Oil Prices and the Cross-Section of Stock Returns
Dayong Huang
Bryan School of Business and Economics
University of North Carolina at Greensboro
Email: d_huang@uncg.edu
Jianjun Miao
Department of Economics
Boston University
Email: miaoj@bu.edu
First version: May 2016
Abstract
We investigate the pricing of oil price changes in the cross-section of stock returns using the US
daily data from 1986 to 2015. We find that stocks with different exposures to oil price changes
do not have a significant return difference in the full sample and in the subsample from 1986 to
2001. There is a negative return spread between a portfolio of stocks with the lowest oil betas
and a portfolio of stocks with the highest oil betas in the subsample from 2002 to 2010 and this
spread can be explained by the Fama-French three factors plus a momentum factor. For the
subsample from 2011 to 2015, the return spread is positive and can be explained by the FamaFrench three factors plus a volatility factor. For both subsamples, the return spread cannot be
explained by the Fama-French five-factor model. We also find that oil betas predict future stock
returns negatively in the past two years.
Keywords: Oil prices, factor model, return spread, cross section.
JEL classification: G12
1
1 Introduction
Oil prices have been volatile, especially in the past decade. Figure 1 presents the daily
nominal front month crude oil futures prices over the sample period from January 1, 1986 (the
earliest date when the daily oil price data is available) to December 31, 2015. Oil prices were
around $20 per barrel until the end of 2001. Over the next six years, oil prices skyrocketed,
reaching more than $140 per barrel in the middle of 2007, and then crashed, falling sharply to
around $40 per barrel in July 2008. Oil prices gradually rose to around $100 per barrel in mid2014 and plummeted to around $30 per barrel in the end of 2015.
Oil prices affect the macroeconomy (see Hamilton (2009) and Blanchard and Gali (2009)
among others) and hence should be related to aggregate stock market movements. Bernanke
(2016) finds that oil price changes and aggregate stock returns were positively correlated in the
past five years and this positive correlation is puzzling because a decrease in oil prices should be
good news for oil-importing economies such as the US. This issue has received a considerable
amount of attention in both the academia and the general public and most discussions have
been focused on the relationship between oil prices and aggregate stock returns. The impact of
oil price changes on the cross-section of stock returns has been underexplored. The goal of our
paper is to investigate this issue.
We first estimate oil beta for each individual stock using daily data in each month and
then form 10 portfolios sorted on oil beta. We define the LMHO portfolio as the one that buys
decile 1 of stocks with the smallest oil betas and sells decile 10 of stocks with the highest oil
betas. We track the value-weighted returns in the following month. Next we estimate the
2
average excess returns on all these portfolios using monthly data. We also estimate abnormal
returns (alphas) based on the five-factor model of Fama and French (2015) using monthly data.
This model performs better than the three-factor model of Fama and French (1993, 1996) and
is used as our benchmark. We find that both excess returns and abnormal returns on the LHMO
portfolio are insignificant in the full sample, implying that different exposures to oil price
changes do not generate a significant difference in average stock returns in the full sample.
One reason for this result is that the excess return on the LMHO portfolio switches signs
over time. The average annual excess return alternates between negative and positive signs
from 1986 to 2001, stays positive from 2002 to 2010, and becomes positive from 2011 to 2015.
We then study these three subsamples. We find that the average excess return on the LMHO
portfolio is 0.2% per month and insignificant in the subsample from January 1986 to December
2001. In the subsample from January 2002 to December 2010, the average excess return on the
LMHO portfolio is -0.71% per month with a t-value of -1.33. Although this number is not
statistically significant, it is economically significant as the implied annual return is a quite
impressive number of -8.52%. After adjusting for risks using the Fama-French five factors, we
find that the abnormal return is -0.99% per month and marginally significant with a t-value of
-1.75. The GRS test (Gibbons, Ross, and Shanken (1989)) rejects the null hypothesis that the
regression intercepts of the 10 portfolios are all equal to zero, with the p-value equal to 0.10.
Thus the Fama-French five-factor model has a hard time to explain the cross-sectional return
spread in this subsample.
Using the Fama-French three factors (Fama and French (1993, 1996)) plus a momentum
factor of Jagadeesh and Titman (1993) and Carhart (1997), we find that the abnormal return on
3
the LMHO portfolio is slightly lower and is equal to -0.87% per month with a t-value of -1.71.
The GRS test gives a p-value of 0.32 and cannot reject the null hypothesis of joint zero
intercepts. We also find that stocks with the lowest (highest) oil betas have a negative (positive)
slope on the momentum factor. Since the average return on the momentum factor is positive,
the negative loading on the momentum factor explains most of the negative average return of
the LMHO portfolio.
For the subsample from January 2011 to December 2015, the average excess return on
the LMHO portfolio is 1.08% per month and highly significant with a t-value of 2.17. The
abnormal return relative to the Fama-French five-factor model is 1.12% per month and
significant at the 5% level. The GRS test rejects the hypothesis that the regression intercepts of
the 10 portfolios are all equal to zero, with the p-value equal to 0.04. Thus the Fama-French
five-factor model cannot explain the return spread in this subsample. Using the Fama-French
three factors plus the momentum factor or the volatility factor (Ang et al (2006)) reduces the
return spread and the significance of the GRS test with p-values of 0.09 and 0.13, respectively.
Thus, the volatility factor plays an important role, but the abnormal return on portfolio 10 with
the highest oil betas is still significant at the 5% level. We find that portfolio 10 has a significant
positive loading on the value factor, but the larger positive loading on the volatility factor
relative to portfolio 1 with the lowest oil betas helps explain the relatively lower return on
portfolio 10 in the subsample from January 2011 to December 2015.
What types of firms have high oil betas for this subsample? Using the Fama-MacBeth
(1973) regressions, we find that high oil beta stocks tend to be large stocks, value stocks, stocks
that grow investments aggressively, stocks that have low momentum, and stocks that are
4
volatile. But oil betas do not help predict future returns in this subsample. When restricting to
the more recent subsample from January 2014 to December 2015, we find that high oil betas
predict low future returns, even after many control variables are considered, including the size,
the book-to-market ratio, asset growth, operating profitability, momentum, and stock’s total
volatility. We also find that our results in this subsample are quite robust after controlling for
these potential cross-sectional pricing effects in two-dimensional sorting.
Examining industry-level evidence, we find that the oil industry has the largest positive
average oil beta in the previous month and earns negative average excess returns and negative
abnormal returns in the following month relative to the Fama-French five factor model. The
retail industry has the smallest (negative) average oil beta in the previous month. It earns
positive average excess returns and positive abnormal returns in the following month relative
to the Fama-French five factor model. This result is consistent with the intuition that oil
producers are hurt by the decline of oil prices, but consumers benefit from it.
Bernanke (2016) argues that the recent close relationship between aggregate stock
returns and oil prices may be due to the fact that they both react to a common aggregate
demand factor. Thus it is possible that our oil beta is measured inaccurately if we ignore
variables that capture fluctuations in aggregate demand. To deal with this issue, we follow
Bernanke’s methodology, originally suggested by Hamilton (2014), to decompose oil price
changes into a demand component and a supply component. We examine how oil price
changes due to aggregate demand and aggregate supply affect the cross-section of stock
returns in the recent period from 2011 to 2015, as this subsample gives a significant LMHO
return. Furthermore, we control for each stock’s exposure to uncertainty measured by the VIX,
5
as stock prices and oil prices can both react to uncertainty in addition to fluctuations in
aggregate demand. We then form 10 portfolios sorted separately by betas of the demand
component (or demand component after controlling for uncertainty) and the supply
component of oil price changes. We find that the abnormal return on the LMHO portfolio
related to demand shocks is insignificant only after controlling for uncertainty, but it is still
significant (1.05% per month) at the 5% level for the supply component. This result shows that
the cross-sectional variation in stock returns due to demand-driven oil price changes after
controlling for uncertainty can be explained by the Fama-French five-factor model. But the
variation of stock returns due to the supply-driven oil price changes is still left unexplained.
Our paper makes two important contributions to the literature. First, we are the first to
investigate the impact of oil price changes on the cross-section of stock returns using some new
common risk factors proposed recently in the literature. Second, we show that the FamaFrench five-factor model cannot explain the cross-section of stock returns related to oil price
changes in the two subsamples from 2002 to 2010 and from 2011 to 2015. But the FamaFrench three factors plus a momentum factor or a volatility factor are useful. This finding
should stimulate further studies.
Most studies in the literature focus on the relation between oil price changes and
aggregate stock returns (Jones and Kaul (1996), Huang, Masulis, and Stoll (1996), Sandorsky
(1999), and Kilian and Park (2009)). These studies find some evidence for relations at various
leads and lags, but weak results in contemporaneous correlations. Ready (2015) proposes a
novel method for classifying oil price changes as supply or demand driven and finds that
demand shocks are strongly positively correlated with market returns, while supply shocks have
6
a strong negative correlation. He also examines the impact of these shocks on industry stock
returns. Driesprong, Jacobsen, and Maat (2008) find that oil price changes predict stock market
returns worldwide. They show that investors’ underreaction to information in oil prices causes
a rise in oil prices to lower future stock returns. Cross-sectional studies that incorporate oil
price changes include Chen, Roll, and Ross (1986), Ferson and Harvey (1993), and Demirer,
Jategaonkar, and Khalifa (2015). These studies find mixed evidence for the pricing effect of oil
price changes.
The reminder of the paper proceeds as follows. In Section 2 we discuss the data and
methodology. Section 3 presents empirical results. Section 4 concludes.
2 Data and Methodology
2.1 Data
We use daily data from January 1, 1986 to December 31, 2015. Our stock returns data
correspond to CRSP common shares (SHRCD = 10 or 11) from the NYSE, AMEX and NASDAQ
is the one-month Treasury bill rate. Firm
(EXCH = 1 or 2 or 3). The risk-free rate
fundamentals and accounting variables are taken from the Compustat database. Following
Davis, Fama, and French (2000), Cooper, Gulen, and Schill (2008), Xing (2008), Fama and French
(2008, 2015), Novy-Marx (2013), and Hou, Xue and Zhang (2015), we construct the following
four fundamental variables in a standard way: firm size (MC), book-to-market ratio (BM),
operating-profit-to-book-equity ratio or operating profitability (OPE), and asset growth (AG) or
investment.
7
Other control variables we use include the stock price momentum (MOM), reversal
(REV), and individual stock’s total volatility (TVOL). Following Jegadeesh and Titman (1993), at
the end of each month t, we compute each stock’s cumulative return from month t-13 to t-2,
and take it as the stock price momentum. Reversal is each stock’s return in month t-1. The total
volatility of a stock is the variance of daily stock returns in each month.
