Tuesday, January 24, 2017

January 2017 Data Update 4: Country Risk Update

In my last post, I pointed to currency confusion as one of the side effects of globalization. In this one, I will argue that as companies and investors globalize,  investors and analysts have no choice but to learn how to deal with the rest of the world, both in terms of risk and pricing. One reason that I take a detailed look at country risk and pricing numbers every year is that my valuations and corporate finance rest so heavily on them. 

Why country risk matters
It seems to me an intuitive proposition that a company’s value and pricing can depend upon the geography of its business. Put simply, cash flows generated in riskier countries should be worth less than equivalent cash flows generated in safer ones but there are two follow up propositions worth emphasizing:
  1. Operation, not incorporation: I believe that it is where a company operates that determines its risk exposure, not just where it is incorporated. Thus, you can have US companies like Coca Cola (through its revenues) and Exxon Mobil (from its oil reserves) with substantial emerging market exposure and emerging market companies like Tata Consulting Services and Embraer with significant developed market exposure. In fact, what we face in valuation increasingly are global companies that through the accident of history happen to be incorporated in different countries.
  2. Company, Country and Global Risks: Not all country risk is created equal, especially as you are look at that risk as a diversified investor. Some country risk can be isolated to individual companies and is therefore averaged out as you diversify even across companies in that country. Still other country risk is country-specific and can be mitigated as your portfolio includes companies from across the globe. There is, however, increasingly a portion of country risk that is global, where even a global investor remains exposed to the risk and more so in some countries than others. The reason that we draw this distinction is that risks that can be diversified away will affect only the expected cash flows; that adjustment effectively takes the form of taking into account the likelihood and cash flow consequences of the risk occurring when computing the expected cash flow. The risks that are not diversifiable will affect both the expected cash flows and also the discount rates, with the mode of adjustment usually taking the form of higher risk premiums for equity and debt. That may sound like double counting but it is not, since the expected cash flows are adjusted for the likelihood of bad scenarios and their consequences and the discount rate adjustment is to demand a premium for being exposed to that risk:
    If you make the assumption that all country risk is diversifiable, you arrive at the conclusion that you don't need to adjust discount rates for country risk, a defensible argument when correlations across countries were very low (as in the 1980s) but not any more.
Thus, dealing with country risk correctly becomes a key ingredient of both corporate finance, where multinational companies try to measure hurdle rates and returns on projects in different countries and in valuation, where investors try to attach values or prices to the same companies in financial markets. 

Country Default Risk
Since I have had extended posts on country risk before, I will not repeat much of what I have said before and instead focus this post on just updating the numbers. Simply put, the most easily accessible measures of country risk tend to be measures of default risk:
  1. Sovereign Ratings: Ratings agencies like S&P, Moody’s and Fitch attach sovereign ratings to countries, where they measure the default risk in government borrowing just as they do for individual companies. These ratings agencies often also provide separate ratings for local currency and foreign currency borrowings by the same government. The picture below summarizes ratings by country, in January 2017, and the linked spreadsheet contains the same data.
    Link to live version of map
  2. Government Bond Default Spreads: When a government issues bonds in a foreign currency, that are traded, the interest rate on those bonds can be compared to the risk free rate in a bond issued in the same currency to arrive at measures of default risk for the government. In much of Latin America, for instance, where countries has US-dollar denominated bonds, comparing the rates on those bonds to the US T.Bond rate (of equivalent maturity) provides a snapshot of default risk. The table below summarizes government bond default spreads as of January 1, 2017, for Latin American countries with US dollar denominated bonds:
  3. Sovereign CDS Spreads: This measure of default risk is of more recent vintage and is a market-determined number. It is, roughly speaking, a measure of how much you would have to pay, on an annual basis, to insure yourself against country default and unlike ratings can move quickly in response to political or economic developments in a country, making them both more timely and more volatile measures of country risk. In January 2017, sovereign CDS spreads were available for 64 countries and you can see them in the picture below and download them as a spreadsheet at this link.
    Link to live version of the map
Country Equity Risk
There are many who use country default spreads as a proxy for the additional risk that you would demand for investing in equity in that country, adding it on to a base equity risk premium (ERP) that they have estimated for a mature market (usually the US).
ERP for Country A = ERP for US + Default Spread for Country A
The limitation of the approach is that there are not only are equities affected by a broader set of risks than purely default risk but that even default can have a larger impact on equities in a country than its bonds, since equity investors are the residual claimants of cash flows.

There are broader measures of country risk, taking the form of country risk scores that incorporate political, economic and legal risks, that are estimated by entities, some public (like the World Bank) and some private (like PRS and the Economist). The first is that they tend to be unstandardized, in the sense that each service that measures country risk has its own scoring mechanism, with World Bank scores going from low to high as country risk increases and PRS going from high to low. The second is that they are subjective, with variations in the factors considered and the weights attached to each. That said, there is information in looking at how the scores vary across time and across countries, with the picture below capturing PRS scores by country in January 2017. The numbers are also available in the linked spreadsheet.
Link to live map
I have my own idiosyncratic way of estimating the country risk premiums that builds off the country default spreads. I use a ratio of market volatility, arguing that default spreads need to be scaled to reflect the higher volatility of equities in a market, relative to government bonds in that market. 

Since the volatility ratio can be both difficult to get at a country level and volatile, especially if the government bond is illiquid, I compute volatilities in an emerging market equity index and an emerging market government bond index and use the resulting ratio as a constant that I apply globally to arrive at equity risk premiums for individual countries. In January 2017, I started my estimates with a 5.69% equity risk premium for mature markets (set equal to the implied premium on January 1, 2017, for the S&P 500) and then used a combination of default spreads for countries and a ratio of 1.23 for relative equity market volatility (from the index volatilities) to arrive at equity risk premiums for individual countries.

