Corporate Cash Holdings and the Cross-Sectional Variation In
Asset Returns1
Shane Shepherd
Anderson School at UCLA
First Draft: June 2004
This Draft: June 2007
Abstract:
The level of cash holdings varies significantly across firms. As a low-returning asset,
previous literature has theorized that holding excess cash should harm firm value. We
find that this appears to be true amongst large firms. However, amongst small firms, we
find that market returns rise with cash ratios (cash/assets or cash/market cap). These
higher returns are not due to any known risk factors. Additionally they are accompanied
by superior operating performance – higher growth in assets and growth in cash flow.
Evidence supports the theory that the outperformance results from the easing of financial
constraints upon these firms. The results are most robust for small, growth-oriented firms,
who have the most difficulty accessing capital markets and could benefit the most from
holding cash.
1
Shane Shepherd is with The Anderson School at UCLA and Research Affiliates, LLC. This paper serves
as part of my dissertation at UCLA. I would like to acknowledge the helpful comments of Antonio
Bernardo, Walter Torous, Marc MartosVilas, Brad Barber, Terrance Odean, Richard Roll, and Mark
Garmaise.
1
Electronic copy available at: http://ssrn.com/abstract=1084552
1. Introduction
A high ratio of cash on a firm’s balance sheet is typically viewed as a poor investment
and a drag on returns. However, in this paper we present evidence that a firm’s cash
stockpile, or lack thereof, can strongly and positively influence its market returns and
operating performance. Particularly amongst small and growth oriented firms, and those
with poor access to the capital markets, we find that firms with high levels of cash
significantly outperform their cash-poor peers.
A discrepancy exists in the theoretical motivations for cash holdings. Jensen’s (1986)
famous free cash flow theory explores the agency conflict between stockholders and
management. Managers have many incentives, both personal and organizational, to
increase the size of their firm. This widens the resources under their control, often
increases their compensation, and delivers greater public recognition. In general,
managers have much more control and discretion over cash holdings, as opposed to
outside equity and debt financing, which is subject to board scrutiny. Jensen’s theory
predicts that managers will be likely to squander leftover cash on negative-value projects,
such as ill-advised takeovers or empire-building attempts. If this behavior is prevalent
then stockholders would clearly prefer their managers to return excess cash through
dividends or share repurchases, and either penalize cash-rich companies with lower
valuations, or attempt to oust the current managers.
2
Electronic copy available at: http://ssrn.com/abstract=1084552
However, other theories explore the benefits from holding liquid assets. This benefit
primarily arises because of the wedge between internal and external finance. Myers and
Majluf (1984) construct a transactions cost model in which firms face time-varying and
perhaps prohibitive costs to raising capital. In some cases, firms may be entirely shut out
of the external capital markets and will have to forego positive NPV projects due to lack
of financing. In this case, a cash stockpile acts as a buffer for rainy days, allowing firms
to smooth their investing behavior over time. This theory predicts that firms more likely
to face constraints on their financing capabilities should be more like to hold high levels
of cash reserves.
Of course, these two stories are not mutually inconsistent. In a large cross section of
firms, cash could hold value for some firms and be wasted by others. Firms most likely to
extract value from holding cash are small growth firms, firms with volatile growth
options, firms with a high cost of external capital, and firms subject to financial
constraints by the capital markets. Larger firms that do not face transactions costs to
raising capital, and have fewer growth options, should have lower benefits to holding
cash.
It is not obvious that cash-rich firms are the ones most prone to the agency costs
discussed by Jensen. High free cash flow does not necessarily equate to high cash
holdings. Consider three types of firms: the first type is cash-constrained and cannot fully
invest in its positive NPV projects. This firm will not hold excess cash. The second type
has more than enough capital to invest in positive NPV projects and then, in keeping with
3
Jensen’s prediction, wastes its excess cash on negative NPV projects (or, under a happier
scenario, distributes it to shareholders). This firm will also not hold much cash, but for a
different reason. A third class of firm also enjoys excess cash flow, but accumulates the
capital rather than wasting it on the present span of negative NPV projects – perhaps
waiting to invest in future positive NPV projects. In this situation, only the third type of
company becomes cash-rich. Rather than predicting imminent agency costs, high cash
holdings may instead indicate a history of skilled and disciplined management; firms with
high cash holdings find themselves in that situation precisely because they have skillfully
created excess cash flow in the past, and (more to the point) not invested it poorly but
possibly saved it in anticipation of future investment opportunities.
2. Literature Overview
Previous empirical investigations of cash-rich firms find that, in general, cash-rich firms
are precisely the ones that stand to benefit from financial slack. Opler et al. (1999) find
that firms with strong growth opportunities and risky cash flows tend to hold a large
percentage of their total assets in cash, and larger firms and firms with better credit
ratings tend to hold low levels of cash. Firms with time-varying growth opportunities put
these cash hoards to good use; Harford, Mikkelson, and Partch (2004) find that cash-rich
firms tend to invest more than their industry average during economic downturns, and
this results in higher post-downturn sales growth and superior operating performance.
Looking across the business cycle, Mikkelson and Partch (2003) find that firms with
large persistent cash reserves (those that held more than 25% of their assets in cash and
4
cash equivalents for a five-year period) have operating performance comparable to or
better than a group of matched firms.
Recent literature also holds evidence for agency costs to holding cash. Harford (1999)
uses a probit model to predict takeovers and finds that excess cash holdings (defined as
the residual from a regression which predicts optimal holdings) significantly increase the
probability of a firm undertaking a value-decreasing merger. Furthermore, he finds that
the stock price reaction to the announcement of a bid is decreasing in the amount of
excess cash held by the bidder. Investigating firms that hold cash but are not necessarily
financially constrained, Blanchard, Lopez-de-Silanes, and Shleifer (1994) find that their
small sample of firms who receive an unexpected windfall tend to waste this excess cash.
If cash holds greater value for some firms than for others, then the market value of liquid
assets should differ across firms. Indeed, Pinkowitz and Williamson (2002) find a large
spread in the cross-sectional value of cash holdings. The market prices the average dollar
of corporate cash at $0.97, but they find a tremendous spread, with the value of a dollar
ranging from $0.27 to $1.84 across firms. Cash is valued below par for firms with poor
growth opportunities – those with predictable investment options – and those with large
stockholder-bondholder conflicts (for example, firms close to bankruptcy). These are the
firms likely to fall prey to Jensen’s cash-wasting hypothesis. Those with high and
variable growth options have their cash valued at a premium. These are the firms most
likely to benefit from the advantages provided by financial slack from their internal
capital markets. But perhaps surprisingly, the market values cash at a strong discount for
5
firms that appear to have poor access to the capital markets, and at a premium for firms
that have good access to external finance.
In the first direct study of the effect of cash upon returns, Greenwood (2004) finds
aggregate corporate cash holdings to be a negative predictor for future market returns. He
argues that cash accumulation is a sign of active market timing by managers, in which
overvalued firms issue equity to take advantage of inflated valuations. However, this
study only tangentially addresses the issue of returns to cash-rich firms in the crosssection.
2. Data
A firm’s level of cash holdings obviously depends largely upon firm size. Thus, to
compare firms in the cross-section, some normalization is needed. We employ three
metrics, with largely similar results: cash and short-term investments divided by book
equity, cash and short-term investments divided by market capitalization, and cash and
short-term investments divided by total assets. From the Compustat industrial quarterly
files, we gather information on cash and short-term investments (Data item 36),
stockholder’s equity (Data item 60), and the report date for the quarterly earnings. We
restrict our analysis to firms for which we can identify the report date, to ensure that all
conditioning information is publicly available at the portfolio formation date. We further
restrict the sample to non-financial firms (eliminating all SIC codes between 6000 and
7000) because financial ratios are largely meaningless for such firms, and to United
States common equity (share codes 10 and 11 in CRSP). From CRSP, we gather monthly
6
returns and end-of-month market capitalizations for each company in the sample. We
calculate each of the three cash ratios for each firm in month t using the most recently
available information, and rank firms into deciles based upon these ratios. Summary
results appear in Table 1.
These rankings provide a sizeable spread of cash holdings. The smallest decile of firms
holds an average of 3 million in cash and short-term investments, while the largest holds
around 400 million (although somewhat less for the cash/assets metric). The mean
(median) ratio increases across all metrics from zero to 215% (103%) of equity, 129%
(74%) of market cap, and 60% (60%) of total assets.
However, there are also obvious differences in the size and book to market loadings
across the cash deciles. Both the lowest and the highest decile are composed of smaller
stocks, with the middle deciles drawing larger market capitalizations. The cash/assets
ranking gives the clearest view of the book-to-market effects. Because book value serves
as the denominator of the cash/equity group, and market value serves as in the
denominator of cash/market cap, the book value ranking will mechanically push low
book-to-market stocks into the high deciles, and the market cap ranking will
mechanically push low market cap stocks (and therefore high book-to-market stocks) into
the high deciles. This pattern is clear in Table 1. Since the cash/assets ranking is less
intricately involved with the book-to-market variables, it provides a clearer view of the
true book-to-market effects. As the Myers/Majluf theory predicts, the book-to-market
variable steadily decreases across the deciles, and therefore the growth options (measured
7
as Tobin’s Q) of the company appear to increase with cash holdings. Firms identified as
cash-rich tend to be small, growth-oriented companies – precisely the sort that would face
frictions in raising external capital and that would benefit the most from financial slack
provided by cash holdings. We provide direct evidence of this later.
To be sure that the portfolio returns are not picking up the well documented size or bookto-market effects, we do a second series of rankings that will mitigate the differences in
these characteristics across portfolios. Each month, we place each firm into one of three
size groups based upon the Fama/French monthly 30/40/30 NYSE breakpoints, and also
into one of three book-to-market groups based upon the 30/40/30 NYSE breakpoints. We
then construct three new decile rankings with a two-stage sort, ranking within each of
these nine subgroups on each of the three cash ratios. This controls for a good portion of
the size and book-to-market effects found across the deciles. Table 2 shows the
descriptive statistics for these second-stage rankings. Unless otherwise specified, our
analysis is conducted using these second-stage rankings.
The second stage ranking is still very much determined by the cash holdings. Table 3
shows the correlation matrix for the six different decile rankings with one another and
with the size and book-to-market rankings. The denominator for the normalization of
cash holdings, and the order of ranking, makes little difference; the lowest correlation
between the ranking deciles is 77%. And the correlations of the decile rankings with
market cap and book-to-market ratio show that the second-stage sorts control much better
for those effects. For example, the correlation of the book-to-market ratio with the cash
8
ranking variables drops from –14% (cash/equity), 20% (cash/market cap), and –14%
(cash/assets) to –2%, 7%, and –3%.
