.67∗
|  T-stat |  [1.49] |  [1.87] |  [1.98] |  [1.75] |  [1.75] |  [2.10] |  [3.37] |  [1.30] |  [2.86] |  [3.54] |  [1.94]
12-1 |  Mean |  0.67∗ |  0.09 |  0.82∗∗∗ |  0.85∗∗∗ |  0.46 |  0.75∗∗ |  0.79∗∗∗ |  0.40 |  1.07∗∗∗ |  1.37∗∗∗ |  0.71∗
|  T-stat |  [1.71] |  [0.25] |  [2.68] |  [2.77] |  [1.49] |  [2.45] |  [2.65] |  [1.23] |  [3.42] |  [3.78] |  [1.86]
2-2 |  Mean |  0.65∗ |  0.47 |  1.01∗∗∗ |  0.48 |  0.74∗∗ |  0.53∗ |  0.64∗∗ |  0.89∗∗∗ |  0.69∗∗ |  1.03∗∗∗ |  0.39
|  T-stat |  [1.82] |  [1.49] |  [2.99] |  [1.46] |  [2.37] |  [1.67] |  [2.16] |  [2.87] |  [2.11] |  [3.02] |  [1.25]
7-2 |  Mean |  0.71∗ |  0.32 |  0.59∗ |  0.96∗∗∗ |  0.93∗∗∗ |  0.63∗∗ |  0.58∗∗ |  0.97∗∗∗ |  0.78∗∗ |  1.19∗∗∗ |  0.48
|  T-stat |  [1.88] |  [0.88] |  [1.84] |  [3.28] |  [3.08] |  [1.99] |  [2.01] |  [2.75] |  [2.51] |  [3.28] |  [1.25]
12-2 |  Mean |  0.70∗ |  0.18 |  0.87∗∗∗ |  0.69∗∗ |  0.73∗∗ |  0.81∗∗ |  0.62∗ |  0.48 |  1.05∗∗∗ |  1.26∗∗∗ |  0.56
|  T-stat |  [1.83] |  [0.52] |  [2.81] |  [2.45] |  [2.37] |  [2.53] |  [1.91] |  [1.51] |  [3.35] |  [3.43] |  [1.44]

Table 3: Abnormal returns of decile portfolios, formed on customer momentum
1-1

This table reports abnormal returns of decile portfolios, formed on returns of
customers’ portfolio during the past month. The tables uses 3 different factor
models to control for factor structure of returns, augmenting them with
momentum factor UMD. Alpha is an intercept in Black-Jensen-Scholes regressions
of returns on factors. D10 is the decile portfolio of the firms with the
largest past customers’ returns. L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios.

Panel A: Equally-weighted decile portfolios |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.27 |  0.10 |  0.26 |  0.27 |  0.33∗ |  0.21 |  0.63∗∗∗ |  0.57∗∗∗ |  0.69∗∗∗ |  0.94∗∗∗ |  1.21∗∗∗
|  T-stat |  [-1.35] |  [0.51] |  [1.43] |  [1.62] |  [1.88] |  [1.23] |  [3.41] |  [2.93] |  [3.79] |  [4.13] |  [5.07]
CAPM + UMD |  alpha |  -0.20 |  0.09 |  0.24 |  0.26 |  0.38∗∗ |  0.21 |  0.59∗∗∗ |  0.58∗∗∗ |  0.67∗∗∗ |  0.90∗∗∗ |  1.09∗∗∗
|  T-stat |  [-0.98] |  [0.43] |  [1.32] |  [1.55] |  [2.16] |  [1.23] |  [3.19] |  [2.92] |  [3.67] |  [3.90] |  [4.57]
FF3 |  alpha |  -0.29∗ |  0.13 |  0.23∗ |  0.22∗ |  0.30∗∗ |  0.20 |  0.57∗∗∗ |  0.56∗∗∗ |  0.68∗∗∗ |  0.94∗∗∗ |  1.24∗∗∗
|  T-stat |  [-1.78] |  [0.82] |  [1.69] |  [1.69] |  [2.05] |  [1.42] |  [3.90] |  [3.42] |  [4.47] |  [4.98] |  [5.14]
FF3 + UMD |  alpha |  -0.14 |  0.19 |  0.27∗ |  0.25∗ |  0.41∗∗∗ |  0.25∗ |  0.57∗∗∗ |  0.62∗∗∗ |  0.72∗∗∗ |  0.96∗∗∗ |  1.11∗∗∗
|  T-stat |  [-0.88] |  [1.21] |  [1.93] |  [1.89] |  [2.81] |  [1.78] |  [3.82] |  [3.75] |  [4.63] |  [4.98] |  [4.54]
FF5 |  alpha |  -0.21 |  0.22 |  0.28∗∗ |  0.29∗∗ |  0.37∗∗ |  0.32∗∗ |  0.66∗∗∗ |  0.73∗∗∗ |  0.78∗∗∗ |  1.02∗∗∗ |  1.23∗∗∗
|  T-stat |  [-1.21] |  [1.36] |  [1.98] |  [2.15] |  [2.45] |  [2.24] |  [4.46] |  [4.38] |  [4.95] |  [5.22] |  [4.92]
FF5 + UMD |  alpha |  -0.11 |  0.26 |  0.30∗∗ |  0.30∗∗ |  0.44∗∗∗ |  0.34∗∗ |  0.65∗∗∗ |  0.76∗∗∗ |  0.80∗∗∗ |  1.03∗∗∗ |  1.14∗∗∗
|  T-stat |  [-0.65] |  [1.57] |  [2.11] |  [2.23] |  [2.95] |  [2.40] |  [4.35] |  [4.51] |  [5.00] |  [5.20] |  [4.55]

Panel B: Value-weighted decile portfolios |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.67∗∗∗ |  -0.43 |  -0.19 |  0.21 |  -0.21 |  0.05 |  -0.05 |  -0.20 |  -0.08 |  0.36 |  1.03∗∗∗
|  T-stat |  [-2.82] |  [-1.61] |  [-0.94] |  [1.09] |  [-1.02] |  [0.24] |  [-0.25] |  [-0.93] |  [-0.35] |  [1.46] |  [3.23]
CAPM + UMD |  alpha |  -0.55∗∗ |  -0.34 |  -0.11 |  0.22 |  -0.16 |  0.15 |  0.01 |  -0.14 |  -0.06 |  0.37 |  0.92∗∗∗
|  T-stat |  [-2.31] |  [-1.24] |  [-0.53] |  [1.11] |  [-0.77] |  [0.78] |  [0.07] |  [-0.66] |  [-0.25] |  [1.47] |  [2.85]
FF3 |  alpha |  -0.59∗∗ |  -0.29 |  -0.19 |  0.24 |  -0.14 |  0.14 |  -0.04 |  -0.15 |  0.00 |  0.47∗ |  1.06∗∗∗
|  T-stat |  [-2.54] |  [-1.13] |  [-0.98] |  [1.30] |  [-0.68] |  [0.72] |  [-0.21] |  [-0.71] |  [0.00] |  [1.92] |  [3.28]
FF3 + UMD |  alpha |  -0.38∗ |  -0.07 |  -0.07 |  0.30 |  -0.03 |  0.32∗ |  0.05 |  -0.04 |  0.08 |  0.55∗∗ |  0.93∗∗∗
|  T-stat |  [-1.65] |  [-0.26] |  [-0.33] |  [1.60] |  [-0.14] |  [1.65] |  [0.25] |  [-0.19] |  [0.35] |  [2.21] |  [2.83]
FF5 |  alpha |  -0.43∗ |  -0.06 |  -0.09 |  0.47∗∗ |  -0.05 |  0.36∗ |  0.08 |  0.02 |  0.25 |  0.56∗∗ |  0.99∗∗∗
|  T-stat |  [-1.77] |  [-0.25] |  [-0.42] |  [2.46] |  [-0.23] |  [1.79] |  [0.37] |  [0.08] |  [1.13] |  [2.24] |  [2.95]
FF5 + UMD |  alpha |  -0.30 |  0.07 |  -0.01 |  0.48∗∗ |  0.02 |  0.46∗∗ |  0.13 |  0.08 |  0.28 |  0.61∗∗ |  0.91∗∗∗
|  T-stat |  [-1.23] |  [0.27] |  [-0.03] |  [2.53] |  [0.11] |  [2.34] |  [0.63] |  [0.37] |  [1.24] |  [2.41] |  [2.69]

Table 4: Abnormal returns of decile portfolios, formed on customer momentum
6-1

This table reports abnormal returns of decile portfolios, formed on returns of
customers’ portfolio during the past 6 months. The tables uses 3 different
factor models to control for factor structure of returns, augmenting them with
momentum factor UMD. Alpha is an intercept in Black-Jensen-Scholes regressions
of returns on factors. D10 is the decile portfolio of the firms with the
largest past customers’ returns. L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios.

Panel A: Equally-weighted decile portfolios |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.09 |  0.06 |  -0.00 |  -0.02 |  0.01 |  0.28 |  0.41∗∗ |  0.22 |  0.53∗∗ |  0.83∗∗∗ |  0.92∗∗∗
|  T-stat |  [-0.43] |  [0.28] |  [-0.02] |  [-0.11] |  [0.06] |  [1.44] |  [2.40] |  [1.20] |  [2.47] |  [3.29] |  [3.51]
CAPM + UMD |  alpha |  0.04 |  0.24 |  0.13 |  0.06 |  0.08 |  0.25 |  0.39∗∗ |  0.14 |  0.41∗ |  0.62∗∗ |  0.58∗∗
|  T-stat |  [0.20] |  [1.24] |  [0.68] |  [0.33] |  [0.41] |  [1.26] |  [2.25] |  [0.74] |  [1.92] |  [2.49] |  [2.36]
FF3 |  alpha |  -0.04 |  0.08 |  -0.01 |  -0.04 |  -0.06 |  0.24 |  0.34∗∗ |  0.19 |  0.55∗∗∗ |  0.82∗∗∗ |  0.86∗∗∗
|  T-stat |  [-0.26] |  [0.45] |  [-0.06] |  [-0.31] |  [-0.32] |  [1.43] |  [2.35] |  [1.13] |  [3.05] |  [4.36] |  [3.33]
FF3 + UMD |  alpha |  0.20 |  0.37∗∗ |  0.21 |  0.10 |  0.05 |  0.23 |  0.33∗∗ |  0.10 |  0.46∗∗ |  0.62∗∗∗ |  0.42∗
|  T-stat |  [1.28] |  [2.16] |  [1.30] |  [0.70] |  [0.31] |  [1.35] |  [2.24] |  [0.62] |  [2.53] |  [3.34] |  [1.76]
FF5 |  alpha |  0.10 |  0.29 |  0.13 |  0.03 |  0.02 |  0.34∗∗ |  0.34∗∗ |  0.05 |  0.49∗∗∗ |  0.87∗∗∗ |  0.78∗∗∗
|  T-stat |  [0.59] |  [1.55] |  [0.75] |  [0.20] |  [0.13] |  [1.98] |  [2.23] |  [0.29] |  [2.64] |  [4.47] |  [2.89]
FF5 + UMD |  alpha |  0.25 |  0.47∗∗∗ |  0.27 |  0.12 |  0.09 |  0.32∗ |  0.33∗∗ |  0.00 |  0.43∗∗ |  0.72∗∗∗ |  0.47∗
|  T-stat |  [1.61] |  [2.69] |  [1.64] |  [0.86] |  [0.51] |  [1.86] |  [2.16] |  [0.03] |  [2.33] |  [3.81] |  [1.90]

Panel B: Value-weighted decile portfolios |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.32 |  -0.13 |  -0.09 |  -0.20 |  -0.15 |  -0.05 |  0.28 |  -0.29 |  0.25 |  0.45∗ |  0.78∗∗
|  T-stat |  [-1.24] |  [-0.56] |  [-0.39] |  [-1.05] |  [-0.64] |  [-0.22] |  [1.47] |  [-1.38] |  [0.93] |  [1.82] |  [2.23]
CAPM + UMD |  alpha |  -0.07 |  0.12 |  0.05 |  -0.08 |  -0.06 |  -0.07 |  0.27 |  -0.29 |  0.11 |  0.30 |  0.37
|  T-stat |  [-0.29] |  [0.52] |  [0.23] |  [-0.43] |  [-0.28] |  [-0.32] |  [1.42] |  [-1.34] |  [0.41] |  [1.21] |  [1.13]
FF3 |  alpha |  -0.24 |  -0.05 |  -0.04 |  -0.19 |  -0.18 |  -0.04 |  0.26 |  -0.30 |  0.38 |  0.47∗∗ |  0.71∗∗
|  T-stat |  [-0.96] |  [-0.22] |  [-0.17] |  [-1.01] |  [-0.76] |  [-0.18] |  [1.39] |  [-1.40] |  [1.49] |  [2.06] |  [2.05]
FF3 + UMD |  alpha |  0.14 |  0.32 |  0.18 |  -0.01 |  -0.06 |  -0.05 |  0.27 |  -0.29 |  0.28 |  0.33 |  0.19
|  T-stat |  [0.60] |  [1.39] |  [0.79] |  [-0.08] |  [-0.27] |  [-0.23] |  [1.37] |  [-1.34] |  [1.09] |  [1.43] |  [0.57]
FF5 |  alpha |  0.00 |  0.25 |  0.07 |  -0.08 |  -0.13 |  0.04 |  0.30 |  -0.44∗∗ |  0.41 |  0.48∗∗ |  0.48
|  T-stat |  [-0.01] |  [1.03] |  [0.30] |  [-0.39] |  [-0.53] |  [0.19] |  [1.53] |  [-2.02] |  [1.59] |  [2.00] |  [1.34]
FF5 + UMD |  alpha |  0.25 |  0.48∗∗ |  0.22 |  0.04 |  -0.05 |  0.02 |  0.30 |  -0.42∗ |  0.34 |  0.38 |  0.13
|  T-stat |  [1.01] |  [2.08] |  [0.92] |  [0.19] |  [-0.21] |  [0.11] |  [1.50] |  [-1.91] |  [1.31] |  [1.57] |  [0.38]

Table 5: Abnormal returns of decile portfolios, formed on customer momentum
12-1

This table reports abnormal returns of decile portfolios, formed on returns of
customers’ portfolio during the past 12 months. The tables uses 3 different
factor models to control for factor structure of returns, augmenting them with
momentum factor UMD. Alpha is an intercept in Black-Jensen-Scholes regressions
of returns on factors. D10 is the decile portfolio of the firms with the
largest past customers’ returns. L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios.

