Country Risk, Return and Pricing: The Global Landscape in January 2015

Advance warning: I apologize. I went a little over-the-top with heat maps in this post. They are neat, though, since you can hover over a country (with your mouse or clicker) and you should be able to see the data for that country. If you prefer your data in old-fashioned tables, they are available as links at the bottom of the post.

I am happy to say that I am done with my data updates for 2015. While I like analyzing data, I am glad that I will not have to work with really big and sluggish excel spreadsheets until next year. Following up on my last post on US equity risk premiums, I thought it would make sense in this one to go global and take a look at the numbers, as they stand in January 2015. I am well aware that the global landscape of pricing and risk can change at the drop of a hat, but knowing what they changed is always useful. 

Country Returns

The US market had a good year in 2014, with the S&P 500 delivering a total return of 13.48% for the year. However, it was not not the best performing market for the year, as quite a few emerging markets ranked higher. The table below lists the dozen best-performing and dozen worst-performing markets, in both local currency and US dollar terms, for 2014.

Stock Market Returns: Calendar Year 2014

As an investor, I am not sure how these numbers should affect my investment choices for the coming year. While the contrarian in me is telling me to avoid the winning markets and seek out the losing ones, I have learned through painful experiences to respect momentum enough not to be a blind contrarian. As a consequence, I will stick with my base plan of valuing individual companies, no matter what country they are incorporated or operate in, and looking for under valued companies. 

Country Risk

To estimate value for companies, though, I do have to wrestle with measuring risk globally, and variations in risk across countries. For the last two decades, I have been trying to measure country risk and have reported my estimates annually. While I am ultimately interested in measuring exposure to equity risk, I start with measures of default, because they are more easily available and are easier to connect to expected returns.

The most widely reported measure of sovereign default risk are sovereign ratings, with Moody’s, Standard and Poor’s and Fitch all providing this service. The attached spreadsheet uses the January 1, 2015, data from S&P and Moody’s to provide a comprehensive listing of the local currency sovereign ratings of all rated countries. The following picture captures the breakdown, at least in January 2015, of countries by broad ratings classes (using Moody's sovereign ratings where available and S&P's converted into Moody's ratings, for a few countries):

Sovereign Ratings: January 2015

One critique of sovereign ratings is that they often lag developments in the real world, with changes in ratings occurring well after investors have priced in the changes. The sovereign CDS market provides market-based estimates of sovereign default risk, though only 68 countries have sovereign CDS spreads (far fewer than the 142 countries that have sovereign ratings available on them). The table below lists the sovereign CDS spreads for all available markets in January 2015 (and is also available as a downloadable spreadsheet):

Sovereign 10-year CDS Spreads: January 2015

Both sovereign ratings and CDS spreads are measures of default risk and investors in search of a wider measure of country risk (which extends beyond default into political and legal risk) may want to take a look at the composite risk scores reported by the Political Risk Services (PRS) group. The PRS composite risk score, is scaled to 100, with higher numbers reflecting more risk. Using the most recent values for these scores, I have created a risk map:

The red spots in the map are the most risky countries in the world, at least according to the PRS measure in January 2015.

As I mentioned up front, my end game requires equity risk premiums by country. I started with the sovereign ratings (since they are available for more countries and then used the look up table below to estimate default spreads by country. 

After scaling up these default spreads to reflect the fact that equities collectively are riskier than bonds, I obtained country risk premiums for each country. I made a judgment that the equity risk premium  (5.78%, which I approximate to 5.75%) that I obtained for the S&P 500 was a good measure of the mature market risk premium and adding the country risk premium yields the total equity risk premium for each country). To complete the picture, I used the PRS scores for those frontier markets (such as Libya, Syria, Iran and Iraq) that are not rated by the rating agencies, found rated countries with similar scores and averaged out their equity risk premiums. While you can download the spreadsheet that contains my estimates of equity and country risk premiums by clicking here, the picture below captures the risk differences across the world.

Note that I have used 5.75% as the mature market premium not just for the US but for all countries rated Aaa by Moody's and that the equity risk premiums for non-Aaa rated countries reflects the additional country risk premium. Again, the red spots on the map represent the countries with the highest equity risk premiums. 

At this stage, if you are primarily an investor in US companies, you may be heaving a sigh of relief that you don’t have to deal with these country risk measures, but your relief should be short lived. Since a company’s risk exposure comes from where it operates and not from where it is incorporated, most US companies (including small ones) operate in multiple countries and it is the weighted average of the equity risk premiums across countries that should be used in valuing these companies.

Country Pricing

It is true that some markets are riskier than others, but a knee jerk avoidance of the riskiest markets may not always be a good strategy. After all, at the right price even the riskiest market can be a bargain. To get a measure of how global markets are being priced right now, I estimated a range of pricing multiples including price earnings ratios. EV/EBITDA and price to book value ratios for all of the countries in my sample. Note, as you look at these numbers, that some of the markets (especially in Africa) that I look at are very small with only a handful of listings and that the ratios computed for these markets have to be taken with a grain of salt. 

Starting with PE ratios, here is what I find, as I look across the globe at the start of 2015.

The countries with the lowest PE ratios are highlighted in red, though hovering over the spots will give you a quick measure of why they might not be bargains.