For countries that had both sovereign CDS spreads and sovereign ratings, I was able to get different measures of equity risk premium using either. For countries that had only a sovereign rating, I used the default spread based on that rating to estimate equity risk premiums (see lookup table here). For those countries that also had sovereign CDS spreads, I computed alternate measures of equity risk premiums using those spreads. Finally, for those frontier countries (mostly in the Middle East and Africa) that were neither rated nor had sovereign CDS spreads, I used their PRS scores to attach very rough measures of equity risk premiums (by looking at other rated countries with similar PRS scores). The picture below summarizes equity risk premiums by country and the link will give you the same information in a spreadsheet.
Link to live map
Closing
The one prediction that we can also safely make for next year is that just as we have each year since 2008, there will be at least one and perhaps even two major shocks to the global economic system, precipitated by politics or by economics or both. Those shocks affect all markets globally, but to different degrees and it behooves us to not only be aware of the impact after they happen but be proactive and start building in the expectation that they will happen into our required returns and values.

YouTube Video


Datasets
  1. Sovereign Ratings by Country, S&P and Moody's on January 1, 2017
  2. Sovereign CDS spreads (ten-year) on January 1, 2017
  3. Political Risk Services (PRS) scores by country, January 1, 2017
  4. Equity Risk Premiums and Country Risk Premiums by country on January 1, 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: Death and Taxes in January 2017- Changes Coming?
  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: A Pricing Update in January 2017

Friday, January 20, 2017

January 2017 Data Update 3: Cracking the Currency Code

There was a time in the not so distant past, where analysts could do their analysis in their local currencies and care little or not at all about foreign currencies, how they moved and why. This was particularly true for US analysts in the last half of the last century, where the US dollar was the unchallenged global currency and the US economy bestrode the world. Those days are behind us and it is almost impossible to do valuations or corporate financial analysis without understanding how to deal with currencies correctly. Since the perils of misplaying currencies can be catastrophic, I decided to spend this post getting up to speed on the basics of how currency choices play out in valuation and where the numbers stand at the start of 2017.

A Currency Primer in Valuation

In intrinsic valuation, the value of an asset is the expected cash flows on that asset, discounted back at a risk adjusted discount rate.
Note that there is no currency specification in the DCF equation and that analysts are given a choice of currencies. So, what currency should you use in valuing a company? While some analysts view this choice rigidly as being determined by the country in which the company operates in or the currency that it reports its financial statements in, there are two basic propositions that govern this choice.
  1. The first is that currency is a measurement mechanism and that you should be able to value any company in any currency, since all it will require is restating cash flows, growth rates and discount rates in that currency
  2. The second is that in a robust DCF valuation, your value should be currency invariant. Put differently, the value of Petrobras should be unchanged, whether you value the company in nominal Brazilian Reais ($R), US dollars or Euros. 
The second proposition may strike some as impractical, since risk free rates vary across currencies and some currencies, like the $R, have higher risk free rates than others, like the US dollar. But the key to understanding currency invariance is recognizing that currency choices affect both your cash flows and your discount rate and if you are being consistent about your currency estimates, those effects should cancel out.
Intuitively, picking a high inflation currency will lead to higher discount rates but also to higher cash flows and growth rates. In fact, if the currency effect is a pure inflation effect, you can see very quickly that you could make your valuation currency-free by doing your entire analysis in real terms, where you cash flows reflect only real growth (without the boost offered by inflation) and your discount rate is built on top of a real risk free rate. Your value should be again equivalent to the value you would have obtained by using the currency of your choice in your valuation.

To make these estimation choices real, consider valuing a company that derives half its cash flows in the United States (in US dollars) and half in Brazil (in nominal $R). You can value the company in US dollars, and to do so, you would have to estimate its cost of capital in US $ and convert the portion of its cash flows that are in $R to US$ in future years; that would require forecasting exchange rates. Alternatively, you can value the company in $R, converting the portion of cash flows in US$ to $R and then estimating a cost of capital in $R. This may sound simple, even trivial, but a whole host of estimation challenges lie in wait. 

Expected Exchange Rates
If you want to make your valuations currency invariant, and inflation is what sets currencies apart, the way to estimate expected future exchange rates is to assume purchasing power parity, where exchange rates move to capture differential inflation. Specifically, you can get from the current exchange rate of local currency (LC) for the foreign currency (FC) to an expected exchange rate in a future year (t) using the expected inflation rates in the two currencies: 
Simply put, if the inflation in the local currency is 5% higher than the inflation in the US$, you are assuming that the local currency will depreciate about 5% a year. I know that exchange rate movements deviate from purchasing power parity significantly over short and perhaps even extended periods and that expected inflation can be difficult to estimate in many currencies, but there is a simple reason why you should stick with this simplistic way of forecasting exchange rates, at least when it comes to valuation. First, it is far easier (and less expensive) that creating a full-fledged exchange rate forecasting model or paying a forecaster, especially because you have to forecast exchange rate changes over very long time periods. Second, it forces you to be explicit about your inflation expectations and by extension, at least be aware of inconsistencies, where you assume one measure of inflation for exchange rates (and cash flows) and another for discount rates. (You can use forward exchange rates for the near years, as long as you are willing to then use interest rate differentials as proxies for inflation differentials.)

But what if you have strong views on the future direction of exchange rates that deviate from inflation expectations? I would argue that you should not bring them into your company valuations for a simple reason. If you incorporate your idiosyncratic exchange rate forecasts into cash flows and value, your final valuation of a company will be a joint consequence of your views on the company and of your views on exchange rates, with no easy way to separate the two. Thus, if you expect the Indian rupee to appreciate over the next five years, rather than depreciate (given your expectations of inflation in the rupee), you will find most Indian companies that you value to be cheap. If that conclusion is being driven by your exchange rate views, why invest in Indian companies when there are far easier and more profitable ways of playing the exchange rate game?

Currency Costs of Capital
Let's start with the challenge of estimating costs of capital in different currencies. There are two general approaches that you can use to get there. One is to compute the cost of capital in a  currency from the ground up, starting with a risk free rate and then estimating and adding on risk premiums to arrives at costs of equity, debt and capital. The other is to compute the cost of capital in a base currency (say the US dollars) and then converting that cost of capital to the local currency.

Currency Risk Free Rates
Every economics student, at some point early in his or her education, has seen the Fisher equation, where the nominal interest rate is broken down into an expected inflation component and an expected real interest rate:
Nominal Interest Rate = Expected Inflation + Expected Real Interest Rate
Note that this is neither a theory nor a hypothesis, but a truism, if you add no constraints on either the expected inflation and real interest rate. It is also a powerful starting point for thinking about what goes into a risk free rate and why it changes over time. It is as you add constraints on the components of interest rates that you start making assumptions which may or may not be true, and require testing. You could assume, for instance, that actual inflation in the most recent periods is a reasonable proxy for expected inflation in the future and that the real interest rate can be approximated to by the real growth rate in the economy in the most recent period (not an unreasonable assumption in mature economies). In fact, it is this proposition that I used in my last post on US markets to estimate intrinsic T.Bond rates that I compared to actual rates. I will use this framework as my back up as I look at four different ways of estimating risk free rates in different currencies.