3. Returns Results
A. Calendar Time Returns
We analyze the returns to the cash portfolios by estimating a calendar time series of
returns to each portfolio. Stocks are placed into portfolios based upon the first-stage cash
rankings each month from January of 1970 until December of 2001. Equally weighted
monthly returns are then estimated for various holding periods (1 month, 3 months, 1
year, 3 years, and 5 years) 2. For all holding periods, the average monthly returns increase
nearly monotonically as we move from low-cash firms to cash-rich firms. The largest
impact comes in the first month, although cash-rich firms outperform even at long
horizons. For a one-month holding period, the high-cash portfolio outperforms the lowcash portfolio by 88 basis points (equity ranking), 115 basis points (market cap ranking)
and 64 basis points (assets ranking), and this difference is statistically significant at the
1% level for the first two groups. The one-year mean monthly outperformance is 54 basis
points (equity), 98 basis points (market cap), and 48 basis points (assets).
These results are not, in general, robust to value-weighted returns. Table 5 shows the
value-weighted returns for each ranking strategy, and only one of the nine high minus
low portfolios provides returns significantly different from zero at the 5% level. This
suggests that small stocks are primarily driving the observed excess returns.
9
B. Three-Factor Model
Although the equally weighted cash-rich firms clearly outperform the low-cash firms, it
may be due to the correlated size and book-to-market factors. To control for beta, size,
and book-to-market effects, we run a three-factor model time series regression on the
monthly portfolio returns, following Fama and French (1993). We estimate the equation
(R
pt
− R ft ) = α j + β j ( Rmt − R ft ) + s j SMBt + h jVMGt + ε jt ,
where Rpt is the monthly portfolio return, Rft is the monthly return on T-Bills, Rmt is the
monthly return on a value-weighted market index, SMBt is the return on a value-weighted
portfolio of small stocks minus the return on a value-weighted portfolio of big stocks, and
VMGt is the return on a value-weighted portfolio of high book-to-market stocks minus the
return on a value-weighted portfolio of low book-to-market stocks.
The results appear in Table 6. The alphas from this regression increase nearly
monotonically from the low-cash decile to the high-cash decile, with (for the 1-month
holding period) the high-cash decile outperforming the low-cash decile by 97 basis points
per month (equity ranking), 120 basis points per month (market cap ranking) and 97 basis
points per month (asset ranking). Quite strikingly, the alphas increase nearly
monotonically across the deciles, but somewhat surprisingly, betas do not significantly
vary across the deciles 3. For all three rankings, the high-cash portfolios are weighted
more toward small stocks than either the low or middle deciles, indicating that large
2
Results for the 3-month, 3-year and 5-year horizons are not reported.
This result is puzzling, as extant theory predicts that an equity beta should reflect the weighted sum of the
component asset betas. If we assume the beta on cash holdings to be zero, equity betas should decrease
across the portfolio deciles as their cash holdings increase, but this is not found in the data. One possible
explanation is that the beta of the noncash assets of firms increases along with the firms’ cash holdings.
3
10
stocks tend to hold lower levels of cash. And under all three rankings, the low-cash firms
have a higher loading on the HML factor, indicating that they tend to be composed more
of value stocks, while the high-cash portfolios contain more growth firm. But even so, the
differences in these factor loadings cannot explain the returns results, as the risk-adjusted
alphas remain highly significant and monotonic.
C. Ordered sorts
As a second method to control for alternative factors, we examine returns to portfolios
formed on the ordered sort rankings. Table 7 shows the nominal returns to the secondstage ranking portfolios. The effect still increases nearly monotonically across deciles,
and the cash-rich portfolio outperforms the cash-poor portfolio at the 5% significance
level in all strategies except the cash/assets ranking, 1-month holding period. As before,
using value-weighted returns weakens the results. Controlling for the size and book-tomarket factors actually strengthens the observed returns patterns. Rather than being the
cause of the excess returns, the size and book to market factors seem to diminish the
effect. While the cash-rich firms tend to be smaller (thus benefiting from the small-stock
effect), they also tend to be growth firms (thus being handicapped by the book-to-market
effect).
The three-factor model regressions reported in Table 9 show similar risk-adjusted
outperformance for the higher-cash deciles on the second-stage sorts. The highest and
lowest decile portfolios both weight more heavily towards small stocks than the middle
deciles, although the differences in loadings are not statistically significant, and the high
11
cash portfolios are heavily tilted towards growth stocks. However, the alphas still
increase monotonically across the deciles and are highly significant. Neither the ordered
sorts nor the three-factor model can explain the return performance of the portfolios;
higher cash portfolios consistently outperform lower cash portfolios, and the returns grow
nearly monotonically under every metric.
D. Size and Book-to-Market Subgroups
These outperformance patterns are not robust to value-weighted returns. This implies that
the effect is much stronger for small stocks than for large cap stocks. If financial slack is
valuable, it should be most valuable for firms that face the most frictions to external
financing. As large firms have more resources, generally better credit ratings, more
contacts and longer-lasting relationships with the banking industry, it seems likely that
they will have an easier time gaining outside financing in difficult market conditions or
poor credit environments. It also seems likely that small companies would benefit the
most from holding cash to avoid the transactions costs of external financing.
In this section, we directly examine the nominal returns to cash holdings amongst the
Fama-French 30/40/30 size bins and the 30/40/30 book-to-market bins. Table 10 shows
the returns to the size portfolios for the one-month holding periods. As expected, the
returns to holding cash decreases as companies grow larger. Small cash-rich firms
outperform their cash-poor counterparts by nearly one percent per month. By contrast, the
gap is only 64 basis points amongst medium sized firms, and large, cash-rich firms
actually underperform their cash-rich counterparts. Table 11 shows the returns to the
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decile portfolios broken down by the book-to-market groups. While the 10 minus 1
spread does not decrease monotonically as we move from growth to value stocks (the
middle group actually shows the greatest spread), there is evidence that the premium does
not exist for value companies. While high-cash value stocks outperform low-cash value
stocks, the difference is not statistically significant; the middle group is the only one that
shows reliable outperformance. Consistent with the value effect, the value stocks
outperform their relevant growth-stocks counterparts in every cash decile.
E. Momentum
Jegadeesh and Titman (1993) document a short-run momentum effect in stock prices,
showing that previous winners tend to continue to outperform while previous losers tend
to underpeform at the three-to-twelve month horizon. It is possible that companies that
hoard cash tend to outperform prior to the ranking date, and that my rankings simply pick
up this well documented momentum effect. To check the robustness of our results, we
examine risk-adjusted alphas from a Fama-French three factor regression plus a
momentum factor. 4
We report results for the one-month holding period, cash/equity and cash/market cap
rankings in Table 12. (Results for other specifications are similar.) All of the equally
weighted portfolios show a negative weighting on the momentum factor. This is likely
due to the mechanical result of smaller stocks correlating negatively with momentum.
(Given two otherwise equal stocks, the smaller one will be more likely to have
experienced a lower return in the past.) The value-weighted results (not reported) show a
13
positive loading on momentum for the cash-poor portfolios and a negative loading for the
cash-rich portfolios. Most importantly, in all specifications, the high-cash portfolios are
generally less subject to the momentum factor than the low-cash portfolios, and the 10-1
difference portfolio shows a negative weighting on the momentum factor. As such, this
raises the alphas from the three-factor Fama-French regressions. Momentum cannot
explained the observed returns to the cash-rich portfolios.
F. Industry controls
Another possible explanation is that cash-rich firms tend to gather in disproportionately
successful or disproportionately risky industries. If industries have heterogeneous and
time-varying costs of equity, and if high-return industries tend to require high cash
holdings, then our results may merely reflect this historical industry outperformance.
We categorize each firm into one of the 48 industry groupings specified in Fama and
French (1997). An examination of this breakdown shows that certain industries do indeed
tend to require more liquidity than others: for example, manufacturing, defense, raw
materials, and wholesaling tend to be low-cash industries, while pharmaceuticals,
business services, computers, and healthcare are disproportionately represented in the
high-cash industries. To explore whether this accounts for the observed return patterns,
we rerun the calendar time portfolios and correct each firm’s monthly return by
subtracting the corresponding industry return for that month. The results in table 13 show
that industry differences cannot account for the return patterns; cash-rich firms tend to
outperform their industry peers, while cash-poor firms tend to underperform. The
4
The momentum portfolio returns are taken from Ken French’s data library.
14
difference portfolios still deliver sizeable and statistically significant returns. Industry
congregations do not seem to explain the observed returns.
G. Post-earnings Announcement Drift
Because the conditioning date for portfolio formation coincides with the quarterly
earnings reports for each firm, information contained in the earnings announcement could
influence the observed returns. However, the calendar-time return method we implement
examines returns starting with the month subsequent to the month containing the earnings
announcement date; there is a lag of at least one day and up to thirty days between the
announcement date and the beginning of the return examination period, so any effect
from the announcement itself will be excluded. Furthermore, the outperformance is
robust to longer time periods; the longer holding periods show that the excess returns
persist long after any news announcement should be incorporated into prices.
However, the companies in the cash-rich deciles to seem to have, on average, more
positive earnings reports than those in the cash-poor deciles. Returns to the portfolios for
the three-day event window around the earnings announcement grow across the
portfolios (see Table 14). The presence of a large cash position on a firm’s balance sheet
could indicate that profits were unexpectedly high in the previous quarter, and that the
firm has not yet invested or distributed the proceeds. Thus, the cash effect may be
correlated with the post-earnings announcement drift effect documented by Bernard and
Thomas (1990). Still, it seems doubtful that one positive earnings surprise could be
enough to move a firm into the higher cash deciles. The median firm in the highest decile
15
holds 75% of its market cap in cash. Assuming an average price/earnings ratio of 20, a
back-of-the-envelope calculation shows that a quarter’s worth of retained earnings would
amount to only 1.25% of a firm’s market capitalization. At least on the high end, the
deciles are clearly not determined by one quarter of earnings surprises.
Additionally, there are at two important distinctions between the cash effect and postearnings announcement drift: first, the cash effect is not concentrated around earnings
announcement dates; and secondly, the post-earnings announcement drift phenomenon
predicts a return reversal in quarter t+4.
To examine these conditions, we conduct an event-time analysis of the returns to the
decile portfolios, examining market-adjusted returns for each day from 505 days before
the earnings announcement until 505 days afterwards. Each day following the earnings
announcement, we assign the portfolios a 1 if their market-adjusted return is positive, and
a 0 for a negative market-adjusted return. The percent of days with positive marketadjusted returns appears in table 14. The returns are not concentrated in earnings periods,
but rather spread across all days in the analysis. From day t+2 to t+275, 66% of the daily
returns to the cash-rich portfolio are persistently greater than the market average, while
only 39% of the cash-poor returns are greater than the market average. And the percent of
outperforming days rises almost monotonically across deciles.
