Equity Valuation – Part 1 Without COVID-19

To understand the current equity market valuation, a good place to start is to do a quick analysis of what market value looked like at the start of the year. We can then look to separate the impact of COVID on the economy and markets. At the time of writing, the S&P is heading towards 3,000.

PE ratio

A simple and decent method of equity valuation is to look at the PE ratio overtime – price of equities divided by their annual Earnings. For the S&P, it is currently just over 20, which is roughly the middle of the range of the past 5 years. Furthermore, it looks even more reasonable when compared with fixed income where bond yields are close to nothing.

Over the same period the stock market has risen strongly. In the chart below, I show that the value of the S&P has doubled in the last decade, as have quarterly earnings (S&P EPS)

Where does earnings growth come from?

The answer is of course that companies are part of the economy and as the economy grows then corporate earnings also grow. In the chart below, we see uninterrupted US GDP growth for the past 10 years and also a rising S&P based upon growing earnings.

This must all be consistent then?

This simple look at equities implies that the rapid rise in equities over the past decade has been fully justified by the fundamentals in the economy.

But I played a little trick in the charts above.
While the stock market, GDP and corporate earnings have all gone up over the past 10 years they have not gone up by the same amount.

  • Nominal GDP has risen by 50%
  • Corporate earnings have doubled
  • The US stock market has tripled

The part I want to look at more closely is the difference between GDP growth and the far more rapid growth in corporate earnings.


Any Reasons to be cautious?

It is remarkable to have such a large rise in earnings when compared to the overall economy and one would expect to see profits as a share of GDP to have risen significantly over the same period. With decent overall growth, then you might expect growth in profits to be better, and to offset this the share of the economy going to workers would be reduced. This would fit the idea that while growth has been good, more of the benefits go to the capital and capital owners and less to workers; in this recovery a lack of real wage growth is often cited as a concern in the face of rising equity prices.

When I look at the data however, it does not fit this intuition. This is a chart of corporate profits as a share of GDP over the past 60 years.

We can see it strongly mean-reverting and so the recent pattern is exactly what we would expect to see. In a recession wages are sticky and do not readily fall; it is companies that have profitability issues. We all know that companies go bust in recessions and in 2001 profits fell to 7% of GDP. In the early stages of a recovery, it is profits which rebound the fastest, wages remain subdued as there is still high unemployment so the gains from GDP growth go to businesses, and by 2014 profits as a share of GDP had nearly doubled to 12%. But later in the cycle this reverses, and profit margins are squeezed, which they have been for the past 5 years.

This does not make sense!

I have just told you two contradictory stories. One is that corporate earnings have been rising rapidly over the past few years, far faster than GDP growth. The other is that profits as a share of GDP have been falling as we would expect late in the economic cycle.

The reason I can tell you two completely different stories is that I have two different data sources. The first is the earnings (S&P EPS) as they are reported by companies. The second is profits as they are recorded in the income method GDP data – known as the NIPA data (National Income and Product Accounts).

These two ways of measuring earnings are not exactly the same. For example, the S+P is only 500 companies whereas NIPA represents the entire US corporate sector. There are also differences in accounting and tax. But logically they are highly similar, and it is no surprise that historically they track very well. Here is a chart from the late 80s until 2014 showing the rise in earnings (EPS) as measured by companies and as measured by the National Accounts. We can see that in the long run, they track pretty well but there are some periods of divergence for instance around the time of the dot com boom in the late 90s.

If I draw the more recent history, we can again see this divergence. The reported earnings have been rising rapidly (white line) while the NIPA measure of profits has been stagnating (orange line).

The relationship is easier to see if we take them as a ratio. In the chart below we see a large spike around the Dot Com bubble (EPS growing more than NIPA), a large spike down during the Great Recession (the opposite) and another large spike higher recently.

These differences are so large that they require an explanation.

  1. Dot Com spike

A bubble emerged in the late 90s with very high PE ratios i.e. companies were expensive compared to earnings. In addition, these reported earnings were inflated; a famous example you may recall was Enron who were highly “creative” with how they recorded and reported their earnings. When the bubble bursts and we move into recession then these accounting methods are not sustainable, and we see the rapid fall in reported earnings and the ratio of reported earnings to NIPA data renormalizes.

There is a danger in that earnings are presented to us by corporates in the most flattering version they can create. In a bull market, there are opportunities to keep presenting this managed version, perhaps similar to how people curate their Instagram feed. Through heavy use of filters and selective framing, someone might look as though they are very attractive with an opulent lifestyle. The recession is the equivalent of when you meet them in person and realise that the reality is not exactly what was promised.