The five factors of Fama and French (2015) include the original three factors of Fama
and French (1993, 1996) and two new factors. The original threes factors are the market excess
return (MKT) defined as the return on the value-weighted market portfolio of all NYSE, AMEX,
and NASDAQ stocks minus the risk-free rate, a size factor (SMB) defined as the return on a
portfolio of small cap stocks minus the return on a portfolio of large cap stocks, and a value
factor (HML) defined as the return on a portfolio of high-book-to-market stocks minus the
return on a portfolio of low-book-to-market stocks. The two new factors are an asset growth
factor or investment factor (CMA) which captures the difference between returns on portfolios
of conservative and aggressive firms in terms of investments, and a profitability factor (RMW)
which captures the difference between returns on portfolios of robust and weak firms in terms
of operating profitability. Additional common factors used in our analysis are: the momentum
factor (MOMF) which is the return on a portfolio that buys the past winner and sells the past
loser, and the total volatility factor (VOL) which is the return on a portfolio that buys stocks
with high total volatility and sells stocks with low total volatility. All these factor data are
downloaded from Ken French’s website (http://mba.tuck.dartmouth.edu).
We use the data of front month crude oil futures on West Texas Intermediate Oil, taken
from the Genesis Financial Technologies and compute daily oil price changes (or the return on
8
oil) as
,
= ln( /
), where
is the oil price at date t. As Huang, Masulis, and Stoll
(1996) and Sadorsky (2001) point out, using oil futures prices has several advantages over using
spot prices. For example, spot prices are more heavily affected by temporary random noise
than futures prices. We find that using spot prices does not affect our results significantly.
To estimate the demand component of oil price changes, we use the data of copper
futures from Genesis Financial Technologies and the yields of the US 10 year T-bond from the
Saint Louis Federal Reserve Bank. We use the percentage changes in the VIX from the Federal
Reserve Bank at Saint Louis to measure uncertainty. Figure 1 presents the time series of oil
prices and copper prices and shows that they follow a similar pattern.
2.2 Methodology
We first estimate oil betas using daily data over the previous month. At the end of each
month, we regress daily stock returns on daily market excess returns and daily returns of crude
oil futures prices in excess of the risk free rate using the following equation
where
−
=
+
+
(
,
−
)+
,
(1)
is the return on stock between date t-1 and date t. The slope of the return on crude
oil futures
is the oil beta. The estimated oil beta may vary across months.
Using these oil betas, we rank all firms from low oil betas to high oil betas in each month
and follow their excess returns in the following month. We then form 10 portfolios based on
the rank of oil betas. The return on a portfolio is the value-weighted average of the returns of
9
all firms in that portfolio. We also track the portfolio level oil betas at the time of portfolio
formation and portfolio level betas in the following month after portfolio formation. The
portfolio-level oil beta is the value-weighted average of oil betas of all stocks that belong to
each portfolio. The LMHO portfolio is the one that longs portfolio 1 with the lowest oil betas
and shorts portfolio 10 with the highest oil betas. The return on the LMHO portfolio is also
equal to the return spread between portfolio 1 and portfolio 10.
At the time of portfolio formation, we delete firms whose prices are less than five
dollars, and firms whose market capitalization are less than the 10 percentile of market
capitalization of NYSE firms. The market capitalization breakpoints of NYSE firms are available
at Kenneth French’s website. We drop these firms to avoid micro-structure issues as they are
hard to trade. Our results are stronger when we include these micro-cap firms.
Our main testing methods follow the classical method of Fama and French (1996, 2015).
We test if the excess returns on decile portfolios formed on oil betas can be explained by the
recently proposed five factors of Fama and French (2015) using time-series regressions with
monthly data. If the returns on oil portfolios can be explained by these five common factors,
the joint test on the abnormal returns or alphas using the GRS test of Gibbons, Ross and
Shanken (1986) shall be insignificant. In addition, we test alternative common factor models.
One of them is the Fama and French three factors plus a momentum factor which buys past
winner and sells past losers (Jagadeesh and Titman (1993) and Carhart (1997)); the other is a
model that includes the Fama-French three factors and a volatility factor that buys stocks with
high volatility and sells stocks with low volatility (Ang et al (2006)).
10
To control for other firm fundamentals or characteristics, we perform double sorting, in
which we first sort on oil betas, and then independently sort on a fundamental or a
characteristics, and we take the intersections of the two sorts to form portfolios. We track the
return on the portfolios in the following month. We rely on the Fama and MacBeth (1973)
regression to identify if oil beta has incremental power to explain future stock returns.
As a robustness check, we decompose changes in oil prices into a component due to
changes in aggregate demand and a component due to changes in aggregate supply, as in
Bernanke (2016). In each month, we project daily oil prices changes on daily copper price
changes
,
and daily changes in 10-year Treasury bond rate ∆"#
using the following
regression
−
,
= $% + $ (
−
,
+& .
) + $ ∆"#
(2)
We take the fitted value as oil price changes related to demand, denoted by
residuals as oil price changes related to supply, denoted by
(
,
'
,
, and the
. Similarly, to remove the
effect of uncertainty on oil price changes, we run the following regression
,
−
= $% + $ (
,
−
) + $ ∆"#
+ $) ∆*"+ +
,
(3)
where ∆*"+ is the percentage change in the daily VIX. The fitted values are the oil price
changes related to the changes in demand and uncertainty, denoted by
are oil price changes related to supply, denoted by
with each one of
'
,
,
(
,
,
',
,
, and
(,
,
(,
,
. We then replace
,
, and the residuals
,
in equation (1)
to estimate the corresponding oil beta. We
form portfolios using these oil betas and repeat the preceding analysis.
11
',
3 Empirical Results
3.1. Results from One-Dimensional Sorting
If oil price changes affect the cross-section of stock returns, we should find that firms with
different exposures to oil price changes earn different returns. We first examine this issue in
the full sample from 1986 to 2015. The first row of Table 1 presents the average monthly excess
returns on the decile portfolios sorted by the past oil betas over the previous month. This row
shows that portfolio 1 with the lowest oil betas earns an average return of 0.45% per month (tvalue = 1.39) and portfolio 10 with the highest oil beta stocks earn an average return of 0.38%
per month (t-value = 1.05). The return spread between portfolio 1 and portfolio 10 is 0.07% per
month (t-value = 0.29). The low t-value shows that the return spread is insignificant. After
adjusting for the Fama-French five factors, we find that the return spread becomes negative
and insignificant. The first row of the bottom panel in Table 1 presents the alphas of the decile
portfolios after adjusting for the Fama-French five factors in the full sample. We find that the
spread of alphas between portfolios 1 and 10 is -0.10% per month with the t-value equal to 0.38.
To see why the average return spread is close to zero in the full sample intuitively, we
compute the annual return spreads from 1986 to 2015, using the average of the monthly
spreads. Figure 2 presents the result and shows that the return spread varies over time. It
alternates between positive and negative signs from 1986 to 2001, stays negative from 2002 to
2010, and becomes positive from 2011 to 2015. The t-values for all years indicate that the
return spreads are significant at the 5% level only in 1988 and 2014. This pattern shows that
12
there is almost no difference in average returns between low oil beta stocks and high oil beta
stocks in the full sample and suggests that the weak result in the full sample is driven by the
sign switching of the return spread in the full sample. We thus focus our analyses on three
subsamples: 1986-2011, 2002-2010, and 2011-2015.
Table 1 presents spreads in excess returns and in alphas for these three subsamples. We
find the following result: From 1986 to 2001, the spread in excess returns is 0.20% per month
(t-value = 0.61) and the spread in alphas relative to the Fama-French five-factor model is 0.17%
per month (t-value = 0.54). Consistent with Figure 2, both spreads are insignificant. From 2002
to 2010, these spreads are -0.71% and -0.99% per month, respectively. The corresponding tvalues are 1.33 and -1.75, indicating that the abnormal returns are marginally significant. By
contrast, from 2011 to 2015, these spreads are 1.08% and 1.12% per month and both are
significant at the 5% level.
Table 2 presents the portfolio-level oil betas at the time of portfolio formation and the
immediate month after the portfolio formation. The purpose of this diagnosis is to ensure high
oil beta portfolios at the time of portfolio formation continue to have high oil betas in the
following month. We find that this pattern holds in all three subsamples. In particular, from
2011 to 2015, low (high) oil beta stocks on average have oil beta of -0.61 (0.68) at the time of
portfolio formation. The beta spread of -1.29 between low and high oil betas is highly
significant. In the month following portfolio formation, low (high) oil beta stocks on average
have oil beta of -0.03 (0.14). The spread of oil betas is -0.17, much less than the oil beta spread
at the time of portfolio formation, but this difference is also highly significant with a t-value of
-7.37. The result in Table 2 is important because it is possible that, although they have high oil
13
betas at the time of portfolio formation, some stocks may become low oil beta stocks after
portfolio formation, and this switch in betas makes it unclear whether it is high oil beta stocks
or low oil stocks will have low returns in the future.
3.2 Can Return Spreads Be Explained by Common Risk Factors?
The next three tables present results from testing if the excess returns on the portfolios
sorted on oil betas can be explained by exposures to common risk factors. We consider the
following three sets of parsimonious common factors: the Fama-French five factors, the FamaFrench three factors plus a momentum factor, and the Fama-French three factors plus a
volatility factor. The purpose is to examine whether stocks with different exposures to oil price
changes have anything to do with any of these common factors. If returns on oil portfolios are
fully captured by their exposures to these common factors, we should expect insignificant
abnormal returns and the GRS test will not be able to reject the null hypothesis of zero joint
abnormal returns.
Table 3 considers the Fama-French five-factor model. The notable findings are as follows.
In the subsample from 1986 to 2001, the low oil beta stocks (portfolio 1) load negatively on the
profitability factor with a coefficient of -0.37 (t-value = -2.44), and the high oil beta stocks
(portfolio 10) load negatively on the asset growth factor with a coefficient of -0.59 (t-value = 6.07). The net result is that the LMHO oil portfolio loads positively on the asset growth factor
and negatively on the profitability factor. This result suggests that high oil beta firms move in
14
lockstep with firms that are aggressive in asset growth and robust in profitability. The GRS test
of 0.67 (p-value = 0.77) indicates that there is no abnormal return in this subsample.
In the subsample from 2002 to 2010, both high oil beta stocks and low oil beta stocks
have negative loadings on the asset growth factor and profitability factor. This result suggests
that they both tend to be firms with aggressive investments and weak profitability. The GRS
test statistic is 1.66 with a p-value of 0.10, suggesting abnormal returns are marginally
significant.
