Original Article
The Relation between Bank Credit Growth and the Expected Returns
of Bank Stocks*
Priyank Gandhi
Rutgers Business School
This version: April 18, 2018
*Rutgers Business School, Rutgers University, New Brunswick 08854; Email:
pgandhi@rbsmail.rutgers.edu. Tel: (574) 631 3450. This paper is based on my dissertation
written at the University of California, Los Angeles. I would like to thank my committee chairs,
Francis Longstaff and Hanno Lustig, for their invaluable guidance. I would also like to thank
Robert Battalio, Antonio Bernardo, Shane Corwin, John Doukas, Andrea Eisfeldt, Mark
Garmaise, Paul Schultz, Eduardo Schwartz, Ivo Welch, Lu Zhang (the editor), an anonymous
referee and seminar participants at the University of California at Los Angeles, the Ohio State
University, Emory University, University of Notre Dame, Casewestern Reserve University,
and the University of Western Ontario for their invaluable comments and suggestions. All
errors are my responsibility.
This article has been accepted for publication and undergone full peer review but has not been through
the copyediting, typesetting, pagination and proofreading process, which may lead to differences
between this version and the Version of Record. Please cite this article as doi:[10.1111/eufm.12179]
This article is protected by copyright. All rights reserved.
Abstract
Higher bank credit growth implies that excess returns of bank stocks over the next one year
are lower by nearly 3%. Credit growth tracks bank stock returns over the business cycle and
explains nearly 14% of the variation in bank stock returns over a 1-year horizon. I argue that
the predictive variation in returns reflects investors’ rational response to a small time-varying
probability of a tail event that impacts banks and bank-dependent firms. Consistent with this
hypothesis, the predictive power, as measured by the absolute magnitude of the coefficient on
credit growth and the adjusted-R2 at the 1-year horizon, depends systematically on variables
that regulate exposure to tail risk.
JEL Codes: G01, G02, G15, G21
Keywords: Bank equity returns, Tail risk, Bank credit growth
_
1
Introduction
Understanding the relation between bank credit and equity markets has important implications for the
link between the financial sector and the real economy, and for the design of widespread government
policies regarding bank regulation and supervision. While a large literature establishes that bank credit
growth is highly pro-cyclical, and that bank credit availability affects borrower firms’ valuation, studies
of the relation between bank credit growth and bank valuations are more limited.1
In this paper, I use a monthly dataset of bank credit in the U.S. over 1960 - 2017 to study its relation
with the returns on an index of bank stocks. This monthly dataset allows me to unmask even short or
mild variations in bank credit, and correlate bank credit more precisely with variations in bank stock
returns. Since the U.S. is also one of the few markets with a long history of equity returns for a broad
cross-section of firms, this allows for additional cross-sectional analysis that the international setting of
other studies does not permit. To my knowledge, this is the first paper that utilizes monthly data on
bank credit and examines how this relation varies in the cross-section.
I run basic forecasting regressions of future bank stock returns on bank credit growth. An increase in
bank credit growth is associated with lower excess returns on bank stocks. That is, bank credit growth
and the excess returns of banks stocks are negatively correlated. A 1% increase in bank credit growth
predicts that excess returns of bank stocks over the next one year are lower by 2.61%. Unlike most other
forecasting relations, bank credit growth tracks bank stock returns over the business cycle. The predictive
power of bank credit growth for bank stock returns is economically large. Over a horizon of one year,
bank credit growth explains 13.52% of the variation in bank stock returns. The annual R2 , when bank
credit is used to forecast bank stock returns, peaks at 17.78% at a horizon of 43 months.
The negative relation between bank credit growth and bank stock returns fits within the framework
of a standard neoclassical model (such as the rare disasters framework of Rietz (1988), Barro (2006),
Gabaix (2008) and Wachter (2013)) and classical models of investment behavior (such as the Q-theory
of investments of Liu, Whited, and Zhang (2009)). In these models, discount rates vary over time, are
counter-cyclical, and reflect the representative agent’s rational response to a small time-varying probability
of a tail event. Banks uses these time-varying counter-cyclical discount rates to determine the cost
of capital and to evaluate projects they can fund. An improvement in macroeconomic conditions (i.e.
1
A notable exception is Baron and Xiong (2017) who use a dataset of 46 countries over 100 years to study the relation
between bank credit and bank stock returns. I discuss below the incremental contribution of this paper compared to theirs.
1
reduction in the probability of tail risk) reduces discount rates, increases the expected present value of
profits, and the optimal rate of investments by banks increases. That is, bank credit grows. Banks are more
sensitive to changes in the probability of a tail event due to their higher leverage, higher proportion of shortdebt that is payable on demand, and lower fraction of tangible assets. In concert, these characteristics
increase the likelihood that a bank (as compared to a typical non-financial firm) will enter distress upon
the realization of a tail event.
An implication of this framework is that the relation between bank credit growth and equity returns
should vary in the cross-section. Since small banks, banks with more leverage, and banks with more
short-term debt are more exposed to tail risk, bank credit should correlate more with equity returns of
banks with these characteristics.2 Indeed, this is exactly what I find in the data. The predictive power of
bank credit growth for bank stock returns, as measured by the absolute magnitude of the coefficient on
bank credit growth and the adjusted-R2 at the 1-year horizon, depends systematically on size, leverage,
and the proportion of short-term debt. The predictive power decreases monotonically in size, as measured
by market capitalization, and increases in leverage, and in the proportion of short-term debt employed by
the bank.
The forecasting relation between bank credit growth and bank stock returns is robust to several
empirical specifications. The results hold when I exclude the data for the financial crisis, and when I
include other forecasting variables used in the literature. Bank credit growth does not predict future cash
flows of bank stocks. It does, however, predict returns for investment banks and bank-dependent firms.
Tail events that impact banks can also affect investment banks. Like banks, investment banks employ
high leverage, rely on short-term debt, do not own substantial tangible assets, and are more sensitive to
changes in the probability of a tail event. Finally, an increase in tail event risk also increases expected
returns of any direct equity claim on bank-funded projects, and this explains the negative correlation
between bank credit growth and returns of bank-dependent firms.
Bank credit growth does not strongly predict returns of any other asset class (non-financial firms,
treasury bonds, or corporate bonds). This is possible if tail events result in large losses for projects
funded by banks, but do not impact other projects in the economy. Examples of such events are the Less
Developed Country Debt crisis of 1982, the Mexico crisis of 1994, the East Asian crisis of 1997, and the
Long-Term-Capital-Management crisis of 1998. Each of these events resulted in a large loss of cash flows
2
Ivashina and Scharfstein (2009) show that banks with more short-term debt are more exposed to tail risk. I show in
Section 3 below that small banks are more exposed to tail risk.
2
from projects funded by banks, and a significant drop in bank profitability and valuation. However, there
was no measurable effect on the performance of other projects in the economy. These crises were not
accompanied by a recession, and other important asset markets such as the stock and housing markets,
were relatively unaffected.3
My paper is related to seminal work by Baron and Xiong (2017) and Schularick and Taylor (2012),
who analyze the relation between bank credit, macroeconomic conditions, and equity markets in more
than 46 countries over 100 years. Schularick and Taylor (2012) show that bank credit expansion increases
macroeconomic (crash) risk in the near future. Baron and Xiong (2017) also document a negative correlation between bank credit and equity returns. However, given the results in Schularick and Taylor (2012),
they attribute this negative relation to the neglect of crash risk by investors.
My results contrast with Baron and Xiong (2017) and Schularick and Taylor (2012) and are consistent
with a significantly different insight: bank credit simply responds to exogenous changes in macroeconomic
conditions as one would expect. As macroeconomic (tail) risk declines, bank credit increases, and viceversa. In other words, worsening macroeconomic conditions increase the risk of potential borrowers (they
hurt borrower’s net worth or collateral values), and forward-looking decisions by bank managers anticipate
time variation in macroeconomic conditions. Thus, the predictive ability of bank credit for equity returns is
due to a simple “price of risk” mechanism: Bank credit increases, but expected equity returns are reduced
with improving macroeconomic conditions. My results are also consistent with the N P V rule (or the
Q-theory) of investments that prescribes banks evaluate lending opportunities by discounting projected
cash flows at discount rates that appropriately reflect investment risk. Thus, bank credit growth should
be negatively correlated with future macroeconomic (tail) risk.
Yet, Baron and Xiong (2017) and Schularick and Taylor (2012) document that bank credit growth
is sometimes positively correlated with future macroeconomic (crash) risk. How does one reconcile the
results? One possibility is that market frictions can sometimes cause deviations from the N P V rule (Qtheory) of investments. An example of such a market friction is the agency cost of leverage. Banks are
highly levered, much more than the typical non-financial firm. Such high leverage can amplify agency
problems in banks and prevent the N P V rule (Q-theory) from working well.
Acharya and Naqvi (2012) and Chen and Manso (2016) show how excessive leverage and liquidity
amplifies agency problems and can sometimes distort the investment decisions of banks, and lead to a
3
One implication of this hypothesis is that banks actively fund projects with higher exposure tail risk. I return to this
question in section 3 below.
3
positive correlation between credit growth and future macroeconomic risk. In Acharya and Naqvi (2012),
macroeconomic risk varies over time. When macroeconomic risk is high, investors hold more bank deposits
as they are insured and considered safe. Higher inflow of deposits results in excessive bank liquidity and
leverage. With no agency problems, banks invest some of these deposits windfalls in safe, liquid assets
to guard against future liquidity shortfalls. That is, bank credit growth appropriately reflects underlying
risk of projects. Standard N P V or Q-theory rules apply and bank credit growth is high (low) when
macroeconomic risk is low (high). However, with agency problems, bank managers use excessive liquidity
to increase credit and underprice risk.4
Chen and Manso (2016) study classic agency problems of investment (debt overhang and asset substitution) when macroeconomic risk varies over time. In their paper equityholders’ decision to invest in
projects depends not only on project risk and cash flows, but also on how investment gains are distributed
among claimholders. Chen and Manso (2016) show that when macroeconomic risk varies over time, equityholders can reduce investments gains captured by debtholders by investing in assets that are more
systematically risky.
Overall, these models suggest that the positive correlation between credit growth and future macroeconomic risk (tail events) depends on excessive leverage (or liquidity) of banks as well as presence of agency
problems. In addition, monetary policy also plays an important role in whether credit growth instigates
or discourages asset bubbles and future macroeconomic risk. Only when banks are excessively levered
(liquid), have agency problems, and face expansionary monetary policy they are likely to underprice risk,
and the correlation between bank credit growth and future macroeconomic risk is positive.
The idea that agency cost of leverage distorts investment decisions of banks and sometimes results
in a positive relation between bank credit growth and future macroeconomic risk is also consistent with
classical accounts of financial crisis, such as those in Kindelberger (1978) and Reinhart and Rogoff (2009).
These papers show that bank credit expansions are not always associated with financial crisis, but often
coincide with abundant bank leverage (liquidity).5
The rest of the paper is organized as follows: I describe the data and present summary statistics in
section Section 2; Section 3 presents key empirical results; Finally, Section 5 concludes.
4
This model also explains why my results vary with size and are stronger for small banks. Large banks benefit from
implicit government guarantees, are more likely to underprice risk, and hence the normal negative relation between credit
growth and bank stock returns implied by the Q-theory and investment CAPM is somewhat weaker for them.
5
Data in Baron and Xiong (2017) and Schularick and Taylor (2012) does not allow them to control for proxies for bank
leverage or agency problems which are simply not available for 46 countries over 100 years.
4
2
Data and summary statistics
In this section, I first present summary statistics for bank stock returns. I then describe how I construct
the time-series for bank credit growth for all banks in the U.S. and present its statistical properties.
2.1
Bank stock returns
To assess the relationship between bank credit growth and bank stock returns, I first form an index of
value-weighted equity returns of all publicly listed banks in the U.S. I collect data on market capitalization
and returns over 1960 - 2017 from the Center for Research on Security Prices (CRSP) for banks. In CRSP,
banks are identified by a Standard Industrial Classification (SIC) code between 6000 - 6199.6 Henceforth,
I refer to the value-weighted index of equity returns for banks by the mnemonic BN . Panel A of Table 1
presents the summary statistics for the value-weighted index of bank stocks (BN ). The first row presents
the summary statistics computed over 1960 - 2005, i.e., it excludes the data for the recent financial crisis
and thereafter. The second row presents the summary statistics over the full sample, 1960 - 2017. The
Table lists the monthly mean, standard deviation, minimum, median, and maximum return expressed in
percentages. For comparison, panel B of Table 1 presents the summary statistics for the CRSP valueweighted index of all stocks. The annualized mean and standard deviation for bank stock returns over
the full sample is 16.80% and 20.68 respectively. As expected the mean and standard deviation for bank
stock returns is higher than that of the average firm in the CRSP value-weighted index, and this could
partly reflect the fact that banks are much more leveraged than the average non-financial firm.
Later, for robustness tests I require returns for an index of investment banks. In CRSP, investment
banks are identified by the SIC code between 6200 - 6299 and 6700 - 6799.7 Henceforth, I refer to the
value-weighted index of investment banks by the mnemonic F T . Table A2 in the appendix provides the
summary statistics for the value-weighted index of investment banks.