1. Government Bond Rate
In this, the most common practice in valuation, analysts assume that the local currency government bond rate is the risk free rate in that currency. To justify this usage, they argue that governments will not default on local currency bonds, since they can always print off enough currency to pay off debt. In table 1, I graph local currency 10-year government bond rates as of January 1, 2017 for those currencies where I was able to obtain them.  


This approach has the advantage of simplicity and is perhaps even intuitively defensible but there are real dangers associated with it. The first is that the government bond may not be liquid and traded and/or the government exercises control over the rate, it is not a market-set rate reflecting demand and supply. The second is that implicit in the use of the government bond rate as the risk free rate is the assumption that governments never default in the local currency. That assumption has been violated at least a half a dozen times just in the last twenty years, thus making the government bond rate a "risky", rather than a risk free, rate. The third is that using government bond rates as local currency risk free rates while using actual inflation rates as expected inflation can lead to both inconsistent and currency dependent valuations. For instance, assume that you decide to value Natura, the Brazilian cosmetics company, in $R and use the Brazilian government $R bond rate of 11.37%, on January 1, 2017, as the risk free rate while using the actual inflation rate of 6.29% (inflation rate last year, according to government statistics) as the expected inflation rate. The value that you estimate for the company will be much lower than the value that you estimate for the company if you valued it in US dollars, with a risk free rate of 2.50% and an expected inflation rate of 2%. The reason for the valuation difference is intuitive. By using the $R numbers, you are effectively using a real risk free rate of 5.08%, when you do your valuation in $R, and only 0.5%, when you do your valuation in US dollars.

2. Government Bond Rate, net of default spread
In this approach, you do not start with the presumption that governments are default free. Instead, you start with the local currency government bond rate and subtract out the portion of that rate that you believe is due to perceived default risk:
Risk free rate in local currency = Local Currency Government Bond rate – Default Spread in Local Currency Government Bond rate
The practical question then becomes how best to estimate the local currency default spread and there are a few approaches, though each comes with limitations. The first is to find a US dollar denominated bond issued by the government in question and netting out the US T.Bond rate, thus getting a default spread on the bond. The second is to use a sovereign CDS spread for the country as a proxy for default risk. In the table below,  Subtracting these default spreads from the local currency bond rates, on the assumption that default risk in both local and foreign currency borrowing is equivalent, would yield local currency risk free rates. Using the sovereign rating-based default spreads, we can estimate the risk free rates in different currencies in January 2017:


This approach comes with its own perils that are layered on top of the assumption that the government bond rate is a market-set interest rate. First, it assumes that the local currency sovereign rating is measuring the default risk in the currency and that you can estimate the default spread based on it. Second, both the rating-based and sovereign CDS default spreads are US dollar based and netting it out against a local currency government bond rate can be viewed as inconsistent.

3. Differential Inflation Based Rates
The third approach is to ignore government bond rates in the local currency entirely, either because you believe that they are not liquid enough to yield reliable numbers or because they contain default risk. Instead, you start with a risk free rate in a currency where you believe that the government bond rate is a reliable measure of the risk free rate (US Treasury Bond, German Euro Bond) and then add to this number the differential inflation rate between the US dollar and the local currency.
Local Currency Risk free Rate = US $ Risk free Rate + (Expected inflation in local currency – Expected inflation in US $)
This is an approximation that works reasonably well when local currency inflation is low (close to the US dollar inflation rate) but the more precise version of this formulation will be based upon compounding, just as the Fisher equation was:
The linked table lists differential inflation based risk free rates in all currencies, using expected inflation rates (the World Bank's estimates) and the US dollar (estimated at about 2%, the difference between the US 10-year T.Bond and TIPs rates).  If you are concerned about being able to forecast expected inflation in the local currency, you should rest easy. As long as you use that same expected inflation rate in your cash flow estimation, your valuation will be inflation-invariant and currency consistent, since the effects of under or over estimating inflation will cancel out.

4. Intrinsic Risk Free Rates
In the differential inflation approach, using the US dollar risk-free rate as the starting point, you are assuming a global real risk free rate, set equal to that rate embedded in the US treasury bond rate as the base for all local currency risk free rates. If you feel uncomfortable with this assumption, you can estimate a synthetic risk free rate from scratch, drawing on the Fisher equation:
Risk free Rate = Expected Real Interest Rate + Expected inflation rate
You can augment this equation with the assumption that long term real growth in an economy will converge on the long term real interest rate. 
Expected Real Interest Rate = Expected Real Growth Rate
Synthetic Risk free Rate = Expected Real Growth Rate + Expected inflation rate
This approach yields the maximum flexibility but it will also create differences in valuations in different currencies. This linked table lists out synthetic risk free rates using this approach, using average real GDP growth as your expected real growth rate. The downside of this approach will be that your valuations will vary across currencies, yielding difficult-to-defend conclusions sometimes, where a company looks cheap when analyzed in US dollars but expensive when valued again in the local currency. The advantage of this approach, as with the differential inflation approach, is that you can estimate risk free rates for many more countries than with the government bond approach.

Currency Cost of Capital
If you start with a  risk free rate in a local currency and build up to a cost of capital using equity risk premiums and default spreads, often available only in dollar-based markets, you are effectively assuming that risk premiums are absolute numbers that don't change as the risk free rate changes. Thus, the equity risk premium of 5.69%, estimated in a dollar-based US market, applies not only to the US dollar risk free rate of 2.45% but also to the Nigerian Naira risk free rate of 10.77%. That is a stretch, since you would expect to risk premium you charge to be higher with the latter than the former. There is an easy and logical fix for it and it lies in the differential inflation approach. Rather than apply it to adjust the US$ riskfree rate to a local currency rate, you could apply it to the cost of equity or capital instead:
Thus, if your cost of capital in US $ is 8%, the inflation rate in $R is 6% and in US$ is 2%, your cost of capital would be 12.24%. (Using the short cut of just adding the differential inflation would yield 12%). As part of my data update, I have reported costs of capital, by industry, in US dollars, for the last two decades. In this year's update, I have added a differential inflation feature allowing you to change that cost of capital to any currency of your choice in this spreadsheet. You will need to input the inflation rate in the local currency to get the costs of capital to update and you are welcome to use either the estimates that I supply in an additional worksheet or enter your own. Remember, though, that you should stay true to whatever this estimate is when estimating growth rates and cash flows in that currency.