In the subsample from 2011 to 2015, the loadings of the returns on high oil beta stocks
(portfolio 10) on the value factor, the size factor, the asset growth factor, and the profitability
factor are 1.37 (t-value = 4.60), 0.37 (t-value=1.81), 0.41 (t-value = 1.34), and -1.48 (t-value = 3.35), respectively. This result implies that the returns on high oil beta stocks tend to co-move
positively with the returns on small value firms or firms with either aggressive investment or
weak profitability. The GRS test statistic is 2.13 with a p-value of 0.04. The null hypothesis of
zero joint abnormal returns is rejected in this subsample.
The result above shows that the Fama-French five-factor model fails to price the returns
on the oil portfolios in the recent sample from 2011 to 2015. We now test if a momentum
factor or a volatility factor, together with the usual Fama-French three factors, can resolve this
challenge. Table 4 presents the results from using the Fama-French three factors and the
momentum factor of Carhart (1997). In the subsample from 2002 to 2010, the returns on high
oil beta stocks (portfolio 10) co-move positively with the returns of past winners. For the
portfolio with the highest oil beta, the loading on the momentum factor is 0.10 and highly
15
significant with a t-value of 2.10. The loadings on the momentum factor increase monotonically
from portfolio 1 with the lowest oil betas to portfolio 10 with the highest oil betas. This explains
why stocks with high oil betas have high returns. The p-value of the GRS test is 0.32, suggesting
that the Fama-French three factors plus the momentum factor can explain the variation in
returns on portfolios with different oil betas in the subsample 2002-2010.
By contrast, in the subsample from 2011 to 2015, the returns on high oil betas stocks comove negatively with the returns of past winners, or co-move positively with past losers. The
loading on the momentum factor for portfolio 10 is -0.13 and its t-value is -1.59. The GRS test
statistic is 1.76 with a p-value of 0.09, rejecting the hypothesis that alphas of the decile
portfolios are all equal to zero. Thus the Fama-French three factors plus the momentum factor
cannot explain the variation in returns on portfolios with different oil betas in the subsample
2011-2015.
Table 5 presents the results from using the Fama-French three factors and a volatility
factor. We find that in all three subsamples, the returns on both high and low oil beta stocks
tend to co-move positively with the return on stocks with high total volatilities. In the
subsample from 2011 to 2015, the high oil beta stocks have the largest exposure to the
volatility factor. The coefficient is 0.34 and highly significant (t-value = 3.98). This can help
explain why stocks with high oil betas have low returns in the subsample 2011-2015. The GRS
test statistic is 1.61 with a p-value of 0.13, indicating that the null hypothesis of zero joint
abnormal returns on the decile portfolios cannot be rejected. But the alpha of portfolio 10 with
the highest oil betas is still significant with a t-value of-2.04.
16
Interestingly, the loadings on the volatility factor are not monotonic with oil betas. It is
possible that among stocks that have large total volatility, some are sensitive to oil price
changes, while some are not and simply vary by themselves. Using oil betas as a sorting
criterion allocates some stocks with high total volatility into high oil beta stocks and others with
high total volatility into low oil beta stocks.
In summary, we find that the Fama-French five-factor model cannot explain the crosssectional variation in stock returns with different exposures to oil price changes in the
subsamples 2002-2010 and 2011-2015. For the subsample from 2002 to 2010, either the
momentum factor or the volatility factor, together with the Fama-French three factors, can
explain this cross-sectional variation. By contrast, the recent sample from 2011-2015 is more
challenging. We find that the Fama-French three factors plus the momentum factor do not help.
The volatility factor together with the Fama-French three factors can marginally explain the
cross-sectional variation with the GRS test p-value equal to 0.13. Moreover, the alpha of
portfolio 10 with the highest oil betas after controlling for these factors is highly significant.
3.3 Results from Cross-Sectional Regressions
What types of firms have high oil betas? We run the Fama and MacBeth (1973)
regressions as follows. We regress oil betas at the time of portfolio formation, by sample
periods, on the following fundamentals including market capitalization, book-to-market ratio,
asset growth, operating profitability, price momentum in the past 12 month, and each stock’s
total volatility at the time of portfolio formation. Table 6 presents the results. In the full sample
17
from 1986 to 2015, high oil beta stocks tend to be large stocks, value stocks, stocks that grow
investments aggressively, stocks that have weak profitability, and stocks that are volatile. There
are variations in the subsamples. From 1986 to 2001, only operating profitability explains oil
betas. In particular, higher oil beta firms have lower operating profits. From 2002 to 2010,
almost all variables are significant in explaining oil betas and the signs seem intuitive. The new
addition is that, high momentum is associated with high oil betas. From 2011 to 2015, high oil
beta stocks are large stocks, value stocks, stocks with aggressive investments, low momentum,
and high volatility. In this sample, the adjusted R-square is 11.52%. Moreover, we find that a
similar pattern holds for the most recent period from 2014 to 2015. The explanation power is
even stronger as the adjusted R-square is 17.96%. But the total volatility is only marginally
significant with the t-value equal to 1.41.
How does the oil beta perform in predicting future returns? Following Fama and
MacBeth (1973), we regress future one-month returns on current oil betas, fundamentals, and
other control variables. Table 7 presents the results. In the full sample from 1986 to 2015, oil
beta has a coefficient of -0.002 (t-value = -1.11). This coefficient has a t-value of -0.8 when we
include control variables. Among the control variables, asset growth, profitability, and total
volatility are significant. This result is consistent with our previous finding that oil price changes
do not have a strong relationship with the cross-section of stock returns in the full sample.
We now consider subsample results. From 1986 to 2001, oil beta by itself has a
coefficient of -0.0034 (t-value = -1.95), but it becomes insignificant when control variables are
included. Among the control variables, asset growth, momentum and total volatility are
significant. From 2002 to 2010, oil beta by itself has a coefficient of 0.0035 (t-value = 0.95).
18
Asset growth, profitability, and total volatility are significant. From 2011 to 2015, oil beta by
itself has a coefficient of -0.0054 (t-value = -1.16). In this period, the only control variable that is
significant is the momentum.
The preceding results indicate that oil beta does not help forecast future returns in both
our full sample and the three subsamples. So what is it that makes oil price changes received so
much attention lately? We then consider the most recent years from 2014 to 2015. We find
that oil beta by itself has a coefficient of -0.0194 (t-value = -1.83). In this period, other control
variables are insignificant. The size, book to market ratio, asset growth, and gross profitability,
as emphasized in the recent asset pricing studies (Fama and French (2015) and Hou, Xue and
Zhang (2014)), do not perform well in forecasting future returns; their t-values are 0.56, -0.91,
1.01, and 0.08, respectively. Momentum has a t-value of 0.6 and volatility has a t-value of -0.86.
In summary, the results presented in Tables 6 and 7 seem consistent with those
reported in Tables 3, 4, and 5. Returns on portfolios formed on oil betas seem to be captured by
common factors such as the Fama-French three factors plus the momentum factor or the
volatility factor. Evidence from 2011 to 2015 and especially from 2014 to 2015 is more
challenging for these common factors. The following two subsections will investigate in more
detail the cross-section of returns in the more recent period from 2014 to 2015 and will
consider other common factors coming from aggregate demand or aggregate supply shocks
from 2011 to 2015, as suggested by Bernanake (2016).
19
3.4 A Closer Look at the Evidence from 2014 to 2015
This subsection focuses on the two years from 2014 to 2015 since this period seems to
pose the most serious challenge to the risk-based explanations of the effect of oil price changes
on the cross-section of stock returns. We consider several robustness checks and also present
some industry-level evidence.
We first show that the cross-sectional variation in stock returns with different exposures
to oil price changes remains large after we control for various fundamental variables. We sort
stocks into five quintiles based on their oil betas in the previous month. We then independently
sort stocks into five quintiles based on one of the following control variables: size, book-tomarket ratio, asset growth, gross profitability, momentum, short-term reversal, and total
volatility. We then obtain 5 × 5 portfolios. Portfolio 15 is the one that buys quintile 1 and sells
quintile 5 along one dimension of sorting in increasing orders. Table 8 presents the twodimensional sorting results.
In the double sorting on oil beta and size, the excess returns and abnormal returns
(relative to the Fama-French five-factor model) on portfolio 15 along the oil beta dimension are
large in four of the size quintiles. They are above 1.2% per month and typically 2.5 standard
deviations from zero. We find similar results from double sorting using the book-to-market,
gross profitability, asset growth, and even momentum or short term reversal. Double sorting on
the oil beta and momentum shows that, the abnormal return on portfolio 15 along the oil beta
dimension is 2.19% per month (t-value = 2.06) among the low momentum stocks, and is 2.33%
per month (t-value = 3.28) among the high momentum stocks. Finally, controlling for the total
20
volatility does not seem to undermine our results in 2014 to 2015. The abnormal return on
portfolio 15 is significant in high volatility portfolio. It is around 2.40% per month (t-value = 2.07)
among stocks with high volatility betas.
As a comparison, the lower right block presents the volatility-oil beta sort in the reverse
order. We find that, controlling for oil beta, the abnormal returns on portfolio 15 along the
volatility dimension, in general, are smaller than the counterparts when we first control for
volatility. The abnormal returns are insignificant in four of the five oil beta quintiles. In quintile
5 with the highest oil betas, the abnormal return is 1.54% per month (t-value = 1.90). Our
finding suggests that both oil betas and total volatility help explain the cross-sectional variation
in stock returns and that oil betas are more informative than total volatility.
Are these high oil beta firms simply oil producers? If yes, the effect of oil price changes
seems easier to understand. We identify industries, to which the high and low oil beta firms
belong. We consider 48 industries identified by the SIC codes as in Fama and French (1997). We
use the 80th percentile and 20th percentile as cutoffs to define high oil beta firms and low oil
beta firms. Panel A of Table 9 shows that the firm-month observations of high oil beta firms in
the oil industry account for the largest fraction (15%) of the total firm-month observations of
high oil beta firms from 2014 to 2015. The other top nine industries that have high oil beta
firms are Business Services (9.6%), Others (8.5%), Machinery (4.6%), Retail (4.6%), Electronic
Equipment (4.3%), Drugs (4.0%), Banking (3.9%), Chemicals (3.6%), and Utilities (3.4%). These
10 industries account for 59% of the firm-month observations that have high oil betas. This
result shows that high oil beta firms are not simply oil producers.
