NYSE: VLO
Valero Energy Corporation
Report updated 6 August 2006
Investment Thesis
Valero Energy Corporation is the largest petroleum refiner
in the United States. Refinery capacity of nearly 3.3 million barrels
per day (mbd), large holdings in pipelines and logistics, and an
extensive retail network give Valero earnings stability and an
enduring competitive advantage. Although refining margins and
earnings are extremely volatile, I believe that emerging
macroeconomic conditions and a lasting imbalance in crude oil
supply and demand will lead to strong performance in Energy
sector stocks. Valero is poised to be a strength leader in this
sector, and I anticipate above-average returns for this stock over
the next two years.
Summary
I have assigned Valero Energy Corp a two year price target
of $95. The following are key assumptions supporting this
valuation:
9 Energy prices will continue to be under pressure from lack of
supply and increasing demand until at least 2010.
9 Price structure and technical indicators show that the crude oil
market is in a healthy trend, and is not in an “overheat” or
overbought condition.
9 Refinery margins will remain strong.
9 Earnings and stock prices will be strong across the sector.
9 Three standard valuation methods (Discounted Cash Flow
(Free Cash Flow to the Firm), Comparable Multiples, and
Dividend Discount Model) support this valuation and suggest
the intrinsic value of this stock is approximately 30%-40%
above its current price.
9 Technical factors support the probability of a price advance at
least to my target price.
VLO, Daily (200 and 49 per Moving Averages)
Analyst: Adam Grimes
Fisher College of Business
The Ohio State University
Columbus, Ohio
Contact: 614.432.5661
grimes.111@osu.edu
Fund: OSU SIM Class (BUS FIN 824)
Fund Manager: Royce West, CFA
Recommendation: BUY
Sector: Energy
Industry: Oil & Gas, Refining and Marketing
Current Stock Price:
Target Price:
$66.18
$95.00
Market Cap:
Outstanding Shares:
ADV:
$40,753MM
615MM
9,320,000
52 Week High:
52 Week Low:
YTD Return:
Beta:
$70.75
$41.29
53.4%
0.99
Last Year Sales:
EPS:
$96,900M
$7.82
VLO, Monthly (200 and 49 per Moving Averages)
Table of Contents
Investment Thesis........................................................................................................................................................................... 1
Summary........................................................................................................................................................................................... 1
Company Overview........................................................................................................................................................................ 3
Refining........................................................................................................................................................................................ 3
Retail, Marketing and Transportation ..................................................................................................................................... 3
Global Macroeconomic Analysis ................................................................................................................................................. 4
Recent Global Economic Growth........................................................................................................................................... 4
Risks to Future Growth ............................................................................................................................................................ 4
Petroleum Market Analysis ........................................................................................................................................................... 5
Market Structure and Overview............................................................................................................................................... 5
Fundamental Forecast ............................................................................................................................................................... 5
A Basic Regression Model for Crude Oil Price..................................................................................................................... 7
Other Forecasting Methods...................................................................................................................................................... 8
Sector Analysis ................................................................................................................................................................................ 9
Petroleum Economics 101........................................................................................................................................................ 9
Drivers of Performance........................................................................................................................................................... 10
Valuation.................................................................................................................................................................................... 10
Industry Analysis: Refining and Marketing ......................................................................................................................... 11
Company Analysis ........................................................................................................................................................................ 12
Sustainable Competitive Advantage? .................................................................................................................................... 12
Financial Statements Analysis................................................................................................................................................. 12
Equity Valuation: Multiples ................................................................................................................................................... 14
Equity Valuation: Dividend Discount Model..................................................................................................................... 15
Equity Valuation: Discounted Cash Flow........................................................................................................................... 17
Technical Position and Assessment ...................................................................................................................................... 19
Summary......................................................................................................................................................................................... 20
Strengths and Opportunities .................................................................................................................................................. 20
Risks and Concerns.................................................................................................................................................................. 20
Conclusions ............................................................................................................................................................................... 20
Appendix 1 Brent Crude Oil Price Forecast ............................................................................................................................ 22
Appendix 2 Valero Financial Statements from Company 10-K ........................................................................................... 23
Appendix 3 Valero Revenue Regression Model and Inputs.................................................................................................. 26
Appendix 4 Valero Discounted Cash Flow Model ................................................................................................................. 27
2
Company Overview1
Valero Energy Corporation (Valero), incorporated in 1981, owns and operates 18 refineries located in the
United States, Canada and Aruba that produce refined products, such as reformulated gasoline (RFG), gasoline
meeting the specifications of the California Air Resources Board (CARB), CARB diesel fuel, low-sulfur diesel fuel
and oxygenates (liquid hydrocarbon compounds containing oxygen). The Company also produces conventional
gasolines, distillates, jet fuel, asphalt, petrochemicals, lubricants and other refined products.
Its business is organized into two segments: refining and retail. The refining segment includes refining
operations, wholesale marketing, product supply and distribution, and transportation operations. The refining
segment is segregated geographically into the Gulf Coast, Mid-Continent, West Coast and Northeast regions. The
retail segment is segregated into two geographic regions: Retail operations in the United States are referred to as the
US System, and retail operations in eastern Canada are referred to as the Northeast System. On September 1, 2005,
Valero completed the merger of Premcor Inc. with and into Valero Energy Corporation.
Valero markets branded and unbranded refined products on a wholesale basis in the United States and
Canada through a bulk and rack marketing network. The Company also sells refined products through a network of
approximately 5,000 retail and wholesale branded outlets in the United States, Canada and Aruba. It is the general
partner of Valero L.P. The retail segment includes company-operated convenience stores, Canadian dealers/jobbers,
truckstop facilities, cardlock facilities and home heating oil operations.
Refining
As of December 31, 2005, Valero's refining operations included 18 refineries in the United States, Canada
and Aruba with a combined total throughput capacity of approximately 3.3 million barrels per day. The Company
processes a slate of feedstocks, including sour crude oils, intermediates and residual fuel oil. For the year ended
December 31, 2005, total throughput volumes for the Gulf Coast refining region averaged 1,364,000 bpd; total
throughput volumes for the West Coast refining region averaged 311,600 bpd; total throughput volumes for the
Mid-Continent refining region averaged 364,500 bpd; and total throughput volumes for the Northeast refining
region averaged 447,800 bpd.
Retail, Marketing and Transportation
The retail segment’s operations include sales of transportation fuels at retail stores and unattended selfservice cardlocks, sales of convenience store merchandise in retail stores, and sales of home heating oil to residential
customers. Valero is an independent retailer of refined products in the central and southwest United States and
eastern Canada.
Sales in the US System represent sales of transportation fuels and convenience store merchandise through
the Company-operated retail sites. During 2005, total sales of refined products through the US System's retail sites
averaged approximately 118,000 bpd. In addition to transportation fuels, its Company-operated convenience stores
sell snacks, candy, beer, fast foods, cigarettes and fountain drinks. Its Company-operated stores are operated
primarily under the brand names Corner Store and Stop N Go. Transportation fuels sold in its US System stores are
sold primarily under the Valero brand, with some sites selling under the Diamond Shamrock brand pending their
conversion to the Valero brand.
Sales in Valero's Northeast System include sales of refined products and convenience store merchandise
through its Company-operated retail sites and cardlocks, sales of refined products through sites owned by
independent dealers and jobbers, and sales of home heating oil to residential customers. The Northeast System
includes retail operations in eastern Canada where it is a supplier of refined products serving Quebec, Ontario and
the Atlantic Provinces of Newfoundland, Nova Scotia, New Brunswick and Prince Edward Island. During 2005,
total retail sales of refined products through the Northeast System averaged approximately 76,300 bpd.
Transportation fuels are sold under the Ultramar brand through a network of 987 outlets throughout eastern
Canada. In addition, the Northeast System operates 89 cardlocks, which are card or key-activated, self-service,
unattended stations that allow commercial, trucking and governmental fleets to buy transportation fuel 24 hours a
day. The Northeast System operations also include a large home heating oil business that provides home heating oil
to approximately 161,000 households in eastern Canada.
1
Adapted from 2005 Company 10-K and Reuters.
3
Global Macroeconomic Analysis
In FY 2005, Valero derived 87.5% of its revenues from domestic operations, 9.2% from operations in
Canada, and the remaining 3.3% from other international refining and retail operations (primarily Aruba and the
Caribbean)2. Valero is not a global player in the sense that many other petroleum companies are: it does not
operate in high geo-political risk areas, it does not have to deal with the intricacies of trans-oceanic shipping, and it is
insulated from most exchange rate fluctuations. However, since the energy and petroleum markets are so strongly
influenced by global economic events, a brief overview of the current world economic condition is in order.
Recent Global Economic Growth3
Real Global GDP rose 4.7% in 2005. China (9.3%) and India (7.6%) were leading drivers of this growth.
Russia and several Baltic FSU states were also in the 6%-7% range, and many other developing economies also
experienced strong growth. Among major industrialized nations, the United States led at 3.5% growth and Italy
trailed with 0% growth. Only 5 countries reported GDP decreases for 2005, ranging from a 3% decline in Iraq to a
7% loss for Zimbabwe. Emerging market countries have shown exceptional strength, but the major industrialized
nations also experienced strong growth. In spite of high energy prices, several major natural disasters, and a variety
of regional wars and conflicts, the 2005 global economy was strong, outperforming many analysts’ expectations.
This expansion has been aided by low inflationary pressure and the strength of global equity markets.
Markets across the globe are experiencing a period of exceptional returns, coupled with volatility and risk premiums
that are near historical lows. Some commentators have suggested that markets are not accurately discounting global
risks, but current market conditions point to a healthy and sustainable trend. Perhaps the strongest warning comes
from yield curves, especially in the US, which are showing a tendency to flatten—this has historically been a
harbinger of softening and even recession—but it is important to note that this relationship has been weaker in the
1990’s and 2000’s than it was in the 1980’s. Other leading indicators (perhaps most significantly the OECD’s
aggregate leading indicators) are rising and imply continued strength.
Risks to Future Growth4
The primary risk to future GDP growth, both domestically and abroad, is inflationary pressures from high
energy costs. Current low core inflation levels may reflect deflationary pressures from globalization, but expect that
global economies will continue to be under pressure for the next few years. Emerging markets, corporations, and oil
exporters have uncharacteristically been net savers, which may have contributed to low long-term interest rates.
(Perhaps this factor is responsible for some of the flattening of the yield curve in the US.) In general, global markets
could be vulnerable to further tightening. Yields are high and volatility in most markets is very low. If interest rates
rise, a feedback loop could develop that would have very negative consequences for global equity markets.
High energy prices themselves continue to be a threat to future growth. The global economy has shown
surprising momentum, and demand for petroleum products has proved resilient even at the current high levels. If
energy prices continue upward, at some point the costs to developing economies will become great enough to stunt
growth. It is also possible that the full effects of the current high prices have not yet been felt. Currently, excess
production capacity remains at historic lows. This environment of constrained supply leaves the market vulnerable
to shortages, squeezes and price shocks. It seems likely that nations and consumers alike are treating these high
energy prices as temporary aberrations. How will economies adapt if it becomes clear that the current levels are
more or less permanent?
Global imbalances pose a threat to future growth as well. Current account surpluses in oil-producing
nations continue to rise, financed largely by the rising current account deficit in the US. This deficit is being
financed easily (due in part to low interest rates), but at some point it must fall and surpluses in other countries must
fall to balance. This adjustment is currently being hampered by high petroleum prices. The ideal mechanism for
this adjustment is a purely market-driven one: Foreign investors must be willing to take large positions in US assets
and bear the risk of US dollar depreciation in the short-term, but a premium for this risk does not appear to be
priced into the market at this time. Policy changes (e.g. more flexible Chinese exchange rates) could also ease this
Company 10-K.
2005 CIA World Factbook and StockVal data.
4 A similar evaluation of future risks, with slightly different priorities, can be found in the IMF’s World Economic Outlook,
April 2006. See, for instance, http://www.imf.org/Pubs/FT/weo/2006/01/index.htm.
2
3
4
adjustment. If this adjustment is not well managed through market forces and policy changes, there is a risk of
serious and far-reaching shocks to global markets.
At some point the risks become so extreme that they cannot be properly evaluated. It is difficult to
estimate the impact of wars, nuclear detonations, bio-terrorism, nuclear terrorism, pandemics, natural disasters and
other extreme scenarios on the global economy. Each event’s impact would vary depending on its scope and
location, but, in the past, major events like 9/11 have not had a lasting impact on world markets. The main risk is
that the global economy is currently a delicately interlocking structure: high current account deficits have arisen in
oil-consuming nations because of the high cost of energy. These deficits are being financed easily due to low
interest rates which are themselves threatened by rising oil prices. Rising oil prices also threaten to push developing
economies into recession and further exacerbate global imbalances. These are the main threats to consider in
coming years.