Moving on to EV/EBITDA multiples and looking for bargains, here is what the global picture looks like in 2015:

The countries that trade at the lowest multiples of EBITDA are in red.

Completing the story with price to book ratios, the visualization yields the following.

Again, it should come as no surprise that the countries that trade at well below book value (colored red) also happen to be countries that are in crises.

Looking across all three multiples and listing out the countries that have the lowest values for each one in aggregate:

Based on market values on 1/1/15 and trailing 12-month financials

It is true that most of the countries on this list are also risky ones but that does end the analysis. If the essence of investing is looking for mismatches, you are looking for countries that trade at low prices (relative to earnings, book value or sales), after adjusting for risk. Much as I would like to go into ways of doing this, I think I have outlived my welcome in this post. So, it has to be put off for another day.

So what now?

If you are an investor, there are very few places left to hide from globalization. Very few companies operate in domestic market vacuums and most are exposed, sometimes in good ways and sometimes in bad ones, to changes in the global market landscape. In 2014, it was Russia that was the game changer in the first part but oil prices played the bigger role in the second half of the year. I am not sure what will move global markets in 2015 but I am sure it will be an interesting year. 

Data Attachments

1. Sovereign Ratings for Countries: January 1, 2015
2. Sovereign CDS Spreads: January 1, 2015
3. Country and Equity Risk Premiums: January 1, 2015
4. PE, PBV and EV/EBITDA multiples by Country: January 1, 2015

Data update blog posts

1. An ERP Retrospective: Looking back (2014) and Looking forward (2015)
2. Country Risk, Return and Pricing: The Global Landscape in January 2015
3. The Tax Story (in January 2015): Myths, Misconceptions and Reality Checks
4. Putting the D in the DCF- The Cost of Capital
5. The X Factor in Business: Excess Returns and Value

An ERP Retrospective: Looking back (2014) and Looking forward (2015)

At the beginning of 2014, the expectation was that government bond rates that had been kept low, at least according to the market mythology, by central banks and quantitative easing, would rise and that this would put downward pressure on stocks, which were already richly priced. Perhaps to spite the forecasters, stocks continued to rise in 2014, delivering handsome returns to investors, and government bond rates continued to fall in the US and Europe, notwithstanding the slowing down of quantitative easing. Commodity prices dropped dramatically, with oil plunging by almost 50%, Europe remained the global economic weak link, scaling up growth became more difficult for China and the US economy showed signs of perking up. Now, the sages are back, telling us what is going to happen to markets in 2015 and we continue to give them megaphones, notwithstanding their  forecasting history. Rather than do a standard recap, I decided to use my favored device for assessing overall markets, the equity risk premium (ERP), to take a quick trip down memory lane and set up for the year to come.

The ERP: Setting the stage
The ERP is what investors demand over and above the risk free rate for investing in equities. as an asset class. At the risk of sounding over-the-top, if there is one number that captures investors' hopes, fears and expectations it is this number, and I have not only posted multiple times on it in the last few years but also updated it every month on my website. In making these updates, I have had to confront a key question of how best to measure the ERP. Many practitioners use a historical risk premium, estimated by looking at how much investors have earned on stocks, relative to the returns on something riskfree ( usually defined as government bills & bonds)). Due to the volatility in stock returns, you need very long time periods of data to estimate these premiums, with "long time period" defined as 50, 75 or even 100 years of data. At the start of each year, I estimate the historical risk premiums for the United States and my January 1, 2015 update is below:

Stocks: S&P 500; T.Bills: 3-mth Bills; T.Bonds: US 10-year
Geometric average is based on compounded returns

Based on this table, the historical equity risk premium for the US is between 2.73% to 8%, depending on the time period, risk free rate and averaging approach used. I will also cheerfully admit that I don't trust or use any of these numbers in my valuations, for three reasons. First, using a historical risk premium requires a belief in mean reversion, i.e., that things will always go back to the way they used to be, that I no longer have. Second, all of these estimates of risk premiums carry large standard errors, ranging from 8.65% for the 2005-2014 estimate (effectively making it pure noise) to 2.32% for the 86-year estimate. Third, it is a static number that changes little as the world changes around you, which you may view as a sign of stability, but I see as denial.

In pursuit of a forward-looking, less noisy and dynamic equity risk premium, I drew on a standard metric in the bond market, the yield to maturity:

As bond price rises (falls), yield to maturity falls (rises)

In the equity market analogue, the bond price is replaced with stock index level, the bond coupons with expected cashflows from stocks, with the twist that the cash flows can continue in perpetuity:

There are both estimation questions (Are cashflows on stocks just dividends, inclusive of buybacks or a broader measure of residual free cashflows to equity?) and challenges (Do you use last year's cash flow or a normalized value? How do you estimate future growth? How do you deal with a perpetuity?), but they are not insurmountable. In my monthly estimates for the ERP for the S&P 500, here are my default assumptions:

This estimate is forward-looking, because it is based on expected future cashflows, dynamic, because it changes as stock prices, expected cash flows and interest rates change, and it is surprisingly robust to alternative assumptions about cash flows and growth. The spreadsheet that I use allows you to replace my default assumptions with yours and check the effects in the ERP. 