Panel A: Equally-weighted decile portfolios |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.20 |  -0.40∗ |  0.13 |  0.22 |  -0.16 |  0.22 |  0.25 |  0.14 |  0.61∗∗∗ |  0.94∗∗∗ |  1.14∗∗∗
|  T-stat |  [-0.89] |  [-1.88] |  [0.66] |  [1.16] |  [-0.87] |  [1.02] |  [1.49] |  [0.78] |  [3.02] |  [3.58] |  [4.04]
CAPM + UMD |  alpha |  0.03 |  -0.17 |  0.22 |  0.32∗ |  0.01 |  0.27 |  0.23 |  0.04 |  0.44∗∗ |  0.67∗∗∗ |  0.64∗∗∗
|  T-stat |  [0.12] |  [-0.83] |  [1.17] |  [1.68] |  [0.07] |  [1.24] |  [1.37] |  [0.24] |  [2.25] |  [2.64] |  [2.64]
FF3 |  alpha |  -0.19 |  -0.39∗∗ |  0.09 |  0.16 |  -0.23 |  0.17 |  0.18 |  0.14 |  0.58∗∗∗ |  0.95∗∗∗ |  1.14∗∗∗
|  T-stat |  [-1.00] |  [-2.01] |  [0.55] |  [0.96] |  [-1.38] |  [0.97] |  [1.21] |  [0.90] |  [3.39] |  [4.55] |  [4.05]
FF3 + UMD |  alpha |  0.15 |  -0.05 |  0.24 |  0.30∗ |  -0.02 |  0.27 |  0.16 |  0.05 |  0.41∗∗ |  0.67∗∗∗ |  0.51∗∗
|  T-stat |  [0.89] |  [-0.26] |  [1.44] |  [1.86] |  [-0.10] |  [1.48] |  [1.10] |  [0.33] |  [2.39] |  [3.31] |  [2.12]
FF5 |  alpha |  0.11 |  -0.27 |  0.07 |  0.26 |  -0.22 |  0.20 |  0.13 |  0.05 |  0.54∗∗∗ |  0.97∗∗∗ |  0.86∗∗∗
|  T-stat |  [0.56] |  [-1.37] |  [0.40] |  [1.57] |  [-1.27] |  [1.06] |  [0.89] |  [0.29] |  [3.00] |  [4.46] |  [2.98]
FF5 + UMD |  alpha |  0.33∗ |  -0.03 |  0.18 |  0.36∗∗ |  -0.06 |  0.26 |  0.13 |  -0.01 |  0.41∗∗ |  0.76∗∗∗ |  0.43∗
|  T-stat |  [1.89] |  [-0.19] |  [1.06] |  [2.17] |  [-0.38] |  [1.41] |  [0.83] |  [-0.05] |  [2.34] |  [3.67] |  [1.72]

Panel B: Value-weighted decile portfolios |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.17 |  -0.64∗∗ |  0.13 |  0.17 |  -0.19 |  0.09 |  0.12 |  -0.27 |  0.41∗ |  0.63∗∗ |  0.79∗∗
|  T-stat |  [-0.59] |  [-2.35] |  [0.63] |  [0.82] |  [-0.85] |  [0.42] |  [0.60] |  [-1.11] |  [1.81] |  [2.31] |  [2.08]
CAPM + UMD |  alpha |  0.15 |  -0.31 |  0.21 |  0.25 |  0.01 |  0.11 |  0.11 |  -0.38 |  0.28 |  0.40 |  0.25
|  T-stat |  [0.55] |  [-1.20] |  [0.98] |  [1.16] |  [0.03] |  [0.48] |  [0.54] |  [-1.54] |  [1.24] |  [1.50] |  [0.71]
FF3 |  alpha |  -0.09 |  -0.53∗∗ |  0.12 |  0.18 |  -0.17 |  0.12 |  0.11 |  -0.20 |  0.41∗ |  0.74∗∗∗ |  0.83∗∗
|  T-stat |  [-0.34] |  [-1.99] |  [0.59] |  [0.83] |  [-0.76] |  [0.56] |  [0.55] |  [-0.80] |  [1.85] |  [2.93] |  [2.17]
FF3 + UMD |  alpha |  0.37 |  -0.05 |  0.23 |  0.29 |  0.09 |  0.17 |  0.11 |  -0.29 |  0.27 |  0.52∗∗ |  0.16
|  T-stat |  [1.40] |  [-0.19] |  [1.07] |  [1.36] |  [0.40] |  [0.79] |  [0.54] |  [-1.18] |  [1.19] |  [2.09] |  [0.44]
FF5 |  alpha |  0.22 |  -0.32 |  0.06 |  0.28 |  -0.06 |  0.12 |  0.16 |  -0.32 |  0.35 |  0.89∗∗∗ |  0.67∗
|  T-stat |  [0.77] |  [-1.17] |  [0.28] |  [1.29] |  [-0.27] |  [0.55] |  [0.74] |  [-1.30] |  [1.53] |  [3.42] |  [1.68]
FF5 + UMD |  alpha |  0.53∗∗ |  0.01 |  0.14 |  0.36 |  0.12 |  0.16 |  0.15 |  -0.38 |  0.26 |  0.71∗∗∗ |  0.19
|  T-stat |  [1.98] |  [0.06] |  [0.66] |  [1.62] |  [0.51] |  [0.72] |  [0.71] |  [-1.50] |  [1.10] |  [2.80] |  [0.52]

Table 6: Summary statistic of long-short portfolios, formed on Customer
momentum

The table reports summary statistic, including percentiles of returns of long-
short portfolios and a market portfolio. L/S5 corresponds to long-short
portfolio, defined as a difference in returns of extreme quintile portfolios.
L/S10 corresponds to long-short portfolio, defined as a difference in returns
of extreme decile portfolios. Mkt is the value-weighted return of all CRSP
stocks(vwretd). Sharpe ratio is computed at annual frequency.

| LS5 | LS10 | Mkt
---|---|---|---
Mean | $0.64$ | $1.06$ | $0.64$
SD | $5.62$ | $6.95$ | $4.40$
min | $-18$ | $-20.72$ | $-23.14$
p05 | $-7.41$ | $-8.98$ | $-7.14$
p25 | $-2.43$ | $-3.05$ | $-1.95$
p50 | $0.45$ | $0.60$ | $1.03$
p75 | $3.28$ | $4.70$ | $3.51$
p95 | $9.08$ | $11.80$ | $7.02$
max | $35.08$ | $41.87$ | $12.43$
Sharpe ratio | $0.45$ | $0.54$ | $0.50$

Performance of $1 (log scale)

The graph describes growth of the investment of $1 in one of the three
portfolios. L/S5 (L/S10) corresponds to long-short portfolio, defined as a
difference in returns of extreme quintile (decile) portfolios. Mkt is the
value-weighted return of all CRSP stocks(vwretd).

Fig. 1.

Table 7: Correlation between factors and macroeconomic variables, 1979-2018

The table reports the correlations between factors and macroeconomic
variables. CMOM is customer momentum factor, constructed from 2x3 sorts on
size and returns of customers’ porfolio in the last month. GDP_gr, PCEC_gr,
PAYEMS_gr and UNRATE_gr are growth rates of GDP, consumption, labor income and
unemployment rate. ***, ** and * represent statistically significant results
at 0.1%, 1% and 10% significance level.

| CMOM | Mkt.RF | SMB | HML | RMW | CMA | RF | UMD | GDP_gr | PCEC_gr | PAYEMS_gr
---|---|---|---|---|---|---|---|---|---|---|---
CMOM |  |  |  |  |  |  |  |  |  |  |
Mkt.RF | 0.08 |  |  |  |  |  |  |  |  |  |
SMB | 0.25*** | 0.16* |  |  |  |  |  |  |  |  |
HML | -0.17** | -0.29*** | -0.26*** |  |  |  |  |  |  |  |
RMW | -0.23*** | -0.28*** | -0.45*** | 0.31*** |  |  |  |  |  |  |
CMA | 0.00 | -0.40*** | -0.10 | 0.69*** | 0.09 |  |  |  |  |  |
RF | 0.05 | -0.11 | -0.17** | 0.07 | -0.02 | 0.13 |  |  |  |  |
UMD | 0.29*** | -0.04 | 0.24*** | -0.35*** | 0.00 | -0.12 | -0.01 |  |  |  |
GDP_gr | -0.05 | 0.18** | -0.05 | -0.04 | 0.02 | -0.05 | 0.42*** | -0.10 |  |  |
PCEC_gr | -0.02 | 0.31*** | 0.04 | -0.18** | -0.09 | -0.08 | 0.45*** | 0.15* | 0.76*** |  |
PAYEMS_gr | -0.01 | 0.17** | -0.09 | -0.08 | -0.02 | -0.03 | 0.03 | 0.03 | 0.53*** | 0.38*** |
UNRATE_gr | 0.10 | -0.19** | 0.03 | 0.03 | -0.02 | 0.03 | 0.16* | 0.03 | -0.32*** | -0.24*** | -0.44***

Table 8: Mean excess returns of decile portfolios, 1978-2018

This table reports excess returns of decile portfolios, formed on customer
momentum. In each month, firms are ranked by the past returns of the portfolio
of their customers. The window over which the past returns are calculated, is
defined by the lag. Lag is defined as (Beginning of return window as a number
of months before the current month)-(End of return window as a number of
months before the current month). L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios. Tenth
portfolio (D10) is the portfolio of the firms with the largest customers’
returns. Standard errors are calculated using Newey-West adjustment.

Panel A1: Equally-weighted portfolios, 1978-2004 |
---|---
lag |  statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
1-1 |  Mean |  0.58 |  0.96∗∗ |  1.00∗∗∗ |  1.01∗∗∗ |  1.14∗∗∗ |  0.93∗∗∗ |  1.60∗∗∗ |  1.40∗∗∗ |  1.55∗∗∗ |  2.10∗∗∗ |  1.52∗∗∗
|  T-stat |  [1.39] |  [2.30] |  [2.62] |  [2.72] |  [3.08] |  [2.67] |  [4.20] |  [3.57] |  [3.88] |  [4.59] |  [4.74]

Panel A2: Equally-weighted portfolios, 2005-2018 |
---|---
lag |  statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
1-1 |  Mean |  0.64 |  0.92∗∗ |  1.11∗∗ |  1.19∗∗∗ |  0.97∗∗ |  0.97∗∗ |  1.05∗∗ |  1.32∗∗∗ |  1.46∗∗∗ |  1.26∗∗∗ |  0.62∗∗
|  T-stat |  [1.30] |  [2.00] |  [2.57] |  [2.70] |  [2.37] |  [2.23] |  [2.31] |  [2.86] |  [3.35] |  [2.67] |  [2.12]

Panel B1: Value-weighted portfolios, 1978-2004 |
---|---
lag |  statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
1-1 |  Mean |  -0.08 |  0.43 |  0.38 |  0.82∗∗ |  0.45 |  0.63 |  0.71∗ |  0.60 |  0.87∗ |  1.22∗∗ |  1.30∗∗∗
|  T-stat |  [-0.17] |  [0.88] |  [0.99] |  [2.06] |  [1.14] |  [1.57] |  [1.79] |  [1.48] |  [1.86] |  [2.47] |  [3.03]

Panel B2: Value-weighted portfolios, 2005-2018 |
---|---
lag |  statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
1-1 |  Mean |  0.79 |  0.28 |  0.89∗∗ |  1.22∗∗∗ |  0.69∗ |  1.14∗∗∗ |  0.68 |  0.54 |  0.65 |  1.41∗∗∗ |  0.62
|  T-stat |  [1.60] |  [0.65] |  [2.04] |  [3.12] |  [1.70] |  [3.08] |  [1.64] |  [1.23] |  [1.56] |  [2.97] |  [1.54]

Table 9: Abnormal returns of equally-weighted decile portfolios, formed on
customer momentum

This table reports abnormal returns of decile portfolios, formed on returns of
customers’ portfolio during the past 1 month. The tables uses 3 different
factor models to control for factor structure of returns, augmenting them with
momentum factor UMD. Alpha is an intercept in Black-Jensen-Scholes regressions
of returns on factors. D10 is the decile portfolio of the firms with the
largest past customers’ returns. L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios.

Panel A: 1978-2004 |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.28 |  0.11 |  0.23 |  0.22 |  0.38 |  0.20 |  0.83∗∗∗ |  0.62∗∗ |  0.69∗∗∗ |  1.21∗∗∗ |  1.49∗∗∗
|  T-stat |  [-1.05] |  [0.40] |  [0.91] |  [0.96] |  [1.59] |  [0.93] |  [3.30] |  [2.35] |  [2.90] |  [3.84] |  [4.60]
CAPM + UMD |  alpha |  -0.21 |  0.12 |  0.17 |  0.19 |  0.49∗∗ |  0.18 |  0.73∗∗∗ |  0.59∗∗ |  0.67∗∗∗ |  1.09∗∗∗ |  1.30∗∗∗
|  T-stat |  [-0.76] |  [0.43] |  [0.67] |  [0.85] |  [2.03] |  [0.80] |  [2.87] |  [2.19] |  [2.74] |  [3.41] |  [3.98]
FF3 |  alpha |  -0.30 |  0.18 |  0.11 |  0.11 |  0.26 |  0.21 |  0.74∗∗∗ |  0.60∗∗∗ |  0.68∗∗∗ |  1.24∗∗∗ |  1.54∗∗∗
|  T-stat |  [-1.33] |  [0.81] |  [0.59] |  [0.61] |  [1.31] |  [1.11] |  [3.63] |  [2.73] |  [3.31] |  [4.65] |  [4.63]
FF3 + UMD |  alpha |  -0.11 |  0.31 |  0.15 |  0.17 |  0.47∗∗ |  0.26 |  0.72∗∗∗ |  0.67∗∗∗ |  0.74∗∗∗ |  1.23∗∗∗ |  1.34∗∗∗
|  T-stat |  [-0.47] |  [1.43] |  [0.79] |  [0.92] |  [2.38] |  [1.38] |  [3.44] |  [2.97] |  [3.53] |  [4.49] |  [3.96]
FF5 |  alpha |  -0.22 |  0.29 |  0.18 |  0.18 |  0.40∗ |  0.36∗ |  0.83∗∗∗ |  0.77∗∗∗ |  0.82∗∗∗ |  1.30∗∗∗ |  1.52∗∗∗
|  T-stat |  [-0.93] |  [1.30] |  [0.92] |  [1.00] |  [1.93] |  [1.89] |  [4.02] |  [3.43] |  [3.91] |  [4.72] |  [4.41]
FF5 + UMD |  alpha |  -0.09 |  0.38∗ |  0.19 |  0.22 |  0.53∗∗∗ |  0.38∗ |  0.80∗∗∗ |  0.79∗∗∗ |  0.85∗∗∗ |  1.28∗∗∗ |  1.37∗∗∗
|  T-stat |  [-0.38] |  [1.67] |  [1.01] |  [1.16] |  [2.63] |  [1.95] |  [3.82] |  [3.48] |  [3.97] |  [4.59] |  [3.96]

