In the chapters leading up to this one, I have focused primarily on individual companies and talked about how stories about these companies drive their valuations. In some cases, though, it is stories about the economy, interest rates, or commodities that drive the valuations of individual companies. In this chapter I focus on these big stories, breaking them down into stories that relate to variables like interest rates and inflation that affect all companies, some that are built on the effects of political and commodity price movements that affect a significant subset of companies, and a few that try to take advantage of lifestyle trends.
Macro Versus Micro Stories
In a micro story you start with a company, and while you consider the market and the competitive landscape in constructing your story, it is the company that is your focus. While that may be the appropriate point of view for many companies, it may not work in businesses in which what happens to your investment is driven primarily by macro variables over which you have little or no control. That is clearly the case with mature commodity companies, where the commodity price path determines the future earnings and the company’s influences are at the margins. It is also often true for cyclical companies, for which the course of the economy will drive the profitability and cash flows in future years. Finally, in some risky emerging markets, the story line for a company is less determined by what happens in the corporate boardrooms and executive suites and more by political and economic developments in the country.
I must confess that I am far more uncomfortable telling macro stories than micro ones, for two reasons. The first is that I feel less in control, since macro variables are driven by forces that are both complicated and global; small changes in one part of the world can cause unexpected shifts in these variables. The second is that I know that my macro forecasting skills leave much to be desired. Consequently, any macro forecasts I build into my story line, even accidentally, can be problematic.
Not all analysts share my dislike for macro models. Some actively pursue them, because the payoff to getting macro bets right is huge; if you can forecast oil prices or interest rates well, your pathway to profitability will be quick and painless. In recent years a new strand of macro investing has become more common, in which investors forecast lifestyle trends and try to invest in companies based on those forecasts. To see the payoff from this investing style, just imagine how much wealth you could have accumulated if you had seen the boom in social media of the last few years and jumped on the bandwagon early. In fact, I would not be surprised if some of the capital flowing into companies like Uber and Airbnb is coming from investors whose narratives are driven by what they see as the explosive growth in the “sharing” market rather than by company-specific stories.
The Steps in a Macro Narrative
The process of building up a macro narrative shares some features with the company narrative process that we described in earlier chapters and deviates from others. It starts with an identification and understanding of the macro variable in question (commodity, cyclicality, or country) and it has to be followed by an assessment of how the company you are trying to value is affected by movements in the macro variable. The final step is a judgment you have to make as to how much you want your valuation to rest on your forecasts of the macro variable and how you plan to build that linkage into your numbers.
If your company’s fortunes are driven primarily by a macro variable or variables, you should start by identifying that variable or variables. Obviously, for an oil company, the macro variable will be oil, but for a mining company, you may have to do a little more digging. With Vale, for instance, a Brazilian mining company that I will value later in this chapter, the key commodity is iron ore, since it accounts for almost three-quarters of Vale’s revenues, and the iron ore price for much of the most recent decade had been driven by growth in China. With cyclical companies, while the economy is the obvious choice for the macro variable, you still have to make a judgment on whether it is just the domestic economy, a broader grouping of economies (say, Latin America), or perhaps even the global economy. Once you have the macro variable pinned down, the next step is to collect the historical data for that variable, looking at its movements over time and, if available, the forces that drive the movements. That history is useful not only to get a measure of what constitutes normal for that variable but also to get a sense of the risk in your company.
The Micro Assessment
The second step in the macro storytelling exercise is to turn your attention to the company that you are trying to value. In assessing your company’s exposure, you are trying to make a judgment on how movements in the macro variable affect your company’s operations. While at first sight this may seem simple, since you would expect oil companies to have higher earnings if oil prices go up, it is critical, because the way an oil company is structured and operates can affect its exposure to oil prices. Thus, an oil company with high-cost reserves may find itself more exposed to oil price variability than one with low-cost reserves, since the company with high-cost reserves will be hurt more by lower oil prices and will benefit more from higher prices. In general, oil companies with high fixed costs will see their earnings respond more violently to oil price changes than oil companies with more flexible cost structures. Finally, oil companies that hedge their output risk, that is, use futures and forward markets to lock in oil prices on future deliveries, will have earnings that are less impacted, at least in the near term, by oil price changes, than companies that do not hedge.