21
Panel A of Table 9 also presents the top 10 industries that have low oil beta firms. We
find that, except for Petroleum and Natural Gas, Machinery, and Chemicals industries, other
top 10 industries that have high oil beta firms are also in the list of top 10 industries that have
low oil beta firms. The remaining three top 10 industries that have low oil beta firms are
Transportation, Insurance, and Medical Equipment.
Panel B of Table 9 presents the average oil betas by industries from 2014 to 2015. We
find that, the 10 industries with the largest oil betas are the following: Petroleum and Natural
Gas, Coal, Mine, Fabricated Products, Gold, Machinery, Entertainment, Electrical Equipment,
Shipbuilding and Railroad Building, Equipment and Utility. These industries have positive oil
betas and low returns in the following month, as most returns in the next month are large and
negative, except for the Entertainment industry. Interestingly the oil industry (Petroleum and
Natural Gas) is not the one with the largest negative abnormal returns (alphas) after adjusting
for the Fama-French five factors. The two industries that have the largest negative alphas are
the Fabricated Products and Coal, albeit they are relatively small in that the number of firms in
those industries is not large. Considering the number of firms and market capitalization, we find
that the two industries having negative alphas still point to Petroleum and Natural Gas and
Machinery. The average alphas of all firms in the oil industry from 2014 to 2015 is -0.48% (tvalue = -2.64). This industry covers 1985 firm-month observations among 49140 total
observations. The average market capitalization of the oil industry is 168 billion. The Machinery
covers 1519 firm-month observations, and its average firm market capitalization is 116 billion.
The 10 industries with the smallest oil betas, from the lowest to the highest, are Retail,
Personal Services, Textiles, Transportation, Restaurants and Hotels, Other, Printing and
22
Publishing, Agriculture, Defense, and Pharmaceutical Products. These industries have negative
oil betas and seem to have large positive returns in the following month. The two industries
that have the largest alphas are Guns and Retail, and Retail seems to count heavily in the
market in terms of market capitalization and the number of firms. Retail covers 2588 firmmonth observations and its average market capitalization is 83 billion. Its abnormal return is
2.08% per month (t-value = 18.69).
3.5. Demand and Supply Driven Oil Price Changes
Bernanke (2016) argues that the recent strong correlation between oil price changes
and aggregate stock returns may be explained by the hypothesis that both are reacting to a
common factor, namely, a softening of global aggregate demand, which hurts both corporate
profits and demand for oil. In this subsection we follow his methodology, which is originally
suggested by Hamilton (2014), to decompose the changes in oil prices into a component due to
demand shocks and a component due to supply shocks. We consider daily data in the sample
period from January 1, 2011 to December 31, 2015. This sample period is almost the same as
that in Bernanke (2016). Unlike his study of aggregate market returns, our analysis focuses on
the impact of oil price changes on the cross-section of stock returns.
We use copper futures prices and the US 10 year T-bond rate to capture demand
changes. The rational of using copper prices suggested by Hamilton (2014) and Bernanke (2016)
is that the drop of the price of copper has nothing to do with the success of people getting
more oil out of the rocks in Texas and North Dakota. Softness in demand for commodities like
23
copper may be an indicator of new weakness in the economy. Similarly, yield on 10-year US
Treasury bond indicates weakness in the world economy. In unreported results we have also
considered the value of the US dollar to capture demand changes as in Hamilton (2014) and
Bernanke (2016). We find that the results are similar.
We regress the oil price changes on copper prices changes and the 10-year T-bond rate
in each month using equation (2). The fitted value is the oil price changes as predicted by the
demand changes and the residual is treated as oil price changes related to supply shocks. Using
oil price changes related to demand and supply shocks, we calculate two corresponding oil
betas. We form portfolios sorted on these two betas in the previous month. We then conduct a
similar analysis as in previous subsections.
Table 10 presents the results. The upper panel presents excess returns and the bottom
panel presents abnormal returns using the Fama and French five-factor model. The first block
and the second block present the returns of portfolios formed on oil betas related to demand
and oil betas related to supply. The excess return on the LMHO portfolio formed on oil betas
related to the demand component of oil price changes is 0.73% per month (t-value = 1.65). The
excess return on the LMHO portfolio formed on oil betas related to the supply component of oil
price changes is 1.00% per month (t-value = 1.65). The abnormal returns are 0.98% per month
(t-value = 2.13) and 0.95% per month (t-value = 2.01). This result indicates that the abnormal
returns based on the Fama-French five-factor model are still significant for both the demanddriven oil price changes and supply-driven oil price changes.
24
Bernanke (2016) argues that the recent stock market fluctuations have been
accompanied by elevated volatility. When uncertainty or volatility elevates, it is possible that
investors retreat from both oil and stocks, causing stock returns and oil price to move together.
To test whether our results are affected by missing variables such as uncertainty, we augment
our previous demand and supply decomposition with daily percentage changes in the volatility
as measured by the VIX. Specifically, to estimate oil beta related to demand-and-uncertaintydriven oil price changes, we compute the fitted values from regressing daily oil price changes on
copper price changes, 10-year T-bond rate changes and VIX percentage changes using equation
(3), and treat the fitted values as demand-and-uncertainty-driven oil price changes. The
residuals are treated as supply-driven oil price changes. We then regress individual stock
returns on these oil price changes to estimate oil betas.
The third blocks of Panels A and B of Table 10 present the excess and abnormal returns
on portfolios formed on oil betas related to demand-and-uncertainty-driven oil price changes
and on oil betas related to supply-driven oil price changes. The excess return on the LMHO
portfolio formed on oil betas related to the demand-and-uncertainty-driven oil price changes is
0.33% per month (t-value = 0.83). The abnormal return based on the Fama-French five-factor
model is 0.58% per month (t-value = 1.43). This result shows that, when controlling for
uncertainty, the demand-driven oil price changes do not generate significant abnormal returns.
The intuition is as follows. It is possible that, even though individual stocks have different
exposures to the demand-and-uncertainty-driven oil price changes, they have similar exposures
to the uncertainty component in the demand-and-uncertainty-driven oil price changes. Hence
they have similar stock returns because volatility explains the abnormal returns.
25
The forth blocks of Panels A and B of Table 10 present the results for the supply-driven
oil price changes. The excess return on the LMHO portfolio formed on oil betas related to the
supply-driven oil price changes is 1.11% per month (t-value = 2.22). The abnormal return based
on the Fama-French five-factor model is 1.05% per month and significant with t-value = 2.23.
Thus the return spread due to different exposures to supply-driven oil price changes cannot be
explained by the Fama-French five-factor model.
4 Concluding Remarks
Our extensive empirical analyses have shown that the risk-based theory using the FamFrench five-factor model fails to explain the cross-sectional variation in stock returns with
different exposures to oil price changes in subsamples 2002-2010 and 2011-2015. The FamaFrench three factors plus a momentum factor or a volatility factor can help explain the crosssectional variation. But there are still some unresolved questions for future research. For
example, the abnormal return on the portfolio with the highest oil betas is significant. One may
also consider other ways of decomposition of demand and supply shocks to oil price changes as
in Kilian (2009) and Ready (2015).
It is possible that there are other explanations for the return spreads in the past five
years. First, it is possible that stocks with high oil betas earn lower future returns because they
pay well when future investment opportunities are worsened by the intertemporal capital asset
pricing model of Merton (1973). If high oil beta stocks provide useful hedges, their expected
returns should be lower. As Campbell et al (2012) point out, there are two types of
26
deterioration in investment opportunities: declining expected returns and rising volatilities. In
our sample from 2011 to 2015, we find weak support for this view because the correlation
between oil price changes in the current month and the market return and volatility shocks in
the next month are negligible.
Second, the impact of oil price changes on stock returns may be due to behavioral
biases. Blanchard (2016) argues that herding behavior can explain the recent positive relation
between aggregate stock returns and oil price changes. The recent decline of oil prices was
associated with elevated uncertainty. When some investors sold stocks in panic, others
followed suit, causing the stock market to fall. Driesprong, Jacobsen, and Maat (2008) provide
a behavioral story based on investors’ underreaction to oil price changes to explain why the oil
price changes predict future aggregate returns negatively. We may use a related behavioral
story to understand our cross-sectional results that stocks with high oil betas earn low future
returns in the past five or two years. A decline of oil prices causes investors to be more likely to
sell stocks with high oil betas. This may be due to herding behavior accompanied with elevated
uncertainty. Investors’ underreaction to oil price changes causes the price of stocks with high
oil betas to continue to fall. Thus these stocks should earn lower future returns. This story may
be consistent with our finding of the importance of momentum and volatility factors and the
recent narrative in the media. We leave a serious test of this story for future research.
27
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30
Table 1. Returns and Abnormal Returns of Portfolios Formed on Oil Betas, 1986-2015
To estimate oil betas, we regress each stock’s daily excess returns on the market excess return and daily percentage changes in the price of crude
oil futures (WTI) in excess of the risk-free rate. The slope on oil price changes is the oil beta. At the end of month t-1, we form decile portfolios
based on the oil betas of all stocks, and track their returns in the following month. Portfolio excess return is value-weighted average of the returns
on stocks in each portfolio. Decile 1 (L) includes stocks that have the lowest oil beta and decile 10 (H) has the highest oil betas. Column LMHO
lists returns on a portfolio that buys decile 1 and sells decile 10. The upper panel lists excess returns (Ret) in monthly percentage and their t-values
(t) and the bottom panel lists abnormal returns (Alpha) in monthly percentage and their t-values (t) after adjusting risk exposure using the fivefactor model of Fama and French (2015).
At the time of portfolio formation, we delete firms that have negative or zero book-to-market ratio, that have prices less than five dollars, or whose
market capitalizations are less than the 10 percentile market capitalization among the NYSE firms. Stocks returns are from the CRSP. Factor data,
risk-free rate and NYSE size breakpoints are from Kenneth French’s website. The WTI crude oil future price is from the Genesis Financial
Technologies. The sample is daily from January 1, 1986 to December 31, 2015.