Panel B: 2005-2018 |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -0.24 |  0.11 |  0.33 |  0.39∗ |  0.23 |  0.22 |  0.22 |  0.51∗∗ |  0.70∗∗∗ |  0.42∗ |  0.67∗∗
|  T-stat |  [-0.93] |  [0.42] |  [1.47] |  [1.74] |  [1.08] |  [0.87] |  [0.97] |  [1.98] |  [2.82] |  [1.66] |  [2.25]
CAPM + UMD |  alpha |  -0.19 |  0.08 |  0.36 |  0.39∗ |  0.23 |  0.24 |  0.25 |  0.55∗∗ |  0.72∗∗∗ |  0.47∗ |  0.66∗∗
|  T-stat |  [-0.73] |  [0.31] |  [1.56] |  [1.74] |  [1.05] |  [0.97] |  [1.11] |  [2.12] |  [2.86] |  [1.87] |  [2.22]
FF3 |  alpha |  -0.23 |  0.08 |  0.30 |  0.40∗∗ |  0.20 |  0.20 |  0.23 |  0.51∗∗ |  0.71∗∗∗ |  0.43∗∗ |  0.67∗∗
|  T-stat |  [-1.10] |  [0.41] |  [1.57] |  [2.23] |  [1.13] |  [1.00] |  [1.32] |  [2.19] |  [3.30] |  [2.06] |  [2.23]
FF3 + UMD |  alpha |  -0.20 |  0.07 |  0.32∗ |  0.39∗∗ |  0.20 |  0.22 |  0.25 |  0.54∗∗ |  0.72∗∗∗ |  0.47∗∗ |  0.66∗∗
|  T-stat |  [-0.94] |  [0.33] |  [1.71] |  [2.20] |  [1.14] |  [1.10] |  [1.42] |  [2.31] |  [3.32] |  [2.25] |  [2.20]
FF5 |  alpha |  -0.12 |  0.11 |  0.39∗∗ |  0.45∗∗ |  0.16 |  0.25 |  0.26 |  0.67∗∗∗ |  0.73∗∗∗ |  0.47∗∗ |  0.59∗
|  T-stat |  [-0.55] |  [0.52] |  [1.99] |  [2.42] |  [0.89] |  [1.18] |  [1.39] |  [2.81] |  [3.23] |  [2.16] |  [1.91]
FF5 + UMD |  alpha |  -0.09 |  0.10 |  0.41∗∗ |  0.44∗∗ |  0.17 |  0.27 |  0.27 |  0.69∗∗∗ |  0.73∗∗∗ |  0.50∗∗ |  0.59∗
|  T-stat |  [-0.42] |  [0.45] |  [2.10] |  [2.39] |  [0.90] |  [1.26] |  [1.46] |  [2.91] |  [3.24] |  [2.32] |  [1.89]

Table 10: Abnormal returns of value-weighted decile portfolios, formed on
customer momentum

This table reports abnormal returns of decile portfolios, formed on returns of
customers’ portfolio during the past 1 month. The tables uses 3 different
factor models to control for factor structure of returns, augmenting them with
momentum factor UMD. Alpha is an intercept in Black-Jensen-Scholes regressions
of returns on factors. D10 is the decile portfolio of the firms with the
largest past customers’ returns. L/S corresponds to long-short portfolio,
defined as a difference in returns of tenth and first decile portfolios.

Panel A: 1978-2004 |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  -1.00∗∗∗ |  -0.43 |  -0.35 |  0.04 |  -0.32 |  -0.18 |  -0.07 |  -0.19 |  -0.08 |  0.27 |  1.27∗∗∗
|  T-stat |  [-3.19] |  [-1.14] |  [-1.31] |  [0.16] |  [-1.17] |  [-0.70] |  [-0.24] |  [-0.67] |  [-0.25] |  [0.78] |  [2.94]
CAPM + UMD |  alpha |  -0.84∗∗∗ |  -0.20 |  -0.24 |  0.09 |  -0.20 |  0.04 |  0.08 |  -0.12 |  -0.05 |  0.27 |  1.11∗∗
|  T-stat |  [-2.64] |  [-0.52] |  [-0.90] |  [0.33] |  [-0.72] |  [0.16] |  [0.30] |  [-0.40] |  [-0.17] |  [0.78] |  [2.52]
FF3 |  alpha |  -0.85∗∗∗ |  -0.12 |  -0.39 |  0.05 |  -0.26 |  -0.05 |  -0.12 |  -0.10 |  0.06 |  0.43 |  1.28∗∗∗
|  T-stat |  [-2.68] |  [-0.32] |  [-1.47] |  [0.20] |  [-0.94] |  [-0.19] |  [-0.43] |  [-0.36] |  [0.19] |  [1.27] |  [2.87]
FF3 + UMD |  alpha |  -0.59∗ |  0.27 |  -0.22 |  0.19 |  -0.08 |  0.24 |  0.06 |  0.04 |  0.15 |  0.51 |  1.11∗∗
|  T-stat |  [-1.86] |  [0.76] |  [-0.83] |  [0.72] |  [-0.27] |  [0.92] |  [0.22] |  [0.14] |  [0.46] |  [1.47] |  [2.42]
FF5 |  alpha |  -0.66∗∗ |  0.12 |  -0.28 |  0.33 |  -0.16 |  0.23 |  0.04 |  0.07 |  0.41 |  0.58∗ |  1.24∗∗∗
|  T-stat |  [-2.02] |  [0.31] |  [-1.01] |  [1.26] |  [-0.57] |  [0.85] |  [0.13] |  [0.24] |  [1.33] |  [1.65] |  [2.68]
FF5 + UMD |  alpha |  -0.49 |  0.37 |  -0.17 |  0.38 |  -0.04 |  0.41 |  0.15 |  0.15 |  0.42 |  0.62∗ |  1.11∗∗
|  T-stat |  [-1.52] |  [1.01] |  [-0.62] |  [1.47] |  [-0.14] |  [1.52] |  [0.51] |  [0.51] |  [1.34] |  [1.73] |  [2.38]

Panel B: 2005-2018 |
---|---
Model |  Statistic |  D1 |  D2 |  D3 |  D4 |  D5 |  D6 |  D7 |  D8 |  D9 |  D10 |  L/S
CAPM |  alpha |  0.00 |  -0.42 |  0.15 |  0.55∗∗ |  0.04 |  0.58∗∗ |  0.01 |  -0.18 |  -0.02 |  0.62∗∗ |  0.61
|  T-stat |  [0.01] |  [-1.50] |  [0.57] |  [2.41] |  [0.15] |  [2.23] |  [0.05] |  [-0.64] |  [-0.07] |  [2.11] |  [1.49]
CAPM + UMD |  alpha |  0.07 |  -0.44 |  0.18 |  0.53∗∗ |  0.03 |  0.58∗∗ |  0.01 |  -0.14 |  0.04 |  0.64∗∗ |  0.57
|  T-stat |  [0.23] |  [-1.57] |  [0.71] |  [2.32] |  [0.12] |  [2.23] |  [0.03] |  [-0.51] |  [0.13] |  [2.20] |  [1.38]
FF3 |  alpha |  -0.01 |  -0.46∗ |  0.14 |  0.55∗∗ |  -0.03 |  0.54∗∗ |  -0.01 |  -0.18 |  -0.01 |  0.56∗∗ |  0.57
|  T-stat |  [-0.02] |  [-1.69] |  [0.53] |  [2.38] |  [-0.12] |  [2.14] |  [-0.04] |  [-0.63] |  [-0.03] |  [1.97] |  [1.38]
FF3 + UMD |  alpha |  0.05 |  -0.47∗ |  0.17 |  0.53∗∗ |  -0.02 |  0.55∗∗ |  -0.01 |  -0.15 |  0.03 |  0.60∗∗ |  0.55
|  T-stat |  [0.15] |  [-1.70] |  [0.65] |  [2.31] |  [-0.07] |  [2.19] |  [-0.04] |  [-0.54] |  [0.11] |  [2.11] |  [1.33]
FF5 |  alpha |  0.08 |  -0.38 |  0.19 |  0.56∗∗ |  0.08 |  0.61∗∗ |  0.07 |  -0.15 |  0.04 |  0.58∗ |  0.50
|  T-stat |  [0.24] |  [-1.32] |  [0.68] |  [2.36] |  [0.30] |  [2.35] |  [0.26] |  [-0.52] |  [0.13] |  [1.95] |  [1.16]
FF5 + UMD |  alpha |  0.12 |  -0.38 |  0.21 |  0.55∗∗ |  0.09 |  0.62∗∗ |  0.07 |  -0.13 |  0.07 |  0.61∗∗ |  0.48
|  T-stat |  [0.37] |  [-1.33] |  [0.78] |  [2.31] |  [0.33] |  [2.39] |  [0.26] |  [-0.45] |  [0.24] |  [2.07] |  [1.12]

Table 11: Summary statistics of characteristics, 1979-2018

This table reports summary statistic of characteristics, including their
percentiles. OP is operating profitability. CAR3 is abnormal return during
3-day window, centered around earnings announcement. Standardized unexpected
earnings (SUE) are defined as the most recent year-to-year change in earnings
per share (EPS), divided by the standard deviation of the changes in earnings
during the recent eight announcements. I define SUE as missing if there are
less than 6 quarterly announcement during 2-year window. Cmom1-1 is the return
of the customers’ portfolio during the last month. Mom12-2 and Mom1-1 are past
returns of the firm at a given lag.

| Cmom1-1 | Mom12-2 | Mom1-1 | log(ME) | log(B/M) | Op | CAR3 | SUE
---|---|---|---|---|---|---|---|---
Mean | $0.91$ | $18.09$ | $1.37$ | $6.34$ | $-0.72$ | $0.25$ | $0$ | $0.07$
SD | $7.84$ | $49.08$ | $11.22$ | $1.96$ | $0.73$ | $0.25$ | $0.07$ | $1.09$
min | $-36.71$ | $-86.71$ | $-46.17$ | $1.15$ | $-4.07$ | $-1.31$ | $-0.32$ | $-3.04$
p05 | $-11.76$ | $-40.77$ | $-16.27$ | $3.27$ | $-2$ | $-0.05$ | $-0.11$ | $-1.86$
p25 | $-3.62$ | $-11.31$ | $-5.25$ | $4.94$ | $-1.19$ | $0.14$ | $-0.04$ | $-0.54$
p50 | $0.98$ | $10.35$ | $0.87$ | $6.29$ | $-0.68$ | $0.24$ | $0$ | $0.07$
p75 | $5.46$ | $37.08$ | $7.41$ | $7.59$ | $-0.22$ | $0.35$ | $0.04$ | $0.67$
p95 | $13.50$ | $101.50$ | $20.53$ | $9.72$ | $0.40$ | $0.58$ | $0.12$ | $1.93$
max | $38.21$ | $831.90$ | $76.52$ | $13.01$ | $2.13$ | $4.29$ | $0.41$ | $4.81$

Table 12: Summary statistics of factors, 1979-2018

This table reports summary statistics of factors, including their percentiles.
CMOM, SUEF and CAR3F are factors, constructed from 2x3 sorts on ME and
respective variable (returns of customers’ portfolio during the recent month,
SUE and CAR3). The factors use NYSE breakpoints and are constructed similarly
to UMD. Standard errors are calculated using Newey-West correction.

| Mkt.RF | SMB | HML | RMW | CMA | UMD | CMOM | SUEF | CAR3F
---|---|---|---|---|---|---|---|---|---
Mean | $0.68^{***}$ | $0.17$ | $0.26$ | $0.34^{***}$ | $0.27^{**}$ | $0.61^{***}$ | $0.40^{***}$ | $1.27^{***}$ | $0.18^{**}$
SE | $0.21$ | $0.13$ | $0.16$ | $0.13$ | $0.10$ | $0.22$ | $0.14$ | $0.07$ | $0.07$
T-stat | $[3.22]$ | $[1.31]$ | $[1.57]$ | $[2.67]$ | $[2.55]$ | $[2.78]$ | $[2.88]$ | $[17.31]$ | $[2.46]$
SD | $4.38$ | $2.89$ | $2.92$ | $2.41$ | $1.99$ | $4.45$ | $3.50$ | $1.94$ | $1.57$
min | $-23.24$ | $-15.33$ | $-11.10$ | $-19.06$ | $-6.88$ | $-34.39$ | $-10.55$ | $-8.34$ | $-10.98$
p05 | $-7.09$ | $-4.07$ | $-3.95$ | $-2.82$ | $-2.66$ | $-6.46$ | $-4.85$ | $-1.48$ | $-2.17$
p25 | $-1.94$ | $-1.51$ | $-1.38$ | $-0.81$ | $-0.99$ | $-1.01$ | $-1.54$ | $0.26$ | $-0.59$
p50 | $1.09$ | $0.06$ | $0.04$ | $0.34$ | $0.16$ | $0.70$ | $0.06$ | $1.22$ | $0.31$
p75 | $3.50$ | $1.93$ | $1.72$ | $1.38$ | $1.49$ | $2.88$ | $2.32$ | $2.18$ | $1.01$
p95 | $7.07$ | $4.62$ | $5.30$ | $3.71$ | $3.29$ | $6.63$ | $5.93$ | $4.47$ | $2.46$
max | $12.47$ | $18.75$ | $12.90$ | $13.51$ | $9.56$ | $18.33$ | $19.36$ | $10.12$ | $5.25$

Performance of $1 (log scale)

Fig. 2.
This graph compares performance of momentum factors. ”Cmom” denotes customer
momentum factor CMOM. ”Mom” stands for UMD. To facilitate comparison, customer
momentum factor is scaled to have the same sample standard deviation as price
momentum.