The Closing
If your valuations are sensitive to your currency choice, you face a fundamental problem. You can find the same company, at the same pricing and point in time, to be both under and over valued, an indefensible conclusion. That conclusion, though, is being driven by some aspect of your valuation process that is making your company's fundamentals (risk, growth and cash flow potential) look different when you switch currencies. That, in my view, is a violation of intrinsic valuation and it requires you to make your inflation assumptions explicit and check for consistency. 

YouTube Video


Datasets
  1. Government Bond Rates, Default Spreads and Risk free Rates - By Currency
  2. Inflation Rates, GDP Growth and Fundamental Growth - By Country
  3. Cost of Capital, by Sector - January 2017 (with currency translator)
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: Death and Taxes in January 2017- Changes Coming?
  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: A Pricing Update in January 2017

Friday, January 13, 2017

January 2017 Data Update 2: The Resilience of US Equities!

If asked to list the biggest threats to US equities at the start of 2016, most people would have pointed to the Federal Reserve’s imminent retreat from quantitative easing and the possibility of a slowdown in China spilling into lower global growth. Those fears contributed to a very bad start to 2016 for US stock markets, and as stocks dropped by about 5% in January, those who have warned us about a bubble looked prescient. But the stock market, as is its wont, surprised us again. Not only did US equities come back from those setbacks but it weathered other crises during the year, including the decision by UK voters to exit the EU in June and by US voters to elect Donald Trump as president in November to end the year with healthy gains. As we enter a year with potentially big changes to the US tax code and trade policy looming, it is time to take stock of where we are and where we might be going in the next year.

Stocks and Bonds: Looking Back
The best place to see  how the year unfolded for stocks is to trace out how the S&P 500 (large cap stocks), the S&P 600 (for small cap stocks) and US ten-year treasury bond rate did on a month by month basis through 2016.
Monthly returns, using month-end values
To convert the index values into returns each month, I first computed price changes for the indices each month (and cumulatively over the year) and added the dividends for the year to estimate annual returns of 11.74% for the S&P 500 and 26.46% for the S&P 600; it was a very good year for small cap stocks and a good one for large cap stocks.  I converted the treasury bond rates into bond price changes each month and cumulatively (for a 10-year constant maturity bond) over the year and added the coupon at the start of the year to get a return of 0.58% for the year; the rise in interest rates cause bond prices to drop by 1.68% during the year.

To put these returns in perspective, I added the S&P 500 and treasury bond return for 2016 to my historical data series which goes back to 1928 and computed both simple and compounded (geometric) annual averages in both for the entire period and compared them to a annualized 3-month treasury bill return (which you can think of as the return for holding cash).
Download spreadsheet with historical data

This table (or some variant of it) is used by practitioners to get the equity risk premium for US markets, by subtracting the average return on treasuries (bills or bonds) from the average return on stocks over a historical time period. Using my estimates, I get the following values for the historical equity risk premium for the US market.
Download spreadsheet with historical data
Note that the equity risk premium varies widely, from 2.3% to 7.96%,  depending on how long a time period you use, how you  compute averages (simple or compounded) and whether you use treasury bills or bonds as your measure of a risk free investment. Adding a statistical note of caution, each of these estimated premiums comes with a standard error, reported in red numbers below the estimated number. Thus, if you decide to use 6.24%, the difference between the arithmetic average returns on stocks and bonds from 1928-2016, as your historical risk premium, that number comes with a standard error of 2.26%. That would mean that your true equity risk premium, with 95% confidence, could be anywhere from 1.72% to 10.76% (plus and minus two standard errors).

Stocks: Looking forward
Looking at the past may give us comfort but investing is always about the future. I have been a long-time skeptic of historical risk premiums for two reasons.  First, as noted in the table above, they are noisy (have high standard errors). Second, they assume mean reversion, i.e., that US equity markets will revert back to what they have historically delivered as returns and that is an increasingly tenuous assumption. It is for this reason that I compute a forward-looking estimate of the equity risk premium for the US, using the S&P 500 Index as my measure of US stocks. Specifically, I estimate expected cash flows from dividends and buybacks from holding the S&P 500 for the next five years, using the trailing 12-month cash flow as my starting point and an expected growth rate in earnings as my proxy for cash flow growth and use these estimates, in conjunction with the index level on January 1, 2017, to compute an internal rate of return (a discount rate that will make the present value of the expected cash flows on the index equal to the traded level of the index).
Given the level of the index (2238.83 on January 1, 2017) and expected cash flows, I estimate an expected return on 8.14% for stocks and netting out the T.Bond rate of 2.45% on January 1, 2017, yields an implied ERP for the index of 5.69%. That number is down from the 6.12% that I estimated at the start of 2016 but is still well above the historical average (from 1960-2016) for this implied ERP of about 4.11%.

There is one troubling feature to the trailing 12 month cash flows on the S&P 500 that gives me pause. As was the case last year, the cash flows returned by S&P 500 companies represented more than 100% of earnings during the trailing 12 months, an unsustainable pace even in a mature market. I recomputed the ERP on the assumption that the cash payout ratio will decrease over time to sustainable levels, i.e., levels that would allow for enough reinvestment given the growth rate. The results are shown below:
The implied ERP for the index, with payout adjusting to about 82.3% of earnings in year 5, is 4.50%, still higher than historic norms but with a much slimmer buffer for safety. Looking at the next year, though, the potential for tax law changes will roil estimates. Not only are many analysts expecting significant increases in earnings next year of 12-15%, as they expect corporate tax rates to get lowered (at least in the aggregate) but there may also be a return of some of the trapped cash ($2 trillion or higher) back to the US, if that portion of the law is modified. Either change will relieve the pressure on cash flows and make it less likely that you will see dramatic cuts in stock buybacks or dividends.