1986-2015
1986-2001
2002-2010
2011-2015
1 (L)
2
3
4
5
6
7
8
9
10 (H)
LMHO
Ret
0.45
0.67
0.68
0.72
0.62
0.51
0.70
0.58
0.73
0.38
0.07
t
1.39
2.50
2.77
3.06
2.59
2.25
2.86
2.30
2.66
1.05
0.29
Ret
0.57
0.86
0.81
0.90
0.66
0.54
0.89
0.62
0.71
0.37
0.20
t
1.25
2.38
2.33
2.62
1.93
1.68
2.51
1.71
1.83
0.74
0.61
Ret
-0.01
-0.01
0.26
0.21
0.15
0.18
0.21
0.40
0.82
0.69
-0.71
t
-0.02
-0.03
0.56
0.48
0.33
0.40
0.48
0.86
1.64
1.01
-1.33
Ret
0.89
1.31
1.02
1.09
1.34
1.03
0.97
0.80
0.65
-0.19
1.08
t
1.58
2.56
2.16
2.47
3.13
2.53
2.02
1.51
1.01
-0.25
1.94
-0.19
-0.01
-0.02
0.02
-0.07
-0.14
0.00
-0.02
0.15
-0.09
-0.10
1986-2015
Alpha
t
-1.22
-0.11
-0.22
0.21
-0.80
-1.50
-0.04
-0.23
1.28
-0.46
-0.38
1986-2001
Alpha
-0.03
0.16
0.01
0.08
-0.16
-0.18
0.09
-0.01
0.02
-0.20
0.17
t
-0.15
1.08
0.07
0.67
-1.18
-1.24
0.75
-0.05
0.12
-0.89
0.54
Alpha
-0.35
-0.39
0.01
-0.07
-0.10
-0.14
-0.15
0.04
0.64
0.64
-0.99
t
-1.21
-1.85
0.04
-0.45
-0.61
-1.00
-1.03
0.28
2.64
1.60
-1.75
Alpha
-0.17
0.22
-0.03
0.07
0.35
0.18
-0.06
-0.26
-0.41
-1.29
1.12
t
-0.70
1.12
-0.18
0.47
2.87
1.28
-0.36
-1.29
-1.58
-3.32
2.17
2002-2010
2011-2015
31
Table 2. Pre-Formation and Post-Formation Oil Betas, 1986-2015
To estimate oil betas, we regress daily excess returns on the market excess return and daily percentage changes in the price of crude oil futures
(WTI) in excess of the risk-free rate. The slope on oil price changes is the oil beta. At the end of month t-1, we form decile portfolios based on the
oil betas of all stocks, and track their oil betas in the portfolio formation month (Beta Pre) and in the following month (Beta Post). Portfolio level
oil betas are value-weighted average of the oil betas of stocks in each portfolio. The t-values are computed using time series information. Decile 1
(L) includes stocks that have the lowest oil beta and decile 10 (H) has the highest oil betas. LMHO represents a portfolio that buys decile 1 and
sells decile 10.
Port.
1 (L)
2
3
4
-0.68 -0.35 -0.21 -0.12
Beta Pre
-29.31 -27.16 -24.76 -18.27
t
0.00 -0.01 -0.01 -0.01
Beta Post
-0.51 -2.37 -3.90 -2.44
t
5
6
7
8
9 10 (H) LMHO
1986-2001
-0.04 0.04 0.11 0.21 0.35
0.68
-1.36
-8.46 9.50 24.20 30.73 32.72 32.94 -33.32
0.00 0.00 0.00 0.01 0.01
0.03
-0.03
-0.87 -0.31 -0.24 2.18 2.07
3.66
-3.20
-0.52 -0.27 -0.17 -0.10
Beta Pre
-29.37 -25.64 -22.18 -16.92
t
Beta Post -0.03 -0.03 -0.03 -0.03
-3.24 -5.76 -5.67 -6.26
t
2002-2010
-0.04 0.02 0.09 0.17 0.29
-7.62 5.50 16.91 23.93 28.09
-0.02 -0.03 -0.01 0.01 0.06
-5.43 -7.31 -3.11 1.47 7.97
0.57
30.62
0.18
9.96
-1.09
-33.86
-0.21
-9.34
-0.61 -0.30 -0.18 -0.10
Beta Pre
-18.50 -18.20 -15.31 -10.69
t
Beta Post -0.03 -0.04 -0.02 -0.02
-2.90 -5.29 -3.28 -4.50
t
2011-2015
-0.03 0.03 0.10 0.19 0.32
-3.86 4.36 10.95 15.56 19.17
-0.02 0.00 0.00 0.02 0.06
-2.64 -0.23 0.65 2.98 5.68
0.68
19.90
0.14
7.62
-1.29
-22.35
-0.17
-7.37
32
Table 3. Alphas and Betas of Oil Portfolios using the Fama and French Five Factors
The table presents alphas and betas of decile portfolios formed on oil betas as testing assets returns. We use the
Fama and French (2015) five-factor model that includes the market excess returns (MKT), a size factor (SMB), a
value factor (HML), an asset growth factor (CMA), and a profitability factor (RMW). We present alphas, betas and
their t-values. LMHO stands for the difference in returns between portfolio 1 and portfolio 10. The GRS and its pvalue are joint tests of alphas following Gibbons, Ross and Shanken (1989). Returns are in monthly percentage.
Panel A: 1986-2001
Port
alpha
MKT
SMB
HML
CMA
RMW
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(CMA)
t(RMW)
1
2
3
4
5
6
7
8
9
10
-0.03
0.16
0.01
0.04
-0.16
-0.18
0.07
-0.01
0.02
-0.20
1.05
0.97
1.01
0.99
1.01
0.92
1.03
1.00
1.09
1.21
0.18
0.06
-0.01
-0.05
-0.09
-0.13
-0.07
-0.01
0.05
0.22
-0.11
0.05
0.04
0.14
-0.02
0.14
0.13
0.06
0.14
0.00
0.16
0.23
0.26
0.17
0.14
0.13
0.07
-0.13
-0.16
-0.59
-0.37
-0.20
-0.04
-0.06
0.17
-0.03
-0.04
-0.06
-0.04
0.06
-0.15
1.08
0.07
0.40
-1.18
-1.24
0.63
-0.05
0.12
-0.89
19.13
25.19
34.91
35.25
29.04
24.96
34.69
31.31
28.48
21.23
2.66
1.22
-0.24
-1.40
-2.09
-2.76
-1.86
-0.37
1.07
3.05
-0.98
0.68
0.64
2.56
-0.25
1.95
2.12
0.96
1.87
-0.04
1.70
3.50
5.33
3.47
2.31
2.09
1.31
-2.37
-2.45
-6.07
-2.44
-1.87
-0.45
-0.83
1.80
-0.34
-0.55
-0.69
-0.38
0.38
LMHO
0.17
-0.16
-0.03
-0.10
0.75
-0.42
0.54
-1.97
-0.35
-0.65
5.51
-1.96
GRS
0.67
p-value
0.77
alpha
MKT
SMB
HML
CMA
RMW
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(CMA)
t(RMW)
1
2
3
4
5
6
7
8
9
10
-0.35
-0.39
0.01
-0.07
-0.10
-0.14
-0.15
0.04
0.64
0.64
1.06
1.03
0.94
0.93
1.00
0.98
1.01
0.99
1.01
1.10
0.38
0.21
-0.04
-0.07
-0.14
-0.08
-0.01
0.09
-0.03
0.20
0.20
0.25
0.16
0.00
-0.15
0.00
-0.07
-0.13
-0.07
-0.03
-0.36
-0.13
-0.10
0.05
0.05
0.12
0.23
0.08
-0.01
-0.44
-0.32
-0.18
-0.03
0.13
0.22
0.15
0.11
0.15
-0.22
-0.68
-1.21
-1.85
0.04
-0.45
-0.61
-1.00
-1.03
0.28
2.64
1.60
13.33
17.77
22.00
21.17
22.20
24.68
24.93
23.65
15.17
10.05
3.13
2.42
-0.57
-1.11
-2.01
-1.26
-0.18
1.47
-0.30
1.21
1.62
2.78
2.37
0.05
-2.08
-0.04
-1.02
-1.92
-0.65
-0.19
-2.13
-1.04
-1.14
0.52
0.57
1.44
2.67
0.91
-0.09
-1.93
-1.78
-1.40
-0.28
1.31
2.21
1.70
1.21
1.59
-1.45
-2.76
LMHO
-0.99
-0.05
0.18
0.24
0.09
0.36
-1.75
-0.29
0.75
0.96
0.27
1.05
GRS
1.66
p-value
0.10
Panel B: 2002-2010
Panel C: 2011-2015
alpha
MKT
SMB
HML
CMA
RMW
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(CMA)
t(RMW)
1
2
3
4
5
6
7
8
9
10
-0.17
0.22
-0.03
0.07
0.35
0.18
-0.06
-0.26
-0.41
-1.29
1.02
1.03
0.99
0.95
0.95
0.85
1.01
1.09
1.21
1.32
0.36
0.12
0.07
-0.10
-0.21
-0.05
0.06
0.01
0.18
0.37
-0.42
-0.27
-0.20
-0.30
0.01
-0.08
-0.04
0.13
0.62
1.37
-0.07
0.02
0.09
0.01
0.00
-0.13
0.04
-0.09
-0.17
0.41
-0.11
-0.02
0.08
0.49
0.14
0.24
0.02
-0.10
-0.64
-1.48
-0.70
1.12
-0.18
0.47
2.87
1.28
-0.36
-1.29
-1.58
-3.32
14.14
17.58
21.24
22.65
26.52
20.68
22.20
18.58
15.93
11.59
2.82
1.14
0.82
-1.27
-3.31
-0.64
0.79
0.11
1.31
1.81
-2.24
-1.76
-1.62
-2.69
0.08
-0.77
-0.36
0.82
3.11
4.60
-0.36
0.15
0.71
0.04
-0.05
-1.19
0.33
-0.59
-0.85
1.34
-0.41
-0.10
0.46
3.01
1.03
1.52
0.09
-0.44
-2.18
-3.35
LMHO
1.12
-0.31
-0.01
-1.79
-0.48
1.37
2.17
-2.03
-0.03
-4.54
-1.18
2.33
GRS
2.13
p-value
0.04
33
Table 4. Alphas and Betas of Oil Portfolios using the Momentum Factor
The table presents alphas and betas of decile portfolios formed on oil betas as testing assets returns. The factors are a
momentum factor and the Fama and French (1993, 1996) three factors, which include the market excess returns
(MKT), a size factor (SMB), and a value factor (HML). The momentum factor (MOMF) is the return on a portfolio
that long the 10th decile and short the 1st decile of stocks formed on past stock price momentum. We present alphas,
betas and their t-values. LMHO stands for the difference between portfolio 1 and portfolio 10. The GRS and its pvalue are joint tests of alphas following Gibbons, Ross and Shanken (1989). Returns are in monthly percentage.