Table 13: Fama-MacBeth regressions, 1979-2018

This table reports the results of Fama-MacBeth regression of returns on the
characteristics. Cmom1-1 is returns of customers’ portfolio during the last
month. Mom1-1 and Mom12-2 are past returns of stock at lags 1-1 and 12-2. OP
is operating profitability. CAR3 is abnormal return during 3-day window,
centered around earnings announcement. Standardized unexpected earnings (SUE)
are defined as the most recent year-to-year change in earnings per share
(EPS), divided by the standard deviation of the changes in earnings during the
recent eight announcements.

| Dependent variable: Return
---|---
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
Cmom1-1 | 0.04∗∗∗ | 0.04∗∗∗ |  |  |  | 0.04∗∗∗ |  | 0.04∗∗∗ | 0.04∗∗∗
| [3.20] | [3.07] |  |  |  | [3.21] |  | [3.34] | [3.52]
log(ME) |  | -0.12∗∗ |  | -0.13∗∗∗ | -0.10∗∗ | -0.11∗∗ | -0.12∗∗ | -0.14∗∗∗ | -0.13∗∗
|  | [-2.18] |  | [-2.58] | [-2.03] | [-2.12] | [-2.38] | [-2.73] | [-2.50]
log(B/M) |  | 0.13 |  | 0.16 | 0.16 | 0.13 | 0.21 | 0.17 | 0.20
|  | [0.92] |  | [1.08] | [1.07] | [0.89] | [1.37] | [1.16] | [1.27]
OP |  |  |  | -0.04 | 0.32 | 0.29 | 0.44 | 0.01 | 0.46
|  |  |  | [-0.09] | [0.71] | [0.62] | [0.96] | [0.03] | [0.98]
Mom12-2 |  |  | 0.01∗∗ | 0.004 |  |  | -0.003 | 0.003 | -0.004
|  |  | [2.06] | [1.34] |  |  | [-1.10] | [0.96] | [-1.54]
Mom1-1 |  |  | -0.02∗∗∗ | -0.03∗∗∗ |  |  | -0.05∗∗∗ | -0.04∗∗∗ | -0.05∗∗∗
|  |  | [-2.70] | [-4.24] |  |  | [-5.87] | [-4.55] | [-6.12]
CAR3 |  |  |  |  | 2.55∗∗ | 2.83∗∗ | 3.16∗∗∗ |  | 3.63∗∗∗
|  |  |  |  | [2.21] | [2.47] | [2.59] |  | [2.93]
SUE |  |  |  |  | 0.87∗∗∗ | 0.87∗∗∗ | 0.92∗∗∗ |  | 0.92∗∗∗
|  |  |  |  | [12.92] | [12.76] | [13.13] |  | [12.98]
Constant | 1.35∗∗∗ | 2.04∗∗∗ | 1.27∗∗∗ | 2.00∗∗∗ | 1.83∗∗∗ | 1.81∗∗∗ | 1.94∗∗∗ | 2.07∗∗∗ | 1.99∗∗∗
| [5.12] | [4.91] | [5.11] | [5.02] | [4.46] | [4.40] | [4.89] | [5.15] | [4.99]
Observations | 25,421 | 25,421 | 25,421 | 25,421 | 25,421 | 25,421 | 25,421 | 25,421 | 25,421
Adjusted R2 | 0.007 | 0.028 | 0.031 | 0.060 | 0.051 | 0.057 | 0.077 | 0.066 | 0.083

Table 14: Factor spanning test of Customer momentum, 1979-2018

This table reports the results of time-series regression of customer momentum
factor CMOM on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| CMOM
| (1) | (2) | (3) | (4) | (5) | (6) | (7)
Mkt.RF | 0.03 | 0.01 | 0.05 | 0.03 | 0.05 | 0.03 | 0.03
| [0.70] | [0.21] | [1.11] | [0.50] | [1.06] | [0.51] | [0.60]
SMB | 0.04 | -0.01 | 0.03 | -0.02 | 0.05 | 0.01 | -0.01
| [0.45] | [-0.08] | [0.43] | [-0.31] | [0.62] | [0.12] | [-0.21]
HML | -0.09 | -0.06 | -0.05 | 0.03 | -0.08 | -0.01 | 0.03
| [-1.04] | [-0.60] | [-0.56] | [0.28] | [-0.90] | [-0.10] | [0.28]
CMA |  | -0.04 |  | -0.11 |  | -0.11 | -0.12
|  | [-0.24] |  | [-0.71] |  | [-0.72] | [-0.80]
RMW |  | -0.15∗ |  | -0.19∗∗ |  | -0.17∗∗ | -0.19∗∗
|  | [-1.77] |  | [-2.49] |  | [-2.04] | [-2.39]
UMD |  |  | 0.12∗ | 0.13∗∗ |  |  | 0.10
|  |  | [1.84] | [2.07] |  |  | [1.40]
SUEF |  |  |  |  | -0.04 | 0.01 | -0.06
|  |  |  |  | [-0.36] | [0.12] | [-0.53]
CAR3f |  |  |  |  | 0.35∗∗ | 0.36∗∗ | 0.26∗
|  |  |  |  | [2.18] | [2.25] | [1.71]
Constant | 0.39∗∗∗ | 0.47∗∗∗ | 0.29∗∗ | 0.38∗∗ | 0.35∗ | 0.39∗ | 0.43∗∗
| [2.84] | [3.13] | [2.10] | [2.55] | [1.69] | [1.82] | [2.00]
Observations | 470 | 470 | 470 | 470 | 470 | 470 | 470
R2 | 0.011 | 0.019 | 0.031 | 0.045 | 0.033 | 0.044 | 0.055
Adjusted R2 | 0.005 | 0.008 | 0.023 | 0.032 | 0.023 | 0.029 | 0.038
Residual Std. Error | 3.495 | 3.489 | 3.463 | 3.447 | 3.464 | 3.452 | 3.436
F Statistic | 1.771 | 1.796 | 3.765 | 3.611 | 3.171 | 3.022 | 3.340

Table 15: Factor spanning test of Momentum, 1979-2018

This table reports the results of time-series regression of momentum factor
UMD on the other factors. CMOM, SUEF and CAR3F are factors, constructed from
2x3 sorts on ME and respective variable (past returns of customers’ portfolio
during the past month, SUE and CAR3). The factors use NYSE breakpoints and are
constructed similarly to UMD.

| Dependent variable:
---|---
| UMD
| (1) | (2) | (3) | (4) | (5)
Mkt.RF | -0.20∗∗ | -0.11 | -0.07 | -0.21∗∗ | -0.07
| [-2.24] | [-1.30] | [-1.20] | [-2.23] | [-1.29]
SMB | 0.04 | 0.12 | 0.18 | 0.04 | 0.17
| [0.34] | [0.95] | [1.50] | [0.32] | [1.51]
HML | -0.41∗∗ | -0.66∗∗∗ | -0.32∗∗ | -0.39∗∗ | -0.31∗∗
| [-2.22] | [-3.49] | [-2.00] | [-2.20] | [-2.04]
CMA |  | 0.50 |  |  |
|  | [1.50] |  |  |
RMW |  | 0.33 |  |  |
|  | [1.41] |  |  |
SUEF |  |  | 0.73∗∗∗ |  | 0.73∗∗∗
|  |  | [4.84] |  | [4.92]
CAR3f |  |  | 1.01∗∗∗ |  | 0.98∗∗∗
|  |  | [3.81] |  | [3.59]
CMOM |  |  |  | 0.17∗∗ | 0.10
|  |  |  | [2.05] | [1.34]
Constant | 0.85∗∗∗ | 0.60∗∗ | -0.39 | 0.79∗∗∗ | -0.42∗
| [4.61] | [2.31] | [-1.55] | [4.37] | [-1.74]
Observations | 474 | 474 | 474 | 470 | 470
R2 | 0.083 | 0.126 | 0.362 | 0.101 | 0.368
Adjusted R2 | 0.078 | 0.117 | 0.355 | 0.093 | 0.360
Residual Std. Error | 4.270 | 4.179 | 3.571 | 4.248 | 3.570
F Statistic | 14.270 | 13.500 | 53.050 | 13.060 | 44.890

Table 16: Factor spanning test of Earnings momentum factor (SUEF), 1979-2018

This table reports the results of time-series regression of earnings momentum
factor SUEF on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| SUEF
|  (1) |  (2) |  (3) |  (4) |  (5) |  (6) |  (7) |  (8)
CAR3f |  0.41∗∗∗ |  |  |  |  |  |  |
|  [4.45] |  |  |  |  |  |  |
Mkt.RF |  |  -0.08∗∗ |  -0.04 |  -0.05 |  -0.02 |  -0.08∗∗ |  -0.04 |  -0.02
|  |  [-2.36] |  [-1.16] |  [-1.47] |  [-0.56] |  [-2.33] |  [-1.15] |  [-0.56]
SMB |  |  -0.12∗∗∗ |  -0.08∗∗ |  -0.13∗∗∗ |  -0.10∗∗∗ |  -0.12∗∗∗ |  -0.08∗∗ |  -0.10∗∗∗
|  |  [-3.42] |  [-2.28] |  [-3.36] |  [-3.07] |  [-3.39] |  [-2.26] |  [-3.04]
HML |  |  -0.06 |  -0.19∗∗ |  0.02 |  -0.08 |  -0.06 |  -0.19∗∗ |  -0.08
|  |  [-0.76] |  [-2.39] |  [0.26] |  [-1.01] |  [-0.72] |  [-2.34] |  [-0.99]
CMA |  |  |  0.27∗∗∗ |  |  0.19∗∗ |  |  0.27∗∗∗ |  0.18∗∗
|  |  |  [2.79] |  |  [2.45] |  |  [2.82] |  [2.38]
RMW |  |  |  0.19∗∗∗ |  |  0.13∗∗ |  |  0.19∗∗∗ |  0.13∗∗
|  |  |  [3.22] |  |  [2.18] |  |  [3.22] |  [2.16]
UMD |  |  |  |  0.19∗∗∗ |  0.17∗∗∗ |  |  |  0.17∗∗∗
|  |  |  |  [5.87] |  [5.63] |  |  |  [5.73]
CMOM |  |  |  |  |  |  0.01 |  0.02 |  -0.01
|  |  |  |  |  |  [0.48] |  [0.88] |  [-0.33]
Constant |  1.19∗∗∗ |  1.36∗∗∗ |  1.22∗∗∗ |  1.20∗∗∗ |  1.12∗∗∗ |  1.36∗∗∗ |  1.21∗∗∗ |  1.12∗∗∗
|  [17.83] |  [17.05] |  [14.04] |  [13.63] |  [13.26] |  [16.65] |  [13.42] |  [12.95]
Observations |  474 |  474 |  474 |  474 |  474 |  470 |  470 |  470
R2 |  0.110 |  0.078 |  0.147 |  0.246 |  0.278 |  0.078 |  0.148 |  0.277
Adjusted R2 |  0.108 |  0.072 |  0.138 |  0.240 |  0.268 |  0.070 |  0.137 |  0.266
Residual Std. Error |  1.833 |  1.870 |  1.803 |  1.693 |  1.660 |  1.877 |  1.809 |  1.668
F Statistic |  58.360 |  13.320 |  16.110 |  38.300 |  29.930 |  9.855 |  13.360 |  25.270

Table 17: Factor spanning test of Earnings momentum (CAR3), 1979-2018

This table reports the results of time-series regression of earnings momentum
factor CAR3 on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| CAR3F
|  (1) |  (2) |  (3) |  (4) |  (5) |  (6) |  (7) |  (8)
SUEF |  0.27∗∗∗ |  |  |  |  |  |  |
|  [4.16] |  |  |  |  |  |  |
Mkt.RF |  |  -0.07∗∗ |  -0.04 |  -0.04 |  -0.02 |  -0.07∗∗ |  -0.04 |  -0.03
|  |  [-2.17] |  [-1.49] |  [-1.35] |  [-0.96] |  [-2.20] |  [-1.51] |  [-1.02]
SMB |  |  -0.04 |  -0.03 |  -0.05∗ |  -0.05∗∗ |  -0.04 |  -0.03 |  -0.05∗∗
|  |  [-1.21] |  [-1.08] |  [-1.87] |  [-2.05] |  [-1.38] |  [-1.15] |  [-2.11]
HML |  |  -0.04 |  -0.13∗∗ |  0.03 |  -0.03 |  -0.04 |  -0.13∗∗ |  -0.03
|  |  [-0.92] |  [-2.48] |  [0.74] |  [-0.72] |  [-0.77] |  [-2.31] |  [-0.72]
CMA |  |  |  0.19∗∗ |  |  0.11∗∗ |  |  0.19∗∗∗ |  0.11∗∗∗
|  |  |  [2.44] |  |  [2.42] |  |  [2.58] |  [2.58]
RMW |  |  |  0.06 |  |  0.01 |  |  0.07 |  0.02
|  |  |  [1.34] |  |  [0.29] |  |  [1.49] |  [0.43]
UMD |  |  |  |  0.17∗∗∗ |  0.16∗∗∗ |  |  |  0.16∗∗∗
|  |  |  |  [5.58] |  [5.64] |  |  |  [5.31]
CMOM |  |  |  |  |  |  0.07∗∗∗ |  0.07∗∗∗ |  0.04∗
|  |  |  |  |  |  [2.76] |  [2.97] |  [1.85]
Constant |  -0.16 |  0.25∗∗∗ |  0.18∗∗ |  0.11 |  0.08 |  0.22∗∗∗ |  0.15∗ |  0.07
|  [-1.26] |  [3.50] |  [2.24] |  [1.47] |  [1.13] |  [3.23] |  [1.88] |  [0.94]
Observations |  474 |  474 |  474 |  474 |  474 |  470 |  470 |  470
R2 |  0.110 |  0.046 |  0.077 |  0.246 |  0.255 |  0.067 |  0.101 |  0.265
Adjusted R2 |  0.108 |  0.040 |  0.067 |  0.240 |  0.246 |  0.059 |  0.090 |  0.254
Residual Std. Error |  1.486 |  1.542 |  1.519 |  1.372 |  1.366 |  1.530 |  1.505 |  1.362
F Statistic |  58.360 |  7.492 |  7.831 |  38.320 |  26.670 |  8.381 |  8.712 |  23.840

## Appendix B: Further Results

Table 18: Mean excess returns of equally-weighted quintile portfolios, formed
on customer momentum 1-1

This table reports excess returns of quintile portfolios, formed on customer
momentum. In each month, firms are ranked by the past returns of the portfolio
of their customers. The window over which the past returns are calculated, is
defined by the lag. Lag is defined as (Beginning of return window as a number
of months before the current month)-(End of return window as a number of
months before the current month). L/S corresponds to long-short portfolio,
defined as a difference in returns of fifth and first quintile portfolios.
Fifth portfolio (Q5) is the portfolio of the firms with the largest customers’
returns. Standard errors are calculated using Newey-West adjustment.