Interest Rates: What lies ahead?
With bonds, I will take a different tack. I believe that, rather than waiting on the Fed, the path for interest rates this year will be determined by the path of the economy, with higher real growth and/or higher inflation pushing up rates. Updating a figure that I have used before, where I compare the T.Bond rate to an intrinsic interest rate (computed by adding expected inflation to expected real growth), you do see the beginning of a gap between the two at the end of 2016:
Entering 2017, the ten-year treasury bond at 2.45% is well below the intrinsic risk free of 3.60%, obtained by adding the inflation rate to real GDP growth through much of 2016. It is entirely possible that the economy will revert back to its post-2008 sluggishness or that there will be other shocks to the global economic system that will cause inflation and real growth to recede and interest rates to stay low, but for the moment at least, it looks like interest rates are their journey back to a new normal. If I were advising the Fed, my suggestion is for them is to act quickly on rates (perhaps as early as the next meeting) in order to preserve the fiction that it is they who are setting rates, rather than following them.

PE, CAPE and Bond PE Ratios
I am not a fan of PE crystal ball gazing but I know that there are many who make their market judgments based on PE ratios. Updating a graph that I last used when I posted on CAPE last year to reflect the numbers at the start of the 2017, here is what the updated PE ratios look like for the S&P 500:
Spreadsheet with data
While current PE ratios, in all their variants, are not at 1999 levels, they have clearly climbed back to 2007 levels and are well above historical averages. Scary, right? This will inevitably lead to the warnings about markets overheating and a coming crash, just as it has for much of the last five years. While one of these years, that predicted crash will come, you may want to look at stock PE ratios relative to the PE ratio on a treasury bond today, another comparison that I made in my CAPE post;
Spreadsheet with data
It is true that stocks look expensive today (at 27 times earnings) but they start to look much better when you compare them to bonds (at 40 times earnings). If you are concerned that bond rates will climb this year to reflect higher inflation/real growth, you may be forced to take another look at how you are pricing stocks at that time. There is one final divergence that needs explaining. In the last section, I noted that implied equity risk premiums on the US market look reasonable or even high relative to historical norms (a sign that the market is not over valued) but in this section, I have pointed to PE ratios being higher than historical norms (a sign of stock prices overheating). How do you reconcile the two findings? The answer lies in this final graph:
Spreadsheet with data
While PE ratios have risen over the last five or six years by almost 35-40%, the ratio of price to cash returned to stockholders (in the form of dividends and buybacks) has barely budged for the last five years. Here again, you should heed the warnings in the last section, where I noted that US companies are returning almost 107% of their earnings as cash to stockholders, unsustainable in the long term. If companies abruptly pull back on stock buybacks, the delicate balance that has allowed for the long bull market will be threatened.

The Closing
In summary,  the primary threats to stocks at the start of 2017, whether you look at implied equity risk premiums or PE ratios, come from two sources. The first is that interest rates will rise quickly, without a concurrent increase in earnings, and the second is that companies will  scale back the cash they return to stockholders to get back to a sustainable payout. Is there a reasonable probability that these events could occur? Of course, and if they both do, it will be a bad year for stocks. However, there is almost equal likelihood that as interest rates rise, earnings will rise even more (partly because of higher inflation/growth and partly because of cuts in corporate taxes) and that companies are able to sustain or even augment cash returned to stockholders. If this scenario unfolds, it will be a very good year for stocks. I will predict that you will be hearing from absolutists on both sides of this argument, one side preaching gloom and doom and the other predicting a market surge. I am in awe of the conviction that each side has in its market-timing judgment, but I am afraid that my market crystal ball is much too cloudy for me to make strong market predictions. So, I will do what I have always done, invest in individual stocks that I find to be priced right and accept that I have little or no control over the market.

YouTube Video


Datasets
  1. Historical Returns on Stocks, T.Bond and T.Bills from 1928 to 2016
  2. Implied Equity Risk Premium - January 2017 (Calculation Spreadsheet)
  3. Historical Implied Equity Risk Premiums - 1960 to 2016 
  4. T.Bond Rate - Actual versus Implied from 1954-2016
  5. PE, CAPE, Shiller PE and Bond PE from 1954-2016
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: Death and Taxes in January 2017- Changes Coming?
  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: A Pricing Update in January 2017

Thursday, January 12, 2017

Almost time for class: My Line Up for the Spring Semester!

If you have been reading my blog for awhile, you should be familiar with the routine at the start of every semester. If I am teaching that semester, I list the classes that I will be teaching, describe them briefly and offer ways in which you can follow the classes online, if you are so inclined. This semester is shaping up to be a busy one, with an MBA Corporate Finance class leading the list, followed by an undergraduate Valuation class and closing with a new online valuation certificate class that will be offered by the Stern School of Business.

Corporate Finance
The most important class that I teach is corporate finance, not valuation. Put simply, this class (or at least the version that I subscribe to) is about the first financial principles that govern how to run a business, small or large, private or public and in any market. That sounds like an ambitious agenda but it makes for a fascinating class, where we break down everything that a business does into three categories: investing, financing and dividend decisions. At the risk of summarizing the entire class into a single picture, these are the questions that corporate finance tries to answer:

For a business to be successful, it has to find a singular objective and then make investment, financing and dividend decisions that advance that objective. We start the class by debating what that objective should be and then move into the investment principle, first looking at how best to estimate the hurdle rates (the threshold for a good investment) in a business and then then at measuring the returns on prospective or actual investments. We follow up by discussing whether there is a right mix of debt and equity to use in funding a business as well as the right type of financing (long term or short term, floating or fixed, straight or convertible, currency) for that business. We finish with a discussion of how much cash should be returned to investors in a business in the form of dividends or buybacks, why a business may prefer one form of cash return over another and how much cash (balance) is too much cash. We end the class by bringing all of these principles together in the value of a business, setting up for my next class (Valuation).