In this third step, you will be creating a composite story, in which you bring together your assessments of both the macro variable and the characteristics of the company you are valuing. At this stage, though, you will have to make a decision as to whether you want your valuation to be macro-neutral or to reflect your views on the future direction of the macro variable. Thus, with an oil company, you can value the company based on today’s prices (reflected in the current spot price and in futures prices) or on your forecasts of future oil prices.
If you decide to go the macro-neutral route, you will first have to clean up the company’s financials for any changes in the macro variable between the period of your financials and today. Consequently, if you are valuing an oil company in March 2015, and your most recent financials are from 2014, recognize that your revenues and earnings are from a period when oil prices averaged $70 a barrel and that the oil price was down to less than $50 a barrel in March of 2015. With those cleaned-up financials, you have then ensured that your forecasts for the future avoid bringing in either your views about oil prices, which may deviate from market views, or the views of market experts.
If you want to forecast oil prices, I would recommend that you start with the macro-neutral valuation first and then revalue the company with your forecasted values for the macro variable. If you are wondering why you need to do two valuations, it will help both you and your valuation audience understand the basis for your conclusions. By separating the two valuations, you are making clear how much of your valuation judgment of the company is driven by your views on the company and how much by your macro forecasts. If your valuation of BHP Billiton is $14 per share in the macro-neutral scenario and $18 per share with your commodity price views, and the stock is trading at $15 per share, and you are buying the stock or asking others to do so, you are pegging your entire recommendation on your macro views. If your recommendations do well consistently, that is a testimonial to your macro forecasting skills, and you should perhaps consider easier pathways to making money (such as buying or selling futures on the macro variable in question). If you just break even or underperform, that should be a signal to you and those who use your valuations that you should not be wasting your time (and money) on macro forecasts.
CASE STUDY 13.1: VALUING ExxonMobil IN MARCH 2009
I valued ExxonMobil, the world’s largest oil company, in March 2009, and my base narrative was that it was a mature oil company and that while oil prices in March 2009 had dropped substantially from prices in prior years, I had no sense of where they would go in the future. ExxonMobil reported pretax operating income in excess of $60 billion in 2008, but that reflected the fact that the average oil price during the year was $86.55 per barrel. By March 2009, the price per barrel of oil had dropped to $ 45, and I knew that the operating income for the coming year would be lower as a consequence.
To estimate what ExxonMobil’s operating income would be at the oil price of $45, I drew on a regression in case study 5.2, in which I regressed ExxonMobil’s operating income against the average oil price, using data from 1985 to 2008 to arrive at the following:
Plugging the $45 oil price into this regression, I obtained an estimate for the expected operating income for ExxonMobil of $34.614 billion, which became the basis for the valuation of ExxonMobil in figure 13.1.
Figure 13.1
An oil price–neutral valuation of ExxonMobil, March 2009.
My story line for ExxonMobil is that it is a mature oil company whose earnings will track oil prices. With its significant competitive advantages, it will earn above-average returns on capital while maintaining its conservative financing policy (of not borrowing too much).
Following through on this story line, I assume a 2% growth rate in perpetuity and use the oil-price correct operating income of $34.6 billion to compute both base year income and a return on capital (of close to 21%). Using a cost of capital of 8.18% (against reflective of a mature oil company) allows me to value Exxon’s operating assets at $342.5 billion.
Value the operating assets
Adding the cash ($32,007 million) that Exxon had at the time of this valuation and subtracting out debt ($9,400 million) from the operating asset value of $320,472 million yields a value of equity of $ 343,079 million for the company, a value per share of $69.43.