Secondly, the outperformance does not reverse after a year. The event time study shows
that the returns to the high-cash firms remain high around the earnings window four
16
quarters after the ranking; from day t+250 to t+275 (the month-long period one year after
the original earnings announcement, which should include the next year’s earnings
announcement), the high-cash portfolios systematically outperform the low-cash
portfolios. This appears to be a persistent, long-run phenomenon.
H. Subperiods and Seasonalities
To be sure that a small subsample is not driving the returns, we examine three
subdivisions of the data: calendar time returns by decades, by year, and by month. The
results for the cash/market cap ranking are in table 15. The portfolio returns retain their
general monotonic pattern in all three of the decade subgroups (although the difference
between the top and bottom deciles is sizable but not statistically significant in the
1970s). The yearly returns show that the difference between the top and bottom deciles is
positive for 23 out of the 30 years sampled 5. The seven negative return years average
minus 78 basis points per month, while the 23 positive return years average 159 basis
points. A binomial test rejects the hypothesis that the top and bottom deciles are equally
likely to outperform one another with a p-value of 0.0026. For the monthly returns, only
June and December give negative returns (averaging 75 basis points per month) while the
remaining ten months give positive returns of 144 basis points per month. Even excluding
January’s outsized returns, the remaining eleven months return an average of 58 basis
points per month. A binomial test rejects the hypothesis that the extreme deciles are
equally likely to outperform one another over the months with a p-value of 0.0193. It
appears that this pattern persists across time and is not driven by seasonal effects.
5
The top decile ranked by cash/equity outperforms the bottom in 18 out of 22 years, and the top decile
when ranked by cash/assets outperforms the bottom in 15 out of 22 years.
17
I. Takeovers
Another possible explanation for the outsized returns could be due to a differing takeover
rate across deciles. If cash-rich firms are more attractive takeover candidates, the
premiums paid to these firms could explain their high future returns. Although Jensen
(1986) theorizes that firms with large cash reserves are likely takeover targets, this
hypothesis runs counter to the prevailing empirical literature: Pinkowitz (2004) finds that,
over the 1985-1994 period, cash-rich firms are actually less likely to be targeted by
hostile takeovers, and that when they are acquired, the premium is no higher than for
their cash-poor counterparts. And Harford (1999) finds that not only are cash-rich firms
less likely to be targeted, but they are more likely to make acquisitions, and this on
average hurts shareholder value.
We gather data on 2933 acquisitions of publicly traded companies from the Mergerstat
database from 1992 through 1999. We first examine the takeover rate across deciles, and
then estimate a logit model where the dependent variable is 1 if a company announces
within the next 12 months that it has acquiesced to a takeover offer, and 0 otherwise. The
independent variables are the cash/equity ratio 6, the log of market cap, and the book-tomarket ratio. Table 16 reports the results. In this sample, the cash-rich deciles are taken
over at a higher rate than the other deciles, but the magnitude is small. While 4.8% of
firms in the lowest decile receive a takeover offer within the next 12 months of the
ranking, 5.7% of the highest decile firms receive an offer. Furthermore, the takeover rate
6
The results are robust to cash/market cap ratio.
18
does not increase monotonically across the deciles. The premium paid is lower than
average for the low-cash companies, but does not seem to be related to cash holdings.
(The takeover method, however, clearly depends upon cash holdings; stock buyouts are
much more frequent for cash-rich firms than cash buyouts, and the opposite holds true for
cash-poor firms.) If returns to the high and low cash deciles were otherwise equal, the
difference in takeover rate and the premiums paid explains only 22 basis points per year
of the difference between the extreme decile returns. While takeover premiums
undoubtedly account for a small part of the outperformance of the highest decile over the
lowest decile, they explain neither the magnitude nor pattern of the return differences.
The logit analysis shows that, holding size and book to market constant, a cash position
actually decreases the probability of receiving a takeover offer in the next 12 months. The
coefficient on the cash/equity ratio is negative, although not significantly so. The bookto-market factor is positive and significant, and the log of market cap is negative but
insignificant. Judging from the takeover rates across deciles, it is possible that the cash
effect on takeovers is not linear. To test this possibility, we run the same logit regression
with the log of the cash level and the decile ranking as independent variables. Both of
those coefficients are also reliably negative. Any takeover premium given to the highcash portfolios seems more likely due to their status as smaller stocks, and might
supplement the returns to the highest decile but cannot explain the pattern of returns.
4. Financing Constraints
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One of the main reasons that firms may hold cash is to avoid costly external financing. At
times, some firms may be so financially constrained that external financing is not
available. These financially constrained firms may be financially sound, without any risk
of default or bankruptcy, but lack the funds necessary to invest in all of their positive
NPV projects.
Recent literature has documented significant return patterns amongst financially
constrained firms. Lamont, Polk, and Saa-Requejo (2001) use the Kaplan and Zingales
(1997) measure to classify manufacturing firms, and construct a financially constrained
risk factor. Firms with lower loadings on this factor tend to have higher returns.
Somewhat surprisingly, facing financial constraint does not increase a firm’s risk
premium, but rather lowers the expected return. Gomes, Yaron, and Zhang (2004)
identify financially constrained firms through a GMM analysis on an Euler equation of
optimal investment behavior. They find evidence of a common financing constraints
factor, and that the costs of financial constraints are procyclical, and thus financial
frictions are more important during good economic conditions. Finally, Pinkowitz and
Williamson find that the market values a dollar of cash in a financially constrained firm
at a significantly lower value than a dollar of cash in an unconstrained firm.
A. Measuring Constraints
Although investment-cash flow sensitivities have been widely used to identify financially
constrained firms, beginning with Fazzari, Hubbard, and Peterson (1988), there is good
reason to believe that this not a useful method. Kaplan and Zingales (1997) point out that
20
the relationship between cash flow and investment is likely nonmonotonic, and lacks
good theoretical justification. They develop a measure based on qualitative information
gleaned from SEC filings, augmented with quantitative data, which appears to
successfully identify the degree of financing constraints faced by firms.
We apply Lamont, Polk, and Requejo’s adapted method of Kaplan and Zingales (1997) to
explore whether financing constraints influence firm returns in my sample. Lamont et al.
report in Appendix A the results from an ordered logit model, based on Kaplan and
Zingales’ measure, that identifies financially constrained firms using four ratios: cash
flow to capital, debt to total capital, dividends to capital, and cash to capital. Taking data
from the yearly COMPUSTAT files, we score all firms in our sample on this KZ metric.
We then rank firms into quintiles each year based upon this score. Kaplan and Zingales
report that around 15% of firms are financially constrained at any given time; therefore,
we consider firms in the highest quintile for each year to be financially constrained, and
the other 80% of firms to not be financially constrained. While this distinction is
somewhat crude, it reflects the fact that firms in the highest quintile are more financially
constrained, and is adequate for comparison purposes.
Not surprisingly, the high-cash firms tend to be less financially constrained than low-cash
firms. We find a –0.30 correlation between the KZ quintiles and the cash decile rankings.
We report the full breakdown in Table 17. The percentage of each decile classified as
financially constrained decreases monotonically, except for an uptick in the highest
21
decile. However, only 15% of the highest decile firms are categorized as constrained,
below the global average.
Lamont et al. find that unconstrained firms earn, on average, higher returns than
constrained firms. Since the level of financial constraint drops as cash holdings rise, this
factor could be in part driving the portfolio returns. Table 18 confirms that unconstrained
firms in my sample earn higher returns. There is very little difference in returns for the
first four quintiles, but a decline in returns for the fifth quintile; the groupings of
constrained firms in the bottom 20% may be appropriate. The least constrained firms
earn, on average, 1.46% per month; the most constrained firms return an average of
1.29%. This difference is not statistically significant.
B. Financial Constraints Risk Factor
We next construct a time-series of returns to a financial constraints (FC) factor by
subtracting the lowest quintile return (least constrained) from the highest quintile return
(most constrained) each month. We then augment a Fama-French three-factor model with
this factor to see if the superior performance of unconstrained firms can explain some of
the variation in returns. The results are reported in Table 19. While all portfolios show a
positive loading on the FC factor, indicating all portfolios are at least somewhat
constrained, the low-cash portfolios have the highest loadings, and the loadings generally
decline as cash holdings rise. The difference portfolio has a strong and statistically
significant negative loading on the FC factor. However, this loading does not seem to
22
make much difference in the excess returns; the alphas are little changed from those
reported in the standard three-factor model in Table 9.
C. Avoiding Financial Constraints with Cash Holdings
Since cash-to-capital is one of the four inputs into the KZ measure, firms with high cash
holdings should be, on average, less constrained than other firms. However, it may be
that these firms choose to hold large amounts of cash because they would otherwise face
financial constraints. To test this hypothesis, we rerun the KZ measure based only on the
first three ratios, excluding cash holdings from the estimation. We then rerank firms into
quintiles based on this secondary KZ score. Results are reported in Panel B of Table 17.
The differences are striking. The percentage of financially constrained firms grows,
instead of shrinks, across the cash holdings deciles. Furthermore, the percentage of
highest decile firms that are considered financially constrained more than doubles, to
32.35%.
There are 10,171 firm/quarters that would be considered financially constrained without
their cash holdings, but use their financial slack to avoid this categorization in the full KZ
measure. There is a significant overlap between these constraint-avoiders and the cashrich firms: a third of the constraint-avoiders fall into the highest cash decile, and 17.5
percent of the highest cash decile is a member of this group. If a benefit accrues to
companies for holding cash to avoid financial constraints, it should accrue most strongly
to these firms. To explore whether these firms are driving our results, we conduct some
further analyzes upon this interesting subgroup.
23
First, Table 20 shows that the constraint-avoiders, as a group, experience superior market
returns. An equally weighted calendar-time portfolio returns 1.71% per month with a
one-month holding period, and 1.42% per month with a year holding period. This is
superior to all of the cash holdings portfolio returns. Table 21 examples the alphas from a
three-factor and five-factor regression of these portfolio returns on the market, size, and
book-to-market factors (panel A) and the first three factors plus a momentum and
financial constraint factor (panel B). The risk-adjusted alphas, with the exception of the
one-year horizon value weighted portfolio, are highly positive and significant.
Furthermore, these alphas are higher than those for any of the cash portfolios.
Because the constraint-avoiders are concentrated in the higher cash deciles, any excess
performance will have the strongest impact on the higher cash deciles. To examine the
extent of this impact, we evaluate the performance of the cash portfolios after removing
these constraint-avoiders from the sample. Table 22 shows that, while the general pattern
of returns persists, the effect is much weaker and the monotonicity fails. Since our
categorization of financial constraints is imperfect, and firms certainly face varying
degrees of financial constraints, we would not expect to have removed all firms that
benefit from cash holdings in this manner from the sample. However, it seems clear that
removing those most likely to benefit deteriorates the results significantly.