The first session will be on January 30, 2017, and we will meet every Monday and Wednesday from 10.30-12 until May 8. While you have to be enrolled in the class as a Stern MBA to attend the class physically, you are welcome to follow the class online in one of three forums. In each of these forums, I will post recorded webcasts of the lectures late on Mondays and Wednesdays, with links lecture notes and other material. I will also post the quizzes and exams that I will be giving in class online, with grading templates that you can use to grade yourself.
  1. My website: The primary platform for my class is on the webpage for the class on my website. A one-page listing of the webcasts and other materials can be found at this link. You can watch the streaming videos or download them and also the slides and other links for each class. You can indulge your voyeuristic instincts by reading the emails I send to the class at this link.
  2. Apple iTunes U: If you prefer a more polished and device-friendly platform and you own an Apple device (iPhone or iPad), you should download the iTunes U app from the store and once you have it installed, try entering the code " EXC-JJS-XEA", and the class should show up on your shelf. (If it does not, try this link instead.) As I post the lectures and other material on the site, you should get a notification (if you want) about the posting. If you have an Android device, you have to download the Tunesviewer app to be able to access iTunes U classes. 
  3. YouTube: If you want a more minimalist set up, with limited demands on broadband, you can use YouTube and check out the playlist for the class. Again, as classes get posted, you should see them show on the playlist.
Valuation 
This is a class that I teach almost every semester to the MBAs and this semester, I will be teaching it to undergraduates. That said, I teach exactly the same class to both and this class follows the same structure as my MBA classes. It is a class about attaching a number to an asset or business and we will look at both intrinsic valuation and pricing of both public and private firms. 

Since I provided a much longer introduction when I wrote about my Fall 2016 class, you can read it full at this link. The first session for this class will be January 23, and as with the corporate finance class, you can follow the class online, in one of three ways:
  1. My website: The primary platform for my class is on the webpage for the class on my website. A one-page listing of the webcasts and other materials can be found at this link
  2. Apple iTunes U: If you download the iTunes U app from the store to your Apple device, you can enter the code "FHS-KWW-FPK" for the class. If you prefer a direct link, try this one.
  3. YouTube: You can use YouTube and check out the playlist for the class. As classes get posted, you should see them show on the playlist.
Valuation Certificate
These postings, listing upcoming classes and offering them online, have been a ritual of mine for more than 20 years and one common query I get is whether I can offer certification. My answer, hitherto, has been no, not only because I have no way of testing or grading what you do or providing feedback. This semester, the Stern School of Business has decided to offer an online version of my class as Valuation certification class, with the following features:
  1. Lectures: The class is built around twenty eight lecture sessions, each of which is about 12-20 minutes long. These sessions were recorded in a studio and should much more professional than the online videos that I make and more watchable than my full-length classes.
  2. Timing: The class is scheduled to begin on January 30 and go through mid-May, requiring that you watch about two sessions a week. Each session will come with self-test assessment, practice problems, additional readings and other material to supplement learnings.
  3. Synchronous sessions: Every two weeks, I will use WebEx for a live Q&A session, where you can ask questions about the four sessions from the prior two weeks.
  4. Discussion Boards: If you are enrolled in the class, you will be able to participate in discussion boards organized by valuation topics, posting comments, questions or other links. A teaching assistant will monitor the boards and add to the discussion, if needed.
  5. Quizzes and Exams: Just as in my regular classes, there will quizzes and exams. You will be able to take these exams online and I will grade them. 
  6. Valuation Project: As in my regular class, each person in the certificate program will be both valuing and pricing a company and I will provide mid-semester feedback on the valuation and a final grade assessment at the end of the semester.
There is bad news and good news with this new offering. The first piece of bad news is that it is not free and you have to decide, for yourself, whether the price charged ($425) is worth the experience (and the certificate). The second is that this is Stern's first try at this type of offering; it will have a few hiccups and the number of students will be capped at fifty. If you are interested, you can find out more about the certificate program at this link and even if you are unable to participate or get into the class this semester, it will be offered again to a larger audience, later in the year. The good news, if you decide to be part of the program is that I will treat you like I treat my regular in-class students. I am not sure that even this is good news, since you will hear from me about once every day and you will be sick and tired of me by May 12.

YouTube Video: Valuation Certificate Class Preview


Links
  1. Corporate Finance (MBA):  (a) My website (b) Apple iTunes U (c) YouTube Playlist
  2. Valuation (Undergraduate):  (a) My website (b) Apple iTunes U (c) YouTube Playlist
  3. Stern Valuation Certificate: Stern entry webpage

Wednesday, January 11, 2017

Narrative and Numbers: How a number cruncher learned to tell stories!

When I taught my first valuation class in 1986 at New York University, I taught it with numbers, with barely a mention of stories. It was only with the passage of time that I realized that my valuations were becoming number-crunching exercises, with little holding them together other than historical data and equations. Worse, I had no faith in my own valuations, recognizing how easily I could move my final value by changing a number here and a number there. It was then that I realized that I needed a story to connect the numbers and that I was not comfortable with story telling, and that realization led me to start working on my narrative skills. While I am still a novice at it, I think that I have become a little better at story telling than I used to be and it is this journey that is at the core of my newest book, Narrative and Numbers: The Value of Stories in Business.

Story versus Numbers
What comes more naturally to you, story telling or number crunching? That is the question that I start every valuation class that I teach and my reasons are simple. In a world where we are encouraged to make choices early and specialize, we unsurprisingly play to our strengths and ignore our weaknesses. I see a world increasingly divided between number crunchers, who have abandoned common sense and intuition in pursuit of data analytics and complex models and story tellers, whose soaring narratives are unbounded by reality. Each side is suspicious of the other, the story tellers convinced that numbers are being used to intimidate them and the number crunchers secure in their belief that they are being told fairy tales. It is a pity, since there is not only much that each can learn from the other, but you need skills in investing and valuation. I think of valuation as a bridge between stories and numbers, where every story becomes a number in the valuation and every number in a valuation has a story behind it.

When I introduce this picture in my first class, my students are skeptical, as they should be, viewing it as an abstraction, but I try to make it real, the only way I can, which is by applying it on real companies. I start every valuation that I do in class with a story and try to connect my numbers to that story and I try to be open about how much I struggle to come up with stories for some companies and have much my story has to change to reflect new facts or data with others. I push my students to work on their weaker sides when they do valuations, trying  asking story tellers to pay more heed to the numbers and beseeching number crunchers to work on their stories. Seeking a larger audience, I have not only posted many times on the process but almost every valuation that I have posted on this blog has been as much about the story that I am telling about the company as it is about the numbers. In fact, having written and talked often about the topic, I thought it made sense to bring it all together in a book, Narrative and Numbers, published by Columbia University Press, and available at bookstores near you now (and on Amazon in both physical and Kindle versions). (Update: The hardcover is not available yet outside the United States, but should be accessible in about 4-6 weeks. The Kindle version is available everywhere.)