The ERP in 2014

Using the framework described in the last section, I estimated an equity risk premium of 4.96% for the S&P 500 on January 1, 2014:

During 2014, the S&P 500 climbed 11.39% during the year but also allowing for changes in cash flows, growth and the risk free rate, my update from January 1, 2015, yields an implied equity risk premium of 5.78%:

At the start of each month in 2014, I posted my estimate of the ERP for the S&P 500 on my website. The figure below graphs out the paths followed by the S&P 500 and the ERP through 2014:

The ERP moved within a fairly narrow band for most of the year, ranging from just under 5% to about 5.5%, with the jump to 5.78% at the end of the year, reflecting the updating of the growth rate.

The Drivers of ERP in 2014

To understand the meandering of the ERP during 2014, note that it is determined by four variables:  the level of the index, the base year cash flow, the expected growth and the government bond rate, and is a reflection of the risk that investors perceive in equities. In the figure below, I chronicle the changes in these variables during 2014, at least in my ERP estimates.

A confession is in order. While I update the index levels and government bond rates in real time, I update cashflows once every quarter and the growth rates materially only once a year (at the start of each year). One reason for the precipitous jump in the ERP in the January 1, 2015, update is the updating of the long term growth rate to 5.58% on that date. Updating the cashflow and growth estimates more frequently will smooth out the ERP but not change the starting and ending points.

Perspective: Against history and other markets

When I stated earlier in the post that the ERP was the one number that encapsulated investor hopes and fears, I was not exaggerating, since every statement about the overall market can be restated in terms of the ERP. Thus, if you believe that the ERP at the start of 2015 is around 5.78% (my estimate but you can replace with yours), your market views can be laid bare by how you answer the following question: Given what you perceive as risk in the market, do you think that 5.78% is a fair premium? If your answer is that it (5.78%) is too low (high), you are telling me that you think stock prices are too high (low). 

One reason that I posit that I am not a market timer is because I struggle with this question and there are two simple comparisons that I use for comfort. One is to compare the ERP today to implied quit risk premiums in previous years to see how it measures up to historic norms. The figure below summarizes implied equity risk premiums from 1960 to 2014:

My estimates of ERP at the end of every year from 1960 to 2014

Looking at the historical numbers, the current ERP looks high, not low; it is close to the norm if you use only the post-2008 time period. It is this argument that I used to contest the notion that the market was in a bubble in June 2014.

The other is to compare the equity risk premium to risk premiums in other asset markets. In the bond market, for instance, the default spread for corporate bonds is a measure of the risk premium and the figure below compares the equity risk premium to the Baa default spreads each year from 1960 to 2014:

Baa rates from the Federal Reserve data site (FRED) and Baa default spread computed relative to US 10-year T.Bond rate in that year.

Again, if history is any indication, equity risk premiums do not look inflated, relative to Baa default spread, though it is entirely possible that both spreads are too low. (You can download the data and check for yourself.)

We are and will continue to be inundated by experts, sages and market prognosticators, each wielding their preferred market measures, trying to convince us that markets are under or over valued. In this New York Times article from December 31, 2014, looking at where the market stands going into 2015,  the writer pointed to two widely followed statistics, the Shiller PE, a measure of how stocks are priced relative to inflation-adjusted earnings, and the Buffett ratio, relating the market cap of US stocks to the US GDP, and suggested that both pointed to an over valued markets. Both Robert Shiller and Warren Buffett are illustrious figures, but I think that both statistics are flawed, the Shiller PE, because it does not control for low interest rates, and the Buffett ratio, because of its failure to factor in the globalization of US companies. On market timing, I prefer to set my own course and am not going to be swayed by celebrity name-power in making my judgments.

The Weakest Links

If you believe at this point that I am sanguine about what stocks will do next year, you would be wrong. The nature of equity investing is that it is always coupled with worries and that the best laid plans can be destroyed by events out of your control. The notion that stocks always win in the long term is misplaced and there is a reason why we earn a risk premium for investing in equities. Looking at 2015, these are the three biggest dangers that I see:

1. An Earnings Shock? While current stock prices can be justified based on current cash flows, the cash flows to equity investors in 2014, from dividends and buybacks, represented an unusually high percentage of earnings, which, in turn, were at a high watermark, relative to history.

Based on aggregate numbers for S&P 500

Note that US companies paid out 87.58% of earnings to investors, below the 2007 & 2008 levels, but still well above the historical average (73.68%), and the profit margin of 9.84% in 2014  is the highest in the 2001-2014 time period. Both aggregate earnings and the payout ratio will be tested in the year to come. With aggregate earnings, the first test will be in the near term as the dramatic drop in oil prices in 2014 will play havoc with earnings at oil companies. As I noted in my earlier post on oil prices, lower oil prices may create a net positive benefit for the economy, but the immediate earnings benefit to the rest of the market will be modest. The second test may come from slower economic growth. While the US economy looks like it is on the mend, earnings at US companies are increasingly global, and a slowing down of the Chinese economic machine coupled with more stagnation in Europe, may net out to lower earnings. With the payout ratio, the challenge will be to deliver the earnings growth that investors are expecting, while paying out the high percentage of earnings that they are right now.

2. Fear the Fed? I have made this point before in my posts, but it is worth making again. While the equity risk premium has gone up significantly since the pre-2008 crisis, all of the increase in the risk premium has come from the risk free rate dropping and not from expected returns on stocks increasing.