5. Operating Performance
24
We next provide evidence that these cash-rich companies use their wealth to finance
growth options, leading to higher earnings. In Table 23 we present operating performance
figures for one year and two years following the ranking date. Investment is calculated as
capital expenditures divided by lagged property, plant, and equipment; asset growth is the
ratio of total assets to year-ago total assets; sales growth is calculated as the ratio of sales
in year t to sales in year t-1; and cash flow growth is calculated as cash flow (income
before extraordinary items plus depreciation) during year t to cash flow in year t-1. The
cash flow growth figures are conditional on the firm having positive cash flow in both
year t and year t-1. All ratios are negatively skewed, so we winsorize at the 1% tails
before calculating means.
There is strong evidence that higher cash levels lead to higher capital expenditures. Both
the mean and median investment levels rise with cash ranking deciles, and the highest
cash firms invest, on average, almost twice as much as the lowest cash firms. This trend
continues into the following year. Furthermore, it seems these firms are investing in good
projects; both sales and cash flow grow at faster rates as the portfolios increase in cash
holdings. Finally, we present weaker evidence that this leads to a growth in assets. While
the mean asset growth rises nearly monotonically with the cash deciles, there is little
difference in the median asset growth. This additional investment allows some firms to
do very well, but most do not show better growth in assets then their cash-poor peers.
Additionally, we examine the operating performance of the constraint-avoiders group.
Their investment level matches that of the top cash decile, and their sales and cash flow
25
growth is much higher than any other group. Their mean asset growth and median asset
growth for year t also ranks as the highest, although there is a sharp dropoff in median
asset growth in year t+1. The group of firms for which cash holds the most importance to
investment policy also shows the best operating performance going forward.
6. Implementing a Trading Strategy
Going long the cash-rich portfolios and shorting the cash-poor portfolio could deliver
profits of around one percent per month. Trading costs to this strategy are relatively low
compared to some arbitrage strategies. First, turnover is minimal. While a 1-month
holding period delivers the strongest returns, the results are robust to 3-month and 12month holding periods. Since the conditioning data (quarterly earnings announcements)
arrives every three months, a 3-month holding period strategy seems appropriate: this
will reduce turnover and maximize the recency of the information. Secondly, there is an
83% autocorrelation amongst the cash rankings. This means that maintenance of the
strategy will require some rebalancing every three months, but not major turnover.
The bid/ask spread generates the largest cost to this trading strategy. As the effect is
primarily manifested in small stocks, they may be illiquid and require heavy costs to
trade. Using data from the CRSP NASDAQ-NMS file, we measure the end-of-day
bid/ask spread for each NASDAQ stock in the portfolios for the first month following the
ranking date. The bid/ask spread drops as cash holdings increase, ranging from 5.5% for
the low-cash firms to 4.7% for the high-cash firms. A 40% turnover four times a year
(20% on both the long and short portfolio), and a 5% bid/ask spread will result in costs of
26
8% per year to the portfolio. However, the 5% spread, based on historical NASDAQ
figures, probably overestimates the true trading costs going forward. Rebalancing the
portfolio yearly, with a spread at 3%, reduces returns by only 25 basis points per month.
Finally, it may not be feasible to properly hedge the long position. The lowest decile,
which should be sold sort, has an average market cap of 800 MM (cash/equity ranking)
and 1.4 BB (cash/market cap). If sufficient shares of these smaller stocks are not
available to short, the strategy will bear considerable market risk. Furthermore, the
difference portfolios load heavily on the small and growth factors. Although the literature
considers growth stocks to be less risky than value stocks, neither this exposure nor the
small stock risk can be avoided.
7. Conclusions
Undoubtedly, some firms use spare cash to increase shareholder value, and others waste
it. However, it appears that firms who accumulate cash tend to be exactly those whose
operations stand to benefit the most from this financial slack – small, possibly financially
constrained firms, with time-varying growth options. Previous studies have shown that
firms subject to financial constraints earn lower returns, on average. By holding cash,
financially constrained firms raise both their operating performance and their expected
market return. However, Pinkowitz and Williamson show that the market places a low
value on cash held by firms subject to financial constraints. Financial slack improves
firms’ operating performance, and at least amongst small firms, this financial slack may
not be properly priced in the market.
27
References
Almeida, Heitor, Murillo Campello, and Michael Wesibach, The Cash Flow Sensitivity
of Cash, 2003, working paper.
Bernard, Victor, and Jacob Thomas, 1990, Evidence that Stock Prices do not Fully
Reflect the Implications of Current Earnings for Future Earnings, Journal of
Accounting and Economics, 13, 305-340.
Blanchard, Lopez-de-Silanes, and Andrei Shleifer, 1994, What do Firms do with Cash
Windfalls? Journal of Financial Economics, 36, 337-360.
Bond, Stephen, and Costas Meghir, 1994, Dynamic Investment Models and the Firm’s
Financial Policy
Fama, Eugene F., and Kenneth R. French, 1993, Common risk factors in returns on
stocks and bonds, Journal of Financial Economics, 33, 3-56.
Fama, Eugene, and Kenneth French, 1997, Industry Costs of Equity, Journal of Financial
Economics, 43, 153-193.
Faulkender, Michael, and Rong Wang, Corporate Financial Policy and the Value of Cash,
working paper, Washington University.
Fazzari, Steven, R. Glenn Hubbard, 1988, Bruce Peterson, Financing Constraints and
Corporate Investment, Brookings Papers on Economic Activity, 1, 141-206.
Gomes, Joao, Amir Yaron, and Lu Zhang, 2004, Asset Pricing Implications of Firms’
Financing Constraints, working paper.
Greenwood, Robin, 2004, Aggregate Corporate Liquidity and Stock Returns, working
paper, Harvard Business School.
Harford, Jarrad, 1999, Corporate Cash Reserves and Acquisitions, Journal of Finance, 6,
1969-1997.
Harford, Jarrad, Wayne Mikkelson, and Megan Partch, 2004, The Effect of Cash
Reserves on Corporate Investment and Performance in Industry Declines,
working paper.
Jegadeesh, N. and Sheridan Titman, (1993), Returns to Buying Winners and Selling
Losers: Implications for Stock Market Efficiency, Journal of Finance, 48, 65-91.
Jensen, Michael, 1986, Agency Costs of Free Cash Flow, Corporate Finance, and
Takeovers, American Economic Review, 76, 323-349.
28
Kaplan, Steven, and Luigi Zingales, 1997, Do Investment Cash-Flow Sensitivities
Provide Useful Measures of Financing Constraints? Quarterly Journal of
Economics, 112, 169-215.
Kim, Chang-Soo, David Mauer, and Ann Sherman, 1998, The Determinants of Corporate
Liquidity: Theory and Evidence, Journal of Financial and Quantitative Analysis,
33, 335-359.
Lamont, Owen, Christopher Polk, and Jesus Saa-Requejo, 2001, Financial Constraints
and Stock Returns, Review of Financial Studies, 14, 529-554.
Mikkelson, Wayne, and Megan Partch, 2003, Do Persistent Large Cash Reserves Hinder
Performance?, Journal of Financial and Quantitative Analysis, 38.
Myers, Stewart, and Nicholas Majluf, 1984, Corporate Financing Decisions when Firms
have Investment Information that Investors do not, Journal of Financial
Economics, 13, 187-221.
Opler, Tim, Lee Pinkowitz, Rene Stulz, and Rohan Williamson, 1999, The Determinants
and Implications of Corporate Cash Holdings, Journal of Financial Economics,
51, 3-46.
Pinkowitz, Lee, 2004, The Market for Corporate Control and Corporate Cash Holdings,
working paper, Georgetown University.
Pinkowitz, Lee, and Rohan Williamson, 2002, What’s a Dollar Worth? The Market Value
of Cash Holdings, working paper, Georgetown University.
Whited, Toni, and Goujun Wu, 2004, Financial Constraints Risk, working paper
29
Table 1: Portfolio summary statistics, unconditional rankings
Firms are sorted each month from 1980-2001 into deciles based upon previous month cash ratio. Cash
holdings includes all cash and short-term investments.
Panel A: Deciles sorted by cash/book equity
Decile
Cash (MM) Cash/equity
(mean)
1
3.8
0.007
2
13.8
0.025
3
29.0
0.050
4
50.0
0.087
5
72.9
0.140
6
94.8
0.212
7
99.9
0.306
8
110.1
0.442
9
109.4
0.665
10
425.8
2.159
Cash/equity
(median)
0.007
0.025
0.049
0.084
0.136
0.207
0.301
0.439
0.662
1.036
Book value
(MM)
443.8
522.7
553.0
535.9
499.9
432.3
316.4
244.1
157.6
257.0
Market Cap
(MM)
817.1
1100.9
1317.4
1396.6
1390.2
1522.2
1086.5
830.8
604.7
743.7
BTM
0.92
0.89
0.87
0.85
0.80
0.75
0.70
0.64
0.57
0.53
Cash/mkcap
(median)
0.003
0.014
0.027
0.046
0.071
0.105
0.151
0.221
0.353
0.743
Book value
Market Cap
BTM
459.2
575.9
583.7
530.1
414.6
315.8
276.8
270.0
232.6
279.7
1457.9
2021.3
1991.5
1557.9
1067.9
674.0
497.7
425.7
330.8
365.1
0.60
0.61
0.64
0.65
0.66
0.68
0.72
0.81
1.00
1.55
Book value
Market Cap
BTM
424.3
562.9
617.4
561.2
549.0
460.5
381.1
245.1
167.1
88.4
811.1
1155.6
1558.7
1338.7
1331.6
1407.9
1372.8
874.1
669.3
450.7
0.91
0.90
0.88
0.87
0.83
0.83
0.71
0.65
0.58
0.50
Panel B: Deciles sorted by cash/market cap
Decile
Cash (MM)
1
2
3
4
5
6
7
8
9
10
6.7
28.9
54.5
72.3
78.3
71.9
78.2
98.6
131.8
412.8
Cash/mkcap
(mean)
0.004
0.014
0.028
0.048
0.074
0.109
0.157
0.233
0.396
1.290
Panel C: Deciles sorted by cash/total assets
Decile
Cash (MM)
1
2
3
4
5
6
7
8
9
10
3.4
13.9
32.4
47.2
80.8
96.2
107.5
94.3
89.1
73.8
Cash/assets
(mean)
0.002
0.009
0.017
0.029
0.048
0.080
0.133
0.214
0.342
0.605
Cash/assets
(median)
0.002
0.008
0.016
0.028
0.047
0.078
0.127
0.203
0.327
0.606
30
Table 2: Portfolio summary statistics, second-stage rankings
Firms are placed each month into one of three size bins based on the 30/40/30 NYSE market cap
breakpoints and one of three book-to-market bins based on the 30/40/30 NYSE book-to-market
breakpoints. A second-stage ranking is then done by cash ratio within each of the nine bins.