At the prevailing stock price of $64.83, the stock looked slightly undervalued. However, that reflected the assumption that the oil price of $45 was the normalized price. In figure 13.2 I graphed out the value of ExxonMobil as a function of the normalized oil price.
Figure 13.2
ExxonMobil normalized oil price and value per share.
As the oil price changes, the operating income and the return on capital change; I kept the capital invested number fixed and reestimated the return on capital with the estimated operating income. If the normalized oil price is $42.52, the value per share is $64.83, equal to the current stock price. Put another way, any investor who believed in March 2009 that the oil price would stabilize above $42.52 would find ExxonMobil to be undervalued.
Since the value per share was so dependent on the oil price, it made more sense to allow the oil price to vary and to value the company as a function of this price. One tool for doing this is a simulation, and it involved the following steps:
Step 1: Determine the probability distribution for the oil prices: I used historical data on oil prices, adjusted for inflation, to both define the distribution and estimate its parameters. Figure 13.3 summarizes the distribution.
Figure 13.3
Oil price distribution.
Note that oil prices can vary from about $8 a barrel at the minimum to more than $120 a barrel. While I used the current price of $45 as the mean of the distribution, I could have inserted a price view into the distribution by choosing a higher or lower mean value.1
Step 2: Link the operating results to commodity price: To link the operating income to commodity prices, I used the regression results from ExxonMobil’s history:
Operating income = −$6,395 million + $911.32 million (Average oil price)
This regression equation yields the operating income for ExxonMobil, at any given oil price.
Step 3: Estimate the value as a function of the operating results: As the operating income changed, there were two levels at which the value of the firm was affected. The first was that the changed operating income, other things remaining equal, changed the base free cash flow and the value. The second was that the return on capital was recomputed, holding the capital invested fixed, as the operating income changed. As operating income changed, the return on capital changed, and the firm had to reinvest a different amount to sustain the stable growth rate of 2 percent. While I could also have allowed the cost of capital and the growth rate to vary, I felt comfortable with both those numbers and left them fixed.
Step 4: Develop a distribution for the value: I ran 10,000 simulations, letting the oil price vary and valuing the firm and equity value per share in each simulation. The results are summarized in figure 13.4.
Figure 13.4
ExxonMobil value per share oil price simulation results.
The average value per share across the simulations was $69.59, with a minimum value of $2.25 and a maximum value of $324.42; there is, however, a greater than 50 percent chance that the value per share will be less than $64.83 (the current stock price). As an investor, the simulation gave me a much richer set of information on which to base my decision whether to invest in the company by going beyond just the expected value to a distribution of values. I chose not to buy the stock even though it looked mildly undervalued, partly because the distribution of value did not hold enough allure to me.
The Big Stories
The macro variables that you can build corporate stories around are many, but the three that are most used are commodity, cyclicality, and country. In the first (commodity), you build a story around a commodity-driven company, with the commodity price being the central variable and the company’s expected response to commodity price changes determining value. In the second (cyclicality), the valuation is of a company, and the primary driver of operating numbers is the overall health or lack thereof of the economy. Thus, your story starts with the economy, with the company woven into it, and requires that you link your company’s prospects to how well or badly the economy does. In the third (country), the driver of value for the company is the country in which it is incorporated and where much of its operations are centered, with your views on the country having a much bigger impact on your value than your views on the company.
The Cycles
Macro variables move in cycles, with some cycles lasting longer than others. With commodity companies, these cycles can last decades and vary in length, thus making it difficult to forecast the next phase. In figure 13.5, for instance, the oil price is graphed from 1946 to 2016 in both nominal and constant dollar terms.
Figure 13.5
Oil price cycles for 1946–2016.
One reason for the long cycles in commodity prices is the time lag between exploration decisions and development of reserves. Thus, oil companies that made the decision to buy reserves or start exploration in 2012 or 2013, when oil prices were still in the triple digits, found themselves starting production in 2014 and 2015, when prices had plummeted. As a consequence, it takes time for commodity companies to adjust their operations to new pricing, making oil prices move in the same direction for extended periods.