If the US 10-year T.Bond rate were at 4%, closer to pre-2008 levels, right now, the equity risk premium would be only 3.95%.

3. Crisis, contagion and collapse? If we learned nothing else from 2008, it should be this. We are all part of a global economy, connected at the hip, and while that can yield benefits, the contagion risk has increased, where a crisis in one part of the world spills over into the rest of it. Again drawing on my post on oil, one danger of the sudden collapse in oil prices is that it has not only increased uncertainty about economic growth in the next year but also increased the risk of large, levered oil companies defaulting and sending shockwaves through the rest of the economy.

The perfect storm, of course, would be for all three phenomenon to occur together: a drop in earnings and an increase in interest rates, with an overlay of a global crisis, with catastrophic consequences of stocks. I think that the odds of this happening are low, because the circumstances that cause an earnings collapse are the ones that would keep interest rates low, but I may be missing something. If you disagree, you could take the safe route and hold cash, but unless your probability assessments of a crash are high and a crash is imminent, that does not strike me as prudent. (I have reattached the spreadsheet that I developed for my post on bubbles that you can use to make your own assessment.) 

Bottom line

Like every other year in my investing memory, I start this year both hopeful and fearful, hopeful that financial markets will navigate through whatever the new year will throw at them and fearful that there will be something that will rock them. Given what I know now, I don't see any reason to dramatically alter my exposure to stocks, bonds or real assets, and I will continue to look for stocks that I think are under priced. I wish you the very best in your investment choices this year as well and I hope that no matter what happens to your portfolio, you are healthy and happy!

Attachments: Data Sets

1. Historical Returns for US stocks, T.Bonds and T.Bills: 1928-2014
2. Implied Equity Risk Premiums for S&P 500: 1960-2014
3. ERP, Baa Yields and Real Estate Cap Rates: 1960-2014

Spreadsheets

1. Implied ERP Spreadsheet: January 1, 2015

Data Update 2015 Posts

1. An ERP Retrospective: Looking back (2014) and Looking forward (2015)

The Oil Price Shock: Primary, Secondary and Collateral Effects

In the last few weeks, financial markets have been rocked by the drop in oil prices, and in the process reminded us of three realities. The first is that for all the money that is spent on commodity price forecasting, there is very little that we have to show for it. The second is that all large macroeconomic events create winners and losers and the net effect of this oil price change, whether positive, neutral or negative, may take a while to manifest itself. The third is that investors are generally ill-served by either panicky selling of all things oil-related or the mindless buying of the most beaten-up oil stocks.

Oil: Prices drop and uncertainty climbs

At the start of 2014, the price per barrel of Brent crude oil was approximately $108/barrel, following three years of prices higher than $100/barrel. In fact, there seemed to be little reason to believe, given signs of economic recovery in the United States, that oil prices would drop any time soon. A combination of mild demand shocks (with reduced demand from China) and more noticeable supply shocks conspired to create the price drop, starting in September, accompanied by more uncertainty about future prices:

While much of the attention has been directed at the 40% drop in oil prices, the tripling in implied volatility in oil prices is a worth paying attention to and as I will argue later, could have an effect on not just oil stocks but on the overall market.

The initial stories about the oil price shock were almost all positive, suggesting that lower gas prices would allow consumers to spend more money on retail, restaurants and other businesses, thus boosting the economy. In the first two weeks of December, though, there was an abrupt shift in mood, as the same journalists who were lauding the oil price drop a few weeks ago were pointing their fingers at it as the primary culprit behind worldwide stock price declines in those weeks.

The Clueless Trifecta: Forecasters, Companies and Investors

The most sobering aspect of the oil price collapse is that is truly came out of nowhere, with none of the economic forecasters at the start of 2014 predicting the magnitude of the drop. In early 2014, Bloomberg's survey of the "most accurate" oil price forecasters yielded a forecast of $105 for oil prices for the year, illustrating that "accurate" is a relative term in this market. In a Reuter's poll in December 2013, which surveyed analysts about oil prices in 2014, the lowest price forecast was $75 by Ed Morse, Gobal Head of Commodities Research at Citibank and a longtime bear on oil prices. 

If you believe that oil companies, being closer to the action, were prescient, you would be wrong. Early in 2014, Chevron announced that its budgeting would be based upon oil prices of $110/barrel, with John Watson, the company’s CEO, stating, “There is a new reality in our business… $100/bbl is becoming the new $20/bbl in our business… costs have caught up to revenues for many classes of projects.” and adding that, “If $100 is the new $20, consumers will pay more for oil.” Chevron was not alone in this assessment and oil companies globally made investment, acquisition and production decisions based upon the assumption that triple-digit oil prices were here to stay, which explains why at a $60 oil price or lower, almost a trillion dollars in investments made by oil companies were no longer viable. Looking at airlines, where fuel costs represent a large proportion of operating expenses, there is evidence that fuel hedging follows the oil price, rather than leading it. Fuel hedging peaked in 2008, just as oil prices peaked, and have tracked oil prices down in the years since. 

