January 2019 Data Update 1: A reminder that equities are risky, in case you forgot!

In bull markets, investors, both professional and amateur, often pay lip service to the notion of risk, but blithely ignore its relevance in both asset allocation and stock selection, convinced that every dip in stock prices is a buying opportunity, and soothed by bromides that stocks always win in the long term. It is therefore healthy, albeit painful, to be reminded that the risk in stocks is real, and that there is a reason why investors earn a premium for investing in equities, as opposed to safer investments, and that is the message that markets around the world delivered in the last quarter of 2018.

A Look Back at 2018

The stock market started 2018 on a roll, having posted nine consecutive up years, making the crisis of 2008 seem like a distant memory. True to form, stocks rose in January, led by the FAANG (Facebook, Amazon, Apple, Netflix and Google) stocks and momentum investors celebrated. The first wake up call of the year came in February, first as the market responded negatively to macroeconomic reports of higher inflation, and then as Facebook and Google stumbled from self-inflicted wounds. 

The market shook off its tech blues by the end of March and continued to rise through the summer, with the S&P 500 peaking for the year at 2931 on September 20, 2018.   For the many investors who were already counting their winnings for the year, the last quarter of 2018 was a shock, as volatility returned to the market with a vengeance. In October, the S&P 500 dropped by 6.94%, though it felt far worse because of the day-to-day and intraday price swings. In November, the S&P 500 was flat, but volatility continued unabated. In December, US equities finally succumbed to selling pressures, as a sharp selloff pushed stocks close to the "bear market" threshold, before recovering a little towards the end of the year.  

Over the course of the year, every major US equity index took a hit, but the variation across the indices was modest.

The ranking of returns, with the S&P 600 and the NASDAQ doing worse than the Dow or the SD&P 500 is what you would expect in any down market. With dividends incorporated, the return on the S&P 500 was -4.23%, the first down market in a decade, but only a modestly bad year by historical standards:

I know that this is small consolation, if you lost money last year, but looking at annual returns on stocks in the last 90 years, there have been twenty years with more negative returns. In short, it was a bad year for stocks, but it felt far worse for three reasons. First, after nine good years for the market, investors were lulled into a false sense of complacency about the capacity of stocks to keep delivering positive returns. Second,  the negative returns were all in the last quarter of the year, making the hit seem larger (from the highs of September 2018) and more immediate. Third,  the intraday and day-to-day volatility exacerbated the fear factor, and those investors who reacted by trading faced far larger losses.

The Equity Risk Premium

If you have been a reader of this blog, you know that my favorite device for disentangling the mysteries of the market is the implied equity risk premium, an estimate of the price that investors are demanding for the risk of investing in equities. I back this number out from the current market prices and expected future cash flows, an IRR for equities that is analogous to the yield to maturity on a bond:

As with any measure of the market, it requires estimates for the future (expected cash flows and growth rates), but it is not only forward looking and dynamic (changing as the market moves), but also surprisingly robust and comprehensive in its coverage of fundamentals. 

At the start of 2018, I estimated the equity risk premium, using the index at that point in time (2673.61), the 10-year treasury bond rate on that day (2.41%) and the growth rate that analysts were projecting for earnings for the index (7.05%). 

The equity risk premium on January 1, 2018 was 5.08%. As we moved through the year, I computed the equity risk premium at the start of each month, adjusting cash flows on a quarterly basis (which is about as frequently as S&P does it) and using the index level and ten-year T.Bond rate at the start of each month:

While the conventional wisdom about equity risk premiums is the they do not change much on a day to day basis in developed markets, that has not been true since 2008. In 2018, there were two periods, the first week of February and the month of October, where volatility peaked on an intraday basis, and I computed the ERP by day, during the first week of February, and all through October:

During October, for instance, the equity risk premium moved from 5.38% at the start of the month to 5.76% by the end of the month, with wide swings during the course of the month.