With economic cycles, the consensus is that the cycles tend to be shorter than commodity price cycles, but much of the conventional wisdom comes from research done on the U.S. economy, through the twentieth century. These studies concluded that economic cycles are more predictable than commodity price cycles, but that picture may be distorted by the fact that the U.S. economy through the second half of the twentieth century was exceptional in terms of stability and predictability, partly because of prosperity in the decades after the Second World War and partly because of U.S. dominance of the global economy during that period. While it is true that central banks became more adept at managing economic cycles in the last century, it is entirely possible that with globalization, economic cycles will again become more violent and difficult to forecast.
On country risk, the optimistic view is that all countries will converge on a global norm. However, that will take a long time and there will be stragglers, probably in large numbers, that diverge from norm. Even those emerging market economies that move toward normalcy will have setbacks that wipe out years of progress. In 2014 and 2015, for instance, four of the highest-profile emerging markets (Brazil, Russia, India, and China, or BRIC) all went through crises for different reasons. One measure that captures the investor perception of the risk in a country is the sovereign credit default swap (CDS) over time, and in figure 13.6 I report on the CDS spreads for the BRIC countries for the periods that data has been available.
Figure 13.6
CDS spreads for BRIC countries. The Indian CDS has been traded only once since 2013, and China’s CDS has been traded only once since 2008.
The Predictability
In my experience, there is no aspect of investing that has a worse track record than investment strategies based on macroeconomic forecasting. With commodities, you would be hard-pressed to find a single commodity price reversal (where a commodity on the downside reversed course and starting rising or a rising commodity price started its decline) in the last fifty years that analyst consensus saw coming. With economic cycles, the record is not much better. In fact, if you break down the economic landscape into interest rates, inflation, and economic growth, the forecasts by experts in the field are often no better than forecasts that are made up or based upon purely historical data. With country risk, the herd mentality rules, with emerging market countries heralded as having made the transition to developed market status after a few years of growth and stability, and then just as quickly downgraded to emerging market status after a correction.
This poor track record has not stopped investors—both individuals and institutions—from continuing to make investments based upon their macro views. The reason perhaps lies in the returns that you generate, if you do get it right, and each year, the winners of that year’s macro forecasting contest are anointed as the new market gurus. In 2015 there were a few analysts and portfolio managers who forecast the continued drop in oil price and easily beat the market. This may be cynical of me, but I have a feeling their success will be short-lived and the hubris of macro forecasting will come back to hurt them.
There are four broad strategies that you can adopt when dealing with macro stories and they range the spectrum:
1. Cycle forecasting: The first is to attempt to not just forecast direction but the entire cycle for an extended future period. In this approach, you could forecast dropping oil prices for the next three years, followed by five years of increasing prices, and then a decade of flat prices before prices start to decline again; or in the context of the economy, you could predict that the economy will be strong for two years, followed by a recession in year 3 and a recovery in year 4.
2. Level forecasting: The second is to try to make a judgment on direction for the market, and at the risk of oversimplifying this process, there are two substrategies that you can adopt. The first is to go with momentum, a strategy in which you assume the direction of price movements in the past will continue into the future. In early 2016, for instance, after two years of precipitous drops in oil prices, this would lead you to forecast continued price decline. The second is to be a contrarian and assume that prices are more likely to reverse direction than continue on their existing path; in early 2016 this would yield a prediction of higher oil prices after two years of decline.
3. Normalization: In this approach, rather than forecast cycles or levels, you estimate what you believe is a “normalized” price for that commodity, based either on the historical pricing record or on fundamentals (demand and supply for the commodity). Implicitly, this becomes a forecast of level, since a normal price that is higher than the current price will require the price to climb, and one that is lower will require it go down.
4. The price taker: As a price taker, you concede that you cannot forecast either cycles or normalized prices. Instead, you value the company at today’s level, knowing that it will change shortly thereafter.