Panel A: 1986-2001
Port
alpha
MKT
SMB
HML
MOMF
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(MOMF)
1
2
3
4
5
6
7
8
9
10
0.14
0.28
0.05
0.03
-0.20
-0.12
0.14
0.00
-0.03
-0.27
1.08
0.99
1.02
1.01
1.00
0.93
1.03
1.00
1.09
1.18
0.10
-0.03
-0.09
-0.10
-0.11
-0.17
-0.09
0.02
0.09
0.39
-0.28
0.01
0.11
0.18
0.14
0.16
0.11
-0.02
0.07
-0.19
-0.13
-0.06
0.01
0.02
0.07
-0.02
-0.03
-0.04
-0.01
-0.05
0.65
1.82
0.45
0.27
-1.52
-0.82
1.22
0.00
-0.20
-1.12
21.58
26.83
35.25
37.29
31.83
26.62
37.42
33.40
30.07
20.22
1.59
-0.61
-2.43
-3.04
-2.87
-3.97
-2.78
0.41
2.10
5.45
-3.84
0.22
2.62
4.50
2.98
3.25
2.85
-0.52
1.30
-2.23
-4.54
-2.75
0.79
1.45
4.16
-0.82
-1.96
-2.13
-0.49
-1.54
LMHO
0.0041
-0.09
-0.29
-0.09
-0.08
1.20
-1.16
-2.91
-0.77
-1.69
GRS
0.79
p-value
0.65
alpha
MKT
SMB
HML
MOMF
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(MOMF)
1
2
3
4
5
6
7
8
9
10
-0.55
-0.47
-0.04
-0.03
-0.04
-0.07
-0.04
0.10
0.59
0.32
1.02
1.01
0.91
0.88
0.98
0.96
0.99
1.00
1.12
1.34
0.50
0.25
0.02
-0.04
-0.12
-0.09
-0.07
0.07
-0.13
0.12
-0.01
0.16
0.10
0.02
-0.10
0.06
0.03
-0.06
-0.05
-0.20
-0.13
-0.05
-0.06
-0.03
0.00
0.01
0.04
0.04
0.10
0.10
-2.07
-2.38
-0.26
-0.17
-0.23
-0.49
-0.29
0.70
2.68
0.83
14.92
19.45
24.55
22.23
23.42
26.17
26.74
26.80
19.56
13.15
4.43
2.95
0.34
-0.64
-1.70
-1.47
-1.13
1.13
-1.38
0.71
-0.12
2.01
1.68
0.31
-1.50
1.06
0.59
-1.01
-0.59
-1.28
-4.21
-2.22
-3.42
-1.79
-0.08
0.83
2.52
2.31
3.76
2.10
LMHO
-0.87
-0.32
0.38
0.19
-0.23
-1.71
-2.40
1.74
0.92
-3.79
GRS
1.17
p-value
0.32
alpha
MKT
SMB
HML
MOMF
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(MOMF)
1
2
3
4
5
6
7
8
9
10
-0.17
0.25
0.00
0.11
0.33
0.09
-0.05
-0.26
-0.49
-1.11
1.02
1.02
0.98
0.94
0.96
0.88
1.01
1.09
1.23
1.23
0.39
0.11
0.03
-0.13
-0.22
-0.02
0.05
0.05
0.27
0.35
-0.50
-0.31
-0.18
-0.08
0.10
0.12
-0.04
0.06
0.35
0.45
-0.02
-0.02
-0.01
-0.01
0.02
0.05
0.00
-0.02
0.01
-0.13
-0.67
1.27
0.03
0.71
2.67
0.66
-0.29
-1.26
-1.79
-2.61
14.11
17.44
20.84
20.81
26.63
21.67
22.07
18.53
15.43
9.98
3.52
1.26
0.47
-1.86
-3.97
-0.39
0.72
0.53
2.24
1.82
-3.36
-2.61
-1.85
-0.89
1.39
1.46
-0.45
0.49
2.12
1.79
-0.36
-0.56
-0.29
-0.46
0.78
2.05
-0.08
-0.46
0.28
-1.59
LMHO
0.94
-0.22
0.04
-0.95
0.11
1.73
-1.38
0.18
-2.93
1.08
GRS
1.76
p-value
0.09
Panel B: 2002-2010
Panel C: 2011-2015
34
Table 5. Alphas and Betas of Oil Portfolios using the Volatility Factor
The table presents alphas and betas of decile portfolios formed on oil betas as testing assets returns. The factors are a
volatility factor and the Fama and French (1993, 1996) three factors, which include the market excess returns
(MKT), a size factor (SMB) and a value factor (HML). The volatility factor (VOL) is the return on a portfolio that
longs the 10th decile and shorts the 1st decile of stocks formed on total volatility. We present alphas, betas and their tvalues. LMHO stands for the difference between portfolio 1 and portfolio 10. The GRS and its p-value are joint tests
of alphas following Gibbons, Ross and Shanken (1989). Returns are in monthly percentage.
Panel A: 1986-2001
Port
alpha
MKT
SMB
HML
VOL
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(VOL)
1
2
3
4
5
6
7
8
9
10
-0.02
0.16
0.04
0.03
-0.11
-0.19
0.03
-0.07
0.00
-0.22
1.04
1.01
1.05
1.03
1.03
0.96
1.07
1.01
1.05
1.08
-0.03
-0.01
-0.03
-0.04
-0.03
-0.11
-0.03
0.01
0.02
0.18
-0.02
0.02
0.01
0.07
-0.03
0.08
0.03
0.00
0.19
0.16
0.15
-0.01
-0.07
-0.07
-0.09
-0.06
-0.07
0.00
0.08
0.23
-0.10
1.02
0.31
0.28
-0.88
-1.39
0.30
-0.56
0.03
-0.97
18.77
24.87
34.20
36.11
29.94
25.89
36.39
30.80
27.44
18.09
-0.39
-0.21
-0.65
-1.07
-0.65
-2.17
-0.78
0.30
0.39
2.21
-0.18
0.23
0.23
1.47
-0.42
1.22
0.63
-0.03
2.77
1.55
3.31
-0.39
-2.63
-2.85
-3.34
-2.13
-2.78
0.18
2.60
4.83
LMHO
0.19
-0.04
-0.21
-0.18
-0.08
0.59
-0.42
-1.74
-1.16
-1.18
GRS
0.90
p-value
0.54
Alpha
MKT
SMB
HML
VOL
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(VOL)
1
2
3
4
5
6
7
8
9
10
-0.48
-0.46
-0.03
-0.04
-0.05
-0.11
-0.08
0.08
0.60
0.42
0.97
1.02
0.93
0.93
1.03
1.04
1.06
1.02
1.01
1.01
0.29
0.18
-0.05
-0.05
-0.09
-0.02
0.03
0.13
-0.06
0.06
0.09
0.20
0.13
0.03
-0.10
0.04
0.00
-0.09
-0.11
-0.23
0.16
0.04
0.03
-0.01
-0.04
-0.08
-0.09
-0.04
0.01
0.18
-1.76
-2.25
-0.17
-0.23
-0.34
-0.79
-0.60
0.56
2.55
1.08
11.34
15.90
19.86
19.23
20.51
24.80
24.54
22.29
13.66
8.32
2.42
2.06
-0.69
-0.81
-1.31
-0.35
0.48
2.07
-0.60
0.36
0.78
2.43
2.24
0.56
-1.61
0.74
-0.08
-1.52
-1.13
-1.47
3.34
1.17
1.22
-0.56
-1.34
-3.43
-3.81
-1.80
0.16
2.69
LMHO
-0.90
-0.05
0.23
0.31
-0.02
-1.65
-0.27
0.95
1.44
-0.25
GRS
1.47
p-value
0.15
alpha
MKT
SMB
HML
VOL
t(alpha)
t(MKT)
t(SMB)
t(HML)
t(VOL)
1
2
3
4
5
6
7
8
9
10
0.00
0.28
-0.03
-0.02
0.27
0.06
-0.11
-0.29
-0.24
-0.80
0.92
1.00
0.99
1.01
1.00
0.91
1.04
1.11
1.11
1.04
0.19
0.06
0.05
-0.01
-0.13
0.09
0.11
0.06
0.04
-0.16
-0.40
-0.26
-0.17
-0.11
0.04
0.00
-0.06
0.08
0.41
0.82
0.14
0.04
-0.01
-0.08
-0.06
-0.08
-0.04
-0.01
0.16
0.34
0.02
1.36
-0.16
-0.15
2.24
0.40
-0.68
-1.40
-0.93
-2.04
12.16
15.41
18.94
20.95
25.66
20.50
20.67
16.79
13.26
8.34
1.44
0.53
0.59
-0.14
-2.00
1.19
1.30
0.52
0.29
-0.76
-3.15
-2.42
-1.97
-1.32
0.69
0.02
-0.73
0.74
2.94
3.96
2.60
0.81
-0.36
-2.43
-2.19
-2.49
-1.20
-0.17
2.74
3.98
LMHO
0.80
-0.11
0.35
-1.22
-0.21
1.47
-0.66
1.17
-4.21
-1.71
GRS
1.61
p-value
0.13
Panel B: 2002-2010
Panel C: 2011-2015
35
Table 6. Oil Beta and Firm Characteristics
The table presents slopes and t-values (t) from the Fama-MacBeth (1973) regression of regressing oil
betas at the time of portfolio formation on stock characteristics. Following Fama and French (2015), the
following accounting variables are used: market capitalization (MC), book-to-market ratio (BM), asset
growth (AG), and operating profitability (OPE). Additional control variables include the price momentum
in the past 12 month (MOM) and stocks total volatility (TVOL). The Adj. R2 is the adjusted R-squared.
The Obs. is the number of observations. Our sample is monthly from 1986 to 2015. Each Subsample
starts from January to December. The t-values are Newey-West (1987) with four lags. Three (two, one)
stars indicate significance at 1% (5%, 10%) level.
Sample 1986-2001 2002-2010 2011-2015 2014-2015 1986-2015
Intercept
0.0157*
-0.0445*
-0.0375
-0.042
-0.0114
t
1.84
-1.75
-1.02
-1.41
-1.01
MC
-0.0007
0.0050*
0.0065* 0.0091***
0.0022*
t
-0.76
1.8
1.68
2.98
1.79
BM
0.0028
0.0045 0.0176*** 0.0281*** 0.0058***
t
1.49
1.3
2.91
3.1
3.1
AG
-0.002 0.0149*** 0.0071***
0.0079* 0.0046***
t
-1.21
4.23
2.74
2
2.71
OPE -0.0019** -0.0030***
0.0003
0.0011 -0.0019***
t
-2.13
-3.98
0.33
0.96
-3.33
MOM
-0.0015
0.0486**
-0.0494*
-0.0863*
0.0056
t
-0.47
2.52
-1.81
-1.78
0.66
TVOL
-1.6137
7.0221* 20.8625*
25.4074
4.7585*
t
-0.58
1.72
1.93
1.41
1.67
Adj R2
0.0248
0.0576
0.1152
0.1706
0.0499
Obs.