I also require returns for portfolios of banks and investment banks sorted by size (market capitalization). I compute the value-weighted returns of 5 size-sorted portfolios for each type of intermediary by
employing the standard portfolio formation strategy of Fama and French (1993). In December of each
year, I allocate all firms for each type of financial intermediary to 5 portfolios based on market capitaliza6
This definition is from Ken French’s website. Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.
french/data_library.html
7
This definition is also from Kenneth French’s website. Table A1 in the Appendix provides a brief description of the types
of financial intermediaries (i.e. banks and investment banks) and the types of businesses they are engaged in.
5
tion. I calculate the value-weighted return of each portfolio for each month over the next year. This results
in a monthly time-series from 1960 - 2017 for each size-sorted portfolio. Table A3 in the appendix show
the summary statistics for size-sorted portfolios of banks and financial intermediaries. Panel A presents
the summary statistics for size-sorted portfolios of banks and panel B presents the summary statistics
for the size-sorted portfolios of investment banks, respectively. In each panel, the first column indicates
the type of intermediary. In column 2, portfolio ’1’ refers to the smallest size-sorted portfolio by market
capitalization. Columns 3 - 7 of the Table show the summary statistics for 1960 - 2005 and columns 8 12 show the statistics for the full sample. The statistics are monthly and are expressed in percentages.
Since the properties of size-sorted portfolios of financial firms are well-established, I do not discuss them
in detail here.8 .
CRSP data for equity returns is available from 1926, however, my analysis begins only in 1960. This
start date reflects the fact that there are not enough publicly listed banks for which data is available in
CRSP to allocate to 5 size-sorted portfolios prior to this date. I check that my results are robust to the
start date. However, for consistency, all Tables in section 3 report results starting in 1960. I next describe
my estimate of bank credit growth.
2.2
Aggregate bank credit growth
To estimate aggregate bank credit level for all publicly listed commercial banks in the U.S., I measure
aggregate bank credit growth (henceforth bank credit growth or the variable gtc ). I estimate bank credit
growth by using data from the monthly report on the ’Assets and Liabilities of Commercial Banks in the
U.S.’ (statistical release H.8) issued by the Board of Governors of the Federal Reserve System (FED).
Data from the release is available at http://www.federalreserve.gov/releases/h8/current/default.htm.
I use non-seasonally-adjusted data for bank credit (item code H8/H8/B1001NCBA, column 1 of the
H.8 release) to compute bank credit growth. Bank credit is the total credit extended by all commercial banks in the U.S. to non-financial entities. It includes commercial and industrial loans, real estate
loans, and consumer loans, but excludes interbank loans, repurchase agreements, Federal Funds holdings,
derivative positions, and unearned income on loans. Table A4 in the appendix provides details of the loan
composition. In any given month bank credit is computed as the sum of total securities (T S), commercial
and industrial loans (IL), real estate loans (RL), consumer loans (CL), and miscellaneous loans (M L).
8
See Gandhi and Lustig (2015) for a detailed analysis of returns of size-sorted portfolios of financial firms.
6
Column 1 of the Table indicates the loan category. Columns 2 - 6 present the mean, standard deviation,
minimum, median, and maximum for each loan category expressed as a percentage of total bank credit.
Over 1960 - 2017, T S accounts for 27.52% of bank credit on average in any given month. The corresponding values for IL, RL, CL, and M L are 21.63%, 27.12%, 13.29%, and 9.64% respectively. Traditional
bank loans, computed as a sum of IL, RL, and CL on average account for nearly 62.04% of total bank
credit in any given month. The level of traditional loans never falls below 50% (approx.) of total bank
credit. The statistics for traditional loans are reported in the last line of the Table.
Bank credit growth is the year-on-year growth rate of bank credit for each month from 1960 - 2017.
Further, I assume that data for bank credit growth is unavailable to investors for another 3 months. This
is because data for the H.8 report is collected from a sample of 875 banks on a voluntary basis. With
voluntary participation, it is plausible that a weak bank may choose not to file a report, rendering the H.8
data inaccurate. In order to guard against this, the FED benchmarks the H.8 data against the mandatory
quarterly ’Report of Condition and Income’ (hereafter the Call Report) required to be filed accurately
by all FDIC-insured banks. This verification also ensures that the data submitted by participating banks
is accurate. Since this benchmarking occurs quarterly, the 3-month lag addresses any concerns regarding
the small sample size of banks and the voluntary nature of the H.8 report. Again, I confirm that my
results are robust to variation in the number of lag months.
I focus on bank credit and not any of its subcomponents, such as commercial and industrial, real
estate, or consumer loans, because by definition bank credit is the appropriate measure of aggregate
credit extended by all banks in the BN index. A valid question to ask is if my analysis can be extended
to each subcomponent of bank credit. Unfortunately, this is not possible using this data because the
FED periodically modifies the definitions of data-items in the H.8 release. These mandated changes
in definitions necessitate moving loan items from one category to another, rendering the choice of any
subcomponent problematic. This may also lead to the selection of a time-series for a subcomponent for
which the inter-temporal definition is simply inconsistent. 9 .
Is bank credit an appropriate proxy for the total loans issued by all banks in the BN index? The
answer is yes, even though over the full sample, the maximum number of publicly listed banks for which
returns data is available in CRSP is 818 compared to the nearly 6,911 FDIC-insured banks that operate
in the U.S. in September 2010. Thus, only a small fraction of commercial banks that operate in the U.S.
9
Kashyap and Stein (2000) also document the issue of inconsistent time-series for the Call Report Data to which the H.8
release is bench-marked.
7
are publicly listed. Data from H.8 is still an appropriate proxy as the largest 508 banks, by asset size,
control nearly 90% of all bank assets ($10,916.20 B of the total $12,130.30 B bank assets in the U.S).10 .
Bank balance sheet data is also available from COMPUSTAT or from the Call Report. I use H.8 data
because it is available at a higher frequency, i.e., monthly, and does not have any missing observations, a
problem that plagues these other sources. In section 3, I use these alternative data sources for robustness
tests.
Panel C of Table 1 presents the mean, median, standard deviation, minimum, and maximum values
of bank credit growth. All results are monthly and are expressed in percentages. Over the full sample,
the average value of bank credit growth is 8.22% and its standard deviation is 3.08%. Dickey-Fuller tests
and augmented Dickey-Fuller tests with lags of 4, 8, 10, and 12, and an inclusion of a constant or a
time-trend, always reject the unit-root hypothesis that bank credit growth is non-stationary at a 10% or
higher confidence level.
Figure 1 plots the time-series of bank credit growth. Here, the solid line represents bank credit
growth and the dashed line represents the index of industrial production. The dark regions in the graph
represent NBER recessions and the three light shaded regions represent the three crises related to the
Less-Developed-Country debt crisis of 1982, the Mexico Peso crisis of 1994, and the Long-Term-CapitalManagement crisis of 1998 respectively. The NBER recession dates are published by the NBER Business
Cycle Dating Committee and are available at http://www.nber.org/cycles.html. Kho, Lee, and Stulz
(2000) provide the dates for the Mexico and the LTCM crisis. The dates for the Less-Developed-Country
debt crisis are from FDIC.
From the figure it is clear that bank credit growth is extremely sensitive to recessions and is strongly
correlated with changes in the index of industrial production. To further quantify this sensitivity, I regress
bank credit growth on a set of dummy variable, one for each month the economy is in a recession. Figure
2 presents the results. The dependent variable is bank credit growth and the independent variable is a
T × 12 matrix of dummy variables. Dummy variable D1 equals 1 if a month is the 1st month in an
NBER dated recession and zero otherwise. As the average length of a recession in the post-war period is
12 months, I restrict the number of dummy variables to 12. During the initial months of a recession the
economic and statistical significance of the loadings on the dummy variables is positive and small. This
may be on account of the fact that in the initial months of a recession banks face a run on committed
10
See http://www2.fdic.gov/qbp/2010sep/qbp.pdf.
8
credit lines (an off-balance sheet item), and bank credit increases. However, as the time spent in an
economic contraction increases, bank credit growth falls, so that by the 8th month of a recession bank
credit growth falls to nearly 3% below its unconditional mean.
I check the correlation between bank credit growth and several business cycle variables. Bank credit
growth is positively correlated with industrial production and negatively correlated with the term spread
(the difference in the yield to maturity of the 10-year Treasury bond and the 3-month Treasury Bill).
This confirms that bank credit growth is indeed pro-cyclical. Having described my measure of bank credit
growth and having demonstrated that it is indeed highly pro-cyclical, I turn to my empirical results.
3
Empirical results
In this section, I test if there exists a relationship between aggregate bank credit growth and an index of
bank stock returns. The advantage of carrying out an analysis at the index level is that it dampens any
idiosyncratic noise and makes it easier to identify the empirical relationships.
3.1
Is there a relationship between bank credit growth and bank stock returns?
I plot the cross-autocorrelation between an index of bank stock returns and bank credit growth for various
lags. The first panel in figure 3 shows the results for nominal bank stock returns and the second panel
shows the results for real bank stock returns. Contemporaneous correlation is indicated by the Lag = 0.
Similarly, Lag = 5 refers to the correlation between bank stock returns at time t + 5 and bank credit
growth at time t. The height of the bars represents the magnitude of the cross-autocorrelation and the
dashed horizontal lines represent significance at the 5% level. In panel A, the leading cross-autocorrelation
between bank stock returns and bank credit growth is small but positive. It then switches sign and the
subsequent correlations are reliably negative. This suggests that bank credit growth expected returns of
bank stocks are negatively correlated. That is, if bank credit growth increases at time t, expected returns
of bank stocks decrease. If expected returns at time t fall, prices at time t increase or time t realized
returns are high. However, future realized returns are lower as expected and required by the market.
The negative correlation between bank credit growth and the expected returns of bank stocks is
confirmed in Panel A of Table 2 that shows the results of a forecasting regression that uses bank credit
growth as a predictive variable. The exact specification for the regression is:
9
j=H
X
log(1 + rBN,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αH + βH
log(1 + gtc ) + ǫt+H
(1)
j=1
The dependent variable is the H-period log excess return of the value-weighted index of bank stocks
(BN ) and is computed as log(1 + rBN,t+1 ) − log(1 + rf,t+1 ) + ... + log(1 + rBN,t+H ) − log(1 + rf,t+H ). The
independent variable is bank credit growth or log(1 + gtc ). Panel A of Table 2 shows the values of the
estimated coefficients, statistical significance, and the adjusted-R2 over horizons spanning 3, 6, 9, and 12
months. The horizon value is indicated by H. The first row shows the estimated β on bank credit growth
or gtc , and the second row presents the value of the adjusted-R2 for each horizon. Statistical significance
of the coefficients is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The standard
errors are adjusted for heteroscedasticity, autocorrelation, and overlapping data using Hansen-Hodrick
with 12 lags.
Over a 12-month horizon, a 1% increase in bank credit growth implies a 2.61% fall in excess bank
stock returns. The coefficients on bank credit growth are statistically different from zero at the 5% level
or better at all horizons. Bank credit growth explains 13.52% of the variation in excess returns of the BN
index over a 12-month horizon. The value of the adjusted-R2 peaks at 17.78% at the 43-month horizon
(not reported in the Table).
This predictive relationship between bank credit growth and bank stock returns is economically significant for several reasons. First, unlike other forecasting relationships, bank credit growth tracks excess
returns of bank stocks over the business cycle rather than over the very long-run. Second, the predictive
power of bank credit growth for returns of bank stocks is large compared to that of dividend-yield for
returns of the value-weighted stock market index. Cochrane (2008) shows that over 1926 - 2004, a 1%
increase in dividend yield implies that returns of the value-weighted stock market index over the next year
are higher by 3.83%, with an adjusted-R2 of 7.4%. Third, over 1960 - 2017, the standard deviation of
predicted returns is 5.97% and the unconditional equity premium of bank stocks in this sample is 12.33%.
Hence, the expected excess return of bank stocks vary as much as its unconditional mean.
Panel B of Table 2 documents the results of a similar regression for non-financial firms. Non-financial
firms generally do not issue credit. However, if I interpret credit as the total investment by banks, I can
compare the predictive power of bank credit growth for bank stock returns to the predictive power of
aggregate domestic corporate sector investment growth for stock returns of non-financial firms. In panel
10
B, the dependent variable is the H-period log excess returns of an index of all non-financial stocks. To
form this index I take returns for all firms for which data is available in CRSP, but exclude firms with an
SIC code between 6000 - 6799. The predictive variable is gti , the net quarterly year-on-year growth rate
of gross corporate domestic investments. Data for gross domestic investments is from NIPA, Table 5.1
Line 23. In panel C, the dependent variable is the H-period log excess return of an index of each of the
39 industries other than financial firms for which data is available on Kenneth French’s website11 . The
predictive variable is the net quarterly year-on-year growth rate of industry level investment as measured
by the sum of capital expenditures and changes in inventory for all publicly listed firms within a given
industry for which data is available in COMPUSTAT. I run a separate forecasting regression for each of
the 39 industry indices and report the average statistics.
For the index of non-financial firms (panel B), the coefficient on investment growth is negative and
statistically significant at almost all horizons. However, the magnitude of the coefficient is fairly small
as compared to the results in panel A. For the indices of other industries (panel C), the coefficient on
investment growth is never statistically significant. The coefficient on investment growth for these other
industries is nearly 0 and the adjusted-R2 never rises above 2%. Clearly, the relationship between bank
credit growth and bank stock returns is economically significant, both in itself, and as compared to other
similar forecasting relationships. However, one should not interpret the results in panels B and C of Table
2 as an absolute test of a link between expected returns and a firm’s investment and production decisions.