Lag | Statistic | Q1 | Q2 | Q3 | Q4 | Q5 | L/S
---|---|---|---|---|---|---|---
1-1 | Mean | 0.72∗∗ | 1.03∗∗∗ | 1.00∗∗∗ | 1.38∗∗∗ | 1.64∗∗∗ | 0.93∗∗∗
| T-stat | [2.36] | [3.72] | [3.80] | [4.93] | [5.42] | [5.51]
2-1 | Mean | 0.66∗∗ | 1.11∗∗∗ | 0.98∗∗∗ | 1.14∗∗∗ | 1.60∗∗∗ | 0.94∗∗∗
| T-stat | [2.12] | [3.94] | [3.69] | [4.19] | [5.20] | [4.95]
3-1 | Mean | 0.64∗∗ | 1.09∗∗∗ | 0.94∗∗∗ | 1.15∗∗∗ | 1.46∗∗∗ | 0.82∗∗∗
| T-stat | [2.06] | [3.86] | [3.47] | [4.24] | [4.68] | [4.05]
4-1 | Mean | 0.64∗∗ | 0.86∗∗∗ | 1.14∗∗∗ | 0.97∗∗∗ | 1.46∗∗∗ | 0.82∗∗∗
| T-stat | [2.00] | [3.21] | [4.24] | [3.58] | [4.69] | [3.86]
7-1 | Mean | 0.77∗∗ | 0.71∗∗∗ | 0.89∗∗∗ | 1.04∗∗∗ | 1.46∗∗∗ | 0.69∗∗∗
| T-stat | [2.49] | [2.60] | [3.20] | [3.88] | [4.68] | [3.30]
12-1 | Mean | 0.50 | 0.85∗∗∗ | 0.66∗∗ | 0.87∗∗∗ | 1.58∗∗∗ | 1.08∗∗∗
| T-stat | [1.61] | [3.12] | [2.47] | [3.29] | [4.95] | [4.65]
2-2 | Mean | 1.03∗∗∗ | 1.32∗∗∗ | 1.16∗∗∗ | 1.23∗∗∗ | 1.31∗∗∗ | 0.29
| T-stat | [3.31] | [4.66] | [4.26] | [4.58] | [4.36] | [1.49]
4-2 | Mean | 0.88∗∗∗ | 1.00∗∗∗ | 1.29∗∗∗ | 1.02∗∗∗ | 1.27∗∗∗ | 0.39∗
| T-stat | [2.75] | [3.55] | [4.72] | [3.90] | [4.09] | [1.93]
7-2 | Mean | 0.78∗∗ | 1.03∗∗∗ | 1.02∗∗∗ | 1.09∗∗∗ | 1.31∗∗∗ | 0.53∗∗
| T-stat | [2.45] | [3.67] | [3.80] | [3.93] | [4.29] | [2.47]
12-2 | Mean | 0.61∗ | 0.95∗∗∗ | 0.82∗∗∗ | 0.81∗∗∗ | 1.60∗∗∗ | 0.99∗∗∗
| T-stat | [1.95] | [3.52] | [3.03] | [3.02] | [5.06] | [4.46]
3-3 | Mean | 1.08∗∗∗ | 1.05∗∗∗ | 1.28∗∗∗ | 1.41∗∗∗ | 1.33∗∗∗ | 0.25
| T-stat | [3.57] | [3.80] | [4.55] | [4.97] | [4.35] | [1.34]
7-3 | Mean | 0.97∗∗∗ | 0.91∗∗∗ | 1.14∗∗∗ | 1.22∗∗∗ | 1.27∗∗∗ | 0.30
| T-stat | [3.06] | [3.31] | [4.09] | [4.48] | [4.00] | [1.34]
12-3 | Mean | 0.70∗∗ | 1.00∗∗∗ | 0.73∗∗∗ | 0.97∗∗∗ | 1.49∗∗∗ | 0.79∗∗∗
| T-stat | [2.31] | [3.50] | [2.67] | [3.59] | [4.85] | [3.66]
12-7 | Mean | 0.92∗∗∗ | 0.99∗∗∗ | 0.97∗∗∗ | 1.08∗∗∗ | 1.52∗∗∗ | 0.60∗∗∗
| T-stat | [3.01] | [3.60] | [3.47] | [3.74] | [4.95] | [3.03]

Table 19: Mean excess returns of value-weighted quintile portfolios, formed on
customer momentum 1-1

This table reports excess returns of quintile portfolios, formed on customer
momentum. In each month, firms are ranked by the past returns of the portfolio
of their customers. The window over which the past returns are calculated, is
defined by the lag. Lag is defined as (Beginning of return window as a number
of months before the current month)-(End of return window as a number of
months before the current month). L/S corresponds to long-short portfolio,
defined as a difference in returns of fifth and first quintile portfolios.
Fifth portfolio (Q5) is the portfolio of the firms with the largest customers’
returns. Standard errors are calculated using Newey-West adjustment.

Lag | Statistic | Q1 | Q2 | Q3 | Q4 | Q5 | L/S
---|---|---|---|---|---|---|---
1-1 | Mean | 0.29 | 0.76∗∗∗ | 0.58∗∗ | 0.49∗ | 0.93∗∗∗ | 0.64∗∗
| T-stat | [0.92] | [2.90] | [2.15] | [1.80] | [2.94] | [2.50]
2-1 | Mean | 0.33 | 0.40 | 0.82∗∗∗ | 0.50∗ | 1.18∗∗∗ | 0.84∗∗∗
| T-stat | [1.05] | [1.40] | [2.94] | [1.84] | [3.79] | [3.37]
3-1 | Mean | 0.42 | 0.66∗∗ | 0.57∗∗ | 0.65∗∗ | 1.00∗∗∗ | 0.59∗∗
| T-stat | [1.29] | [2.34] | [2.13] | [2.43] | [3.27] | [2.27]
4-1 | Mean | 0.49 | 0.46∗ | 0.77∗∗∗ | 0.63∗∗ | 0.97∗∗∗ | 0.48∗
| T-stat | [1.46] | [1.70] | [2.93] | [2.18] | [3.22] | [1.67]
7-1 | Mean | 0.57∗ | 0.61∗∗ | 0.55∗ | 0.67∗∗ | 1.05∗∗∗ | 0.48∗
| T-stat | [1.75] | [2.12] | [1.93] | [2.55] | [3.31] | [1.66]
12-1 | Mean | 0.37 | 0.76∗∗∗ | 0.49∗ | 0.64∗∗ | 1.23∗∗∗ | 0.86∗∗∗
| T-stat | [1.07] | [2.84] | [1.75] | [2.21] | [3.95] | [2.62]
2-2 | Mean | 0.55∗ | 0.60∗∗ | 0.67∗∗ | 0.73∗∗∗ | 0.85∗∗∗ | 0.29
| T-stat | [1.81] | [2.06] | [2.28] | [2.70] | [2.88] | [1.22]
4-2 | Mean | 0.65∗∗ | 0.59∗∗ | 0.90∗∗∗ | 0.46∗ | 0.81∗∗ | 0.16
| T-stat | [1.98] | [2.05] | [3.30] | [1.77] | [2.57] | [0.62]
7-2 | Mean | 0.47 | 0.75∗∗∗ | 0.69∗∗ | 0.67∗∗ | 0.94∗∗∗ | 0.47
| T-stat | [1.39] | [2.70] | [2.47] | [2.45] | [3.13] | [1.60]
12-2 | Mean | 0.42 | 0.76∗∗∗ | 0.70∗∗ | 0.50∗ | 1.17∗∗∗ | 0.74∗∗
| T-stat | [1.26] | [2.86] | [2.42] | [1.77] | [3.82] | [2.42]
3-3 | Mean | 0.63∗∗ | 0.40 | 0.78∗∗∗ | 1.05∗∗∗ | 0.92∗∗∗ | 0.29
| T-stat | [2.03] | [1.42] | [2.70] | [3.52] | [3.06] | [1.10]
7-3 | Mean | 0.54 | 0.76∗∗∗ | 1.07∗∗∗ | 0.65∗∗ | 0.76∗∗ | 0.22
| T-stat | [1.63] | [2.62] | [3.90] | [2.31] | [2.47] | [0.75]
12-3 | Mean | 0.54 | 0.71∗∗ | 0.64∗∗ | 0.64∗∗ | 1.15∗∗∗ | 0.61∗∗
| T-stat | [1.63] | [2.47] | [2.17] | [2.19] | [3.78] | [2.10]
12-7 | Mean | 0.65∗∗ | 0.51∗ | 0.68∗∗ | 0.91∗∗∗ | 0.86∗∗∗ | 0.21
| T-stat | [2.11] | [1.72] | [2.35] | [3.22] | [2.68] | [0.70]

Table 20: Summary statistics of characteristics

This table reports summary statistics of characteristics, including their
percentiles for different time subsamples. OP is operating profitability. CAR3
is abnormal return during 3-day window, centered around earnings announcement.
Standardized unexpected earnings (SUE) are defined as the most recent year-to-
year change in earnings per share (EPS), divided by the standard deviation of
the changes in earnings during the recent eight announcements. I define SUE as
missing if there are less than 6 quarterly announcement during 2-year window.
Cmom1-1 is the return of the customers’ portfolio during the last month.
Mom12-2 and Mom1-1 are past returns of the firm at a given lag.

Panel A: 1979-2004 |  |
---|---|---
|  Cmom1-1 |  Mom12-2 |  Mom1-1 |  log(ME) |  log(B/M) |  Op |  CAR3 |  SUE
Mean |  $1.05$ |  $21.75$ |  $1.54$ |  $5.49$ |  $-0.54$ |  $0.25$ |  $0.003$ |  $0.04$
SD |  $8.41$ |  $56.62$ |  $12.05$ |  $1.73$ |  $0.73$ |  $0.21$ |  $0.06$ |  $1.10$
min |  $-36.36$ |  $-86.71$ |  $-46.17$ |  $1.15$ |  $-3.40$ |  $-1.25$ |  $-0.32$ |  $-3.04$
p05 |  $-12.56$ |  $-41.63$ |  $-17.03$ |  $2.80$ |  $-1.82$ |  $-0.06$ |  $-0.10$ |  $-1.89$
p25 |  $-3.79$ |  $-11.51$ |  $-5.62$ |  $4.26$ |  $-1.00$ |  $0.15$ |  $-0.03$ |  $-0.59$
p50 |  $1.04$ |  $11.43$ |  $0.83$ |  $5.32$ |  $-0.48$ |  $0.26$ |  $0.001$ |  $0.06$
p75 |  $5.88$ |  $41.24$ |  $7.96$ |  $6.62$ |  $-0.04$ |  $0.35$ |  $0.03$ |  $0.64$
p95 |  $14.93$ |  $118.60$ |  $22.30$ |  $8.52$ |  $0.58$ |  $0.56$ |  $0.11$ |  $1.89$
max |  $38.21$ |  $831.90$ |  $76.52$ |  $13.01$ |  $2.13$ |  $2.90$ |  $0.41$ |  $4.81$

Panel B: 2005-2018 |  |
---|---|---
|  Cmom1-1 |  Mom12-2 |  Mom1-1 |  log(ME) |  log(B/M) |  Op |  CAR3 |  SUE
Mean |  $0.75$ |  $13.99$ |  $1.17$ |  $7.30$ |  $-0.92$ |  $0.25$ |  $0.003$ |  $0.10$
SD |  $7.14$ |  $38.52$ |  $10.19$ |  $1.74$ |  $0.68$ |  $0.28$ |  $0.08$ |  $1.08$
min |  $-36.71$ |  $-80.19$ |  $-39.70$ |  $2.37$ |  $-4.07$ |  $-1.31$ |  $-0.29$ |  $-3.03$
p05 |  $-11.10$ |  $-39.79$ |  $-15.44$ |  $4.74$ |  $-2.13$ |  $-0.05$ |  $-0.12$ |  $-1.82$
p25 |  $-3.40$ |  $-11.09$ |  $-4.87$ |  $6.13$ |  $-1.32$ |  $0.14$ |  $-0.04$ |  $-0.48$
p50 |  $0.89$ |  $9.25$ |  $0.89$ |  $7.13$ |  $-0.85$ |  $0.22$ |  $0.001$ |  $0.08$
p75 |  $5.04$ |  $32.59$ |  $6.85$ |  $8.27$ |  $-0.46$ |  $0.34$ |  $0.05$ |  $0.71$
p95 |  $12.22$ |  $84.00$ |  $18.38$ |  $10.80$ |  $0.09$ |  $0.62$ |  $0.13$ |  $1.97$
max |  $37.86$ |  $272.40$ |  $61.13$ |  $12.27$ |  $1.11$ |  $4.29$ |  $0.35$ |  $3.41$

Table 21: Summary statistics of factors

This table reports summary statistics of factors, including their percentiles.
CMOM, SUEF and CAR3F are factors, constructed from 2x3 sorts on ME and
respective variable (returns of customers’ portfolio during the recent month,
SUE and CAR3). The factors use NYSE breakpoints and are constructed similarly
to UMD. Standard errors are calculated using Newey-West correction.