Which one should you go with? I cannot give you a categorical answer, because it depends on what your strengths are, but I have three suggestions:
1. Be explicit about which path you pick: If you decide to take one of the four paths listed above, you should be careful not to make midvaluation changes and to be explicit about how that path plays out in your company’s story (and value).
2. Tailor your information collection and analysis to your choice of path: The pathway that you pick will determine where you will be spending your time and resources. Thus, a strategy based on normalization will require that you not only look at past data in making this determination but that you consider the factors that may cause this normal to shift over time.
3. Be honest with yourself when assessing results: Your commodity, cyclical, or country views will affect the values you estimate for companies exposed to these factors. As the facts unfold, you will get to see not only whether your judgments on company value are right, but also how your macro strategy holds up to scrutiny. If you build your commodity company valuations on your price views of commodities, and you find that your record on the latter is no better than random (you are right half the time and wrong the rest), you should consider a change of strategy.
CASE STUDY 13.2: VALE, THE 3C COMPANY, NOVEMBER 2014
Vale is one of the largest mining companies in the world, with its primary holdings in iron ore, and is incorporated and headquartered in Brazil. Vale was founded in 1942 and was entirely owned by the Brazilian government until 1997, when it was privatized. Between 2004 and 2014, as Brazilian country risk receded, Vale expanded its reach both in terms of reserves and operations well beyond Brazil, and its market capitalization and operating numbers (revenues, operating income) reflected that expansion. By early 2014 Vale was the largest iron ore producer in the world and one of the five largest mining companies in the world, both in terms of revenues and market capitalization. Notwithstanding this long-term trend line of growth, 2014 had been an especially difficult period for Vale, as iron ore prices dropped and Brazilian country risk increased, leading into a presidential election that was concluded in October 2014. Figure 13.7 captures both effects.
Figure 13.7
Vale’s commodity and country risk.
Figure 13.8 shows Vale’s stock price between May 2014 and November 2014 and contrasts it with another mining giant, BHP Billiton. While declining commodity prices had affected both companies adversely, note that Vale’s stock price, in figure 13.8, dropped more than twice as much as BHP’s stock price during this period.
Figure 13.8
Vale’s stock price collapse, June to November 2014.
Though there were fundamental reasons for the stock price decline at Vale, the fear factor was clearly also at play, because of Vale’s exposure to commodity and country risk and significant concerns about corporate governance and currency risk factors.
Focusing specifically on commodity prices, higher iron ore prices over the decade, as shown in figure 13.9, leading into the valuation were a prime factor in Vale’s success. It was robust Chinese growth that lifted iron ore prices during this period to highs in 2011.
Figure 13.9
Iron ore prices (monthly $US per metric ton), 1995–2015.
This history shows why making a judgment about a normal price for iron ore will be difficult. If your historical perspective is restricted to just the last few years, the price of iron ore (about $75/metric ton) in November 2014 looked low, but extending that perspective to cover a longer time period (say twenty to twenty-five years), suggests otherwise.
In my narrative, I assumed that Vale was a mature commodity company and that its earnings reflected prevailing iron ore prices ($75/metric ton). Working under the assumption that I could not forecast future iron ore prices, I valued Vale in U.S. dollar terms and assumed that Vale was a mature commodity company growing at 2 percent a year in perpetuity. To estimate the cost of capital, I built off the U.S. 10-year Treasury bond rate as the risk-free rate and used an equity risk premium of 8.25 percent, reflecting a weighted average of the equity risk premiums across the countries where Vale has its reserves (60 percent are in Brazil). I have summarized the valuation in table 13.1.
Table 13.1
Vale—The Dark Side Beckons
Note that I attempted to incorporate the effect of commodity price declines and currency devaluation in the base-year operating income, valuing the company with the depressed income from the last twelve months. The effects of corporate governance were captured in the investment and financing choices made by the firm, with reinvestment and return on invested capital measuring the investment policy and the debt mix in the cost of capital reflecting financing policy. Finally, the country risk was incorporated into the equity risk premium (in which I used risk premium weighted by the geographic distribution of Vale’s reserves) and the default spread in the cost of debt. The value per share that I got with this combination of assumptions was $19.40, well above the share price of $8.53 on November 18, 2014. I did buy the stock at the time, based on my story and the valuation that came out of it, a decision I came to regret, but more on that later!