  1. Great Recession

This rapid fall in reported earnings is easily explained as a result of huge write-downs taken by financial firms. This is a good example of an item that is recorded by companies as a change in earning, but not included in GDP data. Once the write-downs have finished, the ratio between reported earnings and NIPA profits renormalizes.

  1. Now

I have been searching for a good explanation of this divergence and am yet to find one. One plausible idea is stock buybacks, but this is not true as they are adjusted for in the earnings data. Other sources of divergence such as tax are real but do not come close to explaining the large difference.

Could the NIPA data be wrong?
This is data that will get revised, but it would take something extraordinary for GDP revisions to change corporate profits by the 40% divergence we have seen to EPS

Could it be financial accounting manipulation?
Some argue the rules are so much stricter now, so it is not possible. Surely what we learned from the last crisis is that Rating Agencies having strict rules on how to make a security AAA that enabled smart bankers to arbitrage those rules. I do not believe we could ever have rules so strict that smart bankers cannot find ways to optimise them. This does not mean that people must be lying or breaking the law. Bear in mind virtually everything that Enron did to inflate their earnings was legal.

Could it be offshoring profits?
There could be something here and it is a very murky and complex area. We know that the large tech companies have found ways to limit their onshore earnings, keeping profits in countries where they have to pay no tax but this is hardly a new phenomenon. It is something I will be looking into as an explanation.

Could it just not be a problem?
Maybe this is the first time we have ever seen a large, rapid, permanent shift in the relationship between reported earnings and NIPA data. Maybe. Most people in effect seem to be assuming this and financial markets are not at all concerned.

What if the NIPA data is right?

If the NIPA data is correct, then the PE ratio is currently far higher than 20. In the previous two cycles it was reported earnings that correct, not the NIPA data, and the timing of the correction is during a recession. As Warren Buffet said “You only find out who is swimming naked when the tide goes out”.

Summary

My view of value before COVID was that it was reasonable to belong to equity markets early in 2020. I was aware of the NIPA data divergence as an issue, but it has been an issue for a long time and it has not been a market driver. The trend towards higher earnings and higher equity prices had been very strong and I believed that it would take an event or catalyst to reverse it. If we are heading into a significant recession, then this may be the time we understand if the relationship between reported earnings and GDP profits will reconnect.

The Denial Index

The standard index and best way of measuring the volatility in the US stock market is the VIX.  (it’s an index of average implied volatility of listed options on the S&P of about 30 days expiry)

It is also known at the “Fear Index”.
But I think it should better be termed the “Denial Index”

When does VIX spike?

Volatility rises on negative shocks when stock markets drop rapidly.  This can be attributed to fear.  When the fear subsides, the stock market recovers, and the volatility drops.

This is where the index gets its name.

There are plenty of examples, take the equity market drop during 2015

Picture 1

Or the really huge drop during the financial crisis of 2008

Picture 2

The most recent sell-off also looks the same
i.e. the stock market drops and volatility rises.
So we can expect that volatility will drop again, when the market recovers.

Picture 3

If we look more closely at the examples, what we are really seeing is when there is a sharp drop in share prices, people do not believe it will last.  They do not accept that the new lower price is the correct one, and think that a rapid bounce back is likely.  What they are afraid of is that in the short term, the market will carry on going lower, but they do not expect that to last either.

In a real economic crisis, the fall in stock prices is for good reason and is here to stay.  Once the market reaches this level of acceptance, then we can have stock prices stay low, but volatility falls back to previous lower levels.  This is exactly what we saw in 2008/09.

Picture 4

If this crisis does turn out to be long lasting and real, as I currently expect, then we are still only in Stage 2  i.e. Awareness grows. Faith policy makers remains strong, even though the policy makers do not yet understand they are not in control. Markets believe it will be ok – e.g. positive reaction to Bear Sterns bail out.  (1st half of 2008)

The positive market reaction to the US stimulus package and Trump suggesting that the US be reopened is a sign that the market’s faith in policy makers remains strong.  The “Denial Index” still suggests that we expect these lower prices not to last and that a sustained recovery is imminent.  We will all find out if this faith is justified.

 

Framework for valuing equities Part V – Relationships between Components

In Framework for Equity Valuation Part II I laid out this approach.


Breakdown of the Equity Price

Using some very simple algebra, I split the equity price into components:

Price = (Price/ Earnings) * Earnings

Price = (Price/ Earnings) * ( Earnings / Nominal GDP) * Nominal GDP

In this work, there was an assumption that the components are independent. I will now examine if this is sensible.

Are P/E and E/GDP independent?