Petroleum Market Analysis
Market Structure and Overview
No market is easy to forecast but the petroleum market has several features that make it exceptionally
difficult. Even the availability of fundamental data is a problem because there is concern that some nations (notably
India and China) may not be reporting complete, accurate and unbiased data. The petroleum market is subject to
political intervention and manipulation at several levels, and it often over-reacts in the short term to global events.
In addition, it is inaccurate to think of one petroleum market because there are many markets in many
regions for a wide range of raw products and distillates. Even focusing on one specific product is difficult—crude
oil itself has hundreds of varieties and grades that reflect its geographic origin and ease of processing. These grades
trade at discounts and premiums so there is not one world crude oil price. The three most significant grades of
crude oil are North Sea Brent which is probably the best proxy for world crude price, West Texas Intermediate
Crude which is the standard for US crude oil pricing, and NYMEX futures which are the domestic standard for
crude futures.
In the short term, crude oil prices tend to be extremely volatile, often overreacting to threats of shortages,
transportation disruptions, regulatory changes and geopolitical events. Political events and statements from
governments in major producing areas cause temporary price spikes. Margins flex and the relationships between
crude and distillate pricing changes daily in most markets, but petroleum prices are remarkably stable in the longterm and lend themselves fairly well to supply/demand analysis.
Fundamental Forecast
For the past several years, energy prices have been high, even on an inflation-adjusted basis.
Figure 1 shows the price of West Texas Intermediate Crude oil, in real dollars. As oil prices soar to new nominal
highs, media commentators frequently point out that oil is not actually at historic highs when considered on an
inflation-adjusted basis. This statement, while true, may lead one to overlook some significant facts.
West Texas Intermediate Crude, $/bbl.
First, when adjusted for inflation, crude oil is
not at historic highs, but it is still higher than it has
been anytime in the past 20 years. Oil has never stayed
at such high levels for more than 2 years, and in the
past has spiked to these levels as a result of geopolitical
events and supply shocks. This time, oil prices have
risen to these high levels in a steady, multi-year trend
without significant political shocks. The question we
all need to ask is, “Is this time different?” I believe that
due to serious supply/demand imbalances, the answer
is “yes”. A look at some fundamental supply/demand
numbers illustrates the problem.
Figure 1. WTI $/bbl (Inflation-adjusted)
Current oil use is 86.8 mbd5 and is projected to
grow about 2.1% each year to 2010, with most of this
$60
$50
$40
$30
$20
$10
5
Jan-06
Jan-04
Jan-02
Jan-00
Jan-98
Jan-96
Jan-94
Jan-92
Jan-90
Jan-88
Jan-86
Jan-84
Jan-82
Jan-80
Jan-78
Jan-76
Jan-74
Jan-72
Jan-70
$0
Data in this section from EIA estimates, PIRA, BP and Platt’s data sources.
5
new demand (40%) coming from China and India. Depletion of working fields is currently about 4 mbd annually
(and accelerating). Thus total new additions of 27.5 mbd are needed to match demand to 2010.
In light of these demand numbers, the supply side situation appears bleak. From 2003-2006, non-OPEC
non-FSU output has grown 180,000 bbl/day each year. Best-case estimates for this component of supply hold it
rising at the same rate for the next decade, but many analysts expect it to decline. In the same time period, FSU
nations were forecasting 700,000 bbl/day in annual additions, but 2004 and 2005 saw no new additions due to
internal politics. If the FSU is able to match that forecast every year to the end of the decade, that would bring
potential annual non-OPEC additions to 1 mmb/d.
OPEC forecasts sustainable total (not annual) new additions of 4.6 mmb/d by 2010, but every OPEC
member has consistently missed projections for several years: Iran and Iraq are experiencing serious logistical
problems getting their oil to market, Venezuelan production is slipping in both quantity and quality, and major
Kuwaiti fields are experiencing annual depletions near 5%. Consensus on realistic OPEC additions centers around
2.3 mmb/d, mostly from Saudi Arabia and the UAE, both of which hold massive potential reserves.
Some analysts forecast net additions of 3 mmb/d from Algeria, Libya, and Nigeria, but other analysts
characterize these estimates as “heroic”. These countries have many political issues to resolve and would need to
significantly expand their infrastructure to sustain those levels of production. Even if this is possible, the best-case
estimates for new additions (from CERA) call for 11.5 mmb/d new additions, still leaving a significant shortfall
between supply and demand. I believe that this shortfall will put continued pressure on petroleum prices, at least
through the end of the decade. Prices significantly below current levels ($70 bbl) seem extremely unlikely, and the
possibility of shortages accompanied by high volatility and price spikes remains a real threat.
It is difficult to imagine circumstances that could lead to lower oil prices in the next few years. Perhaps the
most likely would be recession and weakening of demand in high-demand regions, but demand has been
exceptionally resilient in the face of higher prices. The IMF’s rule of thumb, that a $10 increase in the price of a
barrel of crude oil (sustained for one year) will decrease world GDP growth rate by .5%, may not apply to the
rapidly expanding economies of China and India. In some sense, we find ourselves in uncharted territory with these
price levels and this new demand.
It is also possible that massive new reserves could be discovered, but this seems extremely unlikely. Most of
the world’s conventional reserves have probably been discovered. New reserves are likely to come from deep
offshore wells or in the form of difficult-to-refine ultra-heavy unconventional reserves. It is difficult to estimate the
impact of a breakthrough in new energy technology. Emergence of a technology like cold fusion, or more efficient
renewable energy sources, could have dramatic effects on petroleum prices. Even if new technology significantly
reduced our commitment to oil, there would still be a need for liquid fuels and a use for petroleum products for
many years to come.
Perhaps the best case for lower energy prices comes from a possible accounting rule change. Current rules
only permit oil companies to report proved reserves on financial statements6, and nearly all oil companies follow this
policy in all reporting. Many government agencies follow GAPP regulations and also only report proved
conventional reserves. Thus, current figures do not account for extra-heavy and unconventional reserves, which are
many times larger than conventional reserves. If these rules were changed, the apparent supply of crude oil, at least
on financial statements and reports, could double or triple overnight. Even though the actual amount of oil in the
ground would not have changed, it seems likely that world markets could overreact. This could perhaps result in a
downward price shock and a period of sustained lower prices.
The future is always uncertain and forecasts must involve a large margin of error. However, when
considering the future of oil prices, it seems obvious that the risks are skewed to the upside. Events which would
lead to higher prices and volatility seem much more likely than scenarios that could lead to lower prices. I attempted
to quantify these risks into a regression model that would give a forecast range of crude oil prices for the next 10
years.
For a discussion on the SEC’s rules for reserve reporting, see Yergin, Daniel. “How Much Oil is Really Down There?” The
Wall Street Journal. April 17, 2006. p. A18.
6
6
A Basic Regression Model for Crude Oil Price7
Since Energy sector companies’ earnings and stock prices are so closely related to energy prices, I thought it
necessary to have a fundamental price forecast model in place for crude oil. Several models were attempted, one
incorporating over 40 factors. However, the data needed to build and test such a model is not available in sufficient
resolution in the public domain, so in the end I settled on a simpler, “back of the envelope” regression model. This
model is adequate for our forecasting purposes, but it has raised several questions for further investigation. Annual
data on regional production and consumption obtained from the BP Statistical Review of World Energy 2006 were used
as inputs in regression equations.
World supply was divided and re-categorized into several regions and regressions were run on the whole
basket. It was generally found that the supply to crude oil price relationship was linear and resulted in highly
significant regression equations (p values under .05 and R2 values over .85). The regression coefficient for certain
areas (notably North American production) was negative, but this probably indicates that supply is not always the
explanatory variable – some regions may hold back production to be able to produce more in response to higher
prices rather than higher production necessarily leading to lower prices. In addition, some significant production
(US) is consumed internally and never goes to the world market so it may not have the expected impact on oil
prices. Middle Eastern production also showed a sometimes loose relationship to crude oil prices; this may reflect
intervention of politics in the supply/demand equation. Based on these initial regression runs, supply from North
America, Central America, Europe, Middle East and Africa were included in the final model. Asia-Pacific supply
was not included in the model due to low significance in the regression runs.
The supply model appeared adequate (high R2 values) without a demand component, but economic
common sense said demand needed to be included in the forecast. However, the demand side of the equation
proved significantly more difficult to quantify. Initial regressions of the regional baskets showed poor results (pvalues well over .20). Visual inspection of scatterplots showed that the demand relationships were curves. (Why is
the supply relationship linear but demand is not?) Linear transformations resulted in better fits, but several regions
continued to show low p-values (Asia, Middle East, and Africa). Middle Eastern consumption was probably
satisfied by domestic production, insulating that demand from the global marketplace. African and Asian demand
may have been too low for much of the period analyzed to have a significant impact on prices. In the end, North
and Central American demand was aggregated into one variable, Middle East and African demand was eliminated
from the equation, and Asian demand was retained in spite of low p-values.
A note on the regression models developed in this report: This analyst is a trader with 10 years experience in the futures
markets and I have seen many regression models fail for various reasons. In my experience, the most common reasons for
regression models failing are: blindly developing mathematically correct models without understanding the economic
implications of such models (especially include/exclude decisions on explanatory variables), and not accounting for the
interaction of behavioral factors in the marketplace (expecting too much accuracy from regression models’ predictions.) As
such, the methodology I followed in all regressions was to first identify explanatory variables that made economic sense and
then verify the relationships with regression. I did not do shotgun-type exploratory regressions on a wide basket of variables,
but I was often surprised at the explanatory variables that did not have mathematical significance.
There was sometimes a tradeoff between developing a model that was mathematically correct and one that made
economic sense. For instance, the regression model for crude price has a problem with multicollinearity. (This is because
supply/demand figures tend to be highly correlated.) To produce a regression model that did not have issues with
multicollinearity, the number of explanatory variables had to be reduced to 3, but at this point the model ceases to make
economic sense! The partial multicollinearity among the independents inflates the standard errors and makes assessment of the
relative impacts of each independent unreliable, but it does not bias the estimates of the coefficients. Since the purpose of this model
(and all regressions in this report) is predictive rather than analytical, this type of multicollinearity is not a problem at all.
7
7
Many of the difficulties probably arise because
$120
demand does not follow simple, Econ 101 rules—some
regions have extremely inelastic demand and some major
$100
consumers maintain large stocks which they may utilize at
high prices and replenish at lower prices. Changes in supply
$80
may represent movement along the supply curve, a shift of
$60
the supply curve, or even deliberately restricted supply to
force higher prices. In addition, these correlations have not
$40
been stable over time. For instance, Asian demand was not
a significant part of world consumption before 2000, so the
$20
regression model showed low p-values for Asia. Thus the
include/exclude decisions had to be made based on a
$0
combination of mathematical principals and economic
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
estimates of the variable’s likely future impact.
Figure 2. Brent Crude Oil US$ / bbl
For the forecast inputs, a baseline of recent
(predicted by regression)
historical years’ averages was used as a starting point for
each variable. As price creeps above $80/bbl, demand was
reduced slightly for each region. Demand, particularly in the Asia Pacific region, rebounds to historical highs once
prices begin to ease after 2010. Supply increases at historical averages (with a random volatility component added to
the inputs) and greatly increases for some regions after 2010 (to model the increase of new supply from
unconventional reserves). The model predicts a high of $103.85/bbl in 2010, and then a steady decline to $66/bbl
by 2015. Figure 2 is a graphical representation of the model prediction and Appendix 1 shows the actual model and
inputs in more detail.
This regression model provides a forecast for Brent crude oil, which is the standard for world pricing.
Regressions were attempted with several other crude oils, but Brent gave the most significant results. This is to be
expected since world supply and demand figures were used as inputs.
Other Forecasting Methods
As a reality check, comparisons were made with several other forecasts and forecasting methods. The US
Energy Information Administration gives three crude oil forecasts8 which differ significantly from my regression
model. I believe the EIA may not be adequately accounting for the behavioral impact of perceived shortages and
may also not be modeling the emergence of unconventional reserves. Thus, the EIA models forecast much less
volatility in prices (the EIA “high price case” has price unchanged at $61.12/bbl to 2009, then begins a gentle curve
upward to $76.30 by 2015), no price drop after 2010, and lower peak levels than my forecast. However, I also used
the EIA cases as inputs into my DCF model and found that, even though the peak oil prices forecast by the EIA are
considerably lower, they generally give higher DCF valuations because the overall sustained price levels are higher.