The Macro Investing Caveat
When a valuation is driven primarily by macro factors, rather than micro ones, there are three consequences for investors that are worth keeping in mind. The first is that the macro cycles that are chronicled, especially with commodities, tend to be long term with upswings and downswings that can last decades. The second is that the macro variables tend to be more difficult to predict using fundamentals than micro variables, because macro variables are interconnected and affected by many forces. Thus, attempts to forecast the price of oil by looking at the production costs and demand for oil have generally not worked very well. The third is that structural shifts in the process can cause breaks from the past that render history moot. As an example, the explosion in shale oil production in the last decade created a supply shock to the oil markets that may have contributed to the oil price collapse in 2014, just as the rise of China as an economic power that was willing to invest unprecedented amounts in infrastructure jolted prices upward in commodity markets in the previous decade.
Let’s assume that you are skilled at calling commodity, economic, or country price cycles or at least at estimating what the normalized prices should be. While I have laid out the process you can use to go from this macro view to valuing individual companies, it is worth asking the question “Why bother?,” since there is a far simpler and more direct way for you to make money from your macro forecasting skills. You can use the forward, futures, and options market, especially with commodities, to make a killing. There are two reasons for separating your corporate stories into macro and micro parts:
1. It will make clear to you, the narrator of the story, how much of your story comes from each component and will allow you to track your performance on each part. Thus, if you buy Conoco and the stock price drops, you can at least assess whether it was because you got the oil price portion of your story wrong or because your story about Conoco as a company was flawed.
2. Your breakdown of the narrative into macro and micro parts is just as critical for someone hoping to act on your story, first, to aid that person in understanding your story and the valuation that emerges from it, and second, to help your listener judge how much faith he or she should have in your story. After all, if you have a woeful track record in forecasting oil prices, and the bulk of your story on Conoco is built on your oil price forecasts, I should be skeptical about your final valuation.
CASE STUDY 13.3: THE VALE MELTDOWN, SEPTEMBER 2015
In case study 13.2, I made a judgment that Vale looked significantly undervalued and followed through on that judgment by buying its shares at $8.53/share. I revisited the company in April 2015, with the stock down to $6.15, revalued it, and concluded that while the value had dropped, it looked undervalued at its then prevailing price. The months between April and September 2015 were not good ones for Vale on any of the macro dimensions. The price of iron ore continued to decline, albeit at a slower rate, partially driven by turmoil in China. The political risk in Brazil not only showed no signs of abating but was feeding into concerns about economic growth and the capacity of the country to repay its debt. The run-up in Brazilian sovereign CDS prices continued, with the sovereign CDS spread rising above 4.50 percent in September 2015 (from 2.50 percent a year earlier). The ratings agencies, as always late to the party, had woken up (finally) to reassess the sovereign ratings for Brazil and had downgraded the country, Moody’s from Baa2 to Baa3 and S&P from BBB to BB+, on both foreign and local currency bases. While both ratings changes represented only a notch in the ratings scale, the significance was that Brazil had been downgraded from investment-grade status by both agencies. Finally, Vale had updated its earnings yet again, and there seemed to be no bottom in sight, with operating income dropping to $2.9 billion, a drop of more than 50 percent from the prior estimates.
It is undeniable that the earnings effect of the iron ore price effect was much larger than I had estimated it to be in November 2014 or April 2015. Updating my numbers, and using the sovereign CDS spread as my measure of the country default spread (since the ratings were not only in flux but did not seem to reflect the updated assessment of the country), the value per share that I got in September 2015 was $4.29, as shown in table 13.2.
Table 13.2
Vale—The Regrets
I was taken aback at the changes in value over the previous valuations, which were separated by less than a year, and attempted to look at the drivers of these changes, as shown in figure 13.10.