From Story to Value: The Sequence
So, how does a story become a valuation? This book is built around a sequence that has worked for me, in five steps, starting with a story, putting the story through a reality check, converting the story into a valuation and then leaving the feedback loop open (where you listen to those who disagree with you the most and try to improve your story).
There is no rocket science in any of these steps and I am sure that this is not the only pathway to converting narrative to value. These steps have worked for me and I use four companies as my lead players to illustrate the process.
  1. Uber, the ride-sharing phenomenon: I start with the story that I told about Uber in June 2014, and the resulting value, and how that story evolved over the next 15 months as I learned more about the company and its market/competition changed.
  2. Amazon, the Field of Dreams Company: Amazon is a story stock that seems to defy the numbers laws and I use it to illustrate how the value for Amazon can vary as a function of the story you tell about it.
  3. Alibaba, the China story: The China big market story has been used to justify the valuations of many companies, but Alibaba is one case where the use of that story is actually merited. In my story, Alibaba continues to dominate the growing Chinese online retail market and my value reflects that, but I also look at how that value will change if Alibaba can replicate its success globally (Alibaba, the Global Story).
  4. Ferrari, the Exclusive Club: I value Ferrari as an exclusive club, leading into its IPO, and explore how that value will change if you assume that it will follow a different business model.
In the later chapters, I bring in other familiar names (at least to those who read my blog), Vale to illustrate how macroeconomic factors affect stories and Yahoo! to examine the effect of the corporate life cycle. In the final part of the book, I turn the focus on management and look at how the story telling skills of top managers can make a significant difference in how a young company is perceived and valued by the market and how that skill set has to shift as the company ages.

Personal, Applied and Live!
This is my tenth book and I have never had more fun writing a book. There are three aspects to this book that I hope come through:
  1. It is a personal book: If you read the book, you will notice that rather than use the formal "we" or "you" through much of the book, I talk about "I" and "my". Before you decide that this is a sign of an ego run wild, I did this because this book is about my journey from an unquestioning trust in numbers to an increasing focus on stories in valuation and my stories about the companies that I value in this book. I don't expect you to buy into my stories. In fact, I hope that you disagree with me and tell your own stories and that this book will help you convert those stories into valuations. 
  2. It is applied: One common theme across all my books is that I believe that financial tools are best illustrated with real companies in real time. That is the reason that I not only chose real companies as my illustrative examples, but companies that many of you will have strong views (positive or negative) about. 
  3. It is live:  The most exciting part of this book, for me, is that is is never going to be complete. The companies that I use in the book are dynamic entities and I am sure that the stories that I have told about them will change, shift and perhaps even break over time. Rather than dread these upcoming changes, I view them as opportunities for me to revisit my stories and valuations and to update them. You will see these updates on this blog but you will also be able to find them at the website for the book, where I also have pulled together YouTube videos and other material relevant to the book.
Closing
I am usually too embarrassed to ask people to buy my books, since many of them are obscenely over priced, one reason that I don't require them even for students in my classes. I feel no such qualms about this book, since it is (I think) priced reasonably and I hope it offers good value for the money. I hope that you will read the book and that that you enjoy it, and if you can learn something that helps you improve your valuation skills, I will view that as icing on the cake. Drawing on one of the themes in the book, where I argue that the key to keep the feedback loop open, I would also like to hear from those of you who don't like the book and what I can do better! I'll try!

YouTube Video

Book Links

Monday, January 9, 2017

January 2017 Data Update 1: The Promise and Perils of "Big Data"!

Each year, for the last 25 years, I have spent the first week playing Moneyball, with financial data. I gather accounting and market data on all publicly traded companies, listed globally, and then try to extract whatever lessons that I can from the data, to use in investing, corporate finance and valuation for the rest of the year. I report the data, classified by industry group and by country, on my website, in the hope that others might find it useful. While, like last year, I will be summarizing what I see in the data in a series of posts over the rest of January, I decided to use this one to both provide some perspective and cautionary notes not only on my data but on numbers, in general.

The Number Cruncher's Delusions
In an earlier post on narrative and numbers, I confessed that I am more naturally a number cruncher than a story teller and that I have learned through experience that focusing entirely on the numbers can lead you astray in valuation and investing. In fact, as you read my posts on what the numbers look like at the start of 2017, it is also worth noting that I am, like all number crunchers, susceptible to three delusions about data:
  1. Numbers are precise: I say, only half jokingly, that when a number cruncher is in doubt, his or her reaction is to add more decimals, in the hope that making a number look more precise will make it so. The truth is that numbers are only as precise as the process that delivers them and in business, that makes them imprecise. Thus, when you peruse the returns on capital or costs of capital that I will be estimating and reporting for both companies and industry groups, please do recognize that the former is an accounting number, where discretionary choices on expensing and depreciation can translate into big changes in returns on capital, and the latter is market number, making it not only a moving target (as interest rates and risk premiums change) but also a function of my estimation choices as well as estimation error in estimating risk premiums and risk parameters. 
  2. Numbers are objective: One of the resentments that number crunchers have about story tellers is that the latter indulge in flights of fancy and are unashamed about bringing their biases into their stories and through them into pricing and investing. The problem, though, is that numbers can be just as biased as stories, with the caveat that it is easier to hide biases with numbers. To give one example, one of the datasets that I will be updating has tax rates paid by US companies in 2016 and I provide three measures of effective tax rates, ranging from a simple average of effective tax rates across all companies in a sector, yielding the lowest values, to a weighted average effective tax rate that is computed only across money-making firms, which yields much higher values. If you are dead-set on making a case that US companies don't pay their fair share in taxes, you will report only the first number and not mention the rest, whereas if you want to show that US companies pay their fair share and more in taxes, you will go with the latter. It is for this reason that I will not claim to be unbiased (since no one is) but I will try to provide multiple measures of widely used variables and leave it to you to decide which one best fits your preconceptions. 
  3. Numbers put you in control: It is human nature to try to be in control and numbers serve us well, in that pursuit. As in other aspects of life, we seem to think that attaching a number to a volatile or uncontrollable variable brings it under control. So, at the risk of stating the obvious, let me say that measuring your return on invested capital is not going to turn bad projects into good ones, just as estimating your interest coverage ratio is not going to make it easier for you to make your interest payments. 
Don't get me wrong! I remain, at heart, a number cruncher but I have a more complicated, and healthier, relationship with data than I used to have. My faith in data has been tempered by my experiences with data, and especially so with the ease with which I have seen it bent to reflect the agenda of the user. I trust numbers, but only after I verify them, and I hope that you will do the same with the data that you find on my site.