Instead it highlights the relative strength of the relationship between expected returns and investment
and production decisions for banks as compared to that for industrial firms. In the next section I test if
this relationship is robust.
3.2
Are the results robust?
As a first robustness test, I check if this predictability is an artifact of the financial crisis of 2007. Panel
A of Table 3 shows the results for the forecasting regression, but excludes data for the crisis years and
thereafter (2006 - 2017). Dropping the data for the years after the crisis does not significantly effect the
key result. As compared to the full sample, the predictive coefficient on bank credit growth at the 1-year
horizon is actually higher by 0.04% (2.65% versus 2.61%) and the adjusted-R2 at the 12-month horizon
11
Data is actually available for 45 industries other than financial firms. However, to ensure that the results in panels B
and C are comparable to those for banks in Panel A, I exclude all industries for which the time-series of returns does not
start in 1960.
11
improves marginally from 13.52% to 13.86%.
Panel B of Table 3 checks if the predictive relationship arises due to movements in the short-term riskfree rate (rf ) which may either induce a positive or a negative correlation between bank credit growth and
expected bank stock returns. As an example, let the risk free rate, rf increase due to exogenous reasons.
If the equity premium of bank stocks is constant, this increases the expected return of bank stocks. Since
banks borrow in the short-term market and lend for the long-term, a high rf may simultaneously lower the
supply for credit. Therefore movement in rf may induce a positive correlation between the future bank
stock returns of and bank credit growth. While this particular scenario is not too much of a concern (it
biases the estimated coefficients downwards), one can imagine alternate scenarios that induce a negative
correlation between bank credit growth and bank stock returns. This negative correlation may arise easily
if the risk-premium of bank stocks also varies and if the long-term risk-free rate also changes along with rf .
Panel B presents the results for the forecasting regression where the dependent variable is the H-period
log real returns of the BN index. The results for real returns are, if anything, stronger. Over the full
sample, a 1% increase in bank credit growth implies that the real returns of the BN index are lower by
5.01% over a 1-year horizon. This test confirms that the predictability does not arise due to variations in
rf and that the negative correlation between bank credit growth and bank stock returns is fairly robust.
Since there is ample empirical evidence that returns of the stock market, government bonds, currencies,
real estate, and even commodities are predictable by business cycle variables, I next check if bank credit
growth proxies for one of the variables. In Table 4, besides bank credit growth, the independent variables
are the lagged excess return of the BN index (log(1+rBN,t )−log(1+rf,t )), the smoothed dividend-yield of
the BN index (log Dt −log Pt ), the term-spread (y10,t −y3,t ), the log of the 1-month real rate (log(1+rr,t )),
the change in the log 1-month nominal rate (∆rn,t ), the relative bill rate defined as the log of the 1-month
nominal risk-free rate less its 12-month simple moving average (rrel,t ), the default-spread (yBAA,t −yAAA,t),
the year-on-year monthly growth rate of industrial production (log(1 + IP Gt )), and change in leverage
(∆LEVt ). All variables are measured at time t. The exact specification of the regression in Table 4 is:
12
j=H
X
log(1 + rBN,t+j ) −
j=1
j=H
X
log(1 + rf,t+j ) =
j=1
c
g
LR
αH + βH
log(1 + gtc ) + βH
(log(1 + rBN,t ) − log(1 + rf,t ))
DP
TS
r
n
+βH
(log Dt − log Pt ) + βH
(y10,t − y3,t ) + βH
log(1 + rr,t ) + βH
(∆rn,t )
rel
DS
IP G
LEV
+βH
(rrel,t ) + βH
(yBAA,t − yAAA,t) + βH
(log(1 + IP Gt )) + βH
(∆LEVt ) + ǫit+H
(2)
For clarity, Table 4 shows only the results at the 12-month horizon (H = 12) over 1960 - 2017. The
robustness test also goes through for other horizons (H = 1, ..., 11) and also over 1960 - 2005. The first
column indicates the predictive variable and each of the remaining columns corresponds to a separate
regression. In column 2, I only include bank credit growth and the lagged excess return of the BN index
as the predictive variables. In column 3, I only include bank credit growth and the smoothed dividendprice ratio of the BN index as the predictive variables, and so on. The last column shows the results for
the full regression specified in equation (2). The first row shows the estimated coefficient on bank credit
growth for each regression. The remaining rows show the estimated coefficients for each of the other
predictive variables. The last row presents the value for the adjusted-R2 . Note that data for aggregate
assets and liabilities of banks required to compute leverage is available only from 1973. To compute
monthly values of leverage over 1960 - 1972, I use the quarterly data from the U.S. Flow of Funds. Using
this data, I first compute the quarterly leverage for all banks in the U.S. over 1960 - 1972, and then use
a linear interpolation to compute monthly leverage values.
Bank credit growth is not a proxy for any other known predictive variable. The magnitude of the
predictive coefficients on bank credit growth reported in Table 4 is comparable to the univariate regression
over the full sample in Table 2. In some cases, the economic significance of the coefficient on bank credit
growth actually increases, and in all cases is still statistically significant at the 10% level or better. The only
other variables that predict the excess returns of bank stocks at the 12-month horizon are the smoothed
dividend-yield of bank stocks, the term-spread, the default spread, industrial production growth, and the
change in leverage. A 1% increase in smoothed dividend-yield results in a 0.15% increase in the excess
return of bank stocks. The economic magnitude of this coefficient is small as compared to the coefficient
on bank credit growth. Also, the coefficient is only statistically significant at the 5% level when I control
13
for other variables (last column).
The interest rate margin is a crucial measure of profitability for banks and depends on the difference
between the long-term and short-term interest rates. Therefore it is not surprising that the term-spread
determines the future excess returns of bank stocks. A higher term-spread implies higher future profits
either through higher interest earnings or through lower cost of funds, or both. A 1% increase in the
term spread results in a 3.47% (4.42% with other controls) increase in the excess returns of bank stocks
over the next one year. Also, note that none of the short-term interest rate variables that may affect the
bank’s cost of funds, predict the future excess returns of bank stocks.
Not surprisingly, an improvement in macroeconomic conditions measured either by a decrease in the
default spread or an increase in the growth rate of industrial production is associated with lower expected
returns on bank stocks. A 1% decrease in the default spread is associated with -8.62% decrease in the
expected returns of bank stocks over a 1-year horizon. Similarly, a 1% increase in the growth rate of
industrial production implies that expected returns on bank stocks decreases by -0.58% at the 1-year
horizon. While these coefficients are significant in the univariate regressions at the 5% and 1% level,
respectively, they are not statistically significant in the last column, when we control for other forecasting
variables.
Finally, as expected, an increase in leverage implies an increase in expected returns of bank stocks.
A 1% increase in bank leverage implies that future bank returns are higher by 0.16%. This coefficient
is not significant by itself but is statistically significant in the full regression. Controlling for leverage
is crucial because bank credit growth’s predictive power may arise from its negative correlation with
leverage. A negative correlation between bank credit growth and leverage may exist if both bank credit
growth and market value of bank stocks drops in each recession. Since a drop in market value of equity
effectively increases bank leverage, this may explain the observed negative correlation between bank credit
growth and bank stock returns. However, as is clear from Table 4, even after controlling for leverage, the
coefficient on bank credit growth is statistically significant with a magnitude of -2.34%
Independent of the variables in Table 4, Lettau and Ludvigson (2001) and Longstaff and Wang (2008)
respectively show that the log consumption-wealth ratio and the size of the credit market also predict
returns of the stock market index. Controlling for the size of the credit market is especially important
because banks are still one of the largest providers of credit in the U.S. economy12 .
12
As per U.S. Flow of Funds Accounts (December 2009) commercial banks account for $765.40 B of the total $2,854.20 B
lent by the financial sector to the rest of the economy via the credit market. This is second only to the amount lent by issuers
14
The variables defined in Lettau and Ludvigson and Longstaff and Wang are available only at a quarterly
frequency whereas all my previous tests use monthly data. Therefore, I first compute the year-on-year
quarterly growth rate in credit using the raw time-series. I also compute the quarterly returns of the BN
index by compounding the monthly return series. Table 5 shows the results. The exact specification of
the regression is:
j=H
X
log(1 + rBN,t+j ) −
j=1
j=H
X
log(1 + rf,t+j ) =
(3)
j=1
c
g
cay
CR1
αH + βH
log(1 + gtc ) + βH
cayt + βH
Int
CR2 Int
+ βH
+ ǫit+H
Divt
Assets t
The independent variables are bank credit growth, cayt as defined in Lettau and Ludvigson (2001), and
the two proxies for the size of the credit market as defined in Longstaff and Wang (2008). Table 5 shows
the estimated coefficients, statistical significance, and the adjusted-R2 1 to 5 quarters (H = 3 ,6 ,9 ,12 ,15)
after bank credit growth is measured. In all cases the size of the credit market does not predict the excess
returns of bank stocks. Cayt is statistically and economically significant but does not explain away the
predictive power of bank credit growth and its predictive direction is also not the same as that of bank
credit growth. While a 1% increase in cayt implies that log excess returns of bank stocks will be higher
by 4.70% over the next one year, the corresponding value for bank credit growth is -1.48% basis points
and is still statistically significant with a t-statistic of -2.49
To summarize the results so far, bank credit growth predicts lower returns for bank stocks and this
effect is robust to the exclusion of data from the crisis years and to the inclusion of other variables known
to predict returns. In the rest of this paper, I investigate if this predictive variation in bank stock returns
arises due to some inefficiency in financial markets or if it simply reflects the rational response of economic
agents to time-variation in real risk.
3.3
Are investors making systematic errors?
One may attribute the predictive variation in bank stock returns to investor overreaction or to bank
management’s rational response to time-variation in expected returns. Investor overreaction can explain
of asset backed securities ($799.80 B). Thus banks account for nearly 27% of the funds lent in the credit market.
15
my results if optimistic investors first overestimate the impact of new loans on future bank cash flows
and drive up the current price of bank stocks (higher initial returns), and then subsequently correct their
beliefs, so that future bank stock prices fall (lower future returns). Arguably, investors should learn and
not make such systematic errors over time so that these effects would not persist.
Table 6 shows the results for the forecasting regression for nominal excess returns and real returns of
bank stocks over 1990 - 2017. Now, a 1% increase in bank credit growth implies a 2.25% fall in nominal
excess returns of bank stocks over the next one year. This is just 0.86 times the estimated coefficient over
the full sample. Variation in bank credit growth still explains nearly 6% of the variation in returns of
bank stocks over the next one year. A similar result is obtained for real returns.
Another reason that the predictive variation in bank stock returns cannot be attributed to investor
overreaction is that the predictive power, as measured by the absolute value of the coefficient and the
adjusted-R2 at the 1-year horizon, increases monotonically in leverage and the proportion of short-term
debt employed by the bank.13 . Investor errors can co-vary negatively with size if better financial information is available for large banks or if investors pay more attention to large banks. However, it is not clear
why investor errors should also increase in leverage or short-term debt. I explore this in further detail in
sub-section 3.6.
3.4
Do managers respond rationally to time-variation in expected returns?
Alternatively, one may also attribute the predictive variation in bank stock returns to bank management’s
rational response to time-variation in expected returns. If the negative correlation between bank credit
growth and bank stock returns is due to time-variation in expected returns linked to standard risk factors,
then bank credit growth should be correlated with the loadings of bank stock returns on these standard
risk factors, or with changes in aggregate risk-premium, or both.
To check bank credit growth’s correlation with the conditional loadings of bank stock returns on
systematic risk factors, I compute 5-year rolling betas of bank stock returns on the three Fama-French
factors - M KT , SM B, and HM L - and two bond market factors - the excess return of an index of longterm government bonds (LT G) and the excess return of an index of investment-grade corporate bonds
(CRD). A negative correlation with these factor loadings implies that a higher credit growth lowers the
exposure of banks stock returns to standard risk factors and hence the expected return required by the
13
See section 3.6 below.
16
average investor to hold bank stock falls. The drop in expected returns equals the change in loadings
times the risk premium of these risk factors.
Figure 4 plots the results. In each panel, the solid line plots bank credit growth and the dashed line
corresponds to one of the five risk factors mentioned above. The left panel in the top row depicts the
correlation between bank credit growth and the 5-year rolling beta on M KT . Each of the remaining
panels show the 5-year rolling betas of bank stocks on SM B, HM L, LT G and CRD respectively.
Over the full sample bank credit growth has a statistically significant correlation with M KT (-0.11,
p < 0.01), HM L (0.11, p < 0.01), and LT G (0.18, p < 0.01). It’s correlation with SM B (-0.02, p = 0.60)
and CRD (0.04, p = 0.30) is statistically not significant. The annual risk price for these factors in the
sample expressed in percentages is respectively:
[6.10 2.36 4.53 2.29 3.96]
This implies that a 1% increase in credit growth results in an increase of nearly 5.56% in the expected
returns of bank stock as measured by the loadings on the standard risk factors mentioned above. This 6%
increase reflects the correlation between bank credit growth and rolling betas times the risk price divided
by the variance of credit growth. These results are robust to the window over which the rolling betas are
estimated and to sub-sample selection.