Panel A: 1979-2018 |  |  |
---|---|---|---
|  Mkt.RF |  SMB |  HML |  RMW |  CMA |  UMD |  CMOM |  SUEF |  CAR3F
Mean |  $0.68^{***}$ |  $0.17$ |  $0.26$ |  $0.34^{***}$ |  $0.27^{**}$ |  $0.61^{***}$ |  $0.40^{***}$ |  $1.27^{***}$ |  $0.18^{**}$
SE |  $0.21$ |  $0.13$ |  $0.16$ |  $0.13$ |  $0.10$ |  $0.22$ |  $0.14$ |  $0.07$ |  $0.07$
T-stat |  $[3.22]$ |  $[1.31]$ |  $[1.57]$ |  $[2.67]$ |  $[2.55]$ |  $[2.78]$ |  $[2.88]$ |  $[17.31]$ |  $[2.46]$
SD |  $4.38$ |  $2.89$ |  $2.92$ |  $2.41$ |  $1.99$ |  $4.45$ |  $3.50$ |  $1.94$ |  $1.57$
min |  $-23.24$ |  $-15.33$ |  $-11.10$ |  $-19.06$ |  $-6.88$ |  $-34.39$ |  $-10.55$ |  $-8.34$ |  $-10.98$
p05 |  $-7.09$ |  $-4.07$ |  $-3.95$ |  $-2.82$ |  $-2.66$ |  $-6.46$ |  $-4.85$ |  $-1.48$ |  $-2.17$
p25 |  $-1.94$ |  $-1.51$ |  $-1.38$ |  $-0.81$ |  $-0.99$ |  $-1.01$ |  $-1.54$ |  $0.26$ |  $-0.59$
p50 |  $1.09$ |  $0.06$ |  $0.04$ |  $0.34$ |  $0.16$ |  $0.70$ |  $0.06$ |  $1.22$ |  $0.31$
p75 |  $3.50$ |  $1.93$ |  $1.72$ |  $1.38$ |  $1.49$ |  $2.88$ |  $2.32$ |  $2.18$ |  $1.01$
p95 |  $7.07$ |  $4.62$ |  $5.30$ |  $3.71$ |  $3.29$ |  $6.63$ |  $5.93$ |  $4.47$ |  $2.46$
max |  $12.47$ |  $18.75$ |  $12.90$ |  $13.51$ |  $9.56$ |  $18.33$ |  $19.36$ |  $10.12$ |  $5.25$

Panel B: 1979-2004 |  |  |
---|---|---|---
|  Mkt.RF |  SMB |  HML |  RMW |  CMA |  UMD |  CMOM |  SUEF |  CAR3F
Mean |  $0.67^{***}$ |  $0.19$ |  $0.42^{*}$ |  $0.38^{**}$ |  $0.40^{***}$ |  $0.87^{***}$ |  $0.63^{***}$ |  $1.27^{***}$ |  $0.30^{***}$
SE |  $0.25$ |  $0.18$ |  $0.22$ |  $0.18$ |  $0.14$ |  $0.23$ |  $0.18$ |  $0.09$ |  $0.08$
T-stat |  $[2.65]$ |  $[1.05]$ |  $[1.93]$ |  $[2.15]$ |  $[2.88]$ |  $[3.75]$ |  $[3.52]$ |  $[13.95]$ |  $[3.86]$
SD |  $4.55$ |  $3.12$ |  $3.09$ |  $2.75$ |  $2.24$ |  $4.41$ |  $3.96$ |  $1.86$ |  $1.48$
min |  $-23.24$ |  $-15.33$ |  $-10.57$ |  $-19.06$ |  $-6.88$ |  $-25.06$ |  $-10.55$ |  $-8.34$ |  $-7.68$
p05 |  $-7.10$ |  $-4.32$ |  $-4.11$ |  $-3.29$ |  $-2.89$ |  $-6.61$ |  $-5.39$ |  $-1.34$ |  $-2.10$
p25 |  $-2.12$ |  $-1.53$ |  $-1.28$ |  $-0.85$ |  $-0.98$ |  $-0.68$ |  $-1.60$ |  $0.27$ |  $-0.46$
p50 |  $1.13$ |  $0.05$ |  $0.50$ |  $0.38$ |  $0.36$ |  $1.04$ |  $0.26$ |  $1.11$ |  $0.37$
p75 |  $3.73$ |  $2.11$ |  $1.95$ |  $1.47$ |  $1.68$ |  $2.95$ |  $2.95$ |  $2.31$ |  $1.12$
p95 |  $7.16$ |  $4.63$ |  $5.57$ |  $3.96$ |  $4.07$ |  $7.31$ |  $6.19$ |  $4.31$ |  $2.55$
max |  $12.47$ |  $18.75$ |  $12.90$ |  $13.51$ |  $9.56$ |  $18.33$ |  $19.36$ |  $8.48$ |  $5.25$

Panel C: 2005-2018 |  |  |
---|---|---|---
|  Mkt.RF |  SMB |  HML |  RMW |  CMA |  UMD |  CMOM |  SUEF |  CAR3F
Mean |  $0.69^{*}$ |  $0.13$ |  $-0.06$ |  $0.25^{*}$ |  $0.002$ |  $0.12$ |  $-0.06$ |  $1.27^{***}$ |  $-0.04$
SE |  $0.38$ |  $0.15$ |  $0.22$ |  $0.14$ |  $0.12$ |  $0.45$ |  $0.17$ |  $0.12$ |  $0.15$
T-stat |  $[1.82]$ |  $[0.87]$ |  $[-0.25]$ |  $[1.77]$ |  $[0.01]$ |  $[0.27]$ |  $[-0.34]$ |  $[10.17]$ |  $[-0.27]$
SD |  $4.05$ |  $2.39$ |  $2.54$ |  $1.56$ |  $1.38$ |  $4.48$ |  $2.29$ |  $2.10$ |  $1.72$
min |  $-17.23$ |  $-4.76$ |  $-11.10$ |  $-3.71$ |  $-3.32$ |  $-34.39$ |  $-6.09$ |  $-6.74$ |  $-10.98$
p05 |  $-6.35$ |  $-3.72$ |  $-3.33$ |  $-1.97$ |  $-2.16$ |  $-5.40$ |  $-4.08$ |  $-1.82$ |  $-2.31$
p25 |  $-1.46$ |  $-1.44$ |  $-1.45$ |  $-0.67$ |  $-1.00$ |  $-1.33$ |  $-1.37$ |  $0.26$ |  $-0.90$
p50 |  $0.99$ |  $0.08$ |  $-0.23$ |  $0.30$ |  $-0.06$ |  $0.38$ |  $-0.05$ |  $1.33$ |  $0.19$
p75 |  $3.12$ |  $1.60$ |  $1.14$ |  $1.17$ |  $0.90$ |  $2.52$ |  $1.30$ |  $2.17$ |  $0.82$
p95 |  $6.79$ |  $3.73$ |  $3.81$ |  $3.04$ |  $2.26$ |  $5.31$ |  $3.54$ |  $4.83$ |  $2.10$
max |  $11.35$ |  $6.81$ |  $8.27$ |  $4.82$ |  $3.67$ |  $12.54$ |  $6.37$ |  $10.12$ |  $4.93$

Table 22: Fama-MacBeth regressions, 1979-2004

This table reports the results of Fama-MacBeth regression of returns on the
characteristics. Cmom1-1 is returns of customers’ portfolio during the last
month. Mom1-1 and Mom12-2 are past returns of stock at lags 1-1 and 12-2. OP
is operating profitability. CAR3 is abnormal return during 3-day window,
centered around earnings announcement. Standardized unexpected earnings (SUE)
are defined as the most recent year-to-year change in earnings per share
(EPS), divided by the standard deviation of the changes in earnings during the
recent eight announcements.

| Dependent variable:
---|---
|  (1) |  (2) |  (3) |  (4) |  (5) |  (6) |  (7) |  (8) |  (9)
Cmom1-1 |  0.05∗∗∗ |  0.06∗∗∗ |  |  |  |  0.05∗∗∗ |  |  0.05∗∗∗ |  0.06∗∗∗
|  [2.93] |  [2.82] |  |  |  |  [3.11] |  |  [3.15] |  [3.33]
log(ME) |  |  -0.14∗ |  |  -0.16∗∗ |  -0.12∗ |  -0.13∗ |  -0.14∗∗ |  -0.17∗∗ |  -0.15∗∗
|  |  [-1.90] |  |  [-2.39] |  [-1.74] |  [-1.83] |  [-2.06] |  [-2.53] |  [-2.18]
log(B/M) |  |  0.27 |  |  0.36∗ |  0.30 |  0.26 |  0.40∗ |  0.38∗ |  0.38∗
|  |  [1.46] |  |  [1.74] |  [1.44] |  [1.26] |  [1.92] |  [1.84] |  [1.81]
OP |  |  |  |  0.08 |  0.48 |  0.45 |  0.74 |  0.18 |  0.78
|  |  |  |  [0.13] |  [0.77] |  [0.70] |  [1.14] |  [0.28] |  [1.18]
Mom12-2 |  |  |  0.01∗∗∗ |  0.01∗∗ |  |  |  0.001 |  0.01∗∗ |  -0.001
|  |  |  [3.44] |  [2.47] |  |  |  [0.16] |  [2.05] |  [-0.30]
Mom1-1 |  |  |  -0.03∗∗∗ |  -0.05∗∗∗ |  |  |  -0.06∗∗∗ |  -0.05∗∗∗ |  -0.07∗∗∗
|  |  |  [-2.83] |  [-4.63] |  |  |  [-5.91] |  [-4.89] |  [-6.08]
CAR3 |  |  |  |  |  3.58∗∗ |  4.02∗∗ |  4.14∗∗ |  |  4.83∗∗∗
|  |  |  |  |  [2.22] |  [2.51] |  [2.44] |  |  [2.80]
SUE |  |  |  |  |  1.01∗∗∗ |  1.00∗∗∗ |  1.04∗∗∗ |  |  1.04∗∗∗
|  |  |  |  |  [11.34] |  [11.05] |  [10.97] |  |  [10.73]
Constant |  1.50∗∗∗ |  2.38∗∗∗ |  1.47∗∗∗ |  2.45∗∗∗ |  2.10∗∗∗ |  2.06∗∗∗ |  2.29∗∗∗ |  2.50∗∗∗ |  2.34∗∗∗
|  [4.49] |  [4.72] |  [4.64] |  [4.99] |  [4.26] |  [4.12] |  [4.70] |  [5.10] |  [4.77]
Observations |  25,421 |  25,421 |  25,421 |  25,421 |  25,421 |  25,421 |  25,421 |  25,421 |  25,421
Adjusted R2 |  0.008 |  0.031 |  0.033 |  0.067 |  0.059 |  0.066 |  0.086 |  0.073 |  0.093

Table 23: Fama-MacBeth regressions, 2005-2018

This table reports the results of Fama-MacBeth regression of returns on the
characteristics. Cmom1-1 is returns of customers’ portfolio during the last
month. Mom1-1 and Mom12-2 are past returns of stock at lags 1-1 and 12-2. OP
is operating profitability. CAR3 is abnormal return during 3-day window,
centered around earnings announcement. Standardized unexpected earnings (SUE)
are defined as the most recent year-to-year change in earnings per share
(EPS), divided by the standard deviation of the changes in earnings during the
recent eight announcements.

| Dependent variable:
---|---
|  (1) |  (2) |  (3) |  (4) |  (5) |  (6) |  (7) |  (8) |  (9)
Cmom1-1 |  0.02 |  0.02 |  |  |  |  0.02 |  |  0.02 |  0.02
|  [1.29] |  [1.22] |  |  |  |  [0.95] |  |  [1.15] |  [1.17]
log(ME) |  |  -0.07 |  |  -0.06 |  -0.06 |  -0.07 |  -0.07 |  -0.06 |  -0.07
|  |  [-1.16] |  |  [-0.98] |  [-1.12] |  [-1.13] |  [-1.26] |  [-1.02] |  [-1.30]
log(B/M) |  |  -0.18 |  |  -0.25 |  -0.13 |  -0.13 |  -0.20 |  -0.26 |  -0.20
|  |  [-1.18] |  |  [-1.48] |  [-0.75] |  [-0.75] |  [-1.18] |  [-1.51] |  [-1.19]
OP |  |  |  |  -0.31 |  -0.03 |  -0.07 |  -0.19 |  -0.35 |  -0.23
|  |  |  |  [-0.76] |  [-0.08] |  [-0.16] |  [-0.47] |  [-0.86] |  [-0.56]
Mom12-2 |  |  |  -0.01 |  -0.01 |  |  |  -0.01∗∗ |  -0.01 |  -0.01∗∗
|  |  |  [-1.22] |  [-1.15] |  |  |  [-2.17] |  [-1.24] |  [-2.28]
Mom1-1 |  |  |  -0.003 |  -0.004 |  |  |  -0.02 |  -0.01 |  -0.02
|  |  |  [-0.30] |  [-0.36] |  |  |  [-1.49] |  [-0.48] |  [-1.63]
CAR3 |  |  |  |  |  0.36 |  0.28 |  1.06 |  |  1.06
|  |  |  |  |  [0.33] |  [0.26] |  [0.87] |  |  [0.89]
SUE |  |  |  |  |  0.58∗∗∗ |  0.59∗∗∗ |  0.65∗∗∗ |  |  0.67∗∗∗
|  |  |  |  |  [6.52] |  [6.75] |  [8.17] |  |  [8.42]
Constant |  1.04∗∗ |  1.34∗ |  0.84∗∗ |  1.06 |  1.25∗ |  1.29∗ |  1.20∗ |  1.14∗ |  1.26∗
|  [2.46] |  [1.81] |  [2.17] |  [1.56] |  [1.70] |  [1.77] |  [1.76] |  [1.65] |  [1.83]
Observations |  22,666 |  22,666 |  22,666 |  22,666 |  22,666 |  22,666 |  22,666 |  22,666 |  22,666
Adjusted R2 |  0.005 |  0.021 |  0.026 |  0.046 |  0.034 |  0.039 |  0.056 |  0.050 |  0.060

Table 24: Factor spanning test for Momentum, 1979-2004

This table reports the results of time-series regression of momentum factor
UMD on the other factors. CMOM, SUEF and CAR3F are factors, constructed from
2x3 sorts on ME and respective variable (past returns of customers’ portfolio
during the past month, SUE and CAR3). The factors use NYSE breakpoints and are
constructed similarly to UMD.

| Dependent variable:
---|---
| UMD
| (1) | (2) | (3) | (4) | (5)
Mkt.RF | -0.12 | -0.03 | -0.05 | -0.13 | -0.06
| [-1.21] | [-0.32] | [-0.72] | [-1.28] | [-0.85]
SMB | 0.11 | 0.18 | 0.20 | 0.10 | 0.19
| [0.68] | [1.26] | [1.43] | [0.65] | [1.42]
HML | -0.26 | -0.58∗∗∗ | -0.34∗ | -0.25 | -0.33∗
| [-1.13] | [-2.60] | [-1.85] | [-1.17] | [-1.93]
CMA |  | 0.55 |  |  |
|  | [1.32] |  |  |
RMW |  | 0.36 |  |  |
|  | [1.36] |  |  |
SUEF |  |  | 0.78∗∗∗ |  | 0.79∗∗∗
|  |  | [4.56] |  | [4.73]
CAR3f |  |  | 0.88∗∗∗ |  | 0.81∗∗∗
|  |  | [2.82] |  | [2.74]
CMOM |  |  |  | 0.21∗∗ | 0.15∗
|  |  |  | [2.44] | [1.85]
Constant | 1.04∗∗∗ | 0.75∗∗ | -0.23 | 0.91∗∗∗ | -0.32
| [4.13] | [2.17] | [-0.68] | [3.60] | [-0.96]
Observations | 312 | 312 | 312 | 312 | 312
R2 | 0.038 | 0.095 | 0.286 | 0.072 | 0.303
Adjusted R2 | 0.028 | 0.080 | 0.275 | 0.060 | 0.289
Residual Std. Error | 4.351 | 4.234 | 3.759 | 4.278 | 3.722
F Statistic | 4.006 | 6.398 | 24.540 | 5.996 | 22.070