A Big Data Skeptic
It is my experience with data that make me skeptical about two of the hottest concepts in business, big data and data analytics, at least as a basis for making money. It is true that companies are collecting more data than ever before on almost every aspect of our lives, with the intent of using that data to make more money off us. In a capitalist society, I remain doubtful that big data will be monetized, for three reasons.
  1. Data is not information: Not all data is created equal. Data that is based on what you do is worth a lot more than what you say will do; a tweet that you are bullish on Apple, Twitter or the entire market is less useful data than a record of you buying Apple, Twitter or the entire market. This is a point worth remembering as the rush is on to incorporate social media data (from Twitter and Facebook) with financial data to create super data bases. In addition, as we collect and store more data, it is worth noting that data is not information. In fact, if data analytics does its job, converting data to information will remain its focus, rather than generating neat looking graphs and obscure statistics. 
  2. If everyone has it (data), no one has it: For data to have value, you have to some degree of exclusivity in access to that data or a proprietary edge on processing that data. It is one of the reasons that investors have been unable, for the most part, to convert increased access to financial data into investing profits.
  3. Not all data is actionable: , To convert that data to profits, you need to be able to find a way to monetize whatever data edge you have acquired. For companies that offer products and services, this will take the form of modifying existing products/services or coming up with new products/services to what you have learned from the data.
As you look at these three factors, it is easy to see why Netflix and Amazon have become illustrative examples for the benefits of big data. They get to observe us (as consumers) in action, Amazon watching what we buy and Netflix observing what we watch on our devices, and that information is not only proprietary but can be used to not only modify product offerings but to also nudge us to act in ways that will be beneficial to the companies. By the same token, you can also see why using big data as an investing advantage will, at best, provide a transitory advantage, and why I feel no qualms about sharing my data. 

Data Details
If you choose to use any of my data, it behooves me to take you through the process by which I collect and analyze the data and offer some cautionary notes along the way. 
  1. Raw Data: The first step in the process is collecting the raw data and I am deeply thankful to the data services that allow me to do this. I use S&P Capital IQ, Bloomberg and a host of specialized services (Moody's, PRS etc.). For company-specific data, the only criteria that I use for including a company is that it has to have a non-zero market capitalization, yielding a total of 42678 firms on January 1, 2017. The data collected is as of January 1, 2017, with market data (stock prices, market capitalization and interest rates) being as of that data but accounting data reflecting the most recent twelve months (which would be through September 30, 2016 for calendar year companies). 
  2. Classification: I classify these companies first by geographic group into five groups - the United States, Japan, Developed Europe (including the EU and Switzerland), Emerging Markets (including Eastern Europe, Asia, Africa and Latin America) and Australia/New Zealand/Canada, a somewhat arbitrary grouping that I am stuck with because of history.
    I also classify firms into 96 industry groups, built loosely on raw service industrial grouping and SIC codes. The number of firms in each industry group, broken down further by geographic grouping, can be found at this link and you can find the companies in each industry grouping at this link.
  3. Key numbers: I generally don't report much macroeconomic data (interest rates, inflation, GDP growth etc.), since there are much better sources for the data, with my favorite remaining FRED (the Federal Reserve data site in St. Louis). I update equity risk premiums not only for the US but for much of the world at the start of every year and will update them again in July 2017. Using the company data, I report on dozens of metrics at the industry group and geographic levels on profitability, cost of capital, relative risk and valuation ratios and you can find the entire listing here.
  4. Computational details: One of the lessons that I have learned from wrestling with the data is that computing even simple statistics requires making choices, which, in turn, can be affected by your biases. Just to provide an example, to compute the PE ratio for US steel companies, I can take a simple average of the PE ratios of companies but that will not only weight tiny companies and very large companies equally but will also eliminate any companies that have negative earnings from my sample (causing bias in my estimates). To eliminate this problem, for most of the industry average statistics, I aggregate values across companies and then compute ratios. With the PE ratio for US steel companies, for instance, I aggregate the net income of all steel companies (including money-losing companies) and the market capitalizations for the same companies and then divide the former by the latter to get the PE ratio. Think of these averages then as weighted averages of all companies in each industry group, perhaps explaining why my numbers may be different from those reported by other services. 
  5. Reporting: I have wrestled with how best to report this data, so that you can find what you are looking for easily. I have not found the perfect template, but here is how you will find the data. For the current data (from January 2017), go to this link. You will see the data classified into risk, profitability, capital structure and dividend policy measures, reflecting my corporate finance focus, and then into pricing groups (earnings multiples, book value multiples and revenue multiples). I also keep archived data from prior years (going back to 1999) at this link. Unfortunately, since I have had to switch raw data providers multiple times in the last 20 years, the data is not perfectly comparable over time, as both industry groupings and data measures change over time. 
  6. Usage: There are two ways you can get the data. For the US data, I have html versions that you can see on your browser. For all of the data, I have excel spreadsheets that you can download for the data. I would strongly encourage you to use the latter rather than the former, since you can then manipulate and work with the data. If you have questions about any of the variables and how exactly I define them, try this link, where I summarize my computational details
In Closing
I am a one-man operation and I am sure that there are datasets that I have not updated or where you find missing pieces. If you find any of these, please let me know, and I will try to fix them. I also don't see myself as a raw data provider, especially on a real-time basis and on individual companies. So, I don't plan to update this data over the course of the year, partly because industry averages should not have dramatic changes over a few months and partly because I have other stuff that I would rather do.

YouTube Video


Data Links
  1. Current Data on my website
  2. Archived Data on my website
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: Death and Taxes in January 2017- Changes Coming?
  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: A Pricing Update in January 2017