After a brutal December, where stocks dropped more than 9% partly on the recognition that global economic growth may slacken faster than expected, I recomputed the equity risk premium at the start of 2019:

The equity risk premium has increased to 5.96%, but a closer look at the differences between the inputs at the start and end of the year indicates how investor perspectives have shifted over the course of the year:

Going into 2019, investors are clearly less upbeat than they were in 2018 about future growth and more worried about future crises, but companies are continuing to return cash at a pace that exceeds expectations.

What now?

I know that you are looking for a bottom line here on whether the numbers are aligned for a good or a bad year for stocks, and I will disappoint you up front by admitting that I am a terrible market timer. As an intrinsic value investor, the only market-related question that I ask is whether I find the current price of risk (the implied ERP) to be an acceptable one; if it is too low for my tastes, I would shift away from stocks, and if it is too high, shift more into them. To gain perspective, I graphed the implied ERP from 1960 through 2018 below:

At its current level of 5.96%, the equity risk premium is in the top decile of historical numbers, exceeded only by the equity risk premiums in three other years, 1979, 2009 and 2011. Viewed purely on that basis, the equity market is more under valued than over valued right now.

I am fully aware of the dangers that lurk and how they could quickly change my assessment and they can show up in one or more of the inputs:

1. Recession and lower growth: While there was almost no talk about a possible recession either globally or in the US, at the start of 2018, some analysts, albeit a minority, are raising the possibility that the economy would slow down enough to push it into recession, at the start of 2019. While the lower earnings growth used in the 2019 computation already incorporates some of this worry, a recession would make even the lower number optimistic. In the table below, I have estimated the effect on the equity risk premium of lower growth, and  note that even with a compounded growth rate of -3% a year for the next five years, the ERP stays above the historical average of 4.19%.
   
2. Higher interest rates: The fear of the Fed has roiled markets for much of the last decade, and while it has played out as higher short term interest rates for the last two years, the ten-year bond rate, after a surge over 3% in 2018, is now back to 2.68%. There is the possibility that higher inflation and economic growth rate can push this number higher, but it is difficult to see how this would happen if recession fears pan out. In fact, as I noted in this post from earlier in the year, higher interest rates, if the trigger is higher real growth (and not higher inflation), could be a positive for stocks, not a negative.
3. Pullback on cash flows: US companies have been returning huge amounts of cash in the form of stock buybacks and dividends. In 2018, for instance, dividends and buybacks amounted to 92% of aggregate earnings, higher than the 84.60% paid out, on average, between 2009 and 2018, but still lower than the numbers in excess of 100% posted in 2015 and 2016. Assuming that the payout will adjust over time to 85.07%, reflecting expected long term growth, lowers the ERP to 5.55%, still well above historical levels.
4. Political and Economic Crises: The trade war and the Brexit mess will play out this year and each has the potential to scare markets enough to justify the higher ERP that we are observing. In addition, it goes without saying that there will be at least a crisis or two that are not on the radar right now that will hit markets, an unwanted side effect of globalization. 

Looking at how the equity risk premium will be affected by each of these variables, I think that the market has priced in already for shocks on at least two of these variables, in the form of lower growth and political/economic crises, and can withstand fairly significant bad news on the other two. 

Bottom Line

I have long argued that it is better to be transparently wrong than opaquely right, when making investment forecasts. In keeping with my own advice, I believe that stocks are more likely to go up in 2019, than down, given the information that I have now. That said, if I am wrong, it will be because I have under estimated how much economic growth will slow in the coming year and the magnitude of economic crises. Odds are that I will see the tell tale signs too late to protect myself fully against any resulting market corrections, but that is not my game anyway. 