Table 25: Factor spanning test for Momentum, 2005-2018

This table reports the results of time-series regression of momentum factor
UMD on the other factors. CMOM, SUEF and CAR3F are factors, constructed from
2x3 sorts on ME and respective variable (past returns of customers’ portfolio
during the past month, SUE and CAR3). The factors use NYSE breakpoints and are
constructed similarly to UMD.

| Dependent variable:
---|---
| UMD
| (1) | (2) | (3) | (4) | (5)
Mkt.RF | -0.27∗∗ | -0.24∗ | -0.13∗ | -0.26∗∗ | -0.12∗
| [-2.01] | [-1.74] | [-1.87] | [-2.04] | [-1.77]
SMB | 0.01 | 0.02 | 0.07 | 0.01 | 0.08
| [0.06] | [0.12] | [0.61] | [0.09] | [0.67]
HML | -0.61∗∗∗ | -0.66∗∗∗ | -0.11 | -0.64∗∗∗ | -0.13
| [-3.28] | [-2.89] | [-0.47] | [-3.16] | [-0.57]
CMA |  | 0.19 |  |  |
|  | [0.50] |  |  |
RMW |  | 0.12 |  |  |
|  | [0.45] |  |  |
SUEF |  |  | 0.82∗∗∗ |  | 0.81∗∗∗
|  |  | [5.31] |  | [5.26]
CAR3f |  |  | 1.13∗∗∗ |  | 1.16∗∗∗
|  |  | [3.12] |  | [3.32]
CMOM |  |  |  | -0.14 | -0.16
|  |  |  | [-0.62] | [-1.14]
Constant | 0.27 | 0.22 | -0.80∗∗ | 0.25 | -0.78∗∗
| [0.74] | [0.62] | [-2.56] | [0.64] | [-2.51]
Observations | 162 | 162 | 162 | 158 | 158
R2 | 0.224 | 0.228 | 0.524 | 0.227 | 0.531
Adjusted R2 | 0.210 | 0.204 | 0.509 | 0.207 | 0.513
Residual Std. Error | 3.984 | 3.999 | 3.139 | 4.032 | 3.161
F Statistic | 15.230 | 9.234 | 34.390 | 11.250 | 28.510

Table 26: Factor spanning test for Customer momentum, 1979-2004

This table reports the results of time-series regression of customer momentum
factor CMOM on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| CMOM
| (1) | (2) | (3) | (4) | (5) | (6) | (7)
Mkt.RF | 0.04 | 0.02 | 0.06 | 0.03 | 0.06 | 0.02 | 0.03
| [0.66] | [0.26] | [0.90] | [0.37] | [0.76] | [0.31] | [0.37]
SMB | 0.05 | -0.01 | 0.03 | -0.05 | 0.05 | -0.005 | -0.05
| [0.55] | [-0.14] | [0.42] | [-0.60] | [0.61] | [-0.06] | [-0.58]
HML | -0.07 | -0.001 | -0.02 | 0.12 | -0.08 | 0.04 | 0.12
| [-0.53] | [-0.005] | [-0.21] | [0.75] | [-0.64] | [0.22] | [0.72]
CMA |  | -0.07 |  | -0.19 |  | -0.17 | -0.20
|  | [-0.34] |  | [-0.93] |  | [-0.85] | [-0.98]
RMW |  | -0.22∗∗ |  | -0.29∗∗∗ |  | -0.25∗∗∗ | -0.28∗∗∗
|  | [-2.39] |  | [-3.47] |  | [-2.63] | [-3.30]
UMD |  |  | 0.17∗∗ | 0.21∗∗∗ |  |  | 0.18∗∗
|  |  | [2.51] | [2.95] |  |  | [2.10]
SUEF |  |  |  |  | -0.07 | 0.02 | -0.10
|  |  |  |  | [-0.48] | [0.16] | [-0.67]
CAR3f |  |  |  |  | 0.49∗∗ | 0.50∗∗ | 0.35∗
|  |  |  |  | [2.36] | [2.40] | [1.93]
Constant | 0.62∗∗∗ | 0.73∗∗∗ | 0.44∗∗ | 0.57∗∗∗ | 0.55∗ | 0.58∗∗ | 0.63∗∗
| [3.18] | [3.50] | [2.24] | [2.76] | [1.93] | [1.97] | [2.18]
Observations | 312 | 312 | 312 | 312 | 312 | 312 | 312
R2 | 0.012 | 0.028 | 0.048 | 0.077 | 0.041 | 0.062 | 0.090
Adjusted R2 | 0.002 | 0.012 | 0.035 | 0.058 | 0.025 | 0.041 | 0.066
Residual Std. Error | 3.958 | 3.938 | 3.892 | 3.844 | 3.911 | 3.880 | 3.829
F Statistic | 1.211 | 1.749 | 3.828 | 4.217 | 2.615 | 2.894 | 3.745

Table 27: Factor spanning test for Customer momentum, 2005-2018

This table reports the results of time-series regression of customer momentum
factor CMOM on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| CMOM
| (1) | (2) | (3) | (4) | (5) | (6) | (7)
Mkt.RF | 0.03 | 0.04 | 0.02 | 0.03 | 0.03 | 0.05 | 0.04
| [0.50] | [0.82] | [0.31] | [0.63] | [0.65] | [0.99] | [0.83]
SMB | 0.01 | 0.05 | 0.02 | 0.05 | 0.02 | 0.06 | 0.06
| [0.17] | [0.63] | [0.18] | [0.63] | [0.20] | [0.65] | [0.72]
HML | -0.16∗ | -0.10 | -0.18∗∗ | -0.13∗ | -0.17∗∗∗ | -0.11 | -0.12
| [-1.91] | [-1.06] | [-2.49] | [-1.74] | [-2.75] | [-1.49] | [-1.63]
CMA |  | -0.18 |  | -0.17 |  | -0.19 | -0.19
|  | [-1.20] |  | [-1.22] |  | [-1.27] | [-1.31]
RMW |  | 0.21 |  | 0.22 |  | 0.21 | 0.22∗
|  | [1.59] |  | [1.62] |  | [1.63] | [1.71]
UMD |  |  | -0.05 | -0.05 |  |  | -0.08∗
|  |  | [-0.81] | [-0.96] |  |  | [-1.65]
SUEF |  |  |  |  | -0.05 | -0.06 | 0.01
|  |  |  |  | [-0.45] | [-0.56] | [0.11]
CAR3f |  |  |  |  | 0.07 | 0.08 | 0.18
|  |  |  |  | [0.38] | [0.47] | [1.19]
Constant | -0.10 | -0.17 | -0.08 | -0.16 | -0.03 | -0.10 | -0.17
| [-0.56] | [-0.93] | [-0.49] | [-0.88] | [-0.15] | [-0.42] | [-0.68]
Observations | 158 | 158 | 158 | 158 | 158 | 158 | 158
R2 | 0.027 | 0.048 | 0.033 | 0.055 | 0.030 | 0.053 | 0.066
Adjusted R2 | 0.008 | 0.017 | 0.008 | 0.017 | -0.002 | 0.009 | 0.016
Residual Std. Error | 2.286 | 2.275 | 2.286 | 2.275 | 2.297 | 2.285 | 2.277
F Statistic | 1.410 | 1.544 | 1.312 | 1.462 | 0.942 | 1.193 | 1.313

Table 28: Factor spanning test for Earnings momentum (SUE), 1979-2004

This table reports the results of time-series regression of earning momentum
factor SUEF on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| SUEF
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
CAR3f |  0.48∗∗∗ |  |  |  |  |  |  |
| [4.14] |  |  |  |  |  |  |
Mkt.RF |  | -0.05 | -0.01 | -0.03 | -0.0002 | -0.05 | -0.01 | -0.0000
|  | [-1.58] | [-0.20] | [-1.19] | [-0.01] | [-1.59] | [-0.22] | [-0.001]
SMB |  |  -0.10∗∗∗ |  -0.07∗ |  -0.12∗∗∗ |  -0.09∗∗ |  -0.10∗∗∗ |  -0.07∗ |  -0.09∗∗
|  | [-2.58] | [-1.73] | [-3.01] | [-2.41] | [-2.62] | [-1.76] | [-2.41]
HML |  | 0.08 |  -0.09∗ |  0.12∗∗∗ | -0.01 | 0.08 |  -0.09∗ | -0.01
|  | [1.39] | [-1.74] | [2.60] | [-0.12] | [1.40] | [-1.71] | [-0.11]
CMA |  |  |  0.29∗∗∗ |  |  0.21∗∗∗ |  |  0.29∗∗∗ |  0.21∗∗∗
|  |  | [3.12] |  | [3.28] |  | [3.27] | [3.25]
RMW |  |  |  0.19∗∗∗ |  |  0.13∗∗ |  |  0.19∗∗∗ |  0.13∗∗
|  |  | [3.35] |  | [2.48] |  | [3.44] | [2.49]
UMD |  |  |  |  0.17∗∗∗ |  0.15∗∗∗ |  |  |  0.15∗∗∗
|  |  |  | [5.33] | [5.24] |  |  | [5.45]
CMOM |  |  |  |  |  | 0.02 | 0.03 | -0.01
|  |  |  |  |  | [0.55] | [1.12] | [-0.25]
Constant |  1.12∗∗∗ |  1.29∗∗∗ |  1.14∗∗∗ |  1.11∗∗∗ |  1.02∗∗∗ |  1.28∗∗∗ |  1.11∗∗∗ |  1.03∗∗∗
| [13.71] | [14.04] | [11.27] | [13.04] | [11.83] | [13.26] | [10.71] | [11.45]
Observations | 312 | 312 | 312 | 312 | 312 | 312 | 312 | 312
R2 | 0.147 | 0.093 | 0.183 | 0.258 | 0.300 | 0.094 | 0.187 | 0.300
Adjusted R2 | 0.145 | 0.084 | 0.169 | 0.248 | 0.286 | 0.082 | 0.171 | 0.284
Residual Std. Error | 1.720 | 1.780 | 1.695 | 1.613 | 1.571 | 1.782 | 1.694 | 1.573
F Statistic | 53.610 | 10.520 | 13.660 | 26.620 | 21.810 | 7.969 | 11.670 | 18.650

Table 29: Factor spanning test for Earnings momentum (SUE), 2005-2018

This table reports the results of time-series regression of customer momentum
factor SUEF on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| SUEF
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
CAR3f |  0.32∗∗ |  |  |  |  |  |  |
| [1.99] |  |  |  |  |  |  |
Mkt.RF |  | -0.03 | -0.03 | 0.02 | 0.02 | -0.03 | -0.02 | 0.02
|  | [-0.59] | [-0.50] | [0.39] | [0.37] | [-0.53] | [-0.40] | [0.43]
SMB |  | -0.02 | -0.02 | -0.03 | -0.02 | -0.02 | -0.01 | -0.02
|  | [-0.46] | [-0.33] | [-0.54] | [-0.48] | [-0.36] | [-0.16] | [-0.33]
HML |  |  -0.40∗∗∗ |  -0.40∗∗ |  -0.29∗ | -0.28 |  -0.42∗∗∗ |  -0.41∗∗ | -0.28
|  | [-3.03] | [-2.50] | [-1.86] | [-1.50] | [-3.07] | [-2.51] | [-1.50]
CMA |  |  | 0.01 |  | -0.03 |  | -0.01 | -0.05
|  |  | [0.04] |  | [-0.22] |  | [-0.09] | [-0.32]
RMW |  |  | 0.03 |  | 0.004 |  | 0.05 | 0.02
|  |  | [0.22] |  | [0.04] |  | [0.42] | [0.21]
UMD |  |  |  |  0.19∗∗∗ |  0.19∗∗∗ |  |  |  0.19∗∗∗
|  |  |  | [3.21] | [3.20] |  |  | [3.19]
CMOM |  |  |  |  |  | -0.03 | -0.03 | -0.003
|  |  |  |  |  | [-0.39] | [-0.50] | [-0.06]
Constant |  1.28∗∗∗ |  1.27∗∗∗ |  1.26∗∗∗ |  1.22∗∗∗ |  1.22∗∗∗ |  1.25∗∗∗ |  1.23∗∗∗ |  1.20∗∗∗
| [10.18] | [9.69] | [9.97] | [9.10] | [9.18] | [9.11] | [9.50] | [8.69]
Observations | 162 | 162 | 162 | 162 | 162 | 158 | 158 | 158
R2 | 0.067 | 0.269 | 0.269 | 0.401 | 0.401 | 0.273 | 0.274 | 0.403
Adjusted R2 | 0.061 | 0.255 | 0.246 | 0.386 | 0.378 | 0.254 | 0.245 | 0.376
Residual Std. Error | 2.030 | 1.809 | 1.820 | 1.643 | 1.653 | 1.826 | 1.837 | 1.671
F Statistic | 11.520 | 19.390 | 11.500 | 26.270 | 17.310 | 14.360 | 9.497 | 14.490