Completing the clueless trifecta, investors have also been behind the curve on oil prices.  Institutional money continued to flow into oil stocks for most of the year and flowed out only in the last quarter as oil stocks tumbled.  The so-called smart money did worse, with hedge funds among the biggest losers in oil stocks, with big names like Icahn and Paulson leading the way with big money-losing bets. If there is any good news for oil price bulls, it is that oil forecasters are now predicting lower oil prices next year, oil companies are reassessing their assumptions about a normal oil price, airlines are reducing or even suspending their hedging and institutional investors are fleeing from oil stocks. Given their collective track record, this may be the best time to bet on rising oil prices.

The Biggest Losers

When oil prices drop, the most immediate impact is on oil producers and the ecosystem that serves them, including equipment and service providers. Within this group, though, the effect can vary depending on geography, size and leverage, as we will see in the nest section.

a. Companies in the oil business
The effect of an oil price change on a oil producing company may seem obvious, but it goes beyond the effect on revenues and earnings in the near term. By changing the payoff to growth and the risk in the company, a change in oil price can have a multiplier effect on value.

With these effects in place, you should expect the most negative effects of declining oil prices to be at highly levered oil companies with costlier reserves and higher fixed costs. 

Let’s look at the numbers. In the last three months, as oil prices have dropped, oil company stocks have taken a pummeling, losing a jaw-dropping $1.7 trillion in market capitalization, as evidenced in the table below, with companies broken down into different sub-businesses:

Source: S&P Capital IQ

Note that the companies at the production and drilling end of the oil cycle have been hurt the most by lower prices, while the companies that have been hurt the least are at refining and distribution end. Within the oil business, the damage also varies across companies. Breaking the numbers down further, here is what we see:

Smaller, lower-rated companies have been hit harder than larger, investment-grade companies, with the carnage being greatest for Latin American companies. In the only surprising (at least to me) finding, firms with the highest profit margins (in terms of EBITDA/Sales) have seen bigger losses in market value than firms with lower margins.  (Update: My first thought on this was that firms with higher EBITDA/Sales might have higher debt ratios and that the debt effect was overwhelming the profitability effect. The table below gives partial support, since it is among the most highly levered firms that you see the high profitability/negative return relationship to be stronger, but there is something else also happening in the background. So, back to the grind..)

Simple averages of 3-month returns across stocks

Note that I was using this measure of profitability as a rough proxy for the cost of reserves owned by companies, since you should expect companies with higher cost reserves to be hurt more by lower oil prices than those with lower cost reserves. As higher oil prices have induced companies to explore for and develop new reserves, the cost of extracting oil is much higher at some of the newer reserves, as this chart for just shale oil reserves in the US indicates:

Source: 

I would take the breakeven prices that analysts report for reserves with a  grain of salt, because computing a true breakeven would require significantly more information about sunk versus incremental as well as fixed versus variable costs of product than we have access to, but the fundamental truth remains. As oil prices drop, the effect on value and viability will vary across reserves and that effect should then percolate through to companies.

b. Oil Exporting Countries
Moving from companies to countries, it is clear that the companies that lose the most from lower oil prices are the big oil exporters. Among those countries, though, the effects will vary (as they did with companies), based upon the cost of extracting oil from the reserves, how much sovereign debt is owed by the country and how dependent they are on oil revenues to balance their books. Countries with higher-cost reserves that are more dependent on oil revenues to meet debt obligations/balance books should be more negatively affected by oil price changes, and the table below provides these statistics:

Between September 16 and December 16, as oil prices retreated, the most vulnerable country (partly because of its dependence on oil for revenues and partly because of geopolitical events) has been Russia. In the graph below, we capture the carnage in changes in the sovereign CDS spread for Russia (a measure of default risk in the country) and in the Russian Ruble.

Looking more broadly, it is clear that the damage is not limited to Russia, as evidenced in this graph of sovereign CDS spreads for four oil exporting countries: Russia, Venezuela, Saudi Arabia and Mexico (with the September 16 CDS price being set at 100 for all four).

The damage has been greatest in Russia and Venezuela, with the Russian CDS increasing 137.83%  and the Venezuelan CDS more than tripling. However,  Saudi Arabia and Mexico, though in much better shape, have also been affected with the Mexican CDS increasing about 58% and the Saudi CDS increasing 65%.

The Ripple Effects

The damage extends beyond the oil business to green energy companies, which have benefited from high oil prices in the last decade, and lenders to oil companies, who feel the effects of increased credit risk. In the table below, I estimate the effect of lower oil prices on green/clean energy companies and corporate bonds issued by energy companies:

As with the oil sector, the extent of the damage varies across sub-groups, greater for the ten largest solar companies than it is for companies across the solar energy chain or more broadly in clean energy. Consistent with the behavior of returns across stocks across ratings classes, investment grade energy bonds were much less affected than below investment grade bonds. 

The winners from lower oil prices are harder to find, at least in the short term. You would expect that companies that have a high proportion of their costs connected to oil prices to gain the most, and the two sectors that were mentioned as beneficiaries were the airlines and trucking companies.

The airlines were the biggest gainers, but note that the collective market value added (about $55 billion across all companies in the sector, globally) was dwarfed by the losses of more than $2 trillion in oil and green energy companies.