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Datasets

1. Historical Returns on Stocks, Bonds and Bills - 1928 to 2018
2. Historical Implied Equity Risk Premiums for US - 1960 to 2018

Spreadsheets

1. Implied ERP for January 1, 2019

January 2019 Data Updates

1. Data Update 1: A Reminder that equities are risky, in case you forgot!
2. Data Update 2: The Message from Bond Markets
3. Data Update 3: Playing the Numbers Game
4. Data Update 4: The Many Faces of Risk
5. Data Update 5: Of Hurdle Rates and Funding Costs!
6. Data Update 6: Profitability and Value Creation!
7. Data Update 7: Debt, neither poison nor nectar!
8. Data Update 8: Dividends and Buybacks - Fact and Fiction!
9. Data Update 9: Playing the Pricing Game!

January 2017 Data Update 10: The Pricing Game!

It's taken me a while to get here, but in this, the last of my ten posts looking at publicly traded companies globally, I look at pricing differences across regions and sectors. I laid out my rationale for looking at pricing in my most recent post on the topic, where I drew a distinction between good companies, good management and good investments, arguing that investing is about finding mismatches between reality (as driven by cash flows, growth and risk) and perception (as determined by the market). 

Multiple = Standardized Price

When looking at how stocks are priced and especially when comparing pricing across stocks, we almost invariably look at pricing multiples (PE, EV to EBITDA) rather than absolute prices. That is because prices per share are a function of the number of shares and are, in a sense, almost arbitrary. Before you respond with indignation, what I mean to say is that I can make the price per share decrease from $100/share to $10/share, by instituting a ten for one stock split, without changing anything about the company. As a consequence, a stock cannot be classified as cheap or expensive based on price per share and you can find Berkshire Hathaway to be under valued at $263,500 per share, while viewing a stock trading at 5 cents per share as hopelessly overvalued. 

The process of standardizing prices is straight forward. In the numerator, you need a market measure of value of  equity, the entire firm (debt + equity) or the operating assets of the firm (debt + equity -cash = enterprise value). If you confused about the distinction, you may want to review this post of mine from the archives. In the denominator, you can scale the market value to revenues, earnings, accounting estimates of value (book value) or cash flows.

As you can see, there is a very large number of standardized versions of value that you can calculate for firms, especially if you bring in variants on each individual variable in the denominator. With net income, for instance, you can look at income in the last fiscal year (current), the last twelve months (trailing) or the next year (forward). The one simple proposition that you should always follow is to be consistent in your definition of multiple.

The "Consistent Multiple" Rule:   If your numerator is the market value of equity (market capitalization or price per share), your denominator has to be an equity measure as well (net income or earnings per share, book value of equity. For example, a price earnings ratio is consistent, since both the numerator and denominator are equity values, and so is an EV to EBITDA multiple. A Price to EBITDA or a Price to Sales ratio is inconsistent, since the numerator is an equity value and the denominator is to the entire business, and will lead to conclusions that are not merited by the fundamentals.

Pricing – A Global Picture

To see how stocks are priced around the world at the start of 2017, I focus on four multiples, the price earnings ratio, the price to book (equity) ratio, the EV/Sales multiple and EV/EBITDA. With each multiple, I will start with a histogram describing how stocks are priced globally (with sub-sector specifics) and then provide country specific numbers in heat maps. 

PE ratio 

The PE ratio has many variants, some related to what period the earnings per share is measured (current, trailing or forward), some relating to whether the earnings per share are primary or diluted and some a function of whether and how you adjust for extraordinary items. If you superimpose on top of these differences the fact that earnings per share reported by companies reflect very different accounting standards around the world, you can already start to see the caveats roll out. That said, it is still useful to start with a histogram of PE ratios of all publicly traded companies around the world: 

Damodaran Online (data)

Note that of the 42,668 firms in my global sample, there were only 25,493 firms that made it through into this graph; the rest of the sample (about 40%) had negative earnings per share and the PE ratios was not meaningful.  While the histogram provides the distributions by regional sub-groups, the heat map below provides the median PE ratio by country: 

Live Heat Map

If you go to the live heat map, you will also be able to see the 25th and 75th quartiles within each country, or you can download the spreadsheet that contains the data.  I mistrust PE ratios for many reasons. First, the more accountants can work on a number, the less trustworthy it becomes, and there is no more massaged, manipulated and mangled variable than earnings per share. Second, the sampling bias introduced by eliminating a large subset of your sample, by eliminating money losing companies, is immense. Third, it is the most volatile of all of the multiples as it is based upon earnings per share.