Panel A: Deciles sorted by cash/book equity
Decile
Cash (MM) Cash/bk
(mean)
1
3.2
0.008
2
9.7
0.028
3
17.2
0.056
4
28.5
0.095
5
44.1
0.145
6
62.7
0.209
7
83.4
0.292
8
109.9
0.403
9
166.5
0.582
10
399.2
1.832
Cash/bk
(median)
0.006
0.023
0.043
0.072
0.112
0.168
0.246
0.353
0.526
0.931
Book value
(MM)
359.7
407.8
406.9
420.4
440.1
443.4
436.7
417.1
370.6
332.0
Market Cap
(MM)
882.3
970.6
997.7
1083.9
1295.0
1317.6
1318.5
1197.2
945.7
888.3
BTM
0.78
0.79
0.78
0.79
0.78
0.77
0.75
0.74
0.73
0.71
Cash/mkcap
(median)
0.003
0.012
0.025
0.042
0.065
0.094
0.132
0.181
0.266
0.495
Book value
Market Cap
BTM
329.8
386.4
391.5
402.5
431.2
447.0
427.2
423.2
401.1
395.5
932.1
1044.1
1176.8
1242.5
1366.7
1323.2
1094.2
1019.0
889.4
810.4
0.70
0.70
0.71
0.72
0.73
0.74
0.75
0.77
0.80
0.98
Book value
Market Cap
BTM
345.9
423.9
453.8
428.0
414.8
438.3
461.1
428.1
383.9
263.6
883.4
1100.3
1211.3
1138.7
1076.6
1220.2
1402.2
1290.7
1014.1
763.3
0.77
0.79
0.79
0.78
0.79
0.77
0.76
0.73
0.72
0.69
Panel B: Deciles sorted by cash/market cap
Decile
Cash (MM)
1
2
3
4
5
6
7
8
9
10
3.1
9.4
17.8
28.4
44.0
60.8
75.1
104.2
162.9
419.3
Cash/mkcap
(mean)
0.004
0.015
0.029
0.047
0.073
0.108
0.155
0.224
0.357
0.953
Panel C: Deciles sorted by cash/total assets
Decile
Cash (MM)
1
2
3
4
5
6
7
8
9
10
3.4
12.0
22.2
30.2
43.4
62.5
87.5
110.1
127.7
143.0
Cash/assets
(mean)
0.003
0.010
0.022
0.039
0.065
0.100
0.151
0.222
0.328
0.555
Cash/assets
(median)
0.002
0.009
0.017
0.028
0.046
0.074
0.119
0.187
0.295
0.552
31
Table 3: Correlation matrix for decile ranks, market capitalization, and book-to-market ratio. C/E is the
cash/equity rank, C/ME is the cash/market cap rank, and C/A is the cash/assets rank. SS refers to the
second-stage sorts.
Panel A: Pearson Correlations
C/E
C/ME
C/A
C/E (SS)
C/ME (SS)
C/A (SS)
C/E
1
0.80
C/ME
1
C/A
C/E (SS)
C/ME (SS)
C/A (SS)
Market Cap
Panel B: Spearman Correlations
C/E
C/ME
0.92
0.77
1
0.91
0.87
0.86
1
0.86
0.92
0.83
0.92
1
0.85
0.82
0.92
0.92
0.88
1
C/A
C/E (SS)
C/ME (SS)
C/A (SS)
C/E
C/ME
C/A
C/E (SS)
C/ME (SS)
C/A (SS)
Market Cap
0.93
0.78
1
0.91
0.87
0.86
1
0.86
0.92
0.83
0.92
1
0.86
0.83
0.92
0.92
0.88
1
1
0.81
1
Market
Cap
-0.02
-0.08
-0.02
0.00
-0.01
-0.00
1
Market
Cap
-0.05
-0.22
-0.04
-0.01
-0.02
-0.00
1
BTM
-0.14
0.20
-0.14
-0.02
0.07
-0.03
-0.06
BTM
-0.26
0.23
-0.24
-0.05
0.03
-0.04
-0.34
Table 4: Average monthly calendar time returns to deciles formed on cash ratios. This table includes
equally weighted returns for holding periods from 1 month to 1 year. (Standard errors in parentheses.)
Panel A: Cash/Equity
Decile
1mo
3mo
1yr
1
1.09
0.95
1.12
(0.32)
(0.29)
(0.28)
2
1.11
1.02
1.13
(0.33)
(0.30)
(0.29)
3
1.20
1.09
1.20
(0.35)
(0.32)
(0.30)
4
1.42
1.16
1.25
(0.37)
(0.32)
(0.31)
5
1.27
1.23
1.35
(0.36)
(0.32)
(0.32)
6
1.42
1.32
1.43
(0.36)
(0.34)
(0.33)
7
1.68
1.45
1.46
(0.39)
(0.35)
(0.35)
8
1.81
1.52
1.52
(0.44)
(0.39)
(0.38)
9
1.73
1.53
1.49
(0.47)
(0.44)
(0.43)
10
1.98
1.63
1.66
(0.46)
(0.42)
(0.43)
Diff 10 - 1
0.88
0.67
0.54
(0.27)
(0.23)
(0.25)
32
Panel B: Cash/Market Cap
Decile
1mo
1
0.84
(0.34)
2
1.03
(0.35)
3
1.00
(0.35)
4
1.16
(0.38)
5
1.45
(0.39)
6
1.37
(0.39)
7
1.41
(0.42)
8
1.54
(0.41)
9
1.90
(0.43)
10
2.00
(0.43)
Diff 10 - 1
1.15
(0.27)
Panel C: Cash/Assets
Decile
1mo
1
0.99
(0.33)
2
1.06
(0.33)
3
1.10
(0.34)
4
1.21
(0.36)
5
1.26
(0.34)
6
1.48
(0.39)
7
1.52
(0.41)
8
1.50
(0.45)
9
1.69
(0.47)
10
1.64
(0.54)
Diff 10 - 1
0.64
(0.38)
3mo
0.95
(0.28)
1.04
(0.30)
1.10
(0.31)
1.21
(0.33)
1.21
(0.33)
1.30
(0.36)
1.42
(0.39)
1.45
(0.42)
1.62
(0.49)
1.61
(0.49)
0.66
(0.30)
1yr
0.91
(0.30)
0.97
(0.30)
1.12
(0.32)
1.21
(0.34)
1.34
(0.36)
1.35
(0.37)
1.45
(0.39)
1.61
(0.40)
1.76
(0.41)
1.89
(0.39)
0.98
(0.21)
3mo
0.88
(0.29)
0.99
(0.30)
1.04
(0.32)
1.15
(0.32)
1.28
(0.33)
1.36
(0.35)
1.44
(0.38)
1.50
(0.43)
1.58
(0.47)
1.65
(0.54)
0.77
(0.36)
1yr
1.07
(0.28)
1.09
(0.29)
1.10
(0.30)
1.30
(0.31)
1.36
(0.32)
1.38
(0.34)
1.51
(0.37)
1.53
(0.41)
1.52
(0.45)
1.56
(0.52)
0.48
(0.35)
33
Table 5: Value-weighted average monthly calendar time returns to deciles formed on cash ratios. This table
includes monthly returns for holding periods from 1 month to 1 year. Returns are weighted by market
capitalization. (Standard errors in parentheses.)
Panel A: Cash/Equity
Decile
1mo
3mo
1yr
1
0.97
0.88
1.04
(0.31)
(0.26)
(0.24)
2
0.87
1.04
1.08
(0.32)
(0.26)
(0.26)
3
1.10
1.14
1.17
(0.32)
(0.28)
(0.27)
4
0.99
1.28
1.23
(0.36)
(0.28)
(0.27)
5
1.17
1.22
1.25
(0.35)
(0.28)
(0.27)
6
1.31
1.15
1.19
(0.36)
(0.31)
(0.30)
7
1.08
1.46
1.47
(0.42)
(0.33)
(0.31)
8
1.29
1.41
1.41
(0.47)
(0.35)
(0.33)
9
1.16
1.49
1.39
(0.52)
(0.43)
(0.40)
10
1.18
1.47
1.39
(0.48)
(0.40)
(0.38)
Diff 10 - 1
0.21
0.58
0.34
(0.34)
(0.25)
(0.24)
Panel B: Cash/Market Cap
Decile
1mo
3mo
1yr
1
0.99
0.89
1.02
(0.34)
(0.26)
(0.27)
2
1.07
1.08
0.97
(0.33)
(0.25)
(0.28)
3
0.76
1.13
1.19
(0.34)
(0.27)
(0.28)
4
1.00
1.19
1.33
(0.39)
(0.29)
(0.31)
5
1.31
1.22
1.45
(0.40)
(0.28)
(0.33)
6
1.15
1.26
1.52
(0.40)
(0.32)
(0.34)
7
1.38
1.43
1.39
(0.42)
(0.33)
(0.34)
8
1.15
1.35
1.33
(0.42)
(0.39)
(0.34)
9
1.40
1.67
1.39
(0.42)
(0.48)
(0.33)
10
1.39
1.33
1.39
(0.37)
(0.42)
(0.34)
Diff 10 - 1
0.39
0.43
0.36
(0.33)
(0.30)
(0.22)
34
Panel C: Cash/Assets
Decile
1mo
1
0.86
(0.32)
2
0.85
(0.31)
3
1.13
(0.32)
4
1.12
(0.33)
5
0.93
(0.32)
6
1.10
(0.39)
7
1.38
(0.41)
8
1.01
(0.41)
9
1.66
(0.52)
10
1.49
(0.55)
Diff 10 - 1
0.62
(0.48)
3mo
1.07
(0.24)
1.04
(0.26)
1.21
(0.280
1.27
(0.29)
1.11
(0.28)
1.25
(0.29)
1.30
(0.33)
1.46
(0.39)
1.50
(0.49)
1.81
(0.55)
0.73
(0.46)
1yr
1.14
(0.24)
1.11
(0.25)
1.22
(0.27)
1.29
(0.28)
1.18
(0.27)
1.22
(0.29)
1.30
(0.32)
1.61
(0.38)
1.31
(0.46)
1.54
(0.52)
0.40
(0.42)
Table 6: Results from a Fama-French Three Factor regression.
This table analyzes the time series of returns from the 1-month holding period, equally weighted portfolios.
(Standard errors in parentheses.)