From a technical perspective, the futures markets appear to be in a healthy and sustainable uptrend.
Markets which are nearing the end of trends usually show
one of two conditions: either volatility expands into a
“buying climax” phase which ends in dramatic price
collapse (perhaps because everyone who wants to buy has
already bought, and a vacuum has been created on the
other side), or resistance will begin to hold and the market
will begin to form one of the classical top formations (head
and shoulders, rounding tops, etc). Current market
conditions do not show either of these conditions—the
market continues to trend higher, with periodic
retracements indicative of a healthy trend.
Figure 3 shows monthly bars of the NYMEX
Crude Oil futures (continuous futures, rolling with open
interest). Notice that, with the exception of a price spike
Figure 3. NYMEX Light Sweet Crude Futures,
around the 1990 Gulf War, the futures traded in a narrow
Monthly Bars
band from $9.30 to $26.00 for 16 years. An old trader’s
8
See, for instance: http://www.eia.doe.gov/oiaf/aeo/index.html.
8
maxim says “the longer the base, the farther into space,” meaning that the longer a market trades in a tight band, the
farther the breakout move will go (in both price and time) when it finally comes. Sixteen years is an exceptionally
long time for a market to base, and such a base could certainly sustain a move beyond current levels. Some signs of
momentum divergence are emerging, but this is to be expected in a long, sustained trend. This market does appear
to be somewhat overdue for a correction. We could easily see 6-8 months of sideways-downward price action
without compromising the integrity of the trend. Any retracement would probably carry at least to the 20 period
exponential moving average (XMA) which is currently around $62. Market geometry suggests that any downward
price action should find support around $54.50 (basis September 2006 futures). Significant violation of this level
would lead us to reassess the integrity of the trend.
One last insight can be gleaned from the pricing structure of the futures markets themselves. Energy
markets are normally backwardated, meaning that farther-out months trade at a discount to the near month. This is
in contrast to most commodities which normally trade in contango, meaning the near month trades at a discount to
the farther out. (The farther out months in most
Table 1. NYMEX Light Sweet Crude Oil Futures9 commodities have carrying costs associated with them
(insurance, warehouse charges, cost of capital, etc.) so they
command a premium price.) There are several
explanations for normally backwardated markets, but the
clearest is that energies are consumption commodities—
some market participants must carry inventory and cannot
afford the risk of selling inventory and going long on
futures if an arbitrage opportunity exists. (Imagine the
heating oil company that has to tell its customers in
December that they have no oil in the tanks but they are
making a ton of money out in the April futures!) Thus,
cost of carry has not traditionally applied to energy futures
markets.
Table 1 shows the current NYMEX futures
contract price structure. Note that the market is actually in
contango (each farther out month trading at a higher price)
until August 2007, at which point the normal backwardated
structure reasserts itself. This unusual “hump” structure
does not mean that the current bull market in energy will end in July 2007, but it does suggest that the futures
markets are expecting higher prices at least that far out. This “elbow” in the price structure will normally move and
flex a bit, sometimes extending a few more months into the future and sometimes coming in closer to the spot
month. The normal expectation would be that, at some point, the market will revert to its normal backwardated
state and this will be a warning of potential price collapse. Currently the market shows no such warning and seems
to be expecting higher prices at least for the next year. This futures pricing relationship is a bellwether to watch for
clues of impending trend changes.
Sector Analysis
Petroleum Economics 101
Energy sector companies’ earnings are closely tied to crude oil prices and to business cycles in the industry.
The industry itself has exhibited a fairly stable and predictable “long cycle” over its 140-year life. In lean times for
energy companies (most recently late 1980’s to mid 1990’s) earnings are low and companies do not have extra funds
to invest in E&P and CapEx. During these times energy prices are also very low. This encourages economic
growth which leads to greater demand for energy products. The low price of energy and crude distillates through
the 1980’s and early 1990’s was certainly a contributing factor to the boom of growth in China and India leading into
the new millennium.
As economies grow, spurred by low energy costs, demand increases which begins to push the price of
energy higher. As energy prices rise, so do oil companies’ corporate profits, and companies are now able to make
9
Data from eSignal.
9
investments which will eventually increase supply. However, many of these investments have long time lags from
the time the first dollar is spent to the time the project returns new oil. For instance, an E&P company drilling a
new oil well in a high-risk area of the world can sometimes see a decade from the initial expenditure to the time the
well reaches peak production. This time lag between expenditure and return in the form of increased supply drives
the cycles in this industry. Economies continue to expand as energy prices rise, and oil companies continue to make
more investments to increase supply.
Eventually, existing supply reaches
capacity and energy prices rise to a point where
economic expansion is restricted. Demand has
outpaced supply growth. Recession sets in,
demand begins to dry up and energy prices
tumble. Previous E&P investments finally
begin to return new supply, and this excess
capacity only serves to drive price down even
faster. After a period of several years, energy
Figure 4. The Business Cycle in the Petroleum Industry
prices are at low and attractive levels, which
begins to fuel economic growth and the cycle
repeats itself. Figure 4 shows the “long cycle” with turning points marked. The regularity of the cycle (with the
exception of the first 25 years)—about 30 years from peak to peak and trough to trough—suggests that we are
currently probably nowhere near a peak in energy prices and can expect higher prices for some time to come. This
assessment is consistent with the higher price levels implied by current pricing structure in the futures markets,
supply and demand models, and many industry analysts’ estimates.
Figure 5. Scatterplot of Cumulative Log Change Energy
Index and WTI price.
Drivers of Performance
The major driver of sector performance is
60
the price of crude oil. Crude oil itself is virtually
y = 0.8454x - 7.3859
Correlation = 0.905
worthless. What value crude has comes from the
R = 0.8189
50
willingness of refiners to purchase it because they
know they can process it and sell the distillates for
40
more than they paid for the crude. Other players
in the industry also attempt to extract value at
30
various points in the process: E&P companies try
to sell crude to refiners for less than they spent
20
finding it and getting it out of the ground; Pipeline
companies charge a fee for moving crude and
10
refined products; and Retailers charge a mark-up
to sell refined products to the public. The whole
0
industry hinges on participants being able to add
$10
$20
$30
$40
$50
$60
$70
$80
Crude Oil $/bbl (Nom inal)
incremental value to crude oil at each stage of the
process.
To investigate the connection between
crude oil prices and Energy stock performance I created an equal-weighted (not market cap-weighted) index of
Energy sector stocks and then calculated a cumulative log change measure of that index. There was no
commercially available index that targeted the specific companies I wanted, so I selected 30 Energy sector stocks for
the broad Energy index, and repeated the process with 12 Refining and Marketing stocks. (Results were very similar
for the broad Energy index and the specific Refining index so only the broad index is discussed here.) Conventional
wisdom says that Energy stocks do well when energy prices rise. The purpose of this exercise was to verify that a
relationship exists and to see if the correlation between stock prices and energy prices was strong enough to justify
the work involved in building a valuation model. Figure 5 shows that a sufficiently strong relationship exists to
warrant further investigation.
Equal weighted index
2
Valuation
Currently, the sector appears to be relatively cheap by most valuation methods. Price to Forward Earnings
is near historic lows, as is Price to EBITDA. Price to Sales is perhaps the most accurate valuation measure for this
10
sector (and least subject to manipulation) and is also in the lowest 30% of its 10-year range. There are several
explanations for these low valuations. The most obvious is that the sector could be simply undervalued. Most of
these valuations began to drop to low levels in 1999 as the price of crude oil began to climb (see Figure 6). It is
possible that the market, maybe thinking the high crude oil prices were an aberration, did not adjust to these
companies’ improved performance with proportionate stock prices. On a more negative note, there is also the
possibility that the market “knows” something we do not. Whenever valuations appear unreasonably low there is
often a problem with the company or sector that may not be apparent at first glance. But in this case, this seems
less likely since this low valuation condition has persisted for 5 years.
Another explanation is that perhaps the denominators of these measures are so inflated due to the oil price
boom that it does not make sense to compare current values to past values. Perhaps there is a new paradigm and
different appropriate multiples should apply to oil companies in this new environment. Perhaps we should be using
the last 5 years’ data as our baseline rather than the past 10? Rather than use these valuations to justify an
investment, I suggest using them as a double check to ascertain that we are not buying something that is already
overpriced. It is impossible to come to any firm conclusion, but at least we can see from these valuations that the
sector is not overpriced.
Figure 6. Valuations for S&P Energy Sector
Industry Analysis: Refining and Marketing
Refineries purchase crude oil and process it into a wide variety of refined products. The distillation process
“cracks” the molecules of crude oil apart and reassembles them into new molecules that are in most cases larger and
lighter (less dense) than the original crude oil. This causes a refinery gain of about 5%—a 42 gallon barrel of crude
oil will produce a little more than 44 gallons of distillates. The exact proportion of distillate yields will vary
depending on the facility, process, and type of crude oil, but the largest yield is gasoline and heating oil. Figure 7
shows an abbreviated list of distillates from a typical of crude oil.
Figure 7. Refined Products (gallons)
There are many different grades of crude oil, differing in
from a Barrel of Crude Oil
density, viscosity and ease of refining. Cheaper grades of crude
oil are harder to refine and are much harder to refine in
compliance with environmental regulations. Refiners who have
converted a percentage of their capacity to sour/heavy-capable
are able to purchase those oils at a discount and sell the refined
products at the same price as if they came from premium oils.
The US also maintains some refineries that are only able to
process premium light-sweet crude oils. Because these refineries
are more expensive to operate, they are only used when domestic
refineries are operating near full utilization.
US refinery capacity is another issue to consider when
evaluating this industry. US refineries often operate at over 95%
capacity, so any unexpected interruption (hurricanes, refinery
11
fires, etc.) can cause price shocks. More refinery capacity is eventually needed, but government regulations have
made it very difficult to build refineries on US soil. Refiners are continually expanding existing facilities, but even
this is becoming more difficult due to new EPA regulations.
Building refineries offshore is also not a good solution—it generally makes sense to site refineries close to
consumers rather than wellheads. If refineries were built overseas, then many smaller transport ships would be
needed to carry the refined products to market; shipping bulk crude oil utilizes the economies of scale of large
tankers. Refineries situated close to the retail market can also modify their output to match seasonal surges in local
demand for specific products. Lastly, governments are imposing an increasing number of restrictions on the quality
of refined products. A refiner operating overseas might find that their products are not marketable in several large
retail markets. With a few exceptions (Valero’s refining operations in the Caribbean are one exception) most refined
products sold in the US are produced on US soil. The US will be facing a refinery capacity problem for some time
to come. This means more volatility in the price of refined products, but also assures refiners of strong margins and
good profits.
Company Analysis
Sustainable Competitive Advantage?
The Energy sector is highly competitive and highly cyclical. Companies must make massive CapEx outlays,
endure operational, regulatory, exchange rate and geopolitical risk, and still have their quarterly earnings largely
dependent on crude oil prices and other factors over which they have no control. Even though this may seem to be
a difficult and unattractive industry, I have identified a few key points that I believe give Valero a lasting competitive
advantage:
9 The US refinery capacity is tight. Barriers to entry in the refinery business are extremely high. Capital
outlays of at least a billion dollars would be needed to build a new refinery10. Current Federal
regulations are so restrictive that it is essentially impossible to build a new refinery. So, the pool of
available refineries is fixed, and Valero has expanded their capacity the only way possible—through
timely acquisitions. Valero is currently the largest US refiner and holds nearly 20% of US capacity.
9 Valero has converted a large proportion of their capacity to sour/heavy capable. This allows them to
capture significant additional profits due to the spread between heavy and light crude prices. However,
I do not believe this is a sustainable competitive advantage. Other refiners are already making the same
changes and within 5 years most of the industry will have the same capability Valero currently does.
9 Valero is integrated with a large midstream pipeline partner (Valero LP). This partnership, aside from
cost savings, also eliminates possibility of holdup and assures their refined products a path to market.
Shipments can be more tightly managed leading to increased efficiency.
9 Valero also has a large retail footprint. Retail margins are significantly less volatile than refining
margins. The presence of a large retail operation has the effect of smoothing earnings.
9 Economies of scale count for something in this business. The giant Integrateds (Exxon, BP) dominate
the industry through their economies of scale and scope. Valero is poised to develop similar strength in
the refining industry through additional acquisitions.
9 Valero has an exceptionally experienced and able management team. Management has demonstrated
good fiscal discipline and a commitment to adding value through acquisitions by being willing to walk
away from projects when bidding wars develop. Valero has become an industry leader through a
combination of organic projects and these well-managed acquisitions.