In the long term, the general consensus seems to be that lower oil prices will be good for the economy and perhaps, even for stock prices. Looking at oil price movements and their effect on the economy, inflation and stock prices over the last 40 years, here is what I find:

Correlation between lagging oil price changes & leading macro variables: 1974-2013

Between 1974 and 2013, there is little evidence that lower oil prices (in either dollar or percentage terms) have had any effect on economic growth (real GDP), interest & inflation rates or stock prices. In fact, the only variable where there is a relationship is with the US dollar, and lower oil prices have led to a weakening of the currency historically. Looking at the trade off, there are two key benefits that come from lower oil prices. The first is that consumers will be spending less on oil (for transportation and heating) and will thus have more money to spend on retail, leisure and other consumer discretionary items. The second is that lower oil prices will reduce inflation, at least in the near term, thus giving central banks a little more wiggle room in monetary policy. There are, however, two potential costs. The first is that with a large enough oil price drop, the financial distress at oil companies and oil exporting countries may spread into the rest of the economy; defaults by large oil companies or a large sovereign borrower can create chaos in the financial markets. The second is that oil prices in free fall are often accompanied by higher uncertainty about future oil prices, as has been the case in the last few weeks, which, in turn, can lead to more uncertainty about overall economic growth, interest rates and inflation. Since these are drivers of the overall equity risk premium, a higher equity risk premium and lower stock prices will ensue. It is true, that the oil price drop in the last few weeks, has been large, relative to history, and that the effects may therefore be different, but that may be one more reason not to wait and see what the macroeconomic effects of these prices will be.

What now?

You may not be a market timer or oil price forecaster but oil prices do have an effect on your portfolio and perhaps on your investment strategy. As you look at the damage created by plunging oil prices, at least to the oil in your portfolio, it is easy to second guess decisions that you made weeks, months or even years ago. I believe that regret and navel gazing is not only pointless but dangerous and that your time will be better spent picking up the pieces and looking forward. Generically, there are four viewpoints that you can have on oil prices: that they will continue to decline (the momentum story), that $60 is the new normal price ($60 is the new $100), that they have fallen too far and will bounce back (the contrarian play) or that any of the above (price agnostic). Within each viewpoint about oil, you can either go for a protective strategy or an aggressive one, with the latter becoming more attractive as your confidence in your viewpoint increases.

You can put me firmly in the "price agnostic" category. The oil price exposure that I have in my portfolios reflects investments that I have made over time in stocks that I perceived as good value at the time that I made them and were not designed primarily to increase my oil price exposure. If I choose to sell them, it will be because I don't view them as good value, given oil prices at the time of the assessment, any more and not because I have a point of view on oil prices.  Thus, my Lukoil investment from about four weeks ago, when oil prices were $77/barrel,  is down about 15%, but  given today's oil price, it is under valued today. My investment timing clearly left much to be desired but selling it today will not get me my money back!

Attachments:

1. Companies in the oil sector: Price changes from 9/16/14 to 12/16/14
2. Oil Prices and Macroeconomic Variables
3. Sovereign CDS spreads for oil exporters: 9/16/14 to 12/16/16

Up, up and away! A crowd-valuation of Uber!

In June 2014, I tried to value Uber and arrived at an estimated value for the firm of $6 billion, an impressive number for a young firm, but well below the VC estimates of value of $17-$18 billion at the time of my post. Much of the reaction was predictable, with readers whose priors were confirmed by my assessment of value liking it and those whose priors were different disagreeing,and sometimes vehemently. Disagreement and debate don't bother me in the least, since they can only advance the valuation narrative, but I do think that putting my narrative and valuation front and center undercut my objective in two ways. First, it made for passive analysis, where you could pick and choose which one of my valuation inputs you agreed with and which ones that you found erroneous, the justifying your prior biases. Second, some who disagreed took the easy way out, arguing that it was my use of an intrinsic value (DCF) model that had led me down the wrong path and that it was therefore unfixable. 

Now that Uber is in the news again, with value estimates of $40 billion and higher floating around, I decided to revisit the valuation, but from a different angle. Rather than presenting my valuation, I want to open the process up and I would like to invite you along for the journey. Like a book or movie where you get to write not just the ending but the entire story, I will provide the architecture and you can build your own valuation story (and value) for Uber. The good news is that this valuation will reflect your views (not mine) on Uber. The bad news is that if you don't like the value, you cannot blame me.

When Narrative drives Value

While my original valuation of Uber was all about the numbers, I followed it up with a post where I argued that if you disagreed with my value, it was not because you had a problem with my estimates (of growth or risk) but because you were taking issue with my narrative. Underlying my original valuation was a story that I was telling about Uber as an urban cab/limo service company that would continue to attract new users into the market, while maintaining its high profit margins. In response to a post by Bill Gurley, venture capitalist investor (and director) in Uber, where I was accused of missing the story by a mile, I conceded that I knew far less than he did about the company and that his narrative for the company - Uber as a car-service for the masses with global networking benefits - would lead to a much higher value for the company. 