Panel A: Cash/Equity
Decile
Alpha
Beta
Small
Value
1
-0.26
0.95
0.72
0.43
(0.16)
2
-0.25
0.97
0.76
0.40
(0.17)
3
-0.21
1.03
0.74
0.46
(0.20)
4
0.01
1.06
0.79
0.37
(0.19)
5
-0.08
1.01
0.87
0.32
(0.16)
6
0.08
0.98
0.93
0.29
(0.17)
7
0.41
0.99
0.88
0.09
(0.18)
8
0.48
1.07
1.09
0.08
(0.20)
9
0.56
1.06
1.03
-0.29
(0.19)
10
0.70
1.07
1.11
-0.03
(0.21)
Diff 10 - 1
0.97
0.11
0.39
-0.47
(0.22)
35
Panel B: Cash/Market Cap
Decile
Alpha
1
-0.52
(0.16)
2
-0.28
(0.17)
3
-0.33
(0.13)
4
-0.07
(0.16)
5
0.21
(0.16)
6
0.10
(0.62)
7
0.18
(0.21)
8
0.24
(0.19)
9
0.60
(0.22)
10
0.68
(0.28)
Diff 10 - 1
1.20
(0.28)
Panel C: Cash/Assets
Decile
Alpha
1
-0.46
(0.19)
2
-0.39
(0.17)
3
-0.33
(0.17)
4
-0.21
(0.20)
5
-0.12
(0.16)
6
0.08
(0.19)
7
0.23
(0.19)
8
0.20
(0.24)
9
0.53
(0.21)
10
0.51
(0.23)
Diff 10 - 1
0.97
(0.27)
Beta
1.00
Small
0.73
Value
0.36
1.00
0.71
0.22
1.05
0.69
0.19
1.00
0.79
0.01
1.02
0.81
-0.01
1.03
0.83
0.02
1.01
0.86
-0.03
1.04
0.93
0.08
1.06
0.94
0.06
0.99
0.89
0.22
-0.01
0.15
-0.14
Beta
1.00
Small
0.72
Value
0.52
1.02
0.71
0.48
1.04
0.70
0.42
1.05
0.65
0.33
1.02
0.70
0.31
1.06
0.83
0.20
1.04
0.86
-0.04
1.04
1.01
-0.05
1.02
1.00
-0.32
1.03
1.20
-0.52
0.03
0.47
-1.05
36
Table 7: Equally weighted returns to deciles formed on second stage cash ratio rankings; monthly returns
for holding periods for 1 month and 1 year; returns equal weighted. (Standard errors in parentheses.)
Cash/Equity
Cash/Market Cap
Cash/Assets
Decile
1mo
1yr
1mo
1yr
1mo
1yr
1
1.04
1.01
0.94
0.95
1.01
0.99
(0.33)
(0.29)
(0.34)
(0.30)
(0.36)
(0.30)
2
1.08
1.05
0.90
1.01
1.07
1.01
(0.34)
(0.30)
(0.34)
(0.31)
(0.34)
(0.30)
3
1.14
1.18
1.30
1.13
1.02
1.12
(0.35)
(0.31)
(0.35)
(0.32)
(0.35)
(0.32)
4
1.26
1.21
1.29
1.22
1.23
1.20
(0.36)
(0.32)
(0.36)
(0.33)
(0.36)
(0.33)
5
1.23
1.26
1.21
1.23
1.25
1.29
(0.36)
(0.33)
(0.37)
(0.34)
(0.37)
(0.34)
6
1.51
1.32
1.35
1.33
1.30
1.34
(0.39)
(0.35)
(0.38)
(0.35)
(0.37)
(0.35)
7
1.46
1.44
1.54
1.48
1.42
1.41
(0.39)
(0.37)
(0.39)
(0.36)
(0.39)
(0.37)
8
1.41
1.52
1.46
1.53
1.55
1.55
(0.41)
(0.40)
(0.40)
(0.39)
(0.42)
(0.39)
9
1.63
1.60
1.44
1.66
1.74
1.62
(0.44)
(0.42)
(0.42)
(0.41)
(0.47)
(0.42)
10
1.66
1.67
2.01
1.74
1.57
1.64
(0.46)
(0.43)
(0.46)
(0.42)
(0.47)
(0.47)
Diff 10 - 1
0.62
0.65
1.01
0.78
0.55
0.64
(0.27)
(0.23)
(0.23)
(0.21)
(0.32)
(0.29)
Table 8: Value-weighted returns to deciles formed on second stage cash ratio rankings; monthly returns for
holding periods for 1 month and 1 year; returns weighted by market cap. (Standard errors in parentheses.)
Cash/Equity
Cash/Market Cap
Cash/Assets
Decile
1mo
1yr
1mo
1yr
1mo
1yr
1
0.76
1.00
0.81
0.99
0.91
1.04
(0.34)
(0.26)
(0.33)
(0.27)
(0.35)
(0.27)
2
1.02
1.03
1.00
0.92
0.92
1.08
(0.32)
(0.26)
(0.33)
(0.27)
(0.32)
(0.27)
3
1.03
1.08
1.16
0.97
0.84
1.18
(0.32)
(0.27)
(0.34)
(0.29)
(0.33)
(0.27)
4
1.14
1.15
1.10
1.14
1.25
1.10
(0.32)
(0.27)
(0.34)
(0.28)
(0.33)
(0.27)
5
0.94
1.13
1.11
1.15
1.15
1.12
(0.36)
(0.28)
(0.38)
(0.27)
(0.36)
(0.28)
6
1.30
1.20
1.03
1.36
1.19
1.20
(0.36)
(0.29)
(0.36)
(0.29)
(0.35)
(0.28)
7
1.12
1.26
1.17
1.29
0.82
1.13
(0.41)
(0.29)
(0.38)
(0.29)
(0.39)
(0.30)
8
1.39
1.44
1.20
1.43
1.22
1.32
(0.44)
(0.32)
(0.42)
(0.33)
(0.45)
(0.33)
9
1.54
1.44
1.25
1.63
1.21
1.43
(0.45)
(0.38)
(0.42)
(0.35)
(0.47)
(0.39)
10
1.19
1.38
1.31
1.28
1.47
1.50
(0.45)
(0.37)
(0.43)
(0.33)
(0.51)
(0.45)
Diff 10 - 1
0.43
0.37
0.49
0.28
0.56
0.46
(0.34)
(0.23)
(0.33)
(0.18)
(0.42)
(0.34)
37
Table 9: Results from a Fama-French Three Factor regression, second-stage sorts.
1-month holding period, monthly returns equally weighted. (Standard errors in parentheses.)
Panel A: Cash/Equity
Decile
Alpha
Beta
Small
Value
1
-0.32
0.96
0.74
0.43
(0.18)
2
-0.29
1.00
0.78
0.38
(0.16)
3
-0.16
1.03
0.66
0.16
(0.15)
4
-0.07
1.02
0.71
0.25
(0.18)
5
-0.06
1.00
0.75
0.15
(0.16)
6
0.24
1.00
0.84
0.08
(0.18)
7
0.14
1.07
0.85
0.10
(0.17)
8
0.16
1.04
0.85
-0.05
(0.17)
9
0.47
0.99
0.96
-0.20
(0.20)
10
0.43
1.06
0.99
-0.13
(0.22)
Diff 10 - 1
0.76
0.09
0.25
-0.56
(0.22)
Panel B: Cash/Market Cap
Decile
Alpha
Beta
Small
Value
1
-0.40
0.97
0.81
0.37
(0.16)
2
-0.44
0.99
0.73
0.34
(0.16)
3
-0.00
1.01
0.69
0.17
(0.16)
4
-0.05
1.05
0.83
0.20
(0.16)
5
-0.08
1.03
0.81
0.12
(0.15)
6
0.07
1.01
0.88
0.10
(0.16)
7
0.26
1.04
0.76
0.04
(0.19)
8
0.21
1.00
0.89
0.02
(0.19)
9
0.24
1.03
0.80
-0.13
(0.20)
10
0.76
1.05
0.94
-0.06
(0.25)
Diff 10 - 1
1.17
0.08
0.13
-0.44
(0.23)
38
Panel C: Cash/Assets
Decile
Alpha
1
-0.26
(0.16)
2
-0.25
(0.17)
3
-0.21
(0.20)
4
0.01
(0.19)
5
-0.08
(0.16)
6
0.08
(0.17)
7
0.41
(0.18)
8
0.48
(0.20)
9
0.56
(0.19)
10
0.70
(0.21)
Diff 10 - 1
0.96
(0.22)
Beta
0.95
Small
0.72
Value
0.43
0.97
0.76
0.40
1.03
0.74
0.46
1.06
0.79
0.37
1.01
0.87
0.32
0.98
0.93
0.29
0.99
0.88
0.09
1.07
1.09
0.08
1.06
1.03
-0.29
1.07
1.11
-0.03
0.12
0.39
-0.46
Table 10: Monthly returns to deciles formed on cash ratio (unconditional sorts), grouped by size based on
Fama/French NYSE breakpoints; 1-month holding period, returns equal weighted.