Price to Book 

In many ways, the price to book ratio confronts investors on a fundamental question of whether they trust markets or accountants more, by scaling the market’s estimate of what a company is worth (the market capitalization) to what the accountants consider the company’s value (book value of equity). The rules of thumb that have been build around book value go back in history to the origins of  value investing and all make implicit assumptions about what book value measures in the first place. Again, I will start with the histogram for all global stocks, with the table at the regional level imposed on it: 

Damodaran Online (data)

The price to book ratio has better sampling properties than price earnings ratios for the simple reason that there are far fewer firms with negative book equities (only about 10% of all firms globally) than with negative earnings. If you believe, as some do, that stocks that trade at less than book value are cheap, there is good news: you have lots and lots of buying opportunities (including the entire Japanese market). Following up, let’s take a look in the heat map below of median price to book ratios, by country. 

Live Heat Map

Again, you can see the 25th and 75th quartiles in either the live map or by downloading the spreadsheet with the data. Pausing to look at the numbers, note the countries shaded in green, which are the cheapest in the world, at least on a price to book basis, are concentrated in Africa and Eastern Europe, arguably among the riskiest parts of the world. The most expensive countries are China, a couple of outliers in Africa (Ivory Coast and Senegal, with very small sample sizes) and Argentina, a bit of a surprise.

EV to EBITDA 

The EV to EBITDA multiple has quickly grown in favor among analysts, for some good reasons and some bad. Among the good reasons, it is less affected by different financial leverage policies than PE ratios (but it is not immune) and depreciation methods than other earnings multiples. Among the bad ones is that it is a cash flow measure based on a dangerously loose definition of cash flow that works only if you live in a world where there are no taxes, debt payments and capital expenditures laying claim on those cash flows. The global histogram of EV to EBITDA multiples share the positive skew of the other multiples, with the peak to the left and the tail to the right: 

Damodaran Online (data)

Again, there will be firms that had negative EBITDA that did not make the cut, but they are fewer in number than those with negative EPS.  Looking at the median EV to EBITDA multiple by country in the heat map below, you can see the cheap spots and the expensive ones. 

Live Heat Map

As with the other data, you can get the lower and higher quartile data in the spreadsheet. As with price to book, the cheapest countries in the world lie in some of the riskiest parts of the world, in Africa and Eastern Europe. China remains among the most expensive countries in the world but Argentina which also made the list, on a  price to book basis, drops back to the pack.

EV to Sales 

If you share my fear of accounting game playing, you probably also feel more comfortable working with revenues, the number on which accountants have the fewest degrees of freedom. Let’s start with the histogram for global stocks: 

Damodaran Online (Data)

Of all the multiples, this should be the one where you lose the least companies (though many financial service companies don’t report conventional revenues) and the one that you can use even on young companies that are working their way through the early stages of the life cycle.  The median EV/Sales ratio for each country are in the heat map below: 

Live Heat Map

You can download more extensive numbers in the spreadsheet. By now, the familiar pattern reasserts itself, with East European and African companies looking cheap and China looking expensive. With revenue multiples, Canada and Australia also enter the overvalued list, perhaps because of the preponderance of natural resource companies in these countries.