While that may sound abstract, the best way to see the link between story telling and number crunching is to take Uber on the valuation process, with you making your judgments at each step of the way. As you make this journey, a few (gentle) reminders of issues that you will face along the way:

1. This is your valuation: Contrary to what you might have been taught in your valuation classes, valuations are and should never be just about the numbers. To the extent that you will be making choices on these number, this will be your estimate of valuation, reflecting not only what you know about the company (and its products, management etc.) but also your personal biases (whether you like the company or not). 
2. You are almost certainly wrong: Lest you view this is an insult, so is my assessment of value and so are the VC’s valuations. It is not because we don't understand valuation or have not done our homework, it is because we are trying to play God and forecast the future. 
3. You should be open to revisiting it: Following up on the last proposition, it stands to reason that the choices you make in valuing Uber today will not be the choices that you will make tomorrow or a week from now. So, keep the door open for changes not just at the margins but in your central narrative.
4. Be willing to act on it: There is no point to valuing companies, if you are not willing to act on your valuations. With Uber, it is true that you and I are restricted in what we can do, since the company is still private. However, it is also clear that the explosive growth in the estimated value of the company sets it on a path to being public (sooner, rather than later), at which point our valuations will become actionable.

Setting the stage
The first step in valuation is assessing where the company is right now and we start off at a disadvantage, because it is amazing how little we know about the operating details of a company that is in the news as much as Uber. According to the company's website, it operates in 51 countries and in about 230 cities on six continents, and it has also expanded its product offerings, both within the car service market (with U4B, directed at businesses and UberPool, allowing for car pooling)and in new markets (with UberRUSH, its delivery service in New York City).

The only updated revenue numbers came from an article in Business Insider, which seems to be one the company's preferred venues for leaking selective information. According to the article, the company projects gross receipts of $10 billion in 2015, up three times from gross receipts in 2014, which in turn more than tripled relative to receipts in 2013. While the company originally kept 20% of these receipts as revenues, it is unclear whether that number has slipped in recent months, as it has gone aggressively for new growth. While I am normally loath to value companies based upon second-hand information, and especially so if the information comes from a leaked corporate document, I am going to assume that the company will generate $3.5 billion in gross receipts for 2014 and that its slice has stayed at 20%, giving it revenues of $700 million for the year. I have no idea whether it is profitable after covering its operating costs, but the impact on the final value of these initial numbers is small enough that it is worth moving forward.

Building your Uber narrative

To set up the link between the narrative that you will be telling for Uber and its value, I will borrow the set-up that I used in this post on narrative and numbers, where I took the key inputs into my valuation and connected them to stories told about companies:

There are thus six steps to the narrative process and your choices at each step will determine the numbers from which we estimate value.

Step 1: Potential Market

In my initial valuation of Uber, I treated it as an urban car-service company and was taken to task rightly for having too cramped a vision of the company. It is quite clear from both its words and actions that Uber has much larger designs and I will leave it to your judgment whether it will succeed. Based on rudimentary research of the potential markets that Uber could be in, here is what I get as a list:

The potential starting market can range from $100 billion (for urban car service) to close to $300 billion (if you treat it as transportation company, going after all of the markets above). Since this is your narrative, its your choice to make and it will have significant value consequences. 

Based on what you know (and think about) Uber, which of the following do you think is its potential market?

Potential Market	Market size (in millions)	Description
A1. Urban car service	$100,000	Taxi cabs, limos & car services (urban)
A2. All car service	$150,000	+ Rental Cars+ Non-urban car service
A3. Logistics	$205,000	+ Moving + Local Delivery
A4. Mobility Services	$285,000	+ Mass Transit + Car Sharing

Step 2: Market Growth

Uber is not only disrupting the existing players in the market that it disrupts but it is also attracting new users into the market, either by attractive non-cab users to try Uber or increasing the usage of car services, in general. Assuming that this process continues, the growth rates in these markets could increase if Uber's services (or Uber-like services) become more widely accessible. Here again, the choice is yours.

Based on the potential market(s) you chose for Uber in step 1, what effect do you see Uber (and Uber-like services) having on the expected growth rate in the market?

New user effect on market growth	Annual growth rate (next 10 years)
B1. No new users (no growth effect)	3.00%
B2. Increase total market by 25% over next 10 years	5.32%
B3. Increase total market by 50% over next 10 years	7.26%
B4. Double market size over next 10 years	10.39%

Step 3: Market Share

Having chosen a potential market and a growth rate in that market, the third step is making a judgment on what market share you would expect Uber to command once the market hits steady stay (in ten years). That choice will depend in large part on whether you think Uber's products/services have network effects, where increased usage of Uber by customers in a market makes it more attractive to other potential customers, and whether you think these network effects are local (in the city/region of usage) or global (in other cities/regions). The arguments for local network effects are easy (the more Uber users there are, the more Uber cars there are, which in turn makes it easier/quicker to get an Uber ride)  but the ones for global network benefits may be more of a stretch (links to credit cards, inertia, uniformity of service, staying with the known). Once you have assessed the pluses and minuses, here are your choices.

Based on your assessment of Uber, what type of network effect (if any) do you see for its products and services?

Network Effect	Market Share	Description
C1. No network effects	5%	Open competition in every market
C2. Weak local network effects	10%	Dominance in a few local markets
C3. Strong local network effects	15%	Dominance in multiple local markets
C4. Weak global network effects	25%	Weak spillover benefits in new markets
C5. Strong global network effects	40%	Strong spillover benefits in new markets

Step 4: Revenue Slice & Operating Costs

Uber gets to keep a portion of the gross receipts paid by users for an Uber service, representing their revenues. That slice was initially set at 20% of the receipts but whether it can stay at that level will depend upon both the markets that Uber decides to operate in and the competition within each market. Thus, if Uber decides to go into the logistics market (moving and local delivery), it will have to accept a much lower slice of revenues, since competition is more intense. Even within the urban car service market, more intense competition from existing players (Lyft) or new entrants could put Uber's revenue slice under pressure. This choice again is yours to make:

Given the markets that you see Uber entering and the competition it faces within those markets, how strong and sustainable are Uber's competitive advantages?