444432
227060
115403
46967
786895
36
Table 7. Predicting Future Returns Using the Oil Betas
The table presents slopes and t-values (t), using the Fama-MacBeth (1973) regression, from regressing the next-month excess returns on the oil
betas and controlling variables. Following Fama and French (2015), the following accounting variables are used: market capitalization (MC),
book-to-market ratio (BM), asset growth (AG), and operating profitability (OPE). Additional control variables include the price momentum in the
past 12 month (MOM) and stocks total volatility (TVOL). The Adj. R2 is the adjusted R-squared. The Obs. is the number of observations. Our
sample is monthly from 1986 to 2015. Each Subsample starts from January to December. The t-values are Newey-West (1987) with four lags.
Three (two, one) stars indicate significance at 1% (5%, 10%) level.
Sample
Intercept
t
Oil Beta
t
MC
t
BM
t
AG
t
OPE
t
MOM
t
TVOL
t
Adj R2
obs
1986-2001
0.0075**
0.0113*
2.12
1.91
-0.0034*
-0.0017
-1.95
-1.02
-0.0003
-0.44
0.0013
0.99
-0.0021***
-3.28
0.0006
1.42
0.0054***
3.49
-2.9287***
-4.74
0.0025
0.0477
483959
443197
2002-2010
0.0056
0.0105
0.86
1.28
0.0035
0.0022
0.95
0.65
-0.0007
-1.2
0.0007
0.47
-0.0016**
-2.05
0.0006**
2.16
-0.0037
-0.77
-1.7677*
-1.76
0.0084
0.0483
235721
226610
2011-2015
0.0120**
0.0139*
2.42
1.8
-0.0054
-0.0049
-1.16
-1.03
-0.0003
-0.58
-0.0005
-0.5
-0.0013
-1.6
0.0002
1.49
0.0041**
2.07
-0.5453
-0.93
0.0106
0.0414
143800
138707
37
2014-2015
0.0032
-0.0038
0.66
-0.36
-0.0194* -0.0198*
-1.83
-1.81
0.0006
0.56
-0.0018
-0.91
0.001
1.01
0
0.08
0.0015
0.6
-0.7513
-0.86
0.0242
0.0655
49321
46881
1986-2015
0.0073**
0.0106**
2.56
2.52
-0.002
-0.0014
-1.11
-0.8
-0.0004
-0.88
0.0009
1.01
-0.0016***
-3.79
0.0005**
2.25
0.0025
1.39
-2.1767***
-4.66
0.0059
0.0469
839435
784995
Table 8. Abnormal Returns of Portfolios Formed on Oil Betas and Control Variables,
2014-2015
At the end of month t-1, we form qunitile portfolios based on the oil beta of each stock, and
independently form quintile portfolios based on one of the following control variables: market
capitalization (MC), book-to-market ratio (BM), asset growth (AG), operating profitability
(OPE), momentum (MOM), short-term reversal (REV), and total volatility (TVOL). Portfolios
are intersections from the two previous independent sorts. Portfolio excess returns (Ret) are
value-weighted average of the returns on stocks in each portfolio. Alphas are computed using the
Fama and French (2015) five factors. The t() represents t-values. Quintile 1 includes stocks that
have the lowest values and quintile 5 includes stocks that have the highest values. The portfolio
15 is a portfolio that buys quintile 1 and sells quintile 5. Returns are in monthly percentage.
MC
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
0.03
-0.38
0.41
0.72
-0.76
1.47
0.51
-0.86
1.37
0.58
-1.29
1.87
1.12
-0.81
1.93
t(Ret)
0.02
-0.33
0.61
0.70
-0.71
2.26
0.56
-0.74
1.83
0.71
-1.30
2.44
1.52
-0.96
2.84
Alpha
0.44
-0.01
0.45
0.80
-0.91
1.71
0.19
-1.00
1.18
0.03
-1.98
2.02
0.38
-1.34
1.71
t(Alpha)
1.04
-0.01
0.57
2.18
-1.87
2.32
0.58
-1.72
1.63
0.08
-3.72
2.58
1.52
-2.72
2.75
BM
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
1.25
-0.13
1.38
1.12
-0.84
1.96
0.97
-1.49
2.46
0.36
-1.07
1.44
-0.51
-0.82
0.31
t(Ret)
1.65
-0.15
2.53
1.26
-0.95
2.51
1.10
-1.44
2.92
0.48
-1.04
1.60
-0.71
-0.79
0.29
Alpha
0.26
-1.23
1.49
0.25
-1.83
2.07
0.73
-1.84
2.57
0.44
-1.21
1.65
-0.37
-0.96
0.59
t(Alpha)
0.66
-2.63
2.27
0.73
-3.25
2.96
1.38
-2.84
2.98
0.88
-2.03
1.75
-0.60
-1.58
0.56
AG
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
0.32
-1.27
1.59
0.50
-0.66
1.16
0.87
-0.62
1.49
1.49
-1.45
2.94
1.20
-0.68
1.89
t(Ret)
0.40
-1.43
1.99
0.70
-0.77
1.50
1.04
-0.78
2.17
1.93
-1.42
2.87
1.28
-0.56
2.09
Alpha
-0.05
-1.42
1.38
0.30
-1.09
1.39
0.16
-0.98
1.15
0.72
-1.78
2.50
0.42
-2.09
2.51
t(Alpha)
-0.15
-2.22
1.67
0.79
-2.10
1.91
0.41
-2.49
1.66
1.23
-2.86
2.40
0.83
-3.23
3.78
OPE
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
0.43
-0.96
1.39
1.00
-1.28
2.29
1.04
-0.74
1.78
0.79
-0.61
1.40
0.80
0.27
0.54
t(Ret)
0.48
-0.89
1.45
1.25
-1.26
2.48
1.23
-0.72
2.02
1.02
-0.69
2.08
1.05
0.33
0.99
Alpha
0.19
-1.22
1.41
0.57
-1.87
2.44
0.22
-1.16
1.38
0.25
-1.36
1.61
0.12
-0.29
0.41
t(Alpha)
0.29
-1.57
1.21
1.46
-2.89
2.84
0.52
-1.80
1.84
0.49
-2.95
2.03
0.27
-0.63
0.61
38
MOM
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
1.13
-1.11
2.24
0.36
-0.71
1.07
0.41
-0.53
0.94
0.83
-0.29
1.11
1.72
-0.29
2.01
t(Ret)
0.98
-0.88
2.00
0.47
-0.89
1.58
0.64
-0.63
1.41
1.03
-0.35
1.90
1.77
-0.27
2.89
Alpha
0.90
-1.29
2.19
-0.34
-0.95
0.61
-0.01
-1.23
1.22
0.35
-1.08
1.42
0.76
-1.57
2.33
t(Alpha)
0.95
-1.54
2.06
-1.06
-1.86
0.93
-0.03
-2.85
1.72
0.98
-2.44
2.40
1.74
-2.30
3.28
REV
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
0.91
-0.99
1.90
1.85
-0.45
2.30
0.28
-0.33
0.62
0.91
-0.99
1.90
0.55
-0.50
1.05
t(Ret)
1.07
-0.84
1.94
2.24
-0.48
3.28
0.39
-0.34
0.83
1.01
-1.06
2.59
0.72
-0.53
1.49
Alpha
0.34
-1.35
1.68
1.39
-1.23
2.62
-0.39
-0.91
0.53
0.48
-1.72
2.20
-0.25
-1.06
0.81
t(Alpha)
0.57
-1.69
2.14
3.40
-1.81
3.23
-0.79
-1.80
0.64
0.93
-3.19
2.66
-0.56
-1.91
1.01
TVOL
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
OIL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
0.68
-0.45
1.12
0.26
-0.63
0.89
0.88
-0.50
1.38
0.97
-1.16
2.13
0.99
-1.49
2.48
t(Ret)
0.98
-0.56
1.72
0.47
-0.67
1.28
1.07
-0.56
1.59
1.12
-1.13
2.62
0.95
-1.30
2.20
Alpha
0.21
-0.40
0.62
0.01
-1.08
1.09
0.14
-1.06
1.19
0.32
-2.08
2.40
0.45
-1.94
2.39
t(Alpha)
0.55
-0.81
0.97
0.03
-1.69
1.43
0.33
-2.31
1.67
0.91
-3.30
3.20
0.59
-2.38
2.07
OIL
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
TVOL
1
5
15
1
5
15
1
5
15
1
5
15
1
5
15
Ret
0.68
0.99
-0.31
0.71
-0.04
0.76
0.66
0.90
-0.24
0.16
0.83
-0.67
-0.45
-1.49
1.04
t(Ret)
0.98
0.95
-0.38
1.21
-0.04
0.74
1.16
0.73
-0.21
0.25
0.64
-0.72
-0.56
-1.30
1.14
Alpha
0.21
0.45
-0.24
0.23
-0.70
0.93
0.13
0.57
-0.44
-0.11
0.12
-0.23
-0.40
-1.94
1.54
t(Alpha)
0.55
0.59
-0.28
0.56
-0.88
1.07
0.53
0.55
-0.40
-0.45
0.16
-0.29
-0.81
-2.38
1.90
39
Table 9 Oil Betas and Industries, 2014-2015
Panel A presents industries to which the high and low oil beta firms belong. High (low) oil betas use 80th percentile and 20th percentile cutoff
values of oil betas. The column “Industry” indicates the numerical number of the industry in the Fama and French (1997) 48 industries. The
column “# of Obs.” presents the number of firm-month observations. The column “%” presents the fraction of firm-month observations of a
particular industry out of the total firm-month observations of high (or low) oil beta firms. Panel B presents value-weighted oil betas and the
Fama-French five-factor alphas by industry. We rank the oil betas of 48 industries from high to low and report the average of next month return
(Ret), the average of oil betas at current month (Oil Beta), the Fama-French five-factor abnormal returns, and their respective t-values of the 10
industries with the largest and smallest oil betas. We compute the five-factor alphas for each stock before we take averages by industry. The
column “Ind” is the industry’s numerical code in the Fama and French 48 industries. The column “MC” is the value-weighted average of June
market capitalization. The column “Obs” is the number of observations in each industry from 2014 to 2015. The row “All” averages (valueweighted) across all firms using all observations (49140) from 2014 to 2015. All returns are in monthly percentage.
Panel A: 2014-2015
Industry # of Obs.