Figure 13.10: Breaking down the meltdown in value
Put simply, my values changed a lot in both sub-periods, but for different reasons. In the November 2014-April 2015 period, the change was from reassessing operating income for iron ore price changes. In the April 2015-September 2015 period, most of the change came from an increase in Brazil country risk.
The biggest reason for the shift in value from November 2014 to April 2015 was the reassessment of earnings (accounting for 81 percent of my value drop), but looking at the difference between my April 2015 and September 2015 valuations, the primary culprit was the uptick in country risk, accounting for almost 61 percent of my loss in value.
If I stayed true to my investment philosophy of investing in an asset only if its price is less than its value, the line of no return had been passed with Vale. I sold the stock, but it was not a decision that I made easily or without fighting through my biases. In particular, I was sorely tempted by two impulses:
1. The “if only”: My first instinct is to play the blame game and look for excuses for my losses. If only the Brazilian government had behaved more rationally, if only China had not collapsed, if only Vale’s earnings had been more resilient to iron ore prices, my thesis would have been right. Not only is this game completely pointless, but it eliminates any lessons that I might be extract from this fiasco.
2. The “what if”: As I worked through my valuation, I had to constantly fight the urge to pick numbers that would let me stay with my original thesis and continue to hold the stock. For instance, if I continued to use the sovereign rating to assess default spreads for Brazil, as I did in my first two valuations, the value I would have obtained for the company would have been $6.65. I could have then covered up this choice with the argument that CDS markets are notorious for overreacting and that using a normalized value (either a ratings-based approach or an average CDS spread over time) would give me a better estimate.
After wrestling with my own biases for an extended period, I concluded that the assumptions that I would need to make to justify continuing to hold Vale would have to be assumptions about the macro environment: that iron ore prices would stop falling and/or that the market has overreacted to Brazil’s risk woes and would correct itself. As a postscript, the stock price fell as low as $2, at which point I did buy shares in Vale. Perhaps, I deserve more punishment before I learn my lesson, but the stock has increased to $5.03, since.
It is said that you can learn more from your losses than from your wins, but the people who like to dish out this advice have either never lost or don’t usually follow their own advice. Learning from my mistakes was hard to do, but looking back at my Vale valuations, here is what I see:
1. The dangers of implicit normalization: While I was careful to avoid explicit normalization, that is, assuming that earnings would return to the average level seen over the last five or ten years or that iron ore prices would rebound, I implicitly built in an expectation of normalization by taking the previous twelve-month earnings as reflective of iron ore prices during that period. At least with Vale, there seems to be a lag between the drop in iron ore prices and the earnings effect, perhaps reflecting precontracted prices or accounting lethargy. By the same token, using the default spread based on the sovereign rating provided a false sense of stability, especially when the market’s reaction to events on the ground in Brazil has been much more negative.
2. The stickiness of political risk: Political problems need political solutions, and politics does not lend itself easily to either rational solutions or speed in resolution. In fact, the Vale lesson for me should be that when political risk is a big component, it is likely to be persistent and can easily multiply, if politicians are left to their own devices.
3. The debt effect: All of the problems besetting Vale were magnified by its debt load, bloated because of its ambitious growth in the prior decade and its large dividend payout (Vale had to pay dividends to its nonvoting preferred shareholders). While the threat of default was not imminent, Vale’s buffer for debt payments had dropped significantly in the previous year, with its interest coverage ratio dropping from 10.39 in 2013 to 4.18 in 2015.
If I had known in November 2014 what I did in September 2015, I would obviously not have bought Vale, but that is an empty statement.
Conclusion
Macro storytelling is trickier than micro storytelling, but with cyclical companies, commodity companies, or companies in very risky emerging markets, you may have no choice. Even if you have good macro forecasting skills, I would suggest that you first value your company, without bringing those skills into play, and then revalue it with your forecasts. That will allow both you and those using your valuations to see how much of your judgment comes from your views on the company and how much comes from the market.