Pricing – Sector Differences 

All of the multiples that I talked about in the last section can also be computed at the industry level and it is worth doing so, partly to gain perspective on what comprises cheap and expensive in each grouping and partly to look for under and over priced groupings. The following table, lists the ten lowest-priced and highest priced industry groups at the start of 2017, based upon trailing PE: 

Multiples by Sector

In many of the cheapest sectors, the reasons for the low  pricing are fundamental: low growth, high risk and an inability to generate high returns on equity or margins. Similarly, the highest PE sectors also tend to be in higher growth, high return on equity businesses. I will leave the judgment to you whether any fit the definition of a cheap company. The entire list of multiples, by sector, can be obtained by clicking on this spreadsheet.

One comparison that you may consider making is to pick and multiple and trace how it has changed over time for an industry group. Isolating pharmaceutical and biotechnology companies in the United States, for instance, here is what I find when it comes to EV to EBITR&D for the two groups over time:

You can read this graph in one of two ways. If you are a firm believer in mean reversion, you would load up on biotech stocks and hope that they revert back to their pre-2006 premiums, but I think you would be on dangerous ground. The declining premium is just as much a function of a changing health care business (with less pricing power for drug companies), increasing scale at biotech companies and more competition. 

Rules for the Road

1. Absolute rules of thumb are dangerous (and lazy): The investing world is full of rules of thumb for finding bargains. Companies that trade at less than book value are cheap, as are companies that trade at less than six times EBITDA or have PEG ratios less than one. Many of these rules have their roots in a different age, when data was difficult to access and there were no ready tools for analyzing them, other than abacuses and ledger sheets. In Ben Graham's day, the very fact that you had collected the data to run his "cheap stock" screens was your competitive advantage. In today's market, where you can download the entire market with the click of a button and tailor your Excel spreadsheet to compute and screen, it strikes me as odd that screens still remain based on absolute values. If you want to find cheap companies based upon EV to EBITDA, why not just compute the number for every company (as I have in my histogram) and then use the first quartile  (25th percentile) as your cut off for cheap. By my calculations, a company with an EV/EBITDA of 7.70 would be cheap in the United States but you would need an EV to EBITDA less than 4.67 to be cheap in Japan, at least in January 2017.
2. Most stocks that look cheap deserve to be cheap: If your investment strategy is buying stocks that trade at low multiples of earnings and book value and waiting for them to recover, you are playing a game of mean reversion. It may work for you, but there is little that you are bringing to the investing table, and there is little that I would expect you to take away. If you want to price a stock, you have to bring in not just how cheap it is but also look at measures of value that may explain why the stock is cheap. 
3. If you are paying a price, you are "estimating" the future: When I do an intrinsic valuation (as I did a couple of weeks ago with Snap), I am often taken to task by some readers for playing God, i.e., forecasting revenue growth, margins and risk for a company with a very uncertain future. I accept that critique but I don't see an alternative. If your view is that using a multiple lets you evade this responsibility, it is because you have chosen not to look under the hood, If you pay 50 times revenues for a company, which is what you might be with Snap, you are making assumptions about revenue growth and margins, whether you like it or not. The only difference between us seems to be that I am being explicit about my assumptions, whereas your assumptions are implicit. In fact, they may be so implicit that you don't even know what they are, a decidedly dangerous place to be in investing.

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Data Links

1. PE, PBV, EV to EBITDA and EV to Sales by Country: January 2017
2. PE, PBV, EV to EBITDA and EV to Sales by Industry Group: January 2017

Data 2017 Posts

1. Data Update 1: The Promise and Perils of Big Data
2. Data Update 2: The Resilience of US Equities
3. Data Update 3: Cracking the Currency Code - January 2017
4. Data Update 4: Country Risk and Pricing, January 2017
5. Data Update 5: A Taxing Year Ahead?
6. Data Update 6: The Cost of Capital in January 2017
7. Data Update 7: Profitability, Excess Returns and Corporate Governance- January 2017
8. Data Update 8: The Debt Trade off in January 2017
9. Data Update 9: Dividends and Buybacks in 2017
10. Data Update 10: The Pricing Game