These results should be interpreted carefully as the loadings of bank stock returns on the standard
risk factors may not have the same interpretation as that for industrial firms. This is because unlike
industrial firms, high leverage for banks does not necessarily imply distress. Further, changes in loadings
of bank stock returns on M KT may simply be related to changes in leverage and may not imply a change
in exposure to systematic risk. However, variation in exposure to standard risk factors is still unlikely to
explain the predictive variation of bank stock returns given that bank credit growth predicts returns even
after I control for the change in leverage.
While bank credit growth has low correlation with loadings of bank stock returns on standard risk
factors, it could still be correlated with changes in the aggregate price of risk. Indeed, the aggregate price
of risk varies over the business cycle. In Section 2 I have already established that bank credit growth
is correlated with business cycle variables. If predictive variation in bank stock returns arises entirely
due to credit growth’s correlation with changes in the aggregate price of risk, I should find that credit
17
growth’s predictive power for other assets, as measured by the absolute magnitude of the coefficient on
credit growth and the adjusted-R2 at the 1-year horizon, should be comparable to that for banks.
The results in Table 4 reassure us that the predictive variation in bank stock returns does not arise
solely from bank credit growth’s correlation with business cycle variables (hence with aggregate price of
risk). Still, in order to estimate what proportion of the predictive variation in bank stock returns may
arise from this correlation, I use bank credit growth to predict returns of a control set of assets other than
banks. Table 7 presents the results. Each column in Table 7 refers to a separate forecasting regression
for a distinct asset class. In the first column, titled T B, the dependent variable is the log excess return
for an index of treasury bonds, CB is the log excess return of an index of investment-grade corporate
bonds, and OI is the log excess return of an index of other industries. Table 7 shows the results only
for H = 12, although results at other horizons are similar. In each case, I also control for all other
business cycle variables shown in Table 4. Data for government and corporate bonds is from GFD14 . To
obtain estimates for the forecasting regression OI, I again run the forecasting regression for each of the
39 industries other than financial intermediaries for which data is available on Kenneth French’s website.
The estimates in the column are average statistics for all 39 industry indices.
The coefficient on bank credit growth at the 12-month horizon is not statistically significant. The
coefficient is at most -0.97 or nearly one-third of its value for bank stock returns. The average coefficient
at the 12-month horizon for the 39 other industries is only -0.97% and is not statistically significant. Of the
39 individual coefficients on bank credit growth at the 12-month horizon, only 3 are statistically significant
at the 5% level or better. In fact, 6 of the coefficients are positive. Thus the relationship between bank
credit growth and the stock returns of intermediaries is not mechanical, and is indeed special for bank
stock returns. A similar regression over 1990 - 2017, for these asset classes shows that the coefficients on
bank credit growth are not economically or statistically significant at any horizon and the values of the
adjusted-R2 ’s are small. Note that in Table 7 for corporate bonds, the high adjusted-R2 of 31.49% reflects
the fact that I include the term-spread and the default spread as predictor variables.
In Table 8 I check if bank credit growth predicts returns for an index of non-financial stocks or an
index of non-financial stocks sorted by various characteristics. For the results in this Table the dependent
variables are the H−period log excess returns of a value-weighted index of non-financial stocks (M KT ), a
value-weighted index of non-financial stocks sorted by size (M KT − SM ), a value-weighted index of non14
On Global Financial Data (GFD) these assets are identified by the item codes TRUSG2M and TRUSACOM respectively.
18
financial stocks sorted by leverage (M KT −HL), and a value-weighted index of non-financial stocks sorted
by short-term debt (M KT − ST ). I have already described the construction of M KT in section 3.1 above.
To form M KT − SM , I sort non-financial firms into 5 portfolios by market capitalization and analyze the
returns for the smallest firms. To form M KT −HL, I sort all non-financial firms into 2 portfolios by assets
and leverage, and analyze firms with highest leverage and smallest market capitalization. Finally, Table 8
also presents the results for an index of non-financial stocks sorted by the proportion of short-term debt.
Again, I restrict myself to the portfolio of non-financial firms with the highest proportion of short-term
debt and the smallest market capitalization. In all cases the coefficient on bank credit growth is negative.
A 1% increase in bank credit growth implies that the returns on an index of nonfinancial firms will be
lower by just 1.42% over the next one year. The coefficient is statistically significant at the 5% level and
bank credit growth explains nearly 11.32% of the returns on the market index. However, bank credit
growth correlates much more strongly with returns for nonfinancial firms with high leverage. The column
titled ‘MKT-HL’ shows that an 1% increase in bank credit growth implies returns for nonfinancial firms
with high leverage is lower by 1.66% over the next one year. The adjusted R2 for this regression is nearly
21%. Thus, bank credit growth appears to have an independent predictive power, outside of its correlation
with other business cycle variables, to predict the returns nonfinancial firms with high leverage, which
may also have high exposure to tail risk.
Finally, I check if predictive variation in returns arises due to variation in some risk that is specific to
financial intermediaries and the kind of projects they fund. I test if bank credit growth predicts the returns
of a value-weighted index of bank-dependent firms and a value-weighted index of investment banks. As
in Kashyap, Lamont, and Stein (1994), I use the absence of a public-debt rating as the proxy for bankdependence. Data for public-debt rating is available from COMPUSTAT only from 1980, so regression
results for this index are reported for 1980 - 2017. I have already described the construction of the index
of financial trading firms, F T , in section 2 above. The results for investment banks is over the full sample,
1960 - 2017.
Each column in Table 9 represents a separate forecasting regression for a distinct index. In the first
column the dependent variable is an index of all bank-dependent firms and in the second column the
dependent variable is index of bank-dependent firms with the smallest market capitalization. The third
and fourth column present the results for bank-dependent firms sorted by leverage and the proportion of
short-term debt. For analysis of portfolios sorted by leverage and short-term debt, as above, I restrict
19
myself to portfolios with the highest leverage or short-term debt and the smallest market capitalization.
The last column presents the results for financial trading firms. To ensure that the results are comparable
to those presented earlier, in each case I control for other business cycle variables.
While credit growth does not predict returns for all bank-dependent firms or for bank-dependent firms
sorted by leverage or short-term debt, it does predicts returns for bank-dependent firms with small market
capitalization. In fact, for small bank-dependent firms the coefficient is almost as large as that for banks,
and the value of the adjusted-R2 is 15.17%. Credit growth also predicts returns of financial intermediaries
other than banks. Over a 12-month horizon, a 1% increase in bank credit growth predicts a 1.53% fall
in excess returns of F T and the adjusted-R2 is 7.29%. The coefficients and the adjusted-R2 are smaller
as compared to those for bank stock returns. This may be due to the fact that bank credit growth is
constructed using only data from banks and does not include any balance sheet data from financial trading
firms. Credit growth also predicts the excess stock returns of size-sorted portfolios of F T and all other
robustness tests performed for bank stocks also go through for investment banks.
Figure 5 suggests the possible source of the predictive power of bank credit growth for the returns
of the F T index. The Figure plots the growth rates of credit market assets held by banks and financial
trading firms. Data is from the U.S. Flow of Funds Accounts, Table L.1, Lines 33-57. I use quarterly,
non-seasonally adjusted data over 1960-2017. Banks include data for commercial banks (line 35), savings institutions (line 40), and credit unions (line 41) and financial trading firms include data for ABS
issuers (line 53), finance companies (line 54), REITS (line 55), brokers and dealers (line 56), and funding
corporations (line 57).
The solid and dashed lines plot the quarterly year-on-year growth rate of credit market assets of banks
and financial trading firms. Over 1960 - 2005, the correlation between the credit market asset growth
rates is 55.13% (p < 0.01). This suggests a possible reason why bank credit growth also predicts the stock
returns of investment banks – factors that cause banks to vary the supply of credit may also be at work
in financial trading firms.
This evidence that bank credit growth predicts returns of banks, financial trading firms, and bankdependent firms but does not have an independent effect, outside of its correlation with business cycle
variables, on the returns of an index of treasury bonds, investment-grade corporate bonds, non-financial
stocks, and non-financial stocks with highest leverage, suggests that the predictive variation in bank stock
returns arises due to investor’s rational response to time-variation in risk that is specific to the assets
20
typically funded by intermediaries. However, the exact nature of this risk is not entirely clear. I next turn
my attention to this question.
3.5
So what is exact nature of the risk?
In this section I argue that the predictive variation in returns reflects the representative agent’s rational
response to a small time-varying probability of a tail event. The nature of the tail event is such that its
realization results in a large loss of cash flows from projects typically funded by bank loans. However, it
may not impact other projects in the economy. Examples of such events (included in the sample) are the
Less Developed Country Debt crisis of 1982, the Mexico crisis of 1994, the East Asian crisis of 1997, and
the Long-Term-Capital-Management crisis of 1998. Each of these events resulted in large losses of cash
flows from projects funded by banks and a significant drop in the profitability and valuation of banks in
the U.S. However, there was no measurable effect on the performance of other projects in the economy.
These crises were not accompanied by recessions, and other important asset markets such as the stock
and housing market, were also relatively unaffected.
Bank management is very sensitive to changes in the probability of a tail event. This high sensitivity
arises from the balance sheet characteristics of banks. Table 10 compares balance sheet characteristics of
publicly listed financial and non-financial firms. Data is from COMPUSTAT over 1960 - 2017. Note that
an average financial firm employs nearly 500% more leverage than an average non-financial firm and has
only 2% of its book value in tangible assets. The market value of the remaining 98% of assets, typically
invested in loans, is difficult to compute. Also a financial firm relies nearly 350% more on short-term debt
than the average non-financial firm. Finally, financial firms can be subject to runs by both depositors
and creditors. Regulators believe that these characteristics combined with the very possibility of a tail
event provide the justification for the special treatment and close regulation of banks and other financial
intermediaries.
That financial firms are more sensitive to tail events is clear from Figure 6. This Figure plots the
monthly rate at which firms ’delist’ from the CRSP data-set by counting the number of delistings in each
month as a fraction of total firms for which returns are available on the CRSP data-set at the beginning of
the year. Only delistings due to liquidation (delisting code = 400) are included in the count. A separate
delisting rate is computed for financial and non-financial firms.
The solid line plots the delisting rate for financial firms and the dashed line plots the delisting rate for
21
non-financial firms. Note that the delisting rate for financial firms increases in almost all recessions and
financial crises while the delisting rate for non-financial firms mostly remains flat or in some cases actually
falls. The baseline rate at which financial firms delist due to liquidation is also nearly 4 times higher than
the delisting rate for non-financial firms. Figure 6 may actually underreport the ’true’ liquidation rate
for financial firms but not so for non-financial firms. This is because regulators often do not allow large,
troubled, financial firms to liquidate, but oversee their acquisition. For example, FDIC reports that 256
banks have failed in the recent crisis since the failure of IndyMac. All of these banks are small by most
standards. Including such ’mergers’ actually increases the ’liquidation rate’ for financial firms.
Since banks are more sensitive to the probability of a tail event, an increase in the likelihood of a large
loss of cash flows implies that projects with lower expected cash flows are not approved for bank funding.
This effectively raises the discount rate used by the bank to evaluate projects, thereby contracting the
supply of credit. Simultaneously, the discount rate used by the bank’s customers (the project manager) to
evaluate projects also increases in the probability of a tail event. In fact many projects with low expected
cash flows that would customarily be taken to the bank for evaluation, may be rejected at the outset by
the project manager herself. This contracts the demand for credit. My mechanism does not require that
I distinguish supply from demand contraction, only that both supply and demand of credit be negatively
correlated with the probability of a tail event - a reasonable assumption. The actual fall in credit level
attributed to supply or demand contraction may differ in each business cycle and may depend on the
financial condition of the bank, the balance sheet strength of the project manager, and the exact nature
of the tail event.
The standard disaster-risk asset pricing framework also implies that the expected returns required
by the average investor to hold bank stocks and any stock in the projects themselves increase in the
probability of the tail risk. This explains the observed negative correlation between credit growth and
expected returns of bank stocks and between credit growth and expected returns of bank-dependent firms
and financial trading firms.
Note that the predictive power of bank credit growth, measured by the absolute value of the predictive
coefficient and the adjusted-R2 at the 12-month horizon, is stronger for banks than for bank-dependent
firms as banks are more exposed to tail risk given their balance sheet characteristics. Further, the negative
correlation between bank credit growth and returns of projects typically funded by banks may be difficult
to empirically detect in the data. This is because in a typical bank’s loan portfolio, projects with most
22
exposure to tail risk include firms that are informationally-difficult, illiquid, and with no alternative
sources of funds. It is unlikely that this subset includes many publicly listed firms, if at all. However,
publicly listed firms are the only kind for which any stock return data is available. As Kashyap, Stein, and
Wilcox (1993) and Chava and Purnanandam (2011) show, it is easy for publicly listed bank-dependent
firms to replace bank credit with alternate sources of credit at times of aggregate economic shock. So the
results in Table 9 may actually understate the impact of bank credit growth on bank-dependent firms and
bank-funded projects.
One implication of this hypothesis is that banks actively fund projects with higher exposure to tail
risk. Measuring the exact loadings of a typical bank’s loan portfolio to tail risk is difficult to assess
without loan-level cash-flow data. Still, I argue that not only do banks actively fund projects with
higher exposure to tail risk, but also that the proportion of such funding has been increasing over time.