Financial Statements Analysis
Valero’s 10-K reports present an exceptionally transparent set of financial statements (see Appendix 2) that
make it very easy to judge quality of earnings. In recent years, the story is a simple one. Two factors are driving
Valero’s rising earnings: the spread between grades of crude oil and strong refinery margins. Valero has already
converted a large percentage of their capacity to sour/heavy crude capable. These difficult to refine grades of crude
trade at lower prices than light, sweet crude, so Valero is often purchasing and using oil $7 to $15 cheaper than the
quoted WTI price. (Page 27 of the 2005 Company 10-K gives specific price differentials11.) The products refined
10
11
Based on conversations with Company IR representatives.
Available from http://www.valero.com/Investor+Relations/Financial+Reports+and+SEC+Filings/Form10-Ks/.
12
from those cheaper grades of crude are equivalent in all respects (though yields may be slightly different) and sell for
the same price as products from more expensive crudes, so this savings passes directly to Operating Income. The
spread between grades of crude oil shows a strong connection to price levels—cheaper grades of crude oil rise and
fall in price more slowly than premium grades. It is reasonable to assume this spread will remain wide as long as oil
prices are high.
Figure 8. Regression of West Texas Refinery Margins and WTI
Results of multiple regression for refinery margins
Summary measures
R-Square
Adj R-Square
StErr of Est
ANOVA Table
Source
Explained
Unexplained
0.7338
0.7181
2.0741
df
1
17
SS
201.58
73.13
MS
201.58
4.30
Regression coefficients
Coefficient
Constant
-3.2898
Crude Oil
0.2515
Std Err
1.4461
0.0367
t-value
-2.2750
6.8455
F
46.86
p-value
0.0000
p-value Lower limit Upper limit
0.0361
-6.3407
-0.2389
0.0000
0.1740
0.3290
Refinery margins in general have been strong as oil prices have risen. Average margin per barrel was $11.14
in 2005, up from $7.44 in 200412. Regression and correlation analysis shows that refinery margins have tracked
crude oil quite closely. (See Figure 8 for a regression of WTI and West Texas refining margins which shows that
approximately 72% of the variation in margins can be explained by variations in crude oil price.) As I modeled the
future performance of Valero, I assumed strong refinery margins through the period of rising oil costs and reduced
margins appropriately as prices began to fall in the forecast model.
Figure 9. DuPont Ratio Analysis for VLO
2001
Profit Margin (Net Income ÷ Revenues)
Total Asset Turnover (Revenues ÷ Average Total Assets)
Return On Invesment (PM * TAT)
Equity Multiplier (Average Total Assets ÷ Average Total Equity)
Return on Equity (ROI * EM)
2002
2003
2004
2005
3.76%
1.6x
6.03%
0.31%
2.01x
0.63%
1.64%
2.52x
4.13%
3.31%
3.12x
10.30%
4.37%
3.15x
13.76%
3.3x
3.4x
3.x
2.6x
2.3x
19.67%
2.15%
12.38%
26.69%
31.40%
DuPont analysis is a useful tool that focuses attention on the three critical elements of good financial
management: operating management, asset management, and capital structure management. Figure 9 gives a
complete DuPont analysis for 5 years of Valero’s financial statements. Profit Margin (Net Income ÷ Revenues) is a
quick measure of operating management. If this ratio is rising across time, as Valero’s is, then management is
controlling costs well while producing new revenues. The fiscal year 2002 numbers show a downward “blip” by
most measures. Management’s claim at the time was that Valero sacrificed some short term gains in that year, took
a loss on some divestments, and made some financing adjustments to prepare for stronger growth and new
acquisitions in the coming years. At the time this might have seemed like a dubious claim, but the excellent
performance in following years substantiates that claim.
Total Asset Turnover (Revenues ÷ Average Total Assets) is a measure of asset management, or what level
of assets is needed to produce a fixed level of sales. If this ratio is rising, the firm is producing more sales from a
given level of assets. However, all ratio-based performance measures are subject to manipulation, and extremely
high TAT levels may indicate that a firm is not replacing assets when it should. This would be a sign of very poor
asset management. Valero in some sense has no peer group because it is the largest independent US refiner. To
make peer group comparisons with companies refining on the scale that Valero does, it was necessary to go to the
big Integrateds and to separate their refining numbers from the other businesses in their statements. This analysis,
12
Valero 2005 10-K, pg. 25.
13
performed on 6 similar companies, showed TAT ratios ranging from 1.5x to 3.5x, so Valero seems to be within
industry standards on Total Asset Turnover. Return on Investment is an intermediary measure of the profitability
of the assets used by the firm. It should be rising over time. With the exception of 2002, Valero’s ROI shows
excellent growth over the past 5 years.
The Equity Multiplier is a quick check on debt levels. It is the only DuPont ratio that should not be rising
over time because a high EM can indicate excessively high debt and all the costs associated with high debt levels.
Industrial firms in general seem to try to maintain Equity Multiples at 3 or below, so it seems Valero may have
reached an acceptable capital structure after the 2002 restructuring.
Return on Equity is a measure of the profitability of the stakeholder’s investment in the firm, and should be
as high as possible over time. However, ROE can be high for the wrong reasons (i.e. high Equity Multiple) so
DuPont analysis cannot be simplified to only ROE. In this case, operating management is good (high PM), asset
management is strong (high TAT), capital management is appropriate (appropriate EM), and ROE is high and rising.
ROE can be assumed to be high for strong business reasons and the firm is judged to be in strong financial
condition.
Figure 10. Selected Financial Statement Ratios for VLO
2001
Inventory Turnovers
Days in Inventory
Accounts Receivable Turns
Dave in Accounts Receivable
Accounts Payable Turns
Days in Accounts Payable
Operating Cycle (days)
8.8x
42
19.4x
19
10.8x
34
60
2002
2003
17.9x
20
24.9x
15
18.1x
20
35
20.1x
18
26.3x
14
18.5x
20
32
2004
22.6x
16
34.5x
11
20.8x
18
27
2005
22.6x
16
30.4x
12
19.3x
19
28
A quick glance at some other ratios will confirm this assessment. Inventory turnover has increased greatly
since 2001 and shows that Valero is making more revenue with fewer inventories. Days in Accounts Receivable is
decreasing, showing that the Company is collecting its debts faster. Theoretically, we would like to also see an
increase in Days in Accounts Payable, showing that Valero was able to pay its creditors a little more slowly and
thereby hold the money within the firm longer. However, this may not be possible, and Valero’s increase in
Accounts Payable turnover is not out of line with Accounts Receivable turnover. The bottom line to this analysis is
that the Operating Cycle, a measure of how quickly the firm is able to convert assets to cash in hand, is decreasing
across time. This is another indication of excellent management.
In all the financial statements, there are no unusual items or red flags. I did an extensive analysis of all
tables and notes in the 10-K and also considered the effect of off-balance sheet items. None of these analyses had a
material impact on the financial picture. The financial statements show strong growth, both organically and through
acquisitions, and increased revenues driven by strong margins and economies of scale. Valero is a model of financial
health and managerial excellence within the industry.
Equity Valuation: Multiples
There are several difficulties with applying multiples valuations to Energy sector stocks at this time.
Table 2 shows common multiples applied to the 2008 values projected by the DCF model for Valero. The
entire energy sector is currently trading at low P/E multiples (currently 9.7x, 10 year average 15.1x, and 10 year high
41.9x). Since current figures may not be a useful guideline, I used the historical sector average P/E multiple in the
valuation for VLO. Price/Book and Price/Free Cash Flow may not be particularly useful valuations for Energy
stocks, but reasonable multiples applied to the asset growth predicted by the DCF model produce stock prices
consistent with the DCF valuation itself. Price/Sales and Price/EBITDA are probably the most useful multiples for
Energy sector valuations, and carrying forward the current multiples to projected 1998 numbers on those metrics
also results in stock prices significantly above the current price.
14
Table 2. Relative Multiples Valuations for VLO (2008 projected numbers)
High
Low
Mean
Current
P/Forward E n/a
P/Sales 0.51
P/Book 3.60
P/EBITDA n/a
P/FCF 17.40
2.60
0.10
0.60
1.50
2.70
7.10
0.19
1.30
5.10
7.10
7.80
0.47
2.70
6.00
8.30
Target
Target
(E,S,B, etc)
Multiple
per share
15.0x
.47x
2.0x
6.0x
12.0x
$6.60
201.7
55.3
16.1
7.8
Target
Price
$99.00
$94.80
$110.60
$96.60
$93.60
Multiples are an admittedly imprecise valuation shortcut, but this exercise has shown that reasonable
multiples estimates, applied to the revenue growth implied by the Crude Oil Regression model and the asset accrual
calculated by the DCF model, give stock values that are in the range I am predicting. No sensitivity analysis has
been done on these valuations because the predictions are obviously extremely sensitive to changes in either input.
Again, rather than using multiples to justify an investment, I suggest using them as a “common sense” double check.
In this case, they support the valuation implied by the other models.
Equity Valuation: Dividend Discount Model
Figure 11. Dividend Discount Model for VLO
Terminal
Value
Stage 2: Low Growth Phase
Stage 1: High Growth Phase
Year 2006
t=
1
Current Dividend $0.24
k= 9.8%
Dividend Growth Rate 25.0%
2007
2
$0.30
9.8%
25.0%
2008
3
$0.38
10.3%
25.0%
2009
4
$0.47
10.5%
25.0%
2010
5
$0.59
11.0%
25.0%
2011
6
$0.73
11.3%
3.0%
2012
7
$0.75
11.5%
3.0%
2013
8
$0.78
11.8%
3.0%
2014
9
$0.80
12.3%
3.0%
2015
10
$0.82
12.5%
3.0%
PV of Cash Flows $0.22
PV of Terminal Value
$0.25
$0.28
$0.31
$0.35
$0.39
$0.35
$0.32
$0.28
$0.25
2016
11
$0.82
9.5%
9.2%
$101.26
NPV $104.26
The Dividend Discount Model (DDM) is another common valuation technique used in the industry.
Finance theory says that a stock’s intrinsic value (“what it’s really worth”) is the present value of the future cash
flows (dividends) from the stock. If it were possible to predict future dividends accurately, and to calculate the
proper associated discount rates for each time period, it would be a trivial exercise to calculate intrinsic value. This
is certainly a viable technique, but it is important to understand some of the difficulties involved in applying this
method to Valero.
Predicting dividend payouts is not easy because companies may make dividend decisions based on many
criteria. In some industries (Energy is one of them), there is no clear link between dividend policy and any income
statement line. (I had several conversations with Valero’s Investor Relations department, who would not discuss
specific issues of dividend policy beyond saying that the company has a general commitment to return value to
shareholders.) In recent quarters, Valero has increased dividends with earnings, but it is difficult to say whether this
represents ongoing payout policy, is intended as a signal to the market, or some combination of the two.
For the DDM, I predicted three stages of dividend growth and made the following assumptions: During
the “oil boom” years predicted by the Crude Oil Regression model, Valero increases dividends 25% each year. This
may seem high on a percentage basis, but is a reasonable dividend rate, basis the projected DCF Income Statement
figures. The actual dollar amounts of the dividends are small because Valero’s 2005 dividend was only $0.19. (Note
that the DCF model’s predicted dividend payouts are not a useful guide here because they reflect neither
management’s attempts to target a specific payout rate nor management’s reluctance to reduce dividends in lean
years.) During the “price collapse” years following 2010, Valero increases dividends 3% each year to keep pace with
inflation. The company might, in fact, reduce dividends during these years, but I made the assumption that
management would not reduce dividends in this environment. At any rate, the model is extremely insensitive to
assumptions at this stage.
15
Figure 12. k Calculation for VLO DDM
Explicit Forecast
Year
risk-free rate
+ risk premium
=k
2006
5.25%
4.50%
9.75%
2007
5.25%
4.50%
9.75%
2008
5.50%
4.75%
10.25%
2009
5.75%
4.75%
10.50%
2010
6.00%
5.00%
11.00%
2011
6.00%
5.25%
11.25%
Terminal
2012
6.00%
5.50%
11.50%
2013
5.75%
6.00%
11.75%
2014
5.75%
6.50%
12.25%
2015
5.50%
7.00%
12.50%
2016
5.25%
4.25%
9.50%
In the DDM, the k value represents the return investors require to hold the stock and is equivalent to the
discount rate in a NPV calculation. It is a combination of the risk free rate (the rate on long-term US government
debt) and a “risk premium” to compensate investors for the risk of holding the stock. I made the following
assumptions in this model: the Fed has shown a proclivity in the past to keep rates high during times of high oil
prices. There is no clear evidence (no correlation between Fed Funds Rate and oil prices) that the Fed takes specific
action to reduce the inflationary impact of high oil prices, but they have exhibited a “pro cyclical” policy of only
cutting rates when oil prices fall. Therefore, I predicted an increase in the risk free rate through the forecast period
from 5.25% to 6%, only dropping the risk free rate after several years of declining oil prices. I assumed a baseline
equity risk premium of 4.5% (based on what many I-banks are using in their models) and a baseline βVLO =1. As oil
prices rise, the model assumes a small increase in the risk premium and only reduces it after several years of
declining oil prices. Whether or not these are realistic assumptions, the model is nearly completely insensitive to
these assumptions.