Panel A: Sorted by cash/equity
Decile
small
med
large
1
1.04
1.02
1.28
(0.35)
(0.33)
(0.35)
2
1.02
1.06
0.72
(0.37)
(0.35)
(0.38)
3
1.25
0.63
1.26
(0.39)
(0.37)
(0.39)
4
1.50
1.09
0.81
(0.40)
(0.37)
(0.42)
5
1.29
1.13
1.57
(0.38)
(0.38)
(0.45)
6
1.46
1.13
1.87
(0.39)
(0.38)
(0.41)
7
1.81
1.09
1.27
(0.42)
(0.40)
(0.48)
8
1.98
0.89
1.29
(0.46)
(0.49)
(0.58)
9
1.76
1.25
1.04
(0.47)
(0.54)
(0.63)
10
1.98
1.71
1.09
(0.47)
(0.54)
(0.55)
Diff 10 - 1
0.94
0.64
-0.07
(0.26)
(0.44)
(0.51)
39
Panel B: Sorted by cash/market cap
Decile
small
med
1
0.82
1.15
(0.39)
(0.37)
2
0.94
1.27
(0.40)
(0.37)
3
1.06
1.08
(0.39)
(0.38)
4
1.21
1.25
(0.42)
(0.43)
5
1.55
1.30
(0.42)
(0.45)
6
1.42
1.21
(0.41)
(0.45)
7
1.41
1.64
(0.44)
(0.51)
8
1.64
1.04
(0.43)
(0.48)
9
1.98
1.50
(0.45)
(0.48)
10
2.04
1.57
(0.45)
(0.50)
Diff 10 - 1
1.21
0.42
(0.29)
(0.42)
Panel C: Sorted by cash/assets
Decile
small
med
1
1.06
1.20
(0.38)
(0.34)
2
1.08
1.06
(0.38)
(0.33)
3
0.95
1.39
(0.37)
(0.36)
4
1.32
1.04
(0.40)
(0.37)
5
1.41
0.97
(0.38)
(0.37)
6
1.58
1.22
(0.43)
(0.41)
7
1.64
1.38
(0.45)
(0.44)
8
1.46
1.23
(0.47)
(0.46)
9
1.64
1.69
(0.47)
(0.54)
10
1.82
1.09
(0.57)
(0.61)
Diff 10 - 1
0.75
-0.11
(0.39)
(0.52)
large
1.08
(0.42)
0.89
(0.41)
1.22
(0.43)
1.40
(0.45)
1.63
(0.65)
1.04
(0.57)
1.67
(0.64)
1.48
(0.61)
1.51
(0.56)
1.21
(0.49)
0.12
(0.48)
large
0.62
(0.38)
0.89
(0.37)
1.32
(0.37)
1.09
(0.41)
1.21
(0.40)
0.79
(0.51)
1.57
(0.53)
0.88
(0.58)
1.79
(0.72)
1.71
(0.75)
1.08
(0.69)
40
Table 11: Monthly returns to deciles formed on cash ratio (unconditional sorts), grouped by book-to-market
based on Fama/French NYSE breakpoints; 1-month holding period, returns equal weighted
Panel A: Sorted by cash/equity
Decile
Low
mid
high
1
0.73
0.93
1.57
(0.40)
(0.29)
(0.39)
2
0.75
1.46
1.36
(0.39)
(0.37)
(0.41)
3
0.51
1.25
1.56
(0.38)
(0.33)
(0.37)
4
1.21
1.39
1.41
(0.45)
(0.37)
(0.39)
5
0.84
1.20
1.98
(0.43)
(0.35)
(0.40)
6
1.00
1.59
2.26
(0.46)
(0.40)
(0.44)
7
1.15
1.46
1.91
(0.48)
(0.42)
(0.52)
8
1.53
1.82
2.21
(0.50)
(0.44)
(0.50)
9
1.46
1.86
2.25
(0.55)
(0.48)
(0.51)
10
1.28
1.89
1.64
(0.56)
(0.52)
(0.54)
Diff 10 - 1
0.56
0.95
0.08
(0.43)
(0.42)
(0.50)
Panel B: Sorted by cash/market cap
Decile
Low
mid
high
1
0.44
1.00
1.63
(0.41)
(0.31)
(0.50)
2
0.78
1.21
0.99
(0.44)
(0.35)
(0.38)
3
0.77
1.30
1.12
(0.46)
(0.33)
(0.40)
4
1.05
1.21
1.44
(0.49)
(0.37)
(0.41)
5
1.37
1.37
1.80
(0.53)
(0.35)
(0.41)
6
1.50
1.38
1.37
(0.52)
(0.38)
(0.36)
7
1.46
1.54
1.81
(0.60)
(0.41)
(0.43)
8
1.00
1.74
2.18
(0.54)
(0.41)
(0.44)
9
1.72
1.86
2.10
(0.63)
(0.45)
(0.44)
10
3.17
1.88
2.03
(1.91)
(0.55)
(0.42)
Diff 10 - 1
2.72
0.88
0.40
(1.84)
(0.45)
(0.44)
41
Panel C: Sorted by cash/assets
Decile
Low
1
0.67
(0.41)
2
0.43
(0.40)
3
0.99
(0.45)
4
0.38
(0.39)
5
1.07
(0.41)
6
1.08
(0.49)
7
1.19
(0.50)
8
1.15
(0.49)
9
1.47
(0.53)
10
1.34
(0.59)
Diff 10 - 1
0.76
(0.45)
mid
0.86
(0.32)
1.18
(0.35)
1.10
(0.33)
1.28
(0.38)
1.07
(0.36)
1.71
(0.38)
1.52
(0.41)
1.57
(0.47)
1.94
(0.46)
1.82
(0.55)
0.95
(0.45)
high
1.57
(0.42)
1.27
(0.37)
1.27
(0.38)
1.64
(0.43)
1.57
(0.39)
1.94
(0.46)
2.25
(0.47)
1.69
(0.48)
2.35
(0.50)
2.39
(0.56)
0.75
(0.54)
42
Table 12: Results from a Fama-French Three Factor regression plus momentum. This table regresses the
time series of returns from the 1-month holding period, equally weighted portfolios, upon the three FamaFrench risk factors, plus a momentum factor. (Standard errors in parentheses.)
Panel A: Cash/Equity
Decile
Alpha
Beta
Small
Value
Momentum
1
-0.109
0.96
0.75
0.37
-0.21
(0.17)
2
-0.078
0.99
0.79
0.33
-0.20
(0.16)
3
0.048
1.02
0.68
0.10
-0.20
(0.14)
4
0.178
1.01
0.73
0.19
-0.24
(0.17)
5
0.170
0.99
0.77
0.10
-0.22
(0.15)
6
0.597
0.98
0.86
0.00
-0.33
(0.16)
7
0.399
1.06
0.87
0.04
-0.24
(0.16)
8
0.408
1.03
0.86
-0.11
-0.23
(0.16)
9
0.810
0.98
0.98
-0.28
-0.32
(0.19)
10
0.732
1.05
1.01
-0.20
-0.29
(0.22)
Diff 10 - 1 0.842
0.09
0.26
-0.58
-0.07
(0.23)
Panel B: Cash/Market Cap
Decile
Alpha
Beta
Small
Value
Momentum
1
-0.214
0.96
0.82
0.33
-0.19
(0.16)
2
-0.283
0.99
0.74
0.30
-0.15
(0.15)
3
0.170
1.00
0.70
0.13
-0.16
(0.16)
4
0.197
1.04
0.84
0.14
-0.24
(0.15)
5
0.082
1.02
0.82
0.08
-0.16
(0.15)
6
0.290
1.00
0.90
0.05
-0.21
(0.15)
7
0.602
1.03
0.78
-0.03
-0.32
(0.17)
8
0.551
0.99
0.91
-0.05
-0.32
(0.17)
9
0.623
1.02
0.82
-0.23
-0.37
(0.18)
10
1.184
1.04
0.97
-0.16
-0.40
(0.23)
Diff 10 - 1 1.398
0.07
0.14
-0.49
-0.21
(0.23)
43
Table 13: Calendar-time portfolio returns to cash decile portfolios, correcting for industry returns. This
table contains equally weighted returns for holding periods from 1 month to 1 year. Firms are categorized
into the 48 Fama-French industry groupings and then the relevant industry return is subtracted from each
firm’s return each month. (Standard errors in parentheses.)
Cash/Equity
Cash/Market Cap
Decile
1mo
1yr
1mo
1yr
1
-0.11
-0.07
-0.19
-0.13
(0.13)
(0.07)
(0.12)
(0.07)
2
-0.02
-0.06
-0.23
-0.12
(0.12)
(0.07)
(0.11)
(0.06)
3
-0.01
0.03
0.13
-0.02
(0.11)
(0.06)
(0.13)
(0.06)
4
0.11
0.05
0.16
0.07
(0.12)
(0.06)
(0.11)
(0.05)
5
0.10
0.12
0.04
0.08
(0.12)
(0.06)
(0.12)
(0.06)
6
0.33
0.19
0.15
0.17
(0.11)
(0.05)
(0.11)
(0.06)
7
0.23
0.24
0.36
0.29
(0.12)
(0.05)
(0.12)
(0.05)
8
0.20
0.31
0.22
0.31
(0.13)
(0.06)
(0.13)
(0.06)
9
0.40
0.38
0.21
0.45
(0.14)
(0.07)
(0.13)
(0.06)
10
0.42
0.44
0.82
0.53
(0.16)
(0.08)
(0.16)
(0.07)
Diff 10 - 1
0.53
0.52
1.01
0.67
(0.20)
(0.11)
(0.19)
(0.09)
Table 14: Event study results. Panel A shows the equally weighted return to each firm for the 3-day
window around the earnings announcement date. Panel B shows the percent of the subsequent 252 days for
which each portfolio’s excess return is positive. Panel C shows the portfolio returns over the next-year
(days 250 to days 275) earnings announcement period.
Decile
1
2
3
4
5
6
7
8
9
10
Diff 10 - 1
Announcement
window return
0.10
0.16
0.18
0.34
0.26
0.23
0.32
0.32
0.32
0.29
0.19
% days > 0
39.7
35.7
37.2
47.4
54.0
62.0
65.3
64.9
60.2
66.7
27.0
Day t+250 to
t+275
0.10
-0.01
0.10
0.04
0.06
0.37
0.28
0.36
0.47
0.41
0.31
44
Table 15: Returns by year and month. This table shows equally weighted returns from the unconditional
cash/market cap ranking. (Standard errors are in parentheses.)
Panel A: Returns grouped by Decade
1
2
3
1970s
0.95
1.03
1.08
4
1.16
5
1.51
6
1.48
7
2.16
8
1.39
9
1.34
10
1.80
1980s
0.98
1.25
1.33
1.30
1.65
1.53
1.53
1.64
2.07
1.57
1990s
0.71
0.84
0.99
1.11
1.44
1.48
1.42
1.61
2.15
2.66
Panel B: Returns grouped by Month
1
2
3
Jan
5.62
6.37
6.77
4
7.00
5
8.59
6
7.42
7
9.17
8
8.72
9
9.79
10
12.12
Feb
1.76
1.56
1.47
1.55
1.87
1.78
2.03
2.91
2.43
1.77
March
0.97
1.55
1.52
1.41
1.06
1.44
1.10
1.53
1.58
1.65
April
0.41
0.96
0.64
0.69
1.09
0.97
0.83
0.68
1.49
1.66
May
0.81
1.00
1.34
1.81
1.26
1.46
2.02
1.96
2.21
1.21
June
0.81
0.75
1.18
0.91
1.89
1.27
1.60
0.86
1.15
0.60
August
-0.05
-0.20
-0.35
0.45
0.42
0.33
0.86
0.62
0.16
1.85
September
-0.80
-1.00
-0.41
-0.47
-0.57
-0.08
-0.31
-0.25
-0.08
-0.27
October
-2.20
-1.02
-2.57
-1.94
-1.27
-0.82
-1.52
-1.81
-1.31
-1.06
November
1.58
1.09
1.24
1.48
1.99
1.91
2.30
1.87
2.38
3.21
December
1.44
1.03
1.21
1.36
1.04
1.09
0.87
0.40
0.98
0.14
Feb-Dec
0.44
0.55
0.50
0.63
0.82
0.85
0.93
0.84
1.02
1.03
45
10-1
0.84
(0.68)
0.58
(0.23)
1.94
(0.36)
10-1
6.50
(1.61)
0.33
(0.99)
0.68
(0.62)
1.24
(0.67)
0.40
(0.55)
-0.21
(0.65)
1.15
(1.29)
0.53
(0.53)
1.13
(0.69)
1.63
(1.07)
-1.29
(0.99)
0.58
(0.24)
Table 16: Takeovers. Panel A shows the takeover rate for the next 12 months for each decile (ranked on
cash/market cap), the average premium for the target company, the amount by which each decile’s yearly
return is increased from takeover activity, and the frequency of stock and cash offers by decile. Panel B
shows the results to a logit model that predicts the probability of becoming a takeover target based on cash
holdings, book-to-market, and size.