First, several papers, beginning with (Diamond, 1984, 1991), Ramakrishnan and Thakor (1984), Fama
(1985), and Boyd and Prescott (1986), develop theoretical models for banks. A common theme is that
banks specialize in extending credit to illiquid, informationally opaque borrowers that cannot (or will not)
access public financial markets. In these models a bank is the only economic entity that possesses the
experience to lend and periodically monitor to such borrowers. It is reasonable to argue that such illiquid,
informationally dense borrowers are more exposed to tail risk. The fact that the market value of these
loans is difficult to compute, as shown in OHara (1993), Berger, King, and O’Brien (1991), Lucas and
McDonald (1987), and Pennacchi (1988) lends further support to this hypothesis.
Second, Boyd and Gertler (1993) show that banks actively seek exposure to projects with tail risk due
to competition from foreign banks, growth of non-bank intermediation, and asset markets (commercial
paper market and securitization). This has forced banks to move into riskier, less-liquid assets over time.
McCauley, Seth et al. (1992), Berger, Klapper, and Udell (2001), and Berger, Klapper, Martinez Peria, and
Zaidi (2008) analyze the impact of foreign banks on bank lending. They find that, in many cases, foreign
banks are subject to lower reserve requirements and can offer better terms of credit. This mainly attracts
larger, better rated, transparent, and older firms away from domestic banks who replace these customers
with even more informationally opaque firms. A similar mechanism is at work due to competition from
non-bank intermediaries and the commercial paper market. Thus, it is reasonable to conclude that the
U.S. banks loan portfolios are more sensitive to tail risk.
Boyd and Gertler (1993) also show that securitization has moved high-quality assets off the bank’s
23
balance sheet. This is true in their sample (1954 - 1990). They argue that securitization, without recourse
to the bank’s balance sheet, succeeds only if loans that are securitized are liquid enough to be sold on the
secondary market. Over 1954 - 1990, at most 30% of a bank’s loan portfolio was securitized. This implies
that most of the loans on a bank’s balance sheet were not sufficiently liquid to be sold on the secondary
market and the risk of such assets was retained by the bank15 .
Finally, regulators certainly believe that exposure of a bank’s balance sheet to such tail risk is not
merely hypothetical. Regulators contend that opaqueness in a bank’s balance sheet assets justifies the
special treatment of banks. Regulators believe that in the event of a fire sale, a bank may not realize
the full value of its portfolio due its illiquidity and opaqueness. This justifies deposit insurance schemes,
operation of a discount window, and the role of lenders of last resort - policies deemed essential to provide
comfort to market participants and avoid bank runs in case a tail event is realized.
Having proposed an entirely plausible driver of the predictive variation in returns, the next section
presents actual evidence that links the predictive variation in returns to tail risk.
3.6
Is predictive variation in returns actually linked to time-variation in tail risk?
If variation in the probability of a tail event causes the negative correlation between bank credit growth and
the expected returns of bank stocks then the predictive power should depend systematically on variables
known to regulate a bank’s exposure to tail risk. Here, predictive power is measured by the absolute
magnitude of the coefficient on gtc and the adjusted-R2 at the 12-month horizon.
In this section, I focus on three such variables - size, leverage, and the proportion of short-term debt.
A priori, I expect that the predictive power monotonically decreases in size. Large banks should have a
lower exposure to tail risk not only because they benefit from an implicit government guarantee but also
because they are well-diversified and have more assets. A similar argument applies to banks with a higher
leverage or a higher proportion of short-term debt. Banks with higher leverage or short-term debt should
be more exposed to tail risk. Hence, predictive power should decrease in size, and increase in leverage
and the proportion of short-term debt. This is confirmed in Tables 11, 12, and 13.
Each panel in Table 11 shows the results for the forecasting regression for each of the 5 size-sorted
portfolios of bank stocks. Here size is measured by market capitalization. The number ’1’ in the first
column refers to the smallest size-sorted portfolio. While bank credit growth predicts negative excess
15
This analysis may not apply to recent run up in bank lending and securitization of sub-prime mortgages. However as I
show in section 3.2 above, my results hold even if this period is excluded from the analysis.
24
returns for all size-sorted portfolios of bank stocks, the magnitude of the coefficient and the adjusted-R2
at the 12-month horizon monotonically decreases in size. A 1% increase in bank credit growth predicts a
2.83% decrease in the excess returns of the smallest banks and explains 13.55% of the variation in excess
returns over the next 12 months. The corresponding numbers for the largest banks are 1.91% and 8.39%
respectively.
To form portfolios sorted by other characteristics, such as leverage or the proportion of short-term
debt, I require bank-level balance sheet data. The Chicago Fed collects balance sheet data for all bank
holding companies (BHC) in the U.S. at a quarterly frequency via the mandatory Call Report required
to be filed by all FDIC-insured institutions. The data is available online at http://www.chicagofed.
org/webpages/banking/financial_institution_reports/bhc_data.cfm. The data is available from 1986
for approximately 5,396 BHCs over 124 quarters (669,104 observations). The Call Report identifies BHCs
using a unique ’Entity Code’ that is mapped to the ’Permco’ in CRSP for all publicly traded banks. This
mapping is available only for BHCs and not for individual banks16 .
I keep data for total balance sheet assets (item BHCK2170), total balance sheet liabilities (item
BHCK2948), total short-term debt (BHCK 2309, 2332, and 3298). I delete observations where total
assets equal zero or is missing. I eliminate BHCs for which a matching Permco is either not available or
for which the observation date lies outside the valid mapping dates. I also delete observations for BHCs
that are inactive or for which stock return data is not available for each of the 5 subsequent quarters
relative to its observation date in the Call Report. At the end of this exercise, I am left with 22,219
observations (179 BHCs each year). This is typical of studies that use this data-set17 .
To form portfolios, I employ the standard portfolio formation strategy of Fama and French (1993).
For example, to construct the portfolios sorted by leverage, in December of each year, I rank all BHCs
by leverage and market capitalization. BHCs for which 4th quarter data is missing are simply dropped
from the analysis for the subsequent year. A newly-listed BHC is not considered until the following year.
I assign the BHCs to 4 portfolios based on leverage and market capitalization and compute the valueweighted return of each portfolio for each month over the next year. I use a double-sort based on the
balance sheet characteristic and market capitalization as size, leverage, and the proportion of short-term
debt employed by the bank may be correlated. This results in the monthly value-weighted returns of each
16
The mapping is available at http://www.newyorkfed.org/research/banking_research/datasets.html and
also includes the dates for which the mapping is valid.
17
For example Cooper, III, and Pattersonc (2003) use data for 213 BHCs over 1986 - 2003.
25
of the 4 portfolios sorted by leverage from January 1987 to December 2017. Data for BHCs is available
from 1986. I reserve one year for lags and hence my data begins only in January 1987.
The results for portfolios sorted by leverage and market capitalization are in Table 12. ’Small’ and
’large’ refer to size as measured by market capitalization and ’low’ and ’high’ refer to leverage. Table 12
presents the results only for H = 12 months. Results at all other horizons are similar. For small banks,
fixing market capitalization but increasing leverage nearly doubles the predictive coefficient on gtc at the
12-month horizon, from -1.58 to -2.44. Similarly, for large banks the predictive coefficient on gtc increases
from -0.87 to -1.66. Keeping leverage constant, but increasing market capitalization decreases the absolute
magnitude of the predictive coefficient on gtc and the value of the adjusted-R2 at the 12-month horizon for
both banks with low and high leverage. For banks with low leverage the coefficient on gtc declines from
-1.58 to -087. The latter is not statistically significant. The value of the adjusted-R2 also declines from
8.95% to just 2.21%. Note that the predictive power is the strongest for small banks with high leverage.
For these banks gtc explains 17.48% of the variation in excess returns over a 1-year horizon.
Finally, the results for portfolios double-sorted by market capitalization and short-term debt are in
Table 13. Portfolio ’small-low’ refers to banks with the lowest market capitalization and lowest proportion
of short-term debt. Portfolio ’large-high’ refers to banks with the highest market capitalization and highest
proportion of short-term debt. Table 13 shows the results only for H = 12 months. As was the case with
leverage, fixing assets and increasing short-term debt increases the coefficient on gtc , while keeping shortterm debt fixed and increasing assets lowers both the coefficient on gtc and the value of the adjusted-R2 .
In this case the effect is strongest for banks with low market capitalization and the highest proportion of
short-term debt for whom bank credit growth explains nearly 12.06% of the variation in returns over a
1-year horizon.
Thus the predictive power of bank credit growth decreases in size, measured by market capitalization,
increases in leverage, and increases in the proportion of short-term debt employed by the bank. But are
these characteristics really linked to higher proportion of tail risk? To address this, Table 14 shows that
the higher predictive power of bank credit growth for small banks, banks with more leverage, or more
short-term debt, is due to the fact that banks with these characteristics are more exposed to tail risk.
Table 14 reports the estimates for the following regression:
ri,t+j − rf,t+j = αi + β1V IX ∆V IXt+j + β2V IX Dt+j ∆V IXt+j + ǫit+H
26
(4)
Here ri,t+j is the return for size-sorted portfolio of banks and non-financial firms at time t + j, rf,t+j
is the return of the risk-free asset at time t + j, ∆V IXt+j is change in the daily implied volatility of the
index options on the S&P500 (V IX) at time t, and Dt+j is a dummy variable that equals 1 if a date lies
within the start and end dates of a recession or a financial crises and equals zero otherwise. Dates for
recessions and financial crisis are same as the ones used in section 2.
In this regression, β1V IX measures the sensitivity of asset i to changes in V IX and β2V IX measures
the increase in this sensitivity in a recession or a financial crisis. The first panel shows the results for
the size-sorted portfolios of banks and the second panel shows the results for the size-sorted portfolios
of non-financial firms. The last column reports the percentage increase in this coefficient in economic
β V IX
recessions ( β2V IX ).
1
Note that the absolute magnitude of the coefficient, β1V IX is higher for large banks and large nonfinancial firms. This may be simply on account of the fact that the V IX represents the implied volatility of
index options on the S&P500 and this would typically include large financial and non-financial firms. One
may also attribute the higher coefficient for small non-financial firms to the fact that these firms usually
have a lower market capitalization than the smallest banks in my sample. The appropriate benchmark for
the increase in sensitivity to tail risk should then be the percentage increase in the coefficient in economic
recessions and financial crises.
Note that only the coefficient, β2V IX , for large and small banks is statistically different from the
coefficient, β1V IX , in economic recessions and crises. For small banks the coefficient on changes in V IX
increases nearly by 100% and this difference is statistically significant. The coefficient for large banks
also increases, but only by 25% and this difference is statistically significant at only the 10% level. The
coefficient for both small and large non-financial firms does not change in times of distress.
A final argument in favor of variation in tail risk explaining the predictive variation in returns is that
bank credit growth also does not significantly predict the cash flows of the bank stocks as measured by
the dividend growth rates. In a forecasting regression of bank credit growth on future dividend growth
rates The coefficient is never statistically significant. Its maximum magnitude over a one year horizon
is 0.02, the adjusted-R2 reaches a maximum value of 0.54% in 8-months, and the sign of the coefficient
is also not stable. This again argues against the predictive variation in returns arising from variation in
sources of risk other than tail risk. This is because standard cash flow risk would show up in the sample
and hence I should also be able to predict future cash flow growth in the sample.
27
3.7
Results from the cross-section
The last set of results I present relate to cross-sectional tests using data for individual bank holding
companies. Generally, it is difficult to conduct cross-sectional tests of disaster models as a particular
firm’s exposure to tail risk is not readily measurable. However in this case, actions by banks themselves
may indicate their exposure to tail risk. When the likelihood of a disaster decreases, banks with the
highest exposure to tail risk increase credit supply the most and consequently experience the largest drop
in the expected equity premium.
For this, I again require bank-level balance sheet data from the Chicago FED. In each quarter, I sort
BHCs into 5 portfolios based on credit growth at the BHC level. Credit growth is defined as the log change
in total loans outstanding over 1 quarter. BHCs with the lowest credit growth are sorted in portfolio 1
and those with the highest credit growth are sorted in portfolio 5. I compute the average return of each
portfolio over 1 year from the portfolio formation date.
Recall from the results in section 3.1 that banks with the largest drop in today’s expected returns
should have the highest contemporaneous realized excess return and the lowest subsequent excess returns.
That is, the results should be analogous to the cross-correlation diagram in Figure 3.
In Figure 7, each bar corresponds to the mean return of portfolio 5 relative to the mean return of
portfolio 1. The first bar plots this relative mean return one quarter after the portfolios are formed. The
statistics are annualized by multiplying by 4 and are expressed in percentages. On the x-axis Q1 − Q4
refer to the quarter after portfolios are formed and the figures in parentheses refer to the t-stats.
Clearly, banks with the highest credit growth initially outperform banks with the lowest credit growth.
The contemporaneous mean return of high credit growth BHCs exceeds that of low credit growth BHCs
by 450 basis point. This difference is statistically significant at the 1% level. However, this soon reverses
and by quarter 4 high credit growth portfolios slightly underperform low credit growth portfolios although
the difference is not statistically significant.