The problem with this DDM is that over 97% of the stock’s value resides in its terminal value. Thus, the
model is extremely sensitive to very small changes in terminal value assumptions and extremely insensitive to
assumptions in the explicit forecast period. In fact, the entire explicit forecast period could be zeroed out and the
model would still give a stock price over $100.
Terminal k value
Table 3. Sensitivity Analysis of Terminal Assumptions in VLO's DDM
Terminal Dividend Growth Rate
9.1%
9.2%
9.3%
($316.46)
($156.73)
($103.48)
$210.68
$626.03
($620.02)
$104.26
$78.95
$154.89
9.00%
9.25%
9.50%
8.9%
$322.47
$92.01
$53.63
9.0%
n/a
$127.61
$63.76
9.4%
($76.86)
($204.67)
$306.78
9.75%
10.00%
$37.86
$29.27
$42.50
$31.90
$48.58
$35.11
$56.87
$39.12
$68.84
$44.28
$87.65
$51.16
10.25%
$23.88
$25.55
$27.51
$29.84
$32.67
$36.16
Table 3 shows a sensitivity analysis of the terminal assumptions in the DDM. Finance theory also tells us
that competitive forces will tend to cause a firm’s long-term profits to revert to the mean (no firm can earn abovenormal profits in the long run.) Because of this, the terminal growth rate cannot exceed the growth rate of the
economy as a whole (long term inflation rate (3%-5%) + long term real GDP growth (2%-3%)) by more than a
small amount. (In the case of an international company, world GDP is used rather than domestic GDP, resulting in
final growth rates about a percent higher. Thus, the long-term growth rate window for international firms is 7%10%.) I used a terminal growth rate of 9.2% and terminal discount of 9.5% which are reasonable assumptions by
these criteria.
In the end, the DDM is probably not a very useful tool for Valero, and this should be viewed more as an
academic exercise than a valid estimate of intrinsic value. DDM gives most accurate valuations in the case of firms
which have stable or predictable growth rates, stable leverage, and which already pay out high dividends which
approximate FCFE. Energy sector stocks currently violate all of those assumptions—earnings are anything but
stable and predictable, leverage is difficult to predict because of very large required CapEx, and many energy
companies pay out very small dividends. Certainly, most of the “problems” associated with the terminal value in
this DDM come from the very small $0.19 starting dividend payment. Using this valuation as another reality check,
we find again that the predicted stock price is consistent with the DCF model and that only reasonable terminal
growth assumptions are required to support the predicted stock price.
16
Equity Valuation: Discounted Cash Flow
The best intrinsic value assessment for Valero comes from the Discounted Cash Flow Model (DCF)
presented in Appendix 4. The model itself is a fairly standard DCF, but some of inputs were derived through
examination of historical data, peer group data or regression modeling. Accounts from the Company’s financial
statements were combined and, in some cases, re-categorized to facilitate comparisons to other Energy sector
companies. (Complete DCF valuations like this one were run on 12 Energy stocks.) The general procedure
followed was:
9 The Crude Oil Price Forecast regression model in Appendix 1 was created and tested. Appropriate inputs for
regional supply and demand growth (blue text on yellow background in the model) were chosen and a forecast
for crude oil price to 2015 was created (see Figure 2).
9 Regression analysis was done on VLO quarterly earnings and crude oil price. Expectations were that changes in
crude price, rather than the absolute level, would have been more significant, but the regression showed a strong
link between actual nominal crude oil price level and revenues. Quarterly and annual data were both used in this
process—the quarterly to give more resolution and the annual to give more breadth. The resulting Revenue
Regression Model is in Appendix 2.
9 The forecast crude price was then used as the input to the Revenue Regression Model. This produced a forecast
revenue stream for VLO. The DCF model allows for multiple revenue cases and modifications, so the EIA
cases were used as secondary case inputs. Peer group assessment and comparison to consensus13 were used as a
check at this stage.
Figure 13. Schematic: Deriving the Revenue Forecast
VLO Revenue
Regression Model
Crude Oil Regression
Model
Output to DCF Model
Revenue Projection
Crude Oil Forecast
Peer Group
(12 companies)
Revenue Regression
Model
Supply/Demand
Estimates
9 DCF Modeling Assumptions: Income Statement
o Revenues: Regression Model.
o CoGS: Based on peer group (group of 12 refiners) observation and regression of CoGS against
revenues.
o SGA: Set as % of Revenues. Modeled moving average of last 3 years.
o Depreciation: Modeled in a separate DCF module. Based on a percentage of prior year’s net PPE
balance and also with depreciation as a percent of revenue.
o Interest Expense: Also a DCF sub-module. Company 10-K was analyzed for debt structure and
associated interest rates. Cash and Equivalents were assumed to be interest-earning. Actual interest
expense/income calculated by model.
o Income Tax Expense: Assumed 34.2% tax rate. The model is insensitive to changes in corporate tax
rate within reason.
o Dividend payout set as an average of last 3 years’ rate (% of net income).
o Other income statement items were modeled based on extrapolations from past levels.
13
StockVal data were used.
17
9 DCF Modeling Assumptions: Balance Sheet
o Surplus Funds: Reconciling item. Not interest-earning, and not included in working capital.
o Cash: Set as % of revenues.
o Receivables: Set as average of last 3 years’ Days Receivables.
o Inventory: Modeled as percent of revenue based on peer group study.
o CapEx: A major case input. CapEx historically had been 2.4% of revenues. Used this level as a
baseline going forward and only adjusted upward in strong earnings years.
o Net PPE: Calculated dynamically as net of prior year’s PPE, CapEx and Depreciation.
o Goodwill: Difficult to model without making assumptions about new acquisitions and impairment.
Model is completely insensitive to Goodwill assumptions so a gentle curve was used in the model.
o Necessary to Finance: The balancing item for Surplus Funds plug. No interest cost associated.
o Accounts Payable: Modeled as average of last 3 years’ Days Accounts Payable.
o Long-term debt: Another major case input. Assumed less borrowing in good earnings years and more
in lean years. This seemed to be the pattern followed by the peer group as well. The companies did
not finance CapEx during good years with debt and, in fact, paid off debt in good years. These
tendencies were modeled in the DCF case.
o APIC: Held constant as percent of total asset value.
o Other Equity Accounts: Assumed historical structure would carry forward unchanged.
o Other Balance Sheet Accounts not listed: Modeled as % of revenues.
9 Free Cash Flow to the Firm was calculated and discounted at WACC which adjusted dynamically to changes in
capital structure each year. Discount rate of 9.51% was suggested by CAPM and was accepted as reasonable.
5% terminal growth assumed. Implied terminal EBITDA multiple is 11.1x, which is reasonable for this
industry.
9 Project EPS is out of the low end of consensus for 2006 and 2007, and near the middle of the range for 2008.
The reason for this may be that the Crude Oil model is predicting lower prices at the end of 2006 than analysts
expect. However, the model also predicts a string of years with strong revenue surges (2008: 56%, 2009: 16%,
2010: 19%). This is not out of line with recent history (2002: 94%, 2004: 44%, 2005: 50%).
9 Unlevered Free Cash Flow growth is volatile but consistent with VLO and peer group history.
Figure 14. Unlevered Free Cash Flow YOY Growth as predicted by DCF
Year
UFCF YOY growth
2006
53%
2007
(57%)
2008
150%
2009
(41%)
2010
76%
2011
(62%)
2012
7%
2013
(50%)
2014
78%
9 The model gives an intrinsic value of $100.16/share, which is a 51.3% upside. Based on these assumptions and
this model’s valuation, this stock is a strong BUY.
9 Using the EIA High Oil Price Case as the revenue driver, the model gives an intrinsic value of $105.72/share,
which is a 59.7% upside. Based on these assumptions and this model’s valuation, this stock is a strong BUY.
9 Using the EIA Reference Oil Price Case as the revenue driver, the model gives an intrinsic value of
$70.788/share, which is a 7.1% upside. Based on this oil price projection the stock is currently fairly valued and
would be a HOLD.
18
2015
125%
Technical Position and Assessment
Figure 15. VLO Monthly Bars
From a technical perspective, VLO appears to
be in an extremely strong trend that is showing no signs
of climax or overheat. This trend appears to be
overdue for a retracement, but any downside should
probably be considered a pullback in a healthy trend.
$70.75 (the all-time high) provides resistance overhead
and $55.50 should be an area of support.
Figure 15 is a monthly (log scale) chart of VLO
with the following indicators: 20 period exponential
moving average (XMA) in red surrounded by Keltner
Channels at 2.5 average true ranges (20 period average)
above and below the XMA. The XMA provides a
rough baseline of what should be considered the mean
price, and the Keltner channels are used to quantify
“strong impulse” moves. The Keltners, set where they
are, contain approximately 95% of the market action, so
a market that spends an extended period of time against
one of the bands, as VLO has, is in an extremely strong
trend.
The 14-period ADX (orange line, bottom
panel) shows a value above 50, though it is currently
Figure 16. VLO Weekly Bars
declining. This indicator is best understood to show
the strength of the trend, with values above 30
indicating a strong trend. As the market pulls back,
ADX will naturally fall, so this is not to be taken as a
sign of impending trend change. The monthly chart
shows an extended trend without any significant
pullback to the XMA since mid 2003. This is unusually
strong trending action, but we can safely say the market
is overdue for a pullback. Notice also that recent
activity (since mid 2005) is forming a gently rounding
top, also indicating that the immediate move may be
losing steam.
Figure 16 is a weekly chart of VLO that
highlights some significant support and resistance
points. A small double bottom was put in around
$55.50 (mid May 2006 and early June 2006) and this
area should provide support. This is also consistent
with expectations from the monthly chart which would
expect to see support around the 20 period XMA on an
initial pullback. That average is currently below $54 but
will move up every month. To the upside, $70.75 is the
all-time high put in on 4/25/06. An extremely ominous sign would be a brief excursion above this level and then a
close below. Such a “failure test” at new highs can be a warning of serious price weakness and even possible trend
change.
Bottom line technical forecast: expect that the next few months (6-8) could see price weakness in VLO.
(The entire Energy sector and even crude oil futures prices are showing similar patterns.) This pullback, if it occurs,
should be seen as an opportunity to add to longs. Look to $55.50 to provide first support to the downside, though a
move that carried even into the $49-$50 range would not be cause for concern. Extended price action (either time
or price level) below $48 would be a bad sign for integrity of the uptrend. To the upside, watch for $70.75 to
provide initial resistance. If the market tests and fails at that level, I would suggest lightening long positions. Several
19
closes above this level would be confirmation of the resumption of the uptrend, though then we would have to be
mindful of potential momentum divergences. Market geometry and structural ratios support the possibility of a
price advance to $100.
Summary
Strengths and Opportunities
9 Largest US refiner commanding 19% of total US refinery capacity. US refinery utilization is near full
capacity and is unlikely to grow anytime in the foreseeable future. Economies of scope and scale apply
in this industry—bigger players are able to extract more value in the form of earnings. The list of
potential acquisition candidates is shrinking, so the Valero should be able to maintain its leadership
position within the industry.
9 A large portion of Valero’s capacity is heavy/sour crude capable and Valero is continuing to convert
existing capacity. While not a sustainable competitive advantage (other refiners lag behind Valero, but
are in the process of making the same conversions), this does offer Valero increased earnings power in
the short-term.
9 Valero seems to have found the ideal degree of integration into a midstream pipeline system.
9 International exposure is limited.
9 Company financials point to strong, well-managed growth and show good prospects for future growth.
There are no unusual items or red flags on the published financial statements.
9 Future performance will be tightly correlated to energy prices. It seems reasonable to expect higher
energy prices at least through the end of the current decade and Energy sector returns should be above
average.
9 Comprehensive DCF valuation suggests an intrinsic value far above the current stock price. The
technical price structure of VLO also supports the possibility of a large price advance. The probabilities
appear to be strongly in favor of a price advance to at least $95.