Table 30: Factor spanning test for Earnings momentum (CAR3), 1979-2004

This table reports the results of time-series regression of earning momentum
factor CAR3 on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| CAR3F
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
SUEF |  0.31∗∗∗ |  |  |  |  |  |  |
| [4.61] |  |  |  |  |  |  |
Mkt.RF |  | -0.03 | -0.01 | -0.01 | -0.003 | -0.03 | -0.01 | -0.004
|  | [-0.96] | [-0.25] | [-0.51] | [-0.12] | [-1.00] | [-0.29] | [-0.16]
SMB |  | -0.01 | -0.01 | -0.03 | -0.03 | -0.02 | -0.01 | -0.03
|  | [-0.36] | [-0.30] | [-0.91] | [-1.21] | [-0.47] | [-0.29] | [-1.19]
HML |  | 0.03 | -0.07 | 0.07 | 0.01 | 0.03 | -0.07 | 0.001
|  | [0.57] | [-1.21] | [1.61] | [0.13] | [0.69] | [-1.17] | [0.03]
CMA |  |  |  0.19∗∗ |  |  0.11∗∗ |  |  0.19∗∗ |  0.12∗∗∗
|  |  | [2.23] |  | [2.45] |  | [2.43] | [2.63]
RMW |  |  | 0.06 |  | 0.02 |  | 0.08 | 0.03
|  |  | [1.19] |  | [0.43] |  | [1.40] | [0.67]
UMD |  |  |  |  0.14∗∗∗ |  0.13∗∗∗ |  |  |  0.12∗∗∗
|  |  |  | [4.41] | [4.58] |  |  | [4.65]
CMOM |  |  |  |  |  |  0.06∗∗∗ |  0.07∗∗∗ |  0.04∗∗
|  |  |  |  |  | [2.81] | [2.92] | [2.05]
Constant | -0.09 |  0.31∗∗∗ |  0.24∗∗ |  0.17∗ | 0.14 |  0.27∗∗∗ |  0.19∗ | 0.11
| [-0.74] | [3.24] | [2.26] | [1.82] | [1.47] | [2.86] | [1.78] | [1.22]
Observations | 312 | 312 | 312 | 312 | 312 | 312 | 312 | 312
R2 | 0.147 | 0.021 | 0.058 | 0.188 | 0.200 | 0.049 | 0.092 | 0.210
Adjusted R2 | 0.145 | 0.011 | 0.043 | 0.178 | 0.184 | 0.037 | 0.074 | 0.192
Residual Std. Error | 1.368 | 1.471 | 1.448 | 1.342 | 1.336 | 1.452 | 1.424 | 1.330
F Statistic | 53.610 | 2.194 | 3.778 | 17.820 | 12.700 | 3.967 | 5.130 | 11.560

Table 31: Factor spanning test for Earnings momentum (CAR3), 2005-2018

This table reports the results of time-series regression of earning momentum
factor CAR3 on the other factors. CMOM, SUEF and CAR3F are factors,
constructed from 2x3 sorts on ME and respective variable (past returns of
customers’ portfolio during the past month, SUE and CAR3). The factors use
NYSE breakpoints and are constructed similarly to UMD.

| Dependent variable:
---|---
| CAR3F
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
SUEF |  0.21∗ |  |  |  |  |  |  |
| [1.71] |  |  |  |  |  |  |
Mkt.RF |  |  -0.10∗ |  -0.09∗ | -0.04 | -0.04 |  -0.10∗ |  -0.09∗ | -0.05
|  | [-1.70] | [-1.67] | [-0.93] | [-0.96] | [-1.80] | [-1.82] | [-1.13]
SMB |  | -0.04 | -0.04 | -0.04 | -0.05 | -0.04 | -0.05 | -0.06
|  | [-0.71] | [-0.69] | [-0.79] | [-0.88] | [-0.78] | [-0.80] | [-1.01]
HML |  |  -0.15∗∗ |  -0.18∗∗ | -0.03 | -0.05 |  -0.15∗∗ |  -0.18∗∗ | -0.04
|  | [-2.20] | [-2.38] | [-0.38] | [-0.78] | [-2.03] | [-2.18] | [-0.58]
CMA |  |  | 0.10 |  | 0.06 |  | 0.11 | 0.08
|  |  | [0.63] |  | [0.56] |  | [0.70] | [0.68]
RMW |  |  | -0.005 |  | -0.03 |  | -0.01 | -0.04
|  |  | [-0.06] |  | [-0.49] |  | [-0.16] | [-0.72]
UMD |  |  |  |  0.20∗∗∗ |  0.20∗∗∗ |  |  |  0.21∗∗∗
|  |  |  | [4.47] | [4.62] |  |  | [4.72]
CMOM |  |  |  |  |  | 0.03 | 0.04 | 0.07
|  |  |  |  |  | [0.37] | [0.47] | [1.23]
Constant | -0.31 | 0.02 | 0.02 | -0.03 | -0.02 | 0.02 | 0.02 | -0.02
| [-1.16] | [0.19] | [0.16] | [-0.28] | [-0.23] | [0.17] | [0.18] | [-0.15]
Observations | 162 | 162 | 162 | 162 | 162 | 158 | 158 | 158
R2 | 0.067 | 0.149 | 0.153 | 0.360 | 0.362 | 0.155 | 0.160 | 0.379
Adjusted R2 | 0.061 | 0.133 | 0.126 | 0.344 | 0.338 | 0.132 | 0.127 | 0.350
Residual Std. Error | 1.669 | 1.604 | 1.610 | 1.395 | 1.402 | 1.618 | 1.623 | 1.400
F Statistic | 11.520 | 9.203 | 5.654 | 22.110 | 14.690 | 6.994 | 4.790 | 13.100

Table 32: Returns of long-short portfolios, formed on customer momentum

This table reports the returns of long-short portfolios, formed on the returns
of customers’ portfolio at a given lag. Type represents a lag, which is
defined as (Beginning of return window as a number of months before the
current month)-(End of return window as a number of months before the current
month). This table reports the results for 2 subsamples. The first subsample
(l1) is restricted to the supplier-customer pairs, where mean size of customer
is less than the size of supplier. Size is defined as market equity. The
second subsample (l2) is restricted to the supplier-customer pairs, where mean
size of customer can not be more than twice the size of supplier. The columns
l1 and l2 report returns of long-short portfolios, sorted on past customers’
returns in restricted subsamples. Full sample has 143,054 observations. l1 and
l2 subsamples have 7462 and 12120 observations respectively. Ew and Vw stand
for equally-weighted and value-weighted portfolios.

|  | Ew, quintiles | Vw, quintiles | Ew, deciles | Vw, deciles
---|---|---|---|---|---
Type | Statistic | l1 | l2 | l1 | l2 | l1 | l2 | l1 | l2
1-1 | Mean | 1.37∗∗ | 0.99∗∗ | 1.92∗∗∗ | 1.04∗∗ | -0.16 | 0.45 | 0.45 | 0.93
| T-stat | [2.15] | [2.04] | [2.74] | [2.00] | [-0.20] | [0.66] | [0.57] | [1.24]
2-1 | Mean | 1.26∗∗ | 0.86∗ | 1.40∗∗ | 0.97∗ | 1.02 | 0.68 | 0.93 | 1.15
| T-stat | [2.06] | [1.68] | [2.10] | [1.72] | [1.26] | [1.01] | [1.06] | [1.53]
3-1 | Mean | 0.49 | 0.40 | 1.06 | 0.48 | 1.39∗ | 0.49 | 1.74∗∗ | 0.64
| T-stat | [0.79] | [0.78] | [1.56] | [0.91] | [1.77] | [0.72] | [1.96] | [0.84]
4-1 | Mean | 0.54 | 0.58 | 1.15 | 0.37 | 0.80 | 0.54 | 1.16 | 1.01
| T-stat | [0.78] | [1.05] | [1.61] | [0.64] | [1.08] | [0.70] | [1.46] | [1.23]
7-1 | Mean | -0.32 | 0.06 | -0.18 | 0.33 | 0.51 | -0.02 | 0.66 | -0.28
| T-stat | [-0.43] | [0.10] | [-0.22] | [0.56] | [0.70] | [-0.03] | [0.91] | [-0.41]
12-1 | Mean | -0.28 | -0.57 | -0.54 | -0.56 | 0.62 | -0.48 | 0.27 | -0.85
| T-stat | [-0.37] | [-0.83] | [-0.70] | [-0.81] | [0.86] | [-0.72] | [0.36] | [-1.11]
2-2 | Mean | 1.30∗∗ | 0.79 | 1.29∗ | 0.17 | 1.12 | 1.15∗ | 0.35 | 0.46
| T-stat | [1.98] | [1.55] | [1.75] | [0.31] | [1.50] | [1.78] | [0.44] | [0.70]
4-2 | Mean | 0.94 | 0.58 | 1.36∗ | 0.19 | 1.16 | 0.94 | 2.04∗∗ | 1.15
| T-stat | [1.34] | [1.05] | [1.88] | [0.32] | [1.52] | [1.31] | [2.55] | [1.52]
7-2 | Mean | -0.21 | 0.74 | -0.01 | 0.90 | 1.38∗ | 0.58 | 1.94∗∗ | 0.76
| T-stat | [-0.27] | [1.20] | [-0.01] | [1.38] | [1.74] | [0.80] | [2.46] | [1.05]
12-2 | Mean | -0.84 | -0.72 | -1.22 | -0.76 | -0.46 | -0.76 | -0.95 | -0.96
| T-stat | [-1.07] | [-1.03] | [-1.56] | [-1.03] | [-0.59] | [-1.09] | [-1.17] | [-1.23]
3-3 | Mean | 0.17 | 0.36 | 0.59 | 0.63 | 0.34 | 0.38 | 0.90 | 1.00
| T-stat | [0.27] | [0.63] | [0.85] | [1.07] | [0.46] | [0.54] | [1.21] | [1.40]
7-3 | Mean | -0.45 | -0.49 | -0.13 | -0.81 | 0.94 | -0.25 | 1.29∗ | 0.14
| T-stat | [-0.67] | [-0.76] | [-0.19] | [-1.15] | [1.34] | [-0.36] | [1.78] | [0.19]
12-3 | Mean | -1.02 | -0.81 | -1.43∗ | -1.04 | -0.04 | -0.30 | -0.89 | -0.48
| T-stat | [-1.36] | [-1.21] | [-1.83] | [-1.44] | [-0.06] | [-0.43] | [-1.24] | [-0.61]
12-7 | Mean | 0.83 | 0.57 | 0.56 | 0.47 | 1.47∗∗ | 0.07 | 1.01 | 0.35
| T-stat | [1.12] | [0.89] | [0.74] | [0.69] | [2.05] | [0.10] | [1.44] | [0.46]

Table 33: Returns of long-short quintile portfolios, formed on customer
momentum across different quintiles of relative customer size

This table reports the returns of long-short quintile portfolios, formed on
the returns of customers’ portfolio at a given lag. Type represents a lag,
which is defined as (Beginning of return window as a number of months before
the current month)-(End of return window as a number of months before the
current month). This table reports the results separately for 5 quintiles of
relative customer size, defined as mean customer size/supplier size. So for
each month I divide the firms into 5 quintiles on relative customer size and
then perform sort on past customers’ returns within each size quintile.
Reported results are the returns of long-short quintile portfolios, formed on
the returns of customers’ portfolio at a given lag. For example, 0.206 in the
column ”Q1” means that the return of long-short quintile portfolio (largest
quintile by past month customers’ returns minus the smallest quintile) is
equal to 0.206 within the subsample of supplier-customer pairs with low (first
quintile) relative customer size.

Weights | Equal | Value |
---|---|---|---
Type | Statistic | Q1 | Q2 | Q3 | Q4 | Q5 | Q1 | Q2 | Q3 | Q4 | Q5
1-1 | Mean | 0.21 | 0.94∗∗∗ | 0.46 | 1.29∗∗∗ | 1.27∗∗∗ | 0.19 | 1.04∗∗∗ | 0.35 | 1.52∗∗∗ | 0.73∗
| T-stat | [0.78] | [2.98] | [1.40] | [4.33] | [3.71] | [0.55] | [2.64] | [0.88] | [3.92] | [1.76]
2-1 | Mean | 0.68∗∗ | 0.42 | 0.60∗ | 1.00∗∗∗ | 0.67∗ | 0.80∗∗ | 0.64∗ | 0.58 | 1.06∗∗∗ | 0.33
| T-stat | [2.35] | [1.31] | [1.65] | [3.15] | [1.73] | [2.34] | [1.66] | [1.50] | [2.63] | [0.70]
3-1 | Mean | 0.66∗∗ | 0.56∗ | 0.71∗ | 0.70∗∗ | 0.90∗∗ | 0.17 | 0.92∗∗ | 0.74∗ | 0.66 | 1.00∗∗
| T-stat | [2.14] | [1.72] | [1.69] | [2.12] | [2.36] | [0.47] | [2.36] | [1.78] | [1.55] | [2.28]
4-1 | Mean | 0.42 | 0.38 | 0.26 | 0.38 | 0.99∗∗ | 0.08 | 0.34 | 0.29 | -0.02 | 1.06∗∗
| T-stat | [1.42] | [1.19] | [0.69] | [1.08] | [2.36] | [0.21] | [0.87] | [0.65] | [-0.04] | [2.19]
7-1 | Mean | 0.35 | 0.17 | 0.44 | 0.17 | 1.26∗∗∗ | 0.15 | 0.14 | 0.05 | -0.28 | 1.21∗∗
| T-stat | [1.14] | [0.52] | [1.14] | [0.43] | [3.00] | [0.40] | [0.35] | [0.12] | [-0.61] | [2.51]
12-1 | Mean | 0.66∗ | 1.00∗∗∗ | 1.39∗∗ | 0.24 | 1.29∗∗∗ | 0.38 | 0.79∗∗ | 1.24∗∗ | 0.54 | 1.46∗∗∗
| T-stat | [1.79] | [2.61] | [2.29] | [0.43] | [2.90] | [0.88] | [1.96] | [1.97] | [0.89] | [2.72]
2-2 | Mean | 0.38 | -0.02 | 0.54 | 0.36 | 0.16 | 0.13 | 0.44 | 0.88∗∗ | 0.18 | 0.35
| T-stat | [1.37] | [-0.05] | [1.28] | [1.04] | [0.40] | [0.38] | [1.16] | [2.09] | [0.42] | [0.75]
4-2 | Mean | 0.55∗ | 0.16 | 0.43 | 0.43 | 0.79∗∗ | 0.01 | 0.00 | 0.38 | 0.05 | 0.97∗∗
| T-stat | [1.85] | [0.52] | [0.99] | [1.16] | [1.97] | [0.02] | [0.00] | [0.86] | [0.11] | [2.03]
7-2 | Mean | 0.63∗ | -0.18 | 0.79∗ | 0.87∗ | 1.00∗∗ | 0.34 | 0.18 | 0.90∗ | 0.95∗ | 1.15∗∗
| T-stat | [1.85] | [-0.48] | [1.83] | [1.91] | [2.22] | [0.89] | [0.42] | [1.94] | [1.73] | [2.21]
12-2 | Mean | 0.62∗ | 0.64 | 1.17∗ | 0.25 | 1.28∗∗∗ | 0.30 | 0.51 | 1.34∗∗ | 0.35 | 1.56∗∗∗
| T-stat | [1.73] | [1.64] | [1.88] | [0.48] | [3.04] | [0.70] | [1.18] | [2.08] | [0.59] | [2.99]
3-3 | Mean | 0.37 | 0.11 | 0.28 | 0.20 | 0.67∗ | 0.31 | 0.42 | 0.30 | 0.34 | 0.73∗
| T-stat | [1.20] | [0.38] | [0.81] | [0.52] | [1.85] | [0.86] | [1.11] | [0.76] | [0.74] | [1.70]