Table 15 formalizes the relationship between credit growth and equity returns of individual banks.
The dependent variable is the log excess return of each BHC measured at t + H. H = 0 refers to the
quarter in which credit growth is measured and H = 1 − 5 refer to each of the subsequent 5 quarters. The
independent variable are the log change in total loans outstanding, log change in book to market, and the
log change in leverage over 1 quarter .
28
In Table 15 the first row shows the intercept, and rows two to four present the estimated coefficient on
credit growth, leverage change, and book to market change respectively. The intercept and the coefficient
are multiplied by 100 and expressed in percentage. The third row presents the value for the adjusted-R2
for each horizon. The last row shows the number of observations. For each regression I include both
quarter and BHC fixed effects. The data is quarterly from 1987 - 2017. The standard errors are adjusted
for heteroscedasticity and autocorrelation using Newey-West with 4 lags.
A 1% increase in credit growth at the BHC level implies that contemporaneous returns are higher by
13.58%. The magnitude of the coefficient declines monotonically with lags and eventually reverses so that
the coefficient for quarter ’5’ is -5.99% (statistically significant at the 1% level).
4
Related literature
Closest to my analysis is the seminal work by Baron and Xiong (2017) and Schularick and Taylor (2012),
who analyze the relation between bank credit, macroeconomic conditions, and equity markets in more than
46 countries over more than 100 years. My study using just data for the U.S. complements the analysis
in Baron and Xiong (2017) well. It is important to study the relation between bank credit growth and
bank stock returns only in the U.S. Calomiris and Gorton (1991) show that regulation and institutional
structures, which differ across countries, can affect the relation between bank credit growth and financial
panics. An institutional feature that should be of particular concern is government ownership of banks.
Porta, Lopez-de Silanes, Shleifer, and Vishny (2002) show that government ownership of banks is pervasive
in many emerging and developed countries. Such government-ownership of banks politicizes the credit
allocation process and can distort the relation between bank credit and macroeconomic conditions. For
example, if macroeconomic conditions worsen, governments can pressurize banks they own or control
to increase rather than contract credit. The U.S., with no substantial government ownership of banks,
provides a near ideal setting and for this reason a study analyzing just U.S. data can be important and
informative.
This paper also relates to three other distinct strands of the literature. First, my work relates to
standard neoclassical models (such as Rietz (1988), Barro (2006), Wachter (2013), and Gabaix (2011))
as well as classic models of investment behavior (such as Zhang (2017), Lin and Zhang (2013), Abel and
Blanchard (1986), and Abel (1983)). In these models, exogenous variation in the probability of a disaster
causes discount rates to vary over time. Discount rates are correlated with business conditions, and are
29
higher at business cycle troughs than at peaks. Because these discount rates in part determine the cost
of capital used to evaluate projects, an improvement in macroeconomic conditions that reduces discount
rates will increase the optimal rate of investment. Yet empirically, a relation between investment growth
and equity returns is barely apparent for nonfinancial firms in aggregate data. Lamont (2000) shows that
this may be because of short-term lags between investment decisions and investment expenditures. For
banks, the relation between credit (investment) growth and bank stock returns is quite evident. Thus, one
implication of my work is that while time lags inherent in planning, delivery, or construction can shift the
correlations between investment growth and returns for nonfinancial firms, these may be less of an issue
for banks, which can immediately adjust their credit decisions to respond to changes in macroeconomic
conditions.
Second, my paper relates to the growing literature that aims to bridge the gap between time-varying
macroeconomic risk and corporate finance. In classical models of corporate finance, firms use the N P V
rule to evaluate investment decisions, discounting projected cash flows at discount rates that appropriately
reflect the risk of the project, investing when the present value of cash flows is positive. However, a growing
literature shows that deviations from the first best (N P V rule) can arise, when macroeconomic risk and
agency problems vary over time. Mello and Parsons (1992), Mauer and Triantis (1994), Leland (1998),
Mauer and Ott (2000), Décamps and Faure-Grimaud (2002), Hennessy (2004), Manso (2008), Morellec
and Schürhoff (2009), Hackbarth and Mauer (2011), and Sundaresan and Wang (2015) among others
study investment decisions of firms under distortions produced by time-varying macroeconomic risk and
debt financing.
Finally, my research also contributes to a growing literature on the relation between equity markets
and balance sheet values of financial intermediaries. Adrian, Etula, and Muir (2014) and Adrian, Moench,
and Shin (2010) provide theoretical and empirical evidence that intermediary book leverage is a priced risk
factor in equity returns. Baron and Xiong (2017) show that an increase in bank credit growth predicts
lower mean equity returns over the subsequent two years, but they do not investigate whether such a
relation varies in the cross-section with bank characteristics.
5
Conclusion
In this paper, I study the empirical linkage between the aggregate bank credit growth and the expected
returns of bank stocks and document that credit growth and the expected returns of bank stocks are
30
negatively correlated. A 1% increase in credit growth implies that excess returns of bank stocks are lower
by nearly 3% over the next year. The annual adjusted-R2 of this regression is nearly 14%. Unlike most
other variables, credit growth tracks bank stock returns over the short-term to intermediate horizon and
its effect is robust to the exclusion of data from the crisis years and to the inclusion of several popular
forecasting variables used in the literature. Credit growth also does not predict future cash flows of bank
stocks as measured by dividend growth rates. Credit growth has limited predictive power for stock returns
of bank-dependent firms but does not predict returns for any other asset class.
I present evidence consistent with the fact that this predictive variation in returns reflects investors’
rational response to a small time-varying probability of a tail event. Realization of a tail event results in
a large loss of cash flows from projects funded by bank loans. Bank management is rational, but very
sensitive to changes in the probability of a tail event. This high sensitivity arises from higher leverage,
shorter debt maturity structure, and lower fraction of tangible assets that characterize the balance sheet
of a typical bank.
These characteristics also increase the likelihood that a bank will enter distress upon the realization
of a tail event. Hence, as the probability of a tail event increases, projects with lower expected cash flows
are rejected by the bank. This effectively raises the discount rate used by the bank to evaluate projects,
thereby contracting the supply of credit. In fact, many projects with low expected cash flows that would
customarily be taken to the bank for evaluation may be rejected at the outset by the project manager
herself, thereby contracting the demand for credit.
The standard disaster-risk asset pricing framework also implies that the expected returns required
by the average investor to hold bank stocks increases in the probability of a tail event. This drives
the observed negative correlation between credit growth and the expected returns of bank stocks. The
disaster-risk framework also explains the fact that credit growth predicts the excess returns of investment
banks (financial trading firms). This is because tail events that impact cash flows of bank-funded projects
may also affect cash flows of projects funded by other intermediaries.
Consistent with this hypothesis, the predictive power, as measured by the absolute magnitude of the
coefficient on credit growth and the adjusted-R2 at the the 1-year horizon, depends systematically on
variables that regulate exposure to tail risk. Predictive power decreases monotonically in size, increases
in leverage, and increases in the proportion of short-term debt employed by the bank. This is because
small banks, banks with higher leverage, or higher proportion of short-term debt, are more exposed
31
to tail risk. I also rule out that the predictive variation in returns reflects investor overreaction or
bank management’s rational response to sources of variation in expected returns other than tail risk.
Historically, the probability of a tail event increases in a recession, therefore the proposed mechanism also
explains the observed correlation between changes in bank credit levels and business conditions.
32
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37
Appendix
The Relationship between Credit Growth and the Expected Returns of Bank Stocks
A
Additional summary statistics
Table A1 presents a brief description of the various types of financial intermediaries and the types of
businesses they are engaged in. Table A2 presents the summary statistics for the value-weighted index
of equity returns of investment banks (F T ). Table A3 presents the summary statistics for portfolios of
banks and investment banks sorted by market capitalization. Table A4 shows the summary statistics for
the various categories of loans that comprise bank credit.
B
Additional results
Table A5 shows the results for the typical forecasting regression using quantile regressions with bank
credit growth of all commercial banks in the U.S. as the predictive variable.
38
Aggregate credit growth (gct)
90
0.1
80
70
0.05
60
50
0
40
Industrial production (2007 = 100)
100
0.15
30
-0.05
May60
Oct65
Apr71
Oct76
Mar82
Sep87
Mar93
Sep98
Feb04
Aug09
Feb15
Month and year
Figure 1. Time-series plot for bank credit growth.
The figure plots bank credit growth for all banks in the U.S. The dashed line represents the level of the index of industrial production.
The dates are indicated on the x-axis. The left-axis references aggregate bank credit growth (gtc ) and the right-axis references the
index of industrial production. The dark shaded regions represent NBER recessions and the three light shaded regions represent the
Less-Developed-Country debt crisis of 1982, the Mexico Peso crisis of 1994, and the Long-Term-Capital-Management crisis of 1998
respectively. Monthly data, 1960 - 2017.
1
Aggregate credit growth (g ct)
0.5
0
-0.5
-1
-1.5
-2
-2.5
1
2
3
4
5
6
7
8
9
10
11
12
Recession month
Figure 2. Loading on recession dummies for bank credit growth.
This Figure presents the estimated coefficients for the regression of bank credit growth on dummy variables, D i that take a value of 1
for the ith month in an NBER dated recession and are zero otherwise. Coefficients are multiplied by 100 and expressed in percentages.
Monthly data, 1960 - 2017.
39
Nominal returns
Cross-correlation
0.1
0
-0.1
-10
-5
0
5
10
5
10
Cross-correlation
Real returns
0.1
0
-0.1
-10
-5
0
Lags
Figure 3. Cross-correlation function.
This figure presents the cross-correlation function between bank stock returns and bank credit growth for various lags. The first panel
plots the cross-correlation function between the nominal excess bank stock returns and bank credit growth. The second panel plots the
cross-correlation function between the real excess bank stock returns and bank credit growth. The height of the bars represents the
magnitude of the cross-correlation and the dashed horizontal lines represent significance at the 5% level. In each panel, Lag = 0 plots
the contemporaneous cross-correlation and Lag = 5 refers to the correlation between returns at time t + 5 and bank credit growth at
time t. Monthly data, 1960 - 2017.
1.76
1.32
0.88
0.44
0.15
0.1
0.05
0
-0.05
Apr71
Dec84
Sep98
1.88
1.11
0.34
-0.43
0.15
0.1
0.05
0
-0.05
May12
Apr71
MKT
2.34
1.09
-0.16
-1.41
Dec84
May12
Sep98
1.51
0.11
-1.29
-2.69
0.15
0.1
0.05
0
-0.05
May12
Apr71
HML
0.15
0.1
0.05
0
-0.05
May60
Sep98
SMB
0.15
0.1
0.05
0
-0.05
Apr71
Dec84
Dec84
Sep98
May12
LTG
0.69
-0.22
-1.13
Oct65
Apr71
Oct76
Mar82
Sep87
Mar93
Sep98
Feb04
Aug09
Feb15
CRD
Figure 4. Correlation between 5-year rolling factor betas and credit growth.
This figure presents the correlation between factor betas and bank credit growth. In each panel the solid line plots the net monthly
year-on-year bank credit growth and the dashed line represents the 5-year rolling beta on one of the standard risk factors. The left panel
in the first row shows the 5-year rolling beta for M KT T . Each of the other panels correspond to the loadings on SM B, HM L, LT G,
and CRD. The dates are indicated on the x-axis. The left-axis references bank credit growth and the right-axis references the 5-year
rolling betas. Monthly data, 1960 - 2017.
40
Credit Portfolio Growth
0.325
0.25
0.175
0.1
0.025
-0.05
May60 Oct65 Apr71 Oct76 Mar82 Sep87 Mar93 Sep98 Feb04 Aug09 Feb15
Month and year
Figure 5. Correlation between bank credit growth and credit growth of investment banks.
This Figure presents the growth rates of credit market assets held by various financial intermediaries. The solid line plots the growth
rate of credit market assets held by banks and the dashed line plots the growth rate of credit market assets held by financial trading
firms. Data is from the U.S. Flow of Funds Accounts, Table L.1, lines 33-57. I use quarterly, non-seasonally adjusted data. Banks
include data for commercial banks (line 35), savings institutions (line 40), and credit unions (line 41); Financial trading firms include
data for ABS issuers (line 53), finance companies (line 54), REITS (line 55), brokers and dealers (line 56), and funding corporations
(line 57). Quarterly data, 1960 - 2017.
41
Industrials Liquidation Rate (%)
Financials Liquidation Rate (%)
0
Mar82
Sep87
Mar93
Sep98
Feb04
Aug09
Feb15
Month and year
Figure 6. Delisting rates
This Figure presents the monthly rate at which firms delist from the CRSP data-set. The solid line plots the delisting rate for financial
firms and the dashed line plots the delisting rate for non-financial firms. The delisting rate is measured by counting the number of
delistings in each year as a fraction of number of firms for which data is available in CRSP at the beginning of that year. Only delistings
due to liquidations (delisting code = 400) are included in the count. A separate delisting rate is computed for financial and non-financial
firms. Financial firms are identified by an SIC code between 6000 - 6799. Monthly data, 1960 - 2017.