Risks and Concerns
9 Future performance will be tightly correlated to energy prices. In the unlikely event that energy prices
fall dramatically, Valero’s earnings and stock price could be adversely affected.
9 International growth potential is limited.
9 Refinery margins and earnings are extremely volatile, even quarter to quarter.
9 Refiners in general face large risks: operational risks (shutdowns, refinery fires, labor disputes),
regulatory risks (EPA and other Federal, State and local regulations), and increased taxes. These risks
translate into above-average potential risk in the stock prices.
9 Some traditional valuation methods (DDM, multiples) do not apply well to energy stocks in the current
environment. This may make it difficult to do a comprehensive valuation. However, it is my opinion
that the DCF valuation is thorough and comprehensive enough to support a position in Valero at this
time.
Conclusions
The benefits clearly outweigh the potential risks for this stock. I am recommending Valero Energy
Corporation as an immediate BUY and am assigning a two year price target of $95. Extended price action below
$48, a shift in the fundamentals of the underlying Energy markets, or a substantial change in Management would be
cause to reassess this recommendation.
The opinions and information contained herein have been obtained or derived from sources believed to be reliable, but the Fisher College of Business makes no
representation as to their timeliness, accuracy or completeness or for their fitness for any particular purpose. This report is not an offer to sell or a solicitation of
an offer to buy any security. The information and material presented in this report are for general information only and do not specifically address individual
investment objectives, financial situations or the particular needs of any specific person who may receive this report. Investing in any security or investment
strategies discussed may not be suitable for you and it is recommended that you consult an independent investment advisor. Nothing in this report constitutes
individual investment, legal or tax advice.
20
Table of Figures
Figure 1. WTI $/bbl (Inflation-adjusted)................................................................................................................................... 5
Figure 2. Brent Crude Oil US$ / bbl (predicted by regression)............................................................................................ 8
Figure 3. NYMEX Light Sweet Crude Futures, Monthly Bars.............................................................................................. 8
Figure 4. The Business Cycle in the Petroleum Industry ...................................................................................................... 10
Figure 5. Scatterplot of Cumulative Log Change Energy Index and WTI price.............................................................. 10
Figure 6. Valuations for S&P Energy Sector........................................................................................................................... 11
Figure 7. Refined Products (gallons)......................................................................................................................................... 11
Figure 8. Regression of West Texas Refinery Margins and WTI......................................................................................... 13
Figure 9. DuPont Ratio Analysis for VLO.............................................................................................................................. 13
Figure 10. Selected Financial Statement Ratios for VLO...................................................................................................... 14
Figure 11. Dividend Discount Model for VLO...................................................................................................................... 15
Figure 12. k Calculation for VLO DDM ................................................................................................................................. 16
Figure 13. Schematic: Deriving the Revenue Forecast .......................................................................................................... 17
Figure 14. Unlevered Free Cash Flow YOY Growth as predicted by DCF...................................................................... 18
Figure 15. VLO Monthly Bars................................................................................................................................................... 19
Figure 16. VLO Weekly Bars..................................................................................................................................................... 19
Table 1. NYMEX Light Sweet Crude Oil Futures................................................................................................................... 9
Table 2. Relative Multiples Valuations for VLO (2008 projected numbers) ..................................................................... 15
Table 3. Sensitivity Analysis of Terminal Assumptions in VLO's DDM ........................................................................... 16
21
Appendix 1 Brent Crude Oil Price Forecast
Results of multiple regression for Brent_Crude price
Summary measures
Multiple R
R-Square
Adj R-Square
StErr of Est
0.9997
0.9994
0.9972
0.6222
ANOVA Table
Source
Explained
Unexplained
SS
1378.4159
0.7743
Regression coefficients
Coefficient
Constant
8040.5
North_America
-0.0173
Cent_America
0.0070
Europe
0.0036
Middle_East
0.0019
Africa
0.0175
Americas
-298.9486
Eurpoe
-588.6594
Asia_Pacific
83.1807
Std Err
613.3
0.0014
0.0028
0.0010
0.0005
0.0025
65.7456
51.5029
38.8649
projected
historical
demand
supply
df
8
2
Date
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
MS
F
172.3020 445.0405
0.3872
t-value
13.1
-12.4639
2.4640
3.5947
3.8176
6.8861
-4.5471
-11.4296
2.1403
p-value
0.0
0.0064
0.1327
0.0694
0.0623
0.0204
0.0451
0.0076
0.1657
p-value
0.0022
Lower limit Upper limit
5401.8
10679.2
-0.0233
-0.0114
-0.0052
0.0193
-0.0007
0.0078
-0.0002
0.0040
0.0066
0.0285
-581.8290 -16.0682
-810.2586 -367.0601
-84.0413 250.4027
YOY Growth Rates For Regression Inputs
Supply
Demand
N_America C-America Europe Middle_East
Africa
Americas Europe Asia_Pac
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
1.91%
6.51%
1.30%
2.01%
4.62%
3.19%
(0.75%)
4.37%
1.53%
5.43%
1.61%
5.47%
4.40%
2.76%
0.94%
5.60%
(0.60%)
6.39%
(0.27%)
5.71%
(1.60%)
2.03%
0.45%
(2.14%)
(3.55%)
(3.02%)
1.98%
(3.02%)
(0.50%)
2.33%
(0.43%)
4.12%
1.65%
1.71%
3.25%
5.36%
3.14%
0.69%
(0.90%)
2.62%
0.01%
(1.35%)
3.35%
(2.68%)
0.95%
0.45%
0.91%
0.76%
1.17%
(1.21%)
5.43%
(6.12%)
1.31%
0.15%
(0.08%)
3.07%
0.88%
(4.11%)
4.20%
8.50%
5.19%
0.83%
0.90%
3.30%
(0.39%)
(2.00%)
3.58%
5.55%
9.81%
3.28%
1.47%
5.49%
(3.54%)
0.00%
(0.21%)
2.16%
6.14%
0.43%
0.77%
1.57%
1.00%
2.00%
1.00%
1.50%
2.00%
0.40%
0.50%
1.25%
(0.50%)
2.00%
(0.50%)
2.00%
4.00%
0.40%
0.50%
3.00%
(1.00%)
2.50%
3.00%
3.00%
6.00%
0.40%
0.50%
2.50%
0.50%
2.50%
2.50%
4.00%
5.00%
0.40%
0.50%
1.50%
(1.00%)
4.00%
4.50%
4.00%
4.00%
0.40%
0.50%
1.00%
2.00%
(1.50%)
(0.50%)
2.00%
3.00%
1.00%
1.25%
2.00%
2.50%
1.00%
1.00%
2.00%
2.00%
1.00%
1.25%
2.50%
2.50%
(2.00%)
2.00%
1.00%
3.50%
1.00%
1.25%
3.00%
3.50%
2.00%
1.50%
(2.00%)
2.00%
1.00%
1.25%
3.50%
2.00%
1.00%
1.00%
3.00%
1.00%
1.00%
1.25%
4.00%
22
Appendix 2 Valero Financial Statements from Company 10-K
Valero Energy Corporation
INCOME STATEMENT
2001
2002
2003
2004
2005
Revenues
14,988
29,048
37,969
54,619
82,162
CoGS
12,745
25,863
33,587
47,797
71,673
846
1,332
1,656
2,141
2,926
6
675
694
705
771
153
258
299
379
458
1,239
920
1,733
3,597
6,334
8.3%
3.2%
4.6%
6.6%
7.7%
Refining Operating Expenses
Retail Selling Expenses
Administrative expenses
EBITDA
EBITDA Margin
Depreciation
EBIT
EBIT Margin
Equity& Min int: Valero LP
238
449
511
618
875
1,001
471
1,222
2,979
5,459
6.7%
1.6%
3.2%
5.5%
6.6%
39
0
(14)
27
41
Dist pref sec of subsid. Trusts
(13)
(30)
(17)
0
Interest and debt expense, net
(89)
(286)
(261)
Other income (expense), net
(260)
0
(266)
(5)
9
15
(48)
52
895
150
987
2712
5284
6.0%
0.5%
2.6%
5.0%
6.4%
Income tax expense
331.3
58.2
365.1
906.0
1,697.0
Net Income
563.6
91.5
621.5
1,806.0
3,587.0
Net Margin
3.8%
0.3%
1.6%
3.3%
4.4%
EBT
EBT Margin
Dividends
Net to ret'd earnings
0.0
0.0
4.3
13.0
13.0
563.6
91.5
617.2
1,793.0
3,574.0
23
Valero Energy Corporation
BALANCE SHEET
2001
2002
2003
2004
2005
ASSETS
Surplus funds
Cash
Restricted Cash
0
0
0
0
0
269
379
369
864
436
77
30
44
25
30
771
1,558
1,328
1,839
3,564
1,453
1,436
1,913
2,318
4,039
177
95
119
175
142
Other current assets
1,389
38
45
45
65
Current Assets
4,136
3,536
3,817
5,265
8,276
Net PPE
7,217
7,412
8,195
10,317
17,856
0
0
265
265
327
334
341
320
311
298
2,211
2,580
2,402
2,401
4,926
502
596
665
833
1,045
14,400
14,465
15,664
19,392
32,728
Accounts receivable, net
Inventories
Deferred income tax assets
Investment in Valero LP
Intangibles
Goodwill
Deferred charges & other assets
Total assets
LIABILITIES
Short-term, current L/T, CLO
2,561
477
0
412
222
Accounts Payable
1,390
1,825
2,288
2,963
5,563
Accrued expenses
421
294
356
519
581
Taxes, other than Income Taxes
320
368
365
480
595
Income taxes payable
Current Liabilities
Necessary to finance
61
43
56
160
344
4,753
3,007
3,064
4,534
7,305
0
0
0
0
0
Long-term debt, less current
2,517
4,494
4,239
3,893
5,109
Deferred income tax liabilities
1,388
1,301
1,605
2,011
3,615
Cap. lease obligations
288
0
6
8
47
Oblig. pref. sec of subsidiary trust
373
373
0
0
0
Minority interest
116
116
0
0
0
763
867
1,015
1,148
1,602
10,197
10,157
9,929
11,594
17,678
68
Other long-term liabilities
Total Liabilities
SHAREHOLDER'S EQUITY
Preferred stock
0
0
201
208
Common stock
1
1
2
3
6
3,469
3,437
3,921
4,358
8,164
APIC
Treasury stock, at cost
Retained earnings
Other equity
Total SH equity
Total Liabilities & SH equity
(150)
(42)
864
914
(41)
1,483
(199)
3,200
(196)
6,673
0
0
0
0
0
4,203
4,308
5,735
7,798
15,050
14,400
14,465
15,664
19,392
32,728
24
STATEMENT OF CASH FLOWS
Net Income
Cash flows from operating activities
Adjustments to reconcile net income to net cash provided by operating activities
Depreciation and Amortization Expense
Gain on sale of investment in Javelina
Impairment of investment in Clear Lake Methanol Partners
Distributions in excess of (less than) equity in earnings of Valero LP
Minority interest in net income of Valero LP
Noncash interest expense and other income, net
Deferred income tax expense
Changes in current assets and current liabilities
Changes in deferred charges and credits and other, net
Net cash provided by operating activities
Cash flows from investing activities
Capital expenditures
Deferred turnaround and catalyst costs
Buyout of assets under structured lease arrangements
Premcor Acquisition, net of cash acquired
Aruba Acquisition, net of cash acquired
St Charles Acquisition
Proceeds from sales of assets to Valero LP
Proceeds from sales of Tesoro notes
Proceeds from sale of the Denver Refinery
Proceeds from liquidation of investment in Diamond-Koch
Proceeds from disposition of the Golden Eagle Business
CapEx & other CF related to Golden Eagle Business
Proceeds from sale of investment in Javelina
General partner contribution to Valero LP
Contingent payments in connection with acquisitions
Other acuisitions, net of cash
(Investment) return of investment in Cameron Highway Oil Pipeline Project
Proceeds from dispositions of PPE and certain home heating oil operations
Minor acquisitions and other investing activities, net
Net cash used in investing activities
Cash flows from financing activities
Cash payment to UDS shareholders in connection with UDS acq.