Competitive Advantage	Revenue Slice	Description
D1. None	5%	Unrestricted entry + No pricing power
D2. Weak	10%	Unrestricted entry+ Some Pricing Power
D3. Semi-strong	15%	Unrestricted entry + Pricing Power
D4. Strong & Sustainable	20%	Restricted entry + Pricing Power

Step 5: Reinvestment Needs

Uber's existing business model, where it acts as an intermediary and does not invest in cars or equipment, has low capital intensity and as a consequence, much of its growth has come with relatively low reinvestment. That could change, if Uber decides to change its business model or if it has to do acquisitions to continue to generate growth. 

Based on the business model that you see Uber adopting as it goes for the market share (that you forecast) in your potential market, which of the following reinvestment policies best fits the company?

Reinvestment	Sales/Capital Ratio (Higher number= Less investment)
E1. Minimal capital needs, no acquisitions	10.00
E2. Minimal capital needs, small acquisitions	5.00
E3. Service company median	3.00
E4. Technology company median	2.50
E5. US company median	2.00
E6. Capital intensive company median	1.50

Step 6: Risk (Cost of capital & Survival risk)

As I noted in the table above, there are types of risk that you have to grapple with in valuation. The first is the risk in operations, which causes revenues and earnings to be volatile over time, and that risk is captured in the risk-adjusted return you demand for investing in the company. In valuation, the cost of capital becomes the measure of this risk-adjusted return and is generally estimated by looking at publicly traded companies (even though Uber is privately held still). Rather than wrestle with the minutiae of inputs into the model, you can make a judgment on where in the cross-sectional distribution of costs of capital across all companies you would put Uber.

Based on your assessment of the risk in the market that Uber is entering and where the company is in its life cycle, what cost of capital would you pick for the company?

Risk Profile	Cost of Capital
F1. Lowest decile of US companies	7.00%
F2. 25th percentile of US companies	7.50%
F3. Median of US companies	8.00%
F4. 75th percentile of US companies	10.00%
F5. Ninth decile of US companies	12.00%

The other risk for a young company is survival risk, i.e., the risk that you are one disaster from shutting operations. That risk increases for smaller companies with small cash holdings, large cash needs and limited access to capital. Given Uber's capacity to raise capital and cash holdings, this risk should be lower.

Your Uber value

Once you have made the choices on the potential market, growth in that market, Uber's market share and revenue slice, the valuation follows. While the number of combinations of assumptions is prohibitively high to show value estimates under each one, I have summarizes the value estimates for at least a subset of plausible choices. (using a sales to capital ratio of 5.00 and a cost of capital of 12% for all the cases)>

If your set of assumptions is not listed above, you can download the spreadsheet, enter your choices and see what the value of Uber is with those choices. If you don't like the value that you get with your narrative choices, I am afraid that it is just a reflection of your choices.

Looking at the range of values that you can obtain ($799 million to $90.5 billion), you may find your worst fears about DCF models, i.e., that they can be used to deliver whatever number you want, vindicated, but that is not the way I see it. Instead, here are four lessons that I draw from this table:

1. Soaring narratives, soaring values: I know that some people view DCF models as inherently conservative and thus unsuited to valuing young companies with lots of potential. As you can see in the table above, if you have a soaring narrative of a huge market, a dominant market share and hefty profit margins, the model will deliver a value to match. Put differently, if you found my original valuation of Uber too low, the fault lies with me for having a cramped vision of what Uber can accomplish and not with the model. It also stands to reason that when you have big differences in value estimates, it is almost always because you have different narratives for a company, not because you have a disagreement on an input number.
2. Not all narratives are made equal: While I have listed out multiple narratives, some of which deliver huge values and some not, not all are equal. Looking forward as investors, some narratives are more plausible than others and thus have better odds of succeeding. Looking back ten years from now, reality will have delivered its own story line for Uber and the narrative that came closest to that reality will be the winner.
3. Narratives need reshaping: The narratives for Uber that you developed are based on what you know today. As events unfold, it is critical that you check your narrative against the facts and tweak, change or even replace the narrative if the facts require those adjustments, which was the point that I made in this post.
4. Narratives matter: Success, when investing in young companies, comes from getting the narrative right, not the numbers. That may explain why some successful venture capitalists can get away being surprisingly sloppy with they numbers. After all, if your skill set includes finding start-ups with strong narratives and picking founders/entrepreneurs who can deliver on those narratives, the fact that you cannot tell the difference between EBITDA and free cash flow or compute the cost of capital will be of little consequence. 

If you are waiting for me to reveal my narrative choices, you will be disappointed. This is your valuation, not mine, and I hope that you like it. If you could please go in and put your narrative choices and resulting value for Uber into this shared Google spreadsheet, we can get a crowd valuation of Uber!

Attachments

1. My post on Uber in June 2014
2. My follow-up post on Uber
3. Valuation of Uber (December 2014)
4. Google Shared Spreadsheet of Uber values