4.5
Mean return
3
1.5
0
-1.5
-3
Figure 7. Relative mean returns for individual banks sorted by credit growth
In each quarter individual bank holding companies (BHCs) are sorted into 5 different portfolios based on credit growth. Credit growth
is measured by the change in the log of total loans outstanding over 1 quarter at the BHC level. BHCs with the lowest credit growth are
sorted into portfolio 1 and BHCs with the highest credit growth are sorted into portfolio 5. Each bar corresponds to the mean return
of portfolio 5 relative to the mean return of portfolio 1. The first bar plots this relative mean return one quarter after the portfolios are
formed. The statistics are annualized by multiplying by 4 and are expressed in percentages. On the x-axis Q1 − Q5 refer to the quarter
after portfolios are formed and the figures in parenthesis refer to the t-stats. Quarterly data, 1987 - 2017.
42
Table 1. Summary statistics.
This Table presents the summary statistics for equity returns and bank credit growth. Panel A presents the summary statistics for equity
returns of the value-weighted index for banks. Panel B shows the summary statistics for bank credit growth. In each panel, Column 1
indicates the years over which summary statistics are computed. Column 2 - 6 present the mean, standard deviation, minimum, median,
and the maximum return. All statistics are monthly and are expressed in percentages. Monthly data, 1960 - 2017.
Index
Mean
1960 − 2005
1960 − 2017
σ
Min
Panel A: Bank equity returns
1.54
5.74
-23.18
1.40
5.97
-23.18
Median
Max
1.52
1.52
26.99
26.99
1960 − 2005
1960 − 2017
Panel B: CRSP value-weighted index
0.92
4.39
-22.54
0.89
4.36
-22.54
1.25
1.21
16.56
16.56
1960 − 2005
1960 − 2017
8.22
7.56
Panel C: Bank credit growth
3.08
1.23
3.56
-5.37
8.30
7.85
17.39
17.39
Table 2. Forecasting regression.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αH + βH
log(1 + gtc ) + ǫt+H
j=1
In panel A, the dependent variable is the excess return of the BN index at time t + j and the independent variable is gtc , the bank credit
growth at time t. In panels B and C, the dependent variable is the H-period log excess returns of an index of all non-financial stocks and
the H-period log excess return of an index of each of the 39 industries other than financial firms for which data is available on Kenneth
French’s website, respectively. The independent variable is gti , the investment growth at time t. In all panels, rf,t+j is the return of
the risk-free asset at time t + j. Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The
standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick with 12 lags. Monthly
data, 1960 - 2017.
H=
c
3
6
βg
t − stat
R2 -adj (%)
-0.56∗∗
(-2.37)
2.27
i
βg
-0.15∗∗∗
t − stat
R2 -adj (%)
(-2.68)
3.29
Panel A: Banks
-1.30∗∗∗
(-2.97)
6.21
9
12
-2.03∗∗∗
(-3.29)
10.32
-2.61∗∗∗
(-3.46)
13.52
-0.35∗∗∗
(-2.33)
5.07
-0.26
(-1.50)
1.89
0.00
(-0.25)
1.25
-0.01
(-0.22)
1.14
Panel B: Non-financial firms
-0.29∗∗∗
(-2.73)
5.61
Panel C: Other industries
i
βg
t − stat
R2 -adj (%)
0.01
(0.16)
0.30
-0.01
(-0.36)
0.72
43
Table 3. Robustness tests.
This Table presents the estimated coefficients for the forecasting regressions:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
log(1 + rf,t+j )
=
g
αH + βH
log(1 + gtc ) + ǫt+H
log(1 + rBN,t+j )
=
g
αH + βH
log(1 + gtc ) + ǫt+H
j=1
j=H
X
c
j=1
In panel A, ri,t+j is the return of the BN index at time t + j, rf,t+j is the return of the risk-free asset at time t + j, and gtc is the bank
credit growth at time t. In panel B, rBN,t+j is the real return of the BN index at time t + j and gtc is the bank credit growth at time
t. Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The standard errors are adjusted for
heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick with 12 lags. Monthly data, 1960 - 2017.
H=
c
βg
t − stat
R2 -adj (%)
c
βg
t − stat
R2 -adj (%)
3
6
9
Panel A: Excluding financial crisis
-0.57∗∗
-1.33∗∗∗
-2.07∗∗∗
(-2.11)
(-2.65)
(-3.19)
2.35
6.40
10.62
-1.04∗∗∗
(-3.48)
6.90
Panel B: Real returns
-2.34∗∗∗
(-3.63)
13.31
44
-3.71∗∗∗
(-3.59)
18.77
12
-2.65∗∗∗
(-3.38)
13.86
-5.01∗∗∗
(-3.48)
22.19
Table 4. Monthly forecasting regression including other variables.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + rBN,t+j ) −
j=1
j=H
X
log(1 + rf,t+j ) =
j=1
c
g
LR
αH + βH
log(1 + gtc ) + βH
(log(1 + rBN,t ) − log(1 + rf,t ))
DP
TS
r
n
+βH
(log Dt − log Pt ) + βH
(y10,t − y3,t ) + βH
log(1 + rr,t ) + βH
(∆rn,t )
rel
DS
IP G
LEV
+βH
(rrel,t ) + βH
(yBAA,t − yAAA,t ) + βH
(log(1 + IP Gt )) + βH
(∆ log LEVt ) + ǫit+H
Here rBN,t+j is the return of the BN index at time t + j, rf,t+j is the return of the risk-free asset at time t + j, gtc is the bank credit
growth at time t, LRt is the lagged log excess return of BN index at time t, DPt is the log dividend-price ratio of BN index at time t,
T St is the term-spread at time t, DSt is the default spread at time t, IP Gt is the net monthly year-on-year growth rate of the index of
industrial production at time t, and LEV is the change in bank leverage at time t. Statistical significance is indicated by *, **, and ***
at the 10%, 5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping
data using Hansen-Hodrick with 12 lags. Monthly data, 1960 - 2017.
Coefficient
c
βg
t − stat
β LR
t − stat
β DP
t − stat
βT S
t − stat
βr
t − stat
βn
t − stat
β rel
t − stat
β DS
t − stat
β IP G
t − stat
β LEV
t − stat
R2 -adj (%)
-1.86∗∗∗
(-2.79)
-0.04
(-0.33)
-2.34∗∗∗
(-3.65)
-1.31∗
(-1.84)
-1.86∗∗∗
(-2.70)
-1.85∗∗∗
(-2.76)
-1.76∗∗∗
(-2.54)
-1.81∗∗∗
(-2.71)
-1.68∗∗∗
(-2.56)
-2.34∗∗∗
(-2.66)
0.15∗∗∗
(2.54)
3.47∗∗
(2.00)
-0.02
(-0.02)
-1.89
(-0.79)
-1.80
(-0.68)
8.62∗∗
(2.03)
-0.58∗
(-1.70)
8.28
13.44
10.98
8.27
8.41
45
8.72
10.83
9.82
0.15
0.15
14.20
-1.76∗∗∗
(-2.58)
0.02
(0.12)
0.15∗∗
(2.06)
4.42∗∗
(2.12)
1.19
(0.93)
-1.74
(-0.90)
1.99
(0.56)
-3.08
(-0.51)
-0.65
(-1.49)
0.16∗
0.16∗
16.27
Table 5. Quarterly forecasting regression including other variables.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + rBN,t+j ) −
j=1
j=H
X
c
g
cay
CR1
log(1 + rf,t+j ) = αH + βH
log(1 + gtc ) + βH
cayt + βH
j=1
Int
CR2 Int
+ βH
+ ǫit+H
Divt
Assets t
Here rBN,t+H is the quarterly return of the BN index at time t + H, rf,t+H is the return of the risk-free asset at time t + H, and gtc
is bank credit growth at time t, cayt is Lettau and Ludvigson (2001) measure of trend deviation in the log consumption-wealth ratio,
Int/Divt is the ratio of total interest received to total dividends received at time t, and Int/Assetst is the total interest received to
total household assets. The data for total interest and total dividends received is from BEA National Income and Product Accounts
Table 2.1 line 14 and line 15. The data for total household assets is from the Federal Flow of Funds Account Table B.100 Line 1. These
definitions are consistent with Longstaff and Wang (2008). Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1%
levels respectively. The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick
with 4 lags. Quarterly data, 1960 - 2015.
H=
3
c
βg
-0.16
(-0.73)
1.57∗∗∗
(3.14)
0.01
(0.21)
-1.21
(-0.43)
3.18
t − stat
β cay
t − stat
β CR1
t − stat
β CR2
t − stat
R2 -adj (%)
6
9
12
15
-0.38
(-1.22)
3.04∗∗∗
(3.27)
0.01
(0.03)
-1.20
(-0.23)
7.76
-0.95∗∗
-1.48∗∗∗
(-2.31)
3.83∗∗∗
(2.89)
0.01
(0.03)
-0.45
(-0.06)
13.01
(-2.49)
4.70∗∗∗
(3.04)
0.01
(0.09)
1.15
(0.12)
19.39
-1.74∗∗
(2.33)
5.63∗∗∗
(3.05)
-0.01
(-0.25)
2.93
(0.28)
23.46
Table 6. Forecasting regression over different sub-samples.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + rBN,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αH + βH
log(1 + gtc ) + ǫt+H
j=1
Here rBN,t+j is the return of the BN index at time t + j, rf,t+j is the return of the risk-free asset at time t + j, and gtc is the bank credit
growth at time t. The first panel shows the result for nominal excess returns and the second panel shows the results for real returns.
Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The standard errors are adjusted for
heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick with 12 lags. Monthly data, 1990 - 2017.
H=
c
βg
t − stat
R2 -adj (%)
c
βg
t − stat
R2 -adj (%)
3
6
9
Panel A: Nominal bank stock returns
-0.30
-0.75∗∗
-1.19∗∗
(-1.48)
(-2.10)
(-2.36)
0.71
2.63
4.86
Panel B: Real bank stock returns
-0.19
-0.60
-1.10∗∗
(-0.70)
(-1.47)
(-2.20)
0.27
1.35
3.39
46
12
-2.25∗∗
(-2.25)
6.23
-1.46∗∗∗
(-2.40)
4.97
Table 7. Forecasting regression for other asset classes.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αiH + βH
log(1 + gtc ) + ǫit+H
j=1
Here ri,t+j is the return of the index of treasury bonds, corporate bonds, and an index of 39 other industries at time t + j, rf,t+j is the
return of the risk-free asset at time t + j, and gtc is the bank credit growth at time t. Statistical significance is indicated by *, **, and ***
at the 10%, 5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping
data using Hansen-Hodrick with 12 lags. Monthly data, 1960 - 2017.
i=
TB
c
βg
t − stat
BC vars
R2 -adj (%)
CB
-0.09
(-1.51)
Y es
5.99
OI
-0.30
(-1.27)
Y es
31.49
-0.97
(-1.40)
Y es
9.19
Table 8. Forecasting regression for non-financial market index.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αiH + βH
log(1 + gtc ) + ǫit+H
j=1
Here ri,t+j is the return of non-financial stocks (M KT ), non-financial stocks with small market capitalization (M KT − SM ), with high
leverage (M KT − HL), and with high short-term debt (M KT − ST ) respectively at time t + j, rf,t+j is the return of the risk-free
asset at time t + j, and gtc is the bank credit growth rate at time t. Statistical significance is indicated by *, **, and *** at the 10%,
5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using
Hansen-Hodrick with 12 lags. Monthly data, 1960 - 2017.
i=
M KT
c
βg
-1.42∗∗
t − stat
C vars
R2 -adj (%)
(-2.19)
Y es
11.32
M KT − SM
M KT − HL
M KT − ST
-1.55
(-1.25)
Y es
14.78
-1.66∗∗∗
-1.25
(-1.45)
Y es
9.69
(-2.55)
Y es
20.61
47
Table 9. Forecasting regression for bank-dependent firms and financial trading firms.
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αH + βH
log(1 + gtc ) + ǫt+H
j=1
Here ri,t+j is the return of bank-dependent firms (BD), bank-dependent firms with small market capitalization (BD − SM ), with
highest leverage (BD − HL), with high short-term debt (BD − ST ), and financial trading firms at time t + j, rf,t+j is the return of the
risk-free asset at time t + j, and gtc is the bank credit growth rate at time t. As in Kashyap, Lamont, and Stein (1994) bank-dependent
firms are identified by an absence of a public debt rating and presence of interest expenses in the income statement. Financial trading
firms are defined in section 2 above. Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively.
The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick with 12 lags. For
bank-dependent firms data with short-term debt, data is monthly over 1980 - 2017. For other columns, data is monthly over 1960 2017.
i=
c
βg
t − stat
BC vars
R2 -adj (%)
BD
-0.68
(-0.78)
Y es
4.26
BD − SM
BD − HL
BD − ST
-2.18∗
(-1.86)
Y es
15.17
-1.04
(-1.01)
Y es
6.62
-0.41
(-0.37)
Y es
32.14
FT
-1.53∗∗
(-2.60)
Y es
7.29
Table 10. Characteristics of financial firms and non-financial firms
This Table compares the characteristics of publicly listed financial and non-financial firms. Data is from COMPUSTAT. In COMPUSTAT
financial firms are identified by an SIC code between 6000 - 6799. For each firm equity is measured as the difference between total
assets (item code at) and total debt. Total debt is computed as a sum of notes payable (N P ) and long-term debt due in 1-year (DD1).
Short-term debt equals notes payable (N P ). Finally, tangible assets equal gross property, plant, and equipment (P P EGT ). Quarterly
data, 1960 - 2017.