30
1479
34
952
48
841
21
457
42
432
36
427
13
398
44
387
14
351
31
339
Total
High Oil Beta
%
Name
15.0%
Oil Petroleum and Natural Gas
9.6%
BusSv Business Services
8.5%
Other
4.6%
Mach Machinery
4.4%
Rtail Retail
4.3%
Chips Electronic Equipment
4.0%
Drugs Pharmaceutical Products
3.9%
Banks Banking
3.6%
Chems Chemicals
3.4%
Util Utilities
Industry # of Obs.
34
1132
48
1132
44
723
13
714
42
702
36
452
40
333
45
330
12
304
31
295
58.0%
Low Oil Beta
%
Name
11.5%
BusSv Business Services
11.5%
Other
7.3%
Banks Banking
7.2%
Drugs Pharmaceutical Products
7.1%
Rtail Retail
4.6%
Chips Electronic Equipment
3.4%
Trans Transportation
3.3%
Insur Insurance
3.1%
MedEq Medical Equipment
3.0%
Util Utilities
59.0%
40
Panel B: Average Beta and Abnormal Returns by Industry, 2014-2015
Ind
Name
Ret
Oil Beta
Alpha
MC
t(Ret)
t(Oil Beta)
t(Alpha)
t(MC)
Obs
30
Oil
Petroleum and Natural Gas
-1.19
0.2862
-0.48
167840
-6.74
42.06
-2.64
45.51
1985
29
Coal
Coal
-5.24
0.2785
-5.52
6884
-3.32
5.46
-8.53
17.23
67
28
Mines
Non-Metallic and Industrial Metal Mining
-1.70
0.1631
-1.12
20881
-2.27
6.19
-2.12
24.35
194
20
FabPr
Fabricated Products
-2.86
0.1433
-9.12
2201
-2.31
3.79
-15.51
21.27
78
27
Gold
Precious Metals
-1.24
0.1168
1.25
12588
-0.57
2.43
2.29
29.31
40
21
Mach
Machinery
-0.08
0.0653
-0.65
116531
-0.52
13.06
-5.58
40.64
1519
7
Fun
Entertainment
1.08
0.0512
2.47
40221
2.31
2.77
12.76
32.93
445
22
ElcEq
Electrical Equipment
-0.61
0.0443
-1.74
21365
-2.28
5.32
-5.28
25.87
510
25
Ships
Shipbuilding, Railroad Equipment
0.22
0.0388
0.07
5456
0.26
1.84
0.15
24.97
128
31
Util
Utilities
0.24
0.0354
1.84
23859
1.75
7.63
21.89
62.05
1984
13
Drugs
Pharmaceutical Products
1.18
-0.0435
1.07
129250
6.76
-3.24
5.63
63.08
1882
26
Guns
Defense
1.75
-0.0489
3.25
45787
3.69
-3.17
20.30
30.79
110
1
Agric
Agriculture
1.75
-0.0496
-3.59
2821
2.05
-2.11
-1.99
18.89
89
8
Books
Printing and Publishing
-0.29
-0.0514
0.46
28049
-0.73
-4.30
1.23
31.13
392
48
Other
Other
1.10
-0.0582
1.60
49593
6.06
-9.02
2.85
45.58
3698
43
Meals
Restaraunts, Hotels, Motels
0.50
-0.0616
-0.96
48816
2.27
-8.93
-4.20
40.85
859
40
Trans
Transportation
0.22
-0.0637
0.24
39547
1.17
-8.66
2.01
46.53
1269
16
Txtls
Textiles
0.78
-0.0667
-6.07
8860
1.16
-3.11
-16.62
20.44
103
33
PerSv
Personal Services
-0.87
-0.0681
1.89
4771
-2.25
-4.15
2.46
27.16
541
42
Rtail
Retail
0.61
-0.0713
2.08
83445
4.62
-14.82
18.69
49.89
2588
1 to 48
All
0.48
0.0015
1.49
100607
15.45
1.14
23.73
177.25
49140
41
Table 10. Returns and Abnormal Returns of Portfolios Formed on Alternative Oil Betas, 2011-2015
This table presents returns on decile portfolios formed on four sets of alternative oil betas. The original oil betas are slopes from a regression of
daily excess returns on the market excess return and daily percentage changes in the price of crude oil future (WTI) in excess of the risk-free rate.
The first set of oil betas are slopes from a regression in which the daily percentage changes in the oil futures price in the previous regression is
replaced by the oil futures price changes related to demand. The second set of betas uses oil price changes related to supply. The third and fourth
sets include daily percentage changes in the VIX as an explanatory variable in estimating oil betas.
To estimate the oil price changes related to demand and supply shocks, in each month, we regress daily oil price changes on daily percentage
changes in the daily percentage change in the price of copper futures and the change in 10 year Treasury bond rate, and the daily percentage
change in the VIX. The fitted value is treated as the oil price changes related to demand shocks and the residual is treated as the oil price changes
related to supply shocks.
Panel A presents excess returns and panel B presents abnormal returns. Decile 1 (L) includes stocks that have the lowest oil beta and decile 10 (H)
has the highest oil betas. LMHO represents the return of a portfolio that buys decile 1 and sells decile 10.
The copper futures price is from the Genesis Financial Technologies. The VIX is from the St. Louis Fed. The sample is daily from 2011 to 2015.
All returns are in monthly percentage.
Panel A: Excess Returns, 2011-2015
1 (L)
2
3
4
5
6
7
Portfolios formed on beta of the demand component of oil price changes
1.10
1.04
1.16
0.75
1.09
0.96
0.96
1.89
2.14
2.52
1.72
2.36
2.07
2.16
8
9
1.09
2.25
1.16
2.07
0.37
0.55
0.73
1.65
Portfolios formed on beta of the supply component of oil price changes
0.91
1.08
1.20
1.10
1.01
1.30
1.12
1.62
2.05
2.55
2.78
2.36
2.95
2.35
0.75
1.39
0.63
1.04
-0.09
-0.13
1.00
2.11
Portfolios formed on beta of the demand component of oil price changes (Controlling for VIX changes)
1.12
0.89
0.98
0.90
1.06
0.86
1.00 0.97 1.06
0.79
1.94
1.91
2.05
2.02
2.41
1.93
2.18 2.05 1.88
1.19
0.33
0.83
Portfolios formed on beta of the supply component of oil price changes (Controlling for VIX changes)
0.91
1.03
1.08
1.19
1.07
1.35
1.00 0.84 0.62
-0.20
1.63
1.91
2.44
2.94
2.58
2.95
2.06 1.64 0.98
-0.28
1.11
2.22
42
10 (H) LMHO
Panel B: FF 5 factor alphas, 2011-2015
1 (L)
2
3
4
5
6
7
Portfolios formed on beta of the demand component of oil price changes
0.20
0.03
0.16
-0.23
-0.02
-0.06
0.07
0.72
0.16
1.23
-1.54
-0.15
-0.41
0.52
8
9
10 (H)
LMH
0.13
0.63
0.10
0.45
-0.78
-2.43
0.98
2.13
Portfolios formed on beta of the supply component of oil price changes
-0.09
-0.01
0.12
0.17
0.11
0.38
0.12
-0.39
-0.07
0.67
1.12
0.71
2.85
0.87
-0.27
-1.68
-0.52
-1.87
-1.04
-2.72
0.95
2.01
Portfolios formed on beta of the demand component of oil price changes (Controlling for VIX changes)
0.17
-0.03
-0.08
-0.02
0.11
-0.10
0.03 0.00 -0.07
-0.41
0.60
-0.13
-0.56
-0.13
0.76
-0.66
0.23 0.00 -0.29
-1.57
0.58
1.43
Portfolios formed on beta of the supply component of oil price changes (Controlling for VIX changes)
-0.10
-0.15
0.05
0.30
0.15
0.38
-0.02 -0.14 -0.54
-1.15
-0.45
-0.79
0.32
1.70
1.14
2.74
-0.14 -0.91 -1.98
-3.02
1.05
2.23
43
198601
198607
198702
198708
198803
198809
198904
198910
199005
199011
199106
199112
199207
199301
199308
199403
199409
199504
199510
199605
199611
199706
199712
199807
199902
199908
200003
200009
200104
200110
200205
200212
200306
200401
200408
200502
200509
200603
200610
200704
200711
200805
200812
200907
201001
201008
201102
201109
201203
201210
201304
201311
201405
201412
201506
Figure 1. Daily WTI Crude Oil Futures Prices and Copper Futures Prices, 1986 to 2015
This figure plots the daily nominal front month crude oil future prices (WTI CL) and copper future prices (Copper HG) from 1986 to
2015. Data is from the Genesis Financial Technologies.
WTI CL 1986-2015
160
140
120
100
80
60
40
20
0
44
198601
198607
198702
198708
198803
198809
198904
198910
199005
199011
199106
199112
199207
199301
199308
199403
199409
199504
199510
199605
199611
199706
199712
199807
199902
199908
200003
200009
200104
200110
200205
200212
200306
200401
200408
200502
200509
200603
200610
200704
200711
200805
200812
200907
201001
201008
201102
201109
201203
201210
201304
201311
201405
201412
201506
Copper HG 1986-2015
500
450
400
350
300
250
200
150
100
50
0
45
Figure 2. Returns of Portfolios Formed on Oil Betas, 1986-2015
To estimate oil betas, we regress daily excess returns on the market excess return and daily percentage changes in the price of crude oil futures
(WTI) in excess of the risk-free rate. The slope on oil price changes is the oil beta. At the end of month t-1, we form decile portfolios based on the
oil beta of each stock, and track their return in the following month. Portfolio excess return is value-weighted average of the returns of stocks in
each portfolio. Decile 1 (L) includes stocks that have the lowest oil beta and decile 10 (H) has the highest oil betas. LMHO represents the return on
a portfolio that buys decile 1 and sells decile 10.
At the time of portfolio formation, we delete firms that have negative or zero book-to-market ratio, that have prices less than five dollars, or whose
market capitalizations are less than the 10 percentile market capitalization among the NYSE firms. Stocks returns are from the CRSP. Factor data,
risk-free rate and NYSE size breakpoints are from Kenneth French. The WTI crude oil future price is from the Genesis Financial Technologies.
The sample is daily from 1986 to 2015. We average monthly returns in each year to obtain returns by calendar years. The left panel presents
returns and the right panel presents t-values in each year.
LMHO return
LMHO t-value
0.03
2.5
0.025
2
0.02
1.5
0.015
1
0.01
0.5
0.005
0
0
-0.5
-0.005
-0.01
-1
-0.015
-1.5
-0.02
-2
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