Financing required to fund cash portion of UDS acquisiton
Decrease in short-term debt, net
Repayment of capital lease obligations
Long-term debt borrowings, net of issuance costs
Long-term debt repayments
Proceeds from cash settlement of PEPS Unit purchase contracts
Redemption of company-obligated preferred securities of subsidiary trust
Proceeds from issuance of common units by Valero LO, net of issuance costs
Cash distributions to minority interest in Valero LP
Proceeds from the sale of common stock, net of issuance costs
Issuance of common stock in connection with employee benefit plans
Common and preferred stock dividends
Purchase of treasury stock
Other
Net cash provided by (used in) financing activities
Valero LP's cash balance as of the date that we ceased consolation of Valero LP
Effect of foreign exchange rate changes in cash
Net increase (decrease) in cash and temporary cash investments
Cash and temporary cash investments at beginning of year
Cash and temporary cash investments at end of year
25
2001
$564
2002
$92
2003
$622
2004
$1,804
2005
$3,590
238
15
271
(153)
(28)
906
449
14
(23)
2
(209)
(53)
272
511
(4)
2
2
287
429
(96)
1,753
618
57
1
10
345
203
(80)
2,958
875
(55)
4
27
255
1,082
21
5,799
(394)
(142)
(55)
(2,717)
8
(1)
(3,302)
(628)
(152)
301
925
(184)
(24)
16
(7)
249
(976)
(136)
(275)
(309)
380
90
(1)
(51)
(106)
94
(41)
(1,331)
(1,292)
(304)
(567)
(541)
(53)
(36)
108
(2,685)
(2,133)
(441)
(2,343)
45
78
(29)
(85)
38
30
(60)
(4,900)
2,053
173
543
(19)
78
(21)
(157)
2,651
255
15
269
(2,055)
(47)
4,517
(2,828)
(14)
102
(42)
(46)
(412)
1
110
269
379
(153)
(289)
4,014
(3,943)
14
(200)
200
(4)
250
99
(51)
(73)
(136)
(336)
40
(10)
379
369
(1)
3,782
(3,718)
406
135
(79)
(318)
207
15
495
369
864
(2)
1,537
(2,414)
227
(106)
(571)
(2)
(1,331)
4
(428)
864
436
Appendix 3 Valero Revenue Regression Model and Inputs
Results of simple regression for Smoothed Quarterly Earnings
Summary measures
Multiple R
0.9791
R-Square
0.9586
StErr of Est
1411.4739
ANOVA table
Source
Explained
Unexplained
df
1
18
SS
830728797
35860655
MS
830728797
1992259
F
417
p-value
0.0000
Regression coefficients
Coefficient
Constant
-5324.37
Crude_Oil
443.04
Std Err
916.01
21.70
t-value
-5.81
20.42
p-value
0.00
0.00
Lower limit
-7248.85
397.46
Upper limit
-3399.90
488.63
Historical
Projected
Valero Energy Corporation Projected Annual Earnings, 3 Oil Price Cases
Date
12/1/2015
12/1/2014
12/1/2013
12/1/2012
12/1/2011
12/1/2010
12/1/2009
12/1/2008
12/1/2007
12/1/2006
12/1/2005
12/1/2004
12/1/2003
12/1/2002
12/1/2001
12/1/2000
12/1/1999
12/1/1998
12/1/1997
12/1/1996
EIA Reference Case
Oil
VLO Revenue
47.79
63,395
46.90
61,821
47.37
62,659
47.65
63,142
47.34
62,604
47.29
62,503
50.14
67,567
52.30
71,390
55.62
77,269
59.10
83,438
59.43
82,162
43.33
54,619
32.15
37,969
29.42
26,976
19.33
14,988
28.46
14,671
26.08
7,961
11.28
5,539
18.32
5,756
25.39
4,991
EIA High Case
Oil
VLO Revenue
66.41
96,392
73.34
108,681
83.94
127,458
89.27
136,896
96.78
150,208
103.85
162,747
89.47
137,263
78.95
118,611
63.08
90,497
54.90
75,996
59.43
82,162
43.33
54,619
32.15
37,969
29.42
26,976
19.33
14,988
28.46
14,671
26.08
7,961
11.28
5,539
18.32
5,756
25.39
4,991
Legend
Inputs
Outputs
Historical
26
Regression Model Case
Oil
VLO Revenue
76.30
113,912
73.31
108,624
70.67
103,937
67.64
98,572
64.99
93,874
62.65
89,732
61.12
87,021
59.87
84,796
59.26
83,727
59.10
83,438
59.43
82,162
43.33
54,619
32.15
37,969
29.42
26,976
19.33
14,988
28.46
14,671
26.08
7,961
11.28
5,539
18.32
5,756
25.39
4,991
t=
27
Discounted Cash Flows
PV of unlevered free cash flow
PV of terminal value
Total Discounted Cash Flows
NPV
Free Cash Flow Calculation
EBIT
+ Depreciation
EBITDA
- CapEx
EBITDA less CapEx
- taxes on EBIT
- Changes in Working Capital
Unlevered free cash flow
VALUATION ANALYSIS
1,222
511
1,733
(976)
757
(418)
(244)
96
17.86%
2003
-2
3,959
3,959
2,813
2,813
58,503
16%
8.8%
8.8%
Debt / Value Ratio
WACC
Cumulative Discount Factor
2006
1
5,458
1,358
6,816
(1,809)
5,007
(1,866)
1,165
4,307
46.21%
2005
0
2008
3
0.0
0.0
34.2%
5.0%
9.51%
5
2
4
4
2009
4
1,560
1,560
17%
8.7%
18.3%
3,580
3,580
16%
8.8%
28.7%
1,933
1,933
17%
8.8%
40.0%
3,123
3,123
18%
8.7%
52.2%
9,928
1,729
11,658
(3,873)
7,784
(3,394)
363
4,753
92.50%
Projected
2010
5
1,091
1,091
23%
8.5%
65.1%
5,896
1,859
7,755
(3,575)
4,181
(2,015)
(363)
1,802
144.36%
2011
6
Revenue case: Brent Regression
CoGS Case: Custom Scenario
CapEx case: Custom Scenario
Long-Term Debt Case: Custom Scenario
4,098
9,491
6,814
1,345
1,435
1,579
5,444
10,926
8,393
(1,990)
(2,964)
(3,430)
3,454
7,962
4,963
(1,401)
(3,244)
(2,329)
(208)
(110)
73
1,846
4,608
2,706
66.48%
100.00%
92.50%
DCF Enterprise Value Calculation
2007
2
Options
Avg Strike Price
Tax rate
Terminal Value Growth
Discount Rate
DCF assumptions
DCF Inputs
Case Assumptions
Revenue Case
CoGS Case
CapEx Case
L/T Debt Case
5,459
875
6,334
(2,133)
4,201
(1,866)
478
2,813
35.35%
2,979
618
3,597
(1,292)
2,305
(1,018)
(105)
1,182
18.24%
Historical
2004
-1
Summary Valuation
Market
DCF
Stock Price
$66.20
$100.16
Diluted Shares
588.0
588.0
Equity Market Value
38,926
58,892
Net Debt
(389)
(389)
Enterprise Value
38,537
58,503
Value per Share
10.8x 16.4x Proj EPS
Enterprise Value Multiple
6.1x
9.2x EBITDA
Implied Upside in DCF
51.3%
1,078
1,078
25%
8.4%
79.0%
499
499
27%
8.3%
93.9%
819
819
24%
8.4%
110.2%
1,697
36,352
38,049
24%
8.4%
127.9%
4,060
90,130
11.1x
2015
10
Terminal value calculation
Projected Free Cash Flow
Terminal Enterprise Value
Implied Term. Value EBITDA Multiple
2014
9
5,897
2,203
8,100
(2,294)
5,806
(2,016)
77
3,867
166.62%
3,200
2,099
5,299
(3,033)
2,266
(1,094)
(204)
968
198.08%
2013
8
0.99
0.9
1.00
5.05%
4.50%
3,429
2,174
5,603
(2,586)
3,017
(1,172)
(123)
1,722
182.82%
5,057
1,989
7,046
(3,258)
3,788
(1,728)
(130)
1,929
167.06%
2012
7
r (CAPM)
risk-free rate
risk premium
β (override)
β (Reuters)
β (Yahoo!)
β (Calculated)
Appendix 4 Valero Discounted Cash Flow Model
SHE
Other Non-Cur Liabs
Indexed Case Inputs (last historical year=100%)
50%
40%
200%
30%
150%
20%
100%
10%
0%
50%
-10%
0%
1999
2001
2003
2005
2007
2009
2011
2013
-20%
2015
Breakdown of Liability and Equity Accts
Long Term Debt
$120,000
Current Liabilities
$100,000
Revenue 5 year CAGR (green)
Revenue Index (Blue)
CoGS Index (Red)
250%
$80,000
$60,000
$40,000
$20,000
$0
2000
2002
2004
Margin and EPS Analysis
2006
2008
2010
2012
2014
Debt/Equity Ratio
12.0%
Gross Margin
100%
10.0%
8.0%
6.0%
Operating
Margin
75%
EBT Margin
50%
Net Margin
25%
value%
debt%
4.0%
2.0%
0.0%
1998
0%
2000
2002
2004
2006
2008
2010
2012
2014
2000
2016
2004
2008
2012
Capital Structure
250,000
200,000
150,000
total assets
current liabilities
noncurrent liabs
she
100,000
50,000
0
2000
I
2002
2004
2006
2008
2010
2012
2014
Sensitivity of Upside Downside to Discount Factor
and Terminal Growth
51.3%
4.00%
4.33%
4.67%
5.00%
5.33%
5.67%
6.00%
200%
150%
100%
Sensitivity of Implied Upside/Downside to Discount Factor and Terminal Growth Rate
0.8%
7.75%
8.50%
9.25%
10.00%
10.75%
11.50%
91.5%
61.1%
39.6%
23.5%
11.1%
1.2%
104.7%
69.6%
45.4%
27.6%
14.1%
3.5%
120.8%
79.5%
52.0%
32.3%
17.5%
6.1%
140.7%
91.4%
59.6%
37.5%
21.3%
8.9%
166.2%
105.7%
68.6%
43.6%
25.5%
12.0%
199.7%
123.4%
79.3%
50.5%
30.3%
15.4%
246.1%
145.9%
92.1%
58.6%
35.8%
19.3%
50%
0%
6.0
0%
5.3
3%
4.6
7% Terminal
12
%
Discount Factor
12
%
11
%
9%
10
%
8%
-50%
9%
Implied Upside/Downside
250%
4.0
0%
Growth Rate
51.3%
30%
32%
34%
36%
38%
28
Sensitivity of Implied Upside/Downside to Beta (top) and Tax Rate (side)
0.3
0.7
1.0
1.3
1.6
352.3%
135.3%
60.6%
24.7%
6.4%
338.7%
128.1%
55.5%
20.6%
2.9%
325.2%
120.8%
50.4%
16.6%
0.6%
311.6%
113.5%
45.3%
12.5%
4.1%
298.1%
106.2%
40.2%
8.5%
7.6%
2.0
11.5%
14.5%
17.4%
20.4%
23.4%
12.25%
6.8%
5.0%
3.1%
0.9%
1.4%
4.0%
6.8%
29
0
0
0
339
2.3%
19
320
Accounting Change
Discontinued Operations
Extraordinary Item
Net Income
Net Margin
Dividends
Net to ret'd earnings
242.1
$1.40
189
0
0
339
Income tax expense
Equity In Affiliates
Minority Interest
Net Income Before Extra. Items
Diluted Weighted Average Shares
EPS
(76)
(7)
528
3.6%
Interest Income(Expense), Net Non-Oper
Other, Net
EBT
EBT Margin
127
0
174
0
0
611
4.2%
14,671
13,760
912
6.2%
Total Revenue
Cost of Revenue, Total