Panel A: Percent of companies taken over and premiums within the next 12 months
Rank
1
2
3
4
5
6
7
Takeover %
4.8
5.1
5.1
5.0
5.0
5.2
5.0
Average
14.4
15.8
15.6
16.5
13.8
17.1 16.8
premium
Yearly return
0.69
0.80
0.79
0.82
0.69
0.88 0.84
due to
takeovers (%)
% Stock
33.2
35.8
36.2
37.6
39.5
42.6 42.7
% Cash
50.9
46.1
46.7
47.5
46.3
43.2 44.3
Panel B: Logit Model
Intercept
Regression 1
2.93
Regression 2
2.88
Regression 3
2.98
Cash/equity
-0.042
(1.64)
Log(Cash)
-0.020
(6.71)
-0.011
(6.42)
46
BTM
0.049
(4.32)
0.048
(4.56)
0.042
(3.61)
8
5.0
19.6
9
5.1
17.0
10
5.7
16.1
0.98
0.86
0.91
44.3
42.4
46.4
42.2
51.3
37.1
Log(ME)
-0.008
(1.75)
-0.011
(2.74)
-0.009
(1.75)
Table 17: Percentage of firms in each cash holdings decile classified as financially constrained under the
KZ measure. Panel A includes the full KZ scoring criteria: cash flow to capital, Tobin’s Q, debt/total
capital, dividends/capital, and cash/capital. Panel B scores companies on only the first three factors. The
bottom 20% of firms in any given year are classified as financially constrained.
Panel A: Percent of firms categorized as constrained under KZ measure, by
decile.
Cash Decile
1
2
3
4
5
6
7
8
9
10
Constrained
5
21.92
19.49
19.29
18.71
16.70
14.15
13.12
11.57
10.94
15.72
4
33.28
30.77
28.26
25.11
22.17
18.49
15.60
12.60
10.18
8.03
3
23.97
25.24
24.29
24.35
24.07
24.08
22.35
19.63
1600
10.14
Unconstrained
2
14.48
17.14
19.19
21.41
23.36
25.32
26.46
27.29
25.05
19.84
1
6.35
7.35
8.97
10.42
13.69
17.95
22.47
28.91
37.83
46.27
Panel B: Percent of firms categorized as constrained under the modified KZ
measure, ignoring cash holdings.
Cash Decile
1
2
3
4
5
6
7
8
9
10
Constrained
5
11.99
11.62
12.77
14.07
13.88
13.58
14.92
15.93
20.28
32.35
4
28.15
25.91
24.22
22.39
21.08
18.86
17.08
16.05
15.13
14.18
3
25.67
24.70
23.97
22.78
21.79
21.38
20.92
19.64
18.25
13.45
Unconstrained
2
20.81
22.74
22.27
22.83
23.34
24.27
23.70
23.92
20.92
15.32
1
13.38
15.02
16.77
17.93
19.91
21.91
23.38
24.46
25.42
24.69
Table 18: Performance of financially constrained firms, by KZ ranking quintile. This table presents
calendar-time returns for one-year holding periods to portfolios grouped into quintiles based on the KZ
scoring. Firms in the bottom quintile are considered financially constrained.
Qunitile
1 year
1 year
(equal)
(value)
1 (unconstrained)
1.463
1.394
(0.38)
(0.28)
2
1.476
1.305
(0.35)
(0.28)
3
1.509
1.300
(0.31)
(0.26)
4
1.404
1.246
(0.31)
(0.25)
5 (constrained)
1.299
1.200
(0.38)
(0.33)
5-1
-0.164
-0.194
(0.15)
(0.21)
47
Table 19: Results from a Fama-French Three Factor regression plus financial constraints factor. This table
regresses the time series of returns from the 1-month holding period, equally weighted portfolios, upon the
three Fama-French risk factors, plus the financial constraints factor. (Standard errors in parentheses.)
Panel A: Cash/Equity
Decile
Alpha
Beta
Small
Value
FC
1
-0.177
0.94
0.68
0.23
0.53
(0.16)
(0.07)
2
-0.124
0.96
0.71
0.18
0.53
(0.15)
(0.06)
3
-0.078
1.01
0.61
0.03
0.37
(0.14)
(0.06)
4
0.135
0.97
0.66
0.03
0.52
(0.17)
(0.07)
5
0.047
0.98
0.71
0.02
0.34
(0.16)
(0.06)
6
0.388
0.96
0.81
-0.05
0.34
(0.18)
7
0.307
1.01
0.81
-0.05
0.36
(0.16)
(0.07)
8
0.283
1.00
0.82
-0.16
0.23
(0.17)
(0.07)
9
0.532
0.95
0.93
-0.30
0.23
(0.20)
(0.08)
10
0.559
1.02
0.98
-0.25
0.26
(0.23)
(0.09)
Diff 10 - 1 0.736
0.07
0.30
-0.49
-0.26
(0.22)
(0.09)
Panel B: Cash/Market Cap
Decile
Alpha
Beta
Small
Value
FC
1
-0.281
0.95
0.75
0.20
0.51
(0.15)
(0.06)
2
-0.261
0.95
0.68
0.14
0.52
(0.14)
3
0.108
0.99
0.62
0.14
0.45
(0.15)
(0.06)
4
0.037
1.03
0.79
0.08
0.32
(0.16)
(0.06)
5
0.025
1.01
0.78
0.01
0.28
(0.15)
(0.06)
6
0.226
0.97
0.85
-0.04
0.37
(0.15)
(0.06)
7
0.427
0.99
0.73
-0.09
0.30
(0.19)
(0.08)
8
0.367
0.95
0.87
-0.11
0.32
(0.19)
(0.08)
9
0.303
1.00
0.75
-0.24
0.26
(0.20)
(0.08)
10
0.943
0.99
0.91
-0.25
0.39
(0.25)
(0.10)
Diff 10 - 1 1.224
0.04
0.16
-0.45
-0.12
(0.22)
(0.09)
48
Table 20: Performance of constraint-avoiders
This table shows the calendar-time monthly returns to firms that avoid being financially constrained
because of their cash holdings. Results are presented for both one-month and one-year holding periods, and
raw returns and market-adjusted returns (subtracting the CRSP equal or value-weighted index from each
return).
Raw returns
Market-adjusted returns
1mo
1yr
1mo
1yr
Equally
1.719
1.422
0.659
0.363
weighted
(0.64)
(0.52)
(0.54)
(0.39)
Value
1.578
0.823
0.517
-0.23
weighted
(0.71)
(0.57)
(0.60)
(0.42)
Table 21: Fama-French three factor regressions for constraint-avoiders. This table presents risk-adjusted
alphas from a factor regression for the subset of firms that avoid being financially constrained via their cash
holdings. Panel A regresses portfolio returns on the Fama-French three factors, and Panel B regresses
portfolio returns on these three factors plus a momentum factor and the financially-constrained factor.
Panel A: Fama-French three-factor model
Equally weighted
Value weighted
1mo
1yr
1mo
1yr
Alpha
0.946
0.473
1.205
-0.127
(0.45)
(0.28)
(0.67)
(0.32)
Beta
0.96
1.02
1.07
1.24
Size
1.46
1.61
1.18
0.97
Value
-0.71
-0.52
-1.23
-0.91
Panel B: Five-factor model
Equally weighted
Value weighted
1mo
1yr
1mo
1yr
Alpha
1.585
0.750
1.783
0.137
(0.44)
(0.28)
(0.68)
(0.33)
Beta
0.98
1.03
1.06
1.25
Size
1.44
1.60
1.15
0.97
Value
-0.85
-0.56
-1.46
-0.94
Momentum
-0.56
-0.26
-0.42
-0.26
FC
-0.11
-0.10
0.15
-0.14
49
Table 22: Performance of cash decile portfolios after removing firms that avoid financial constraints via
their cash holdings.
Decile
1
2
3
4
5
6
7
8
9
10
Diff 10 - 1
1mo
0.468
0.875
1.265
0.945
1.090
1.154
1.135
1.036
1.631
1.236
0.832
(0.39)
Cash/Equity
1yr
0.915
0.936
0.956
1.022
1.013
1.174
1.143
1.236
1.432
1.137
0.221
(0.20)
1mo
0.531
0.921
1.288
0.977
1.030
1.122
1.287
1.207
1.384
1.245
0.761
(0.42)
Cash/Market Cap
1yr
0.774
0.940
0.820
1.041
1.017
1.159
1.198
1.310
1.491
1.230
0.455
(0.16)
Table 23: Operating performance of cash deciles, based on cash/equity rankings. Decile means are
reported, and decile medians appear in parentheses.
Panel A: Investment and Asset Growth
Investment
Asset Growth
Decile
Year t
Year t+1
Year t
Year t+1
1
27.2 (19.0)
25.4 (18.0)
12.9 (7.0)
11.1 (6.2)
2
26.8 (19.0)
24.8 (18.1)
13.3 (6.9)
10.2 (5.9)
3
27.8 (19.5)
26.1 (18.7)
13.5 (7.1)
11.0 (6.3)
4
29.2 (20.2)
27.5 (19.6)
13.6 (7.0)
11.3 (6.2)
5
30.7 (21.2)
28.7 (20.1)
13.7 (7.3)
11.4 (6.3)
6
32.7 (22.1)
30.1 (21.4)
14.1 (7.2)
11.7 (6.5)
7
34.7 (23.3)
31.7 (22.0)
14.5 (7.5)
11.9 (6.5)
8
38.7 (24.9)
33.6 (23.3)
15.1 (7.3)
12.9 (6.8)
9
45.1 (26.9)
37.5 (24.5)
15.7 (6.8)
13.2 (6.5)
10
50.0 (28.0)
43.1 (26.0)
15.8 (5.7)
14.1 (5.9)
Constraint- 48.3 (28.1)
45.1 (25.6)
16.7 (9.7)
17.8 (1.6)
avoiders
Panel B: Sales Growth and Cash flow growth
Sales Growth
Cash Flow Growth
Decile
Year t
Year t+1
Year t
Year t+1
1
12.0 (8.3)
10.4 (7.4)
30.4 (12.7)
29.1 (11.9)
2
12.0 (8.0)
9.8 (7.0)
30.0 (12.6)
27.6 (11.7)
3
12.1 (8.2)
10.6 (7.4)
29.6 (12.8)
29.5 (12.5)
4
12.7 (8.2)
10.6 (7.3)
31.2 (13.1)
28.7 (12.1)
5
13.5 (8.4)
10.8 (7.4)
32.1 (13.0)
30.6 (12.7)
6
13.9 (8.8)
11.3 (7.7)
31.6 (13.2)
30.6 (12.5)
7
15.2 (9.2)
12.4 (8.1)
31.8 (13.2)
31.8 (12.6)
8
17.6 (9.8)
14.4 (8.7)
33.7 (13.9)
33.7 (13.0)
9
22.7 (10.7)
17.2 (9.4)
35.0 (14.2)
35.3 (13.7)
10
27.5 (11.2)
21.9 (10.3)
43.1 (16.2)
43.9 (15.6)
Constraint- 40.0 (13.7)
31.9 (12.0)
103.6 (32.5)
91.1 (28.5)
avoiders
50