Statistic
F inancial
Assets
Equity
Others
12.56
2.44
Tangible assets
Assets
0.02
0.92
ST debt
LT Debt
4.85
1.38
Subject to a run
Y es
No
Dif f
Easy
Fix market value of assets
48
Table 11. Forecasting regression for excess returns of bank portfolios sorted by market capitalization
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αiH + βH
log(1 + gtc ) + ǫit+H
j=1
Here ri,t+j is the return of the ith size-sorted portfolio of banks at time t + j, rf,t+j is the return of the risk-free asset at time t + j,
and gtc is bank credit growth at time t. Column 1 indicates the size-sorted portfolio for which the coefficients are estimated. Portfolio
1 refers to the smallest size-sorted portfolio by market capitalization. Statistical significance is indicated by *, **, and *** at the 10%,
5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using
Hansen-Hodrick with 12 lags. Monthly data, 1960 - 2017.
Size
1
H=
3
6
9
12
c
βg
-0.60∗∗
-1.36∗∗∗
-2.16∗∗∗
t − stat
R2 -adj (%)
(-2.18)
3.01
(-2.71)
6.84
(-2.84)
10.97
-2.83∗∗∗
(-2.72)
13.55
-0.50∗∗
(-2.35)
2.75
-1.16∗∗∗
(-2.85)
6.41
-1.88∗∗∗
(-2.96)
9.95
-2.49∗∗∗
(-2.92)
12.27
-0.50∗∗∗
(-2.67)
2.60
-1.16∗∗∗
(-3.12)
6.17
-1.86∗∗∗
(-3.23)
9.84
-2.46∗∗∗
(-3.09)
12.13
-0.46∗∗
(-2.32)
1.96
-1.09∗∗∗
(-2.84)
5.35
-1.76∗∗∗
(-2.91)
8.70
-2.33∗∗∗
(-2.88)
11.12
-0.38∗∗∗
(-1.67)
1.09
-0.93∗∗∗
(-2.29)
3.55
-1.50∗∗∗
(-2.60)
6.47
-1.91∗∗∗
(-2.48)
8.39
c
2
βg
t − stat
R2 -adj (%)
3
βg
t − stat
R2 -adj (%)
4
βg
t − stat
R2 -adj (%)
5
βg
t − stat
R2 -adj (%)
c
c
c
49
Table 12. Forecasting regression for excess returns of bank portfolios double sorted by market capitalization and leverage
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αiH + βH
log(1 + gtc ) + ǫit+H
j=1
Here ri,t+j is the return of the ith portfolio of bank stocks sorted by market capitalization and leverage at time t + j, rf,t+j is the
return of the risk-free asset at time t + j, and gtc is bank credit growth at time t. Small and large refer to market capitalization and
low and high refer to leverage. Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The
standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick with 12 lags. Monthly
data, 1987 - 2017.
Coefficient
c
Small
βg
t − stat
R2 -adj (%)
Large
βg
t − stat
R2 -adj (%)
c
Low Leverage
High Leverage
-1.58∗∗∗
(-2.63)
8.95
-2.44∗∗∗
(-3.83)
17.48
-0.87
(-1.42)
2.21
-1.66∗∗
(-2.22)
5.66
Table 13. Forecasting regression for excess returns of bank portfolios double sorted by market capitalization and short-term debt
This Table presents the estimated coefficients for the forecasting regression:
j=H
X
log(1 + ri,t+j ) −
j=1
j=H
X
c
g
log(1 + rf,t+j ) = αiH + βH
log(1 + gtc ) + ǫit+H
j=1
Here ri,t+j is the return of the ith portfolio of bank stocks sorted by market capitalization and book value of short-term debt at time
t + j, rf,t+j is the return of the risk-free asset at time t + j, and gtc is bank credit growth at time t. Small and large refer to market
capitalization and low and high refer to short-term debt. Statistical significance is indicated by *, **, and *** at the 10%, 5% and 1%
levels respectively. The standard errors are adjusted for heteroscedasticity, auto-correlation, and overlapping data using Hansen-Hodrick
with 12 lags. Monthly data, 1987 - 2017.
Coefficient
c
Small
βg
t − stat
R2 -adj (%)
Large
βg
t − stat
R2 -adj (%)
c
Low ST Debt
High ST Debt
-2.03∗∗∗
(-2.51)
7.49
-2.74∗∗∗
(-3.45)
12.06
-1.23∗∗
(-2.19)
3.56
-1.63∗
(-1.98)
3.61
50
Table 14. Sensitivity to changes to V IX in economic expansions and recessions
This Table presents the estimated coefficients for the regression:
ri,t+j − rf,t+j = αi + β1V IX ∆V IXt+j + β2V IX Dt+j ∆V IXt+j + ǫit+H
Here ri,t+j is the return for size-sorted portfolio of banks and non-financial firms at time t + j, rf,t+j is the return of the risk-free asset
at time t + j, and ∆V IXt+j is change in the daily implied volatility of the index options on the S&P500 (V IX) at time t. Dt+j is a
dummy variable that equals 1 if a date lies within the start and end dates of a recession or a financial crisis and equals zero otherwise.
The first panel shows the results for banks and the second panel shows the results for non-financial firms. The last column reports the
percentage increase in this coefficient in recessions and financial crisis. Statistical significance is indicated by *, **, and *** at the 10%,
5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity and auto-correlation using Newey-West with 3
lags. Daily data, 1990 - 2009.
Size
β1V IX
β2V IX
Small banks
Large banks
-0.84∗∗∗
-0.72∗∗
-8.28∗∗∗
-2.16∗
85.71
26.09
Small firms
Large firms
-3.00∗∗∗
-7.44∗∗∗
-0.12
0.24
4.00
4.36
% Change
Table 15. Forecasting regression for excess returns of individual bank holding companies
This Table presents the estimated coefficients for the forecasting regression:
c,i
l,i
b,i
g
g
g
log(1 + ri,t+H ) − log(1 + rf,t+H ) = αiH + βH
(∆gtc,i ) + βH
(gtl,i ) + βH
(∆gtb,i ) + ǫit+H
Here ri,t+j is the quarterly return of the ith individual bank holding company (BHC) at time t + H, rf,t+H is the quarterly return of
the risk-free asset at time t + H, gtc,i is the quarterly credit growth, gtl,i is the quarterly change in leverage, and gtb,i is the quarterly
change in book/market ratio of BHC i at time t. I include both time- and BHC-fixed affects. For H = 0 the dependent variable is the
contemporaneous return of the ith BHC in the quarter that credit growth is measured. For H = 1−5, the dependent variable is the return
of the ith BHC in quarters Q1 − Q5 after credit growth is measured. All coefficients are expressed in percentages. Statistical significance
is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity and
auto-correlation using Newey-West with 4lags. Quarterly data, 1987 - 2017.
H=
0
1
c,i
βg
13.58∗∗∗
-2.13∗∗∗
l,i
βg
-0.14∗∗∗
4.62∗∗∗
R2 -adj (%)
TFE
BF E
N
28.06
Y es
Y es
22,219
32.17
Y es
Y es
22,219
2
-2.01
1.39∗∗∗
31.74
Y es
Y es
22,219
51
3
-4.24
0.65
32.02
Y es
Y es
22,219
4
0.16
-2.95∗∗∗
33.44
Y es
Y es
22,219
5
-5.99∗∗∗
0.27
33.32
Y es
Y es
22,219
Table A1. Stock return series.
This Table presents a brief description of each type of financial intermediary. Column 1 indicates the mnemonic used to reference each
type. The industry definitions are from Kenneth French’s web-site and are presented in column 3. Column 4 describes the kind of
business each type of financial intermediary is engaged in.
Mnemonic
Type
SIC code
Description
BN
Banks
6000 - 6199
Deposit banking, related functions,
and fiduciary services. Also includes
firms that extend credit but do not
provide deposit banking services.
FT
Investment banks
6200 - 6299 & 6700 - 6799
Trade securities. Also includes exchanges, clearinghouses, and other allied services.
Table A2. Summary statistics.
This Table presents the summary statistics for equity returns for investment banks. Column 1 indicates the years over which summary
statistics are computed. Column 2 - 6 present the mean, standard deviation, minimum, median, and the maximum return. All statistics
are monthly and are expressed in percentages. Monthly data, 1960 - 2017.
Index
1960 − 2005
Mean
1.61
σ
5.05
Min
-20.73
Median
1.69
Max
19.59
1960 − 2017
1.51
5.14
-21.16
1.64
23.48
Table A3. Summary statistics for equity returns of financial intermediaries sorted by size.
This Table presents the summary statistics for returns of size-sorted portfolios for each type of financial intermediary. Columns 3 - 8
present results for 1960 - 2005 and columns 9 - 14 present results for 1960 - 2017. Column 1 indicates the type of financial intermediary.
In column 2, ’1’ refers to the smallest portfolio by market capitalization. Columns 3 - 7 and 9 - 13 present the mean, standard deviation,
minimum, median, and the maximum return for each size-sorted portfolio. All statistics are monthly and are expressed in percentages.
Monthly data, 1960 - 2017.
Index
Portfolio Mean
σ
Min
Median
BN
1
2
3
4
5
1.54
1.17
1.15
1.10
1.15
6.15
5.32
4.99
5.48
5.70
1960 - 2005
-17.98
-19.20
-19.30
-20.32
-23.76
FT
1
2
3
4
5
1.26
1.14
1.04
1.07
1.09
5.07
5.00
4.43
4.14
5.17
1960 - 2005
-19.67
-21.41
-18.05
-20.76
-21.60
Max
Mean
1.00
0.98
0.99
1.21
1.07
41.31
37.70
22.84
24.99
29.33
1.39
1.03
1.01
0.98
0.99
1960 - 2017
5.96 -17.98
5.11 -19.20
4.91 -19.30
5.67 -24.25
5.97 -23.91
0.88
0.90
0.90
1.08
1.07
41.31
37.70
22.84
24.99
29.33
0.96
1.13
1.04
1.19
1.11
34.29
34.11
26.96
21.92
17.40
1.16
1.03
0.96
0.97
0.98
1960 - 2017
4.79 -19.67
4.77 -21.41
4.25 -18.05
4.16 -20.76
5.29 -23.51
1.01
1.05
1.03
1.10
1.13
34.29
34.11
26.96
21.92
22.43
1
σ
Min
Median
Max
Table A4. Composition of aggregate bank credit.
This Table presents the summary statistics for various categories of loans that comprise aggregate bank credit. In any given month
bank credit is computed as the sum of ’total securities (T S)’, ’total commercial and industrial loans (IL)’, ’total real estate loans (RL)’,
’total consumer loans (CL)’, and ’total miscellaneous loans (M L)’. Column 1 indicates the loan category. Column 2 - 6 present the
mean, standard deviation, minimum, median, and maximum for each loan category as a percentage of total bank credit. All statistics
are monthly and are expressed in percentages. Monthly data, 1960 - 2017.
Loan category
Mean
σ
Min
Median
Max
TS
IL
RL
CL
ML
27.52
21.63
27.12
13.29
9.64
4.03
4.17
9.04
1.94
2.69
22.19
13.14
13.86
9.12
6.47
26.91
22.12
28.44
13.66
8.59
40.94
28.63
43.15
16.80
15.60
T rad.loans
62.04
5.57
47.19
63.68
69.78
Table A5. Quantile regressions.
This Table presents the estimated coefficients for the quantile regression. In panel A, the dependent variable is the excess return of the
BN index at time t + j and the independent variable is gtc , the bank credit growth at time t. In panels B and C, the dependent variable
is the H-period log excess returns of an index of all non-financial stocks and the H-period log excess return of an index of each of the
39 industries other than financial firms for which data is available on Kenneth French’s website, respectively. The independent variable
is gti , the investment growth at time t. In all panels, rf,t+j is the return of the risk-free asset at time t + j. Statistical significance
is indicated by *, **, and *** at the 10%, 5% and 1% levels respectively. The standard errors are adjusted for heteroscedasticity,
auto-correlation, and overlapping data using Hansen-Hodrick with 12 lags. Monthly data, 1960 - 2017.
H=
c
βg
t − stat
R2 -adj (%)
c
βg
t − stat
R2 -adj (%)
c
βg
t − stat
R2 -adj (%)
c
βg
t − stat
R2 -adj (%)
3
Panel A:
-0.85∗
(-2.49)
1.82
6
10th -percentile
9
12
regressions
-3.15∗∗∗
(-5.87)
8.03
-3.07∗∗∗
(-6.92)
9.44
Panel B: 25th -percentile regressions
-0.81∗∗∗
-1.66∗∗∗
-2.25∗∗∗
(-4.65)
(-5.98)
(-6.76)
2.21
4.08
5.45
-2.31∗∗∗
(-6.22)
5.72
Panel C: Median regressions
-0.39∗
-1.09∗∗∗
-1.66∗∗∗
(-2.16)
(-4.46)
(-4.75)
0.45
2.99
3.78
-2.26∗∗∗
(-6.13)
6.09
Panel D: 75th -percentile regressions
-0.34∗
-0.93∗∗
-1.85∗∗∗
(-1.98)
(-3.02)
(-5.95)
0.62
2.00
5.71
-2.99∗∗∗
(-8.02)
8.14
-1.77∗∗∗
(-3.83)
6.12
2