Gross Profit
Gross Margin
Selling/General/Admin. Expenses, Total
Research & Development
Depreciation/Amortization
Interest Expense(Income) - Net Operating
Other Operating Expenses, Total
Operating Income
Operating Margin
2000
INCOME STATEMENT
VLO
255.2
$2.21
23
541
0
0
0
564
3.8%
331
0
0
564
(89)
(18)
895
6.0%
159
0
238
0
0
1,001
6.7%
14,988
13,591
1,398
9.3%
2001
440.4
$0.21
44
47
0
0
0
92
0.3%
58
0
0
92
(286)
(36)
150
0.5%
933
0
449
0
0
471
1.6%
29,048
27,195
1,853
6.4%
Historical
2002
488.0
$1.27
49
573
0
0
0
622
1.6%
365
0
0
622
(261)
26
987
2.6%
993
0
511
0
0
1,222
3.2%
37,969
35,243
2,726
7.2%
2003
552.2
$3.27
77
1,726
0
0
0
1,804
3.3%
906
0
0
1,804
(260)
(9)
2,710
5.0%
1,084
0
618
0
0
2,979
5.5%
54,619
49,938
4,681
8.6%
2004
588.0
$6.11
112
3,478
0
0
0
3,590
4.4%
1,697
0
0
3,590
(266)
94
5,287
6.4%
1,229
0
875
0
0
5,459
6.6%
82,162
74,599
7,563
9.2%
2005
588.0
$5.47
172
3,219
3,391
4.5%
1,761
0
0
3,391
(348)
42
5,152
6.8%
1,544
0
1,358
0
0
5,458
7.2%
75,996
67,637
8,360
11.0%
2006
588.0
$4.00
102
2,349
2,451
3.2%
1,273
0
0
2,451
(413)
39
3,724
4.9%
1,396
0
1,345
0
0
4,098
5.4%
75,997
69,158
6,840
9.0%
2007
588.0
$9.69
245
5,695
5,940
5.0%
3,085
0
0
5,940
(554)
87
9,024
7.6%
2,121
0
1,435
0
0
9,491
8.0%
118,611
105,564
13,047
11.0%
2008
Valero Energy Corp. (Reuters Data)
588.0
$6.60
181
3,882
4,063
3.0%
2,110
0
0
4,063
(723)
82
6,173
4.5%
2,588
0
1,579
0
0
6,814
5.0%
137,263
126,282
10,981
8.0%
2009
588.0
$9.71
253
5,712
5,965
3.7%
3,098
0
0
5,965
(965)
100
9,063
5.6%
2,990
0
1,729
0
0
9,928
6.1%
162,747
148,100
14,647
9.0%
2011
588.0
$4.88
128
2,867
2,995
2.0%
1,556
0
0
2,995
(1,443)
97
4,551
3.0%
2,759
0
1,859
0
0
5,896
3.9%
150,208
139,693
10,515
7.0%
Projected
2010
588.0
$3.73
99
2,195
2,294
1.7%
1,191
0
0
2,294
(1,656)
85
3,486
2.5%
2,537
0
1,989
0
0
5,057
3.7%
136,896
127,314
9,583
7.0%
2012
588.0
$1.52
40
892
932
0.7%
484
0
0
932
(1,864)
80
1,416
1.1%
2,348
0
2,099
0
0
3,200
2.5%
127,458
119,811
7,647
6.0%
2013
588.0
$2.03
54
1,195
1,248
1.1%
648
0
0
1,248
(1,601)
69
1,896
1.7%
2,004
0
2,174
0
0
3,429
3.2%
108,681
101,073
7,608
7.0%
2014
588.0
$4.64
122
2,727
2,849
3.0%
1,480
0
0
2,849
(1,629)
60
4,329
4.5%
1,780
0
2,203
0
0
5,897
6.1%
96,392
86,511
9,880
10.3%
2015
30
0
269
0
948
1,453
86
1,380
4,136
0
15
0
586
540
39
106
1,285
2,677
0
59
0
287
4,308
27
0
807
104
101
1,039
0
0
1,215
0
407
120
2,781
0
0
1
1,249
(44)
322
0
1,527
4,308
Net PPE
Goodwill, Net
Intangibles, Net
Long Term Investments
Other Long Term Assets, Total
Total assets
LIABILITIES
Notes Payable/Short Term Debt
Current Port. of LT Debt/Capital Le
Accounts Payable
Accrued Expenses
Other Current liabilities, Total
Current Liabilities
Necessary to finance
Capital Lease Obligations
Long Term Debt
Minority Interest
Deferred Income Tax
Other Liabilities, Total
Total Liabilities
SHAREHOLDER'S EQUITY
Redeemable Preferred Stock, Tota
Preferred Stock - Non Redeemable
Common Stock, Total
Additional Paid-In Capital
Treasury Stock - Common
Retained Earnings (Accumulated D
Other Equity, Total
Total SH equity
Total Liabilities & SH equity
0
0
1
3,469
(150)
864
18
4,203
14,400
0
288
2,890
116
1,388
763
10,197
506
0
1,390
421
2,436
4,753
7,217
2,211
334
0
502
14,400
2001
2000
BALANCE SHEET
ASSETS
Surplus funds
Cash & Equivalents
Short Term Investments
Total Receivables, Net
Total Inventory
Prepaid Expenses
Other Current Assets, Total
Current Assets
0
0
1
3,437
(42)
914
(1)
4,308
14,465
0
0
4,867
116
1,241
927
10,157
477
0
1,825
294
411
3,007
7,412
2,580
341
0
596
14,465
0
379
0
1,558
1,436
38
126
3,536
Historical
2002
0
201
2
3,921
(41)
1,483
170
5,735
15,664
0
6
4,239
0
1,605
1,015
9,929
0
0
2,288
356
421
3,064
8,195
2,402
320
265
665
15,664
0
369
0
1,328
1,913
45
162
3,817
2003
0
208
3
4,358
(199)
3,200
229
7,798
19,392
0
8
3,893
0
2,011
1,148
11,594
0
412
2,963
519
639
4,534
10,317
2,401
311
265
833
19,392
0
864
0
1,839
2,318
45
200
5,264
2004
0
68
6
8,164
(196)
6,673
335
15,050
32,728
0
47
5,109
0
3,615
1,602
17,678
0
222
5,563
581
939
7,305
17,856
4,926
298
327
1,045
32,728
0
436
0
3,564
4,039
65
172
8,276
2005
0
68
6
8,431
(180)
9,892
323
18,539
34,910
0
47
5,236
0
3,118
1,703
16,371
0
259
4,483
657
867
6,266
18,307
4,926
760
684
1,520
34,910
100
684
0
2,837
4,560
152
380
8,713
2006
0
68
6
9,369
(213)
12,241
317
21,788
39,275
0
47
6,284
0
3,087
1,594
17,486
0
346
4,615
639
875
6,475
18,951
5,200
570
760
1,900
39,275
3,100
950
0
2,897
4,560
114
272
11,894
2007
0
68
6
14,323
(299)
17,936
494
32,528
58,902
0
47
8,835
0
4,968
2,486
26,374
0
422
7,304
954
1,358
10,038
20,480
4,940
835
909
2,135
58,902
16,604
1,779
0
4,699
5,931
170
422
29,604
2008
0
68
6
17,114
(352)
21,818
575
39,230
70,991
0
47
11,359
0
5,652
2,944
31,761
0
527
8,512
1,148
1,572
11,759
22,331
4,693
1,123
1,220
2,883
70,991
23,773
2,059
0
5,265
6,863
226
556
38,742
2009
0
68
6
21,248
(427)
27,530
679
49,104
88,190
0
47
14,992
0
6,709
3,439
39,086
0
648
10,038
1,346
1,867
13,899
24,475
4,458
1,233
1,440
3,472
88,190
35,380
2,441
0
6,298
8,137
248
607
53,112
Projected
2010
0
68
6
23,894
(386)
30,397
627
54,607
98,847
0
47
21,746
0
6,223
3,182
44,240
0
570
9,516
1,236
1,721
13,043
26,190
4,235
1,141
1,272
3,021
98,847
46,585
2,253
0
5,842
7,510
230
568
62,988
2011
0
68
6
24,812
(354)
32,592
572
57,696
102,848
0
47
24,684
0
5,650
2,910
45,152
0
530
8,628
1,135
1,569
11,861
27,459
4,024
1,066
1,196
2,849
102,848
51,324
2,053
0
5,291
6,845
215
527
66,255
2012
0
68
6
25,604
(330)
33,485
532
59,364
106,108
0
47
27,588
0
5,265
2,701
46,744
0
495
8,134
1,053
1,461
11,143
28,394
3,822
975
1,107
2,645
106,108
55,262
1,912
0
4,939
6,373
196
483
69,165
2013
0
68
6
23,835
(281)
34,679
454
58,762
98,728
0
47
23,695
0
4,493
2,305
39,966
0
418
6,866
898
1,246
9,427
28,806
3,631
835
938
2,234
98,728
50,426
1,630
0
4,213
5,434
168
414
62,284
2014
0
68
6
24,125
(249)
37,406
403
61,759
99,971
0
47
23,993
0
3,982
2,045
38,212
0
373
5,871
797
1,105
8,145
28,897
3,450
743
837
1,996
99,971
53,533
1,446
0
3,732
4,820
150
368
64,048
2015
31
2.3%
3.4x
7.9%
2.8x
22.2%
25.x
14.6
25.5
14.3
17.1
21.4
7.5
1.2
0.6
0.6
0.6
1.8
8.0
1.8%
1.2%
na
Accounts Receivable Turnover
Accounts Receivable in Days
Inventory Turnover
Inventory Days
Accounts Payable Turnover
Accounts Payable in Days
Operating Cycle
Current Ratio
Quick Ratio
CFO/Current Liabilities
Solvency Ratios
Debt to Value
Debt to Equity
Interest Coverage Ratio
Working Cap as % of Revenues
CapEx as % of Revenues
CapEx as % Prior Year PPE
2000
Profit Margin
Total Asset Turnover
ROI
Equity Multiplier
ROE
DuPont Analysis
RATIO ANALYSIS
VLO
(2.5%)
1.6%
8.9%
0.7
2.4
11.3
0.9
0.3
0.3
15.9
12.4
29.5
13.6
26.8
19.5x
18.7
3.8%
1.x
3.9%
3.4x
13.4%
2001
2.2%
1.5%
6.2%
0.7
2.4
1.6
1.2
0.6
0.1
13.6
16.9
21.6
18.8
19.4
23.2x
15.7
0.3%
2.x
0.6%
3.4x
2.1%
Historical
2002
1.0%
1.3%
6.9%
0.6
1.7
4.7
1.2
0.6
0.5
9.9
17.1
21.3
21.0
17.3
26.3x
13.9
1.6%
2.4x
4.0%
2.7x
10.8%
2003
0.5%
1.1%
7.5%
0.6
1.5
11.5
1.2
0.6
0.8
6.8
19.0
19.2
23.6
15.5
34.5x
10.6
3.3%
2.8x
9.3%
2.5x
23.1%
2004
0.9%
1.1%
8.5%
0.5
1.2
20.5
1.1
0.5
1.0
6.7
17.5
20.9
23.5
15.6
30.4x
12.0
4.4%
2.5x
11.0%
2.2x
23.9%
2005
2.5%
1.3%
7.6%
0.5
0.9
15.7
1.4
0.6
0.0
11.5
13.5
27.1
15.7
23.2
23.7x
15.4
4.5%
2.2x
9.7%
1.9x
18.3%
2006
2.3%
1.3%
7.3%
0.4
0.8
9.9
1.8
0.6
0.0
13.8
15.2
24.0
15.2
24.1
26.5x
13.8
3.2%
1.9x
6.2%
1.8x
11.2%
2007
1.4%
1.2%
7.6%
0.4
0.8
17.1
2.9
0.6
0.0
9.2
17.7
20.6
20.1
18.1
31.2x
11.7
5.0%
2.x
10.1%
1.8x
18.3%
2008
Valero Energy Corp. (Reuters Data)
1.2%
1.2%
7.7%
0.4
0.8
9.4
3.3
0.6
0.0
8.9
16.0
22.9
19.7
18.5
27.6x
13.2
3.0%
1.9x
5.7%
1.8x
10.4%
2009
1.3%
1.2%
7.7%
0.4
0.8
10.3
3.8
0.6
0.0
8.6
16.0
22.9
19.7
18.5
28.1x
13.0
3.7%
1.8x
6.8%
1.8x
12.1%
Projected
2010
1.1%
1.3%
7.6%
0.4
0.8
4.1
4.8
0.6
0.0
9.6
14.3
25.5
17.9
20.4
24.7x
14.7
2.0%
1.5x
3.0%
1.8x
5.5%
2011
1.1%
1.2%
7.6%
0.4
0.8
3.1
5.6
0.6
0.0
9.4
14.0
26.0
17.7
20.6
24.6x
14.8
1.7%
1.3x
2.2%
1.8x
4.0%
2012
1.1%
1.2%
7.6%
0.4
0.8
1.7
6.2
0.6
0.0
9.2
14.3
25.5
18.1
20.1
24.9x
14.6
0.7%
1.2x
0.9%
1.8x
1.6%
2013
1.1%
1.2%
7.7%
0.4
0.7
2.1
6.6
0.6
0.0
9.6
13.5
27.1
17.1
21.3
23.8x
15.4
1.1%
1.1x
1.3%
1.7x
2.1%
2014
1.3%
1.2%
7.6%
0.4
0.6
3.6
7.9
0.6
0.0
9.8
13.6
26.9
16.9
21.6
24.3x
15.0
3.0%
1.x
2.9%
1.6x
4.6%
2015