I can find no consistent relationship between the two.
There appears to be a mild negative correlation overall, but at times there can be extended periods of both components falling, such as 1967-74 and 2000-2003, or both rising such as 1994-2000.

An intuitive relationship occurs when there is an expectation of a large rise or fall in earnings and the equity price rises or falls in anticipation. This means the PE ratio would rise in anticipation of earnings rising, and then fall back down as earnings expectations are realised. In this situation, I see earnings as the driver and the PE ratio as a passive variable.

A situation where PE was the independent driver was in the 1980s, when a broad fall in yields meant the PE ratio rose without any need for an expectation of a change in earnings. This supports the approach that we can look at the two factors as independent drivers.


Are Growth and PE ratio related?

This is a relationship that is often assumed to exist as we think periods of low growth or recession are associated with low confidence and high awareness of risk. This high “risk premium” means low PE ratio.

But the evidence to support this idea is not so clear. Of the past 9 recessions, the PE ratio only fell twice. There is some evidence to support the idea that the PE ratio falls in the year before the recession in anticipation of an earnings drop, then recovers quickly as those expectations are realised. This happened in 5 of the last 9 recessions so it is still a fairly mild effect.

Are Growth and earnings related?

I find the chart below intuitive and compelling. The reason that recessions drive equity markets down is because recessions drive corporate earnings down. If we look at earnings as a share of GDP from 1 year before the recession to the low during the recession they fell each time. The average fall was 21% with the smallest still a 9% fall and the largest (2008) down a massive 42%.

The rationale for this comes from thinking about the breakdown of national income in the NIPA data (Framework for Equity Valuation Part III Earnings Outlook). If there is downward pressure on nominal GDP whilst wages remain sticky, then the impact is felt in a magnified way in corporate earnings.

The magnitude of changes in earnings are very large during recessions and early recovery, so it is during these periods we should be especially alert when forming an equity outlook. The impact of whether growth is 2.5% or 2.8% is imperceptible by comparison. Lots of work by economists, strategists and asset managers is done to fine tune these types of economic forecast but a) it is not possible for them to be that accurate b) even if you could, the relationship to market prices is so loose as to make it useless information.

Conclusion

There is one important interrelationship we need to be very aware of. In previous recessions, earnings as a share of GDP have fallen rapidly and normally bottomed at around 7%. If that were repeated in the next recession, earnings would need to fall by 40% from current levels.

 

Framework for valuing equities Part IV – PE Outlook

In previous post (Framework for Equity Valuation Part II – Equity Drivers), we have seen that the PE ratio can be an important medium term driver of equity prices. Given the debatable outlook for aggregate corporate earnings, this makes the outlook for the PE ratio a critical factor in forming a view on equities.

Simple PE ratio

It should be very clear from the normalised chart below that the powerful driver of the equity market performance since 2012 has been an expansion of the PE ratio. The S+P has risen by 72% over that period, and the majority of that is explained by PE ratio which has risen from 14 to over 21, an increase of almost 50%.

Can PE ratios go higher from here?

If we look over the long run, it is very rare for the PE ratio to move higher from where we currently are. In fact, it has only happened 3 times; 1991, 1999 and 2009.

In 2 of these 3 examples, high PE ratios were observed during a recession and ensuing bear market with earnings falling even more than prices . For example, in 2009, the high PE ratio was driven by the collapse in earnings not the soaring of equity prices to record highs. These are not helpful precedents for equity bulls right now.

The only previous period where the PE ratio drove the market higher from this level was the dot com bubble. The name given to this period gives a big clue as to what we now think of what happened. If that were to be repeated, then there would be another 40% left in this rally due to PE expansion. This is not impossible but relying on a repeat of the biggest valuation bubble in a century is not reassuring to me.

“Fed Model”

The post I wrote about the Fed model implied that equities represent good value compared with bonds. This generates a counter-argument to my scepticism of a repeat of the dot-com bubble. We have never seen bond yields this low before, so why should we not also see unprecedented low yields in equities (high PE ratio)?

As I explained in my previous post (Framework for valuing equities Part 1- Compared to bonds), I do not think that bonds are good value and so simply beating their performance may not be a high enough benchmark. Most importantly, if QE-driven low yields are pushing up PE ratios, then the termination of QE and rising bond yields should be very harmful for equities.

The other problem is that, even if it is true that holding equities for the next 10 years may work out, the volatility and drawdown you experience may be hard to handle.

For example,
In May 2007 from my equity model (Framework for valuing equities Part 1- Compared to bonds), the expected 10 year return for equities was 8.1% (annualised)

It actually turned out to be 7.1% annualised – which resulted in a total return of almost 100% over 10 years.

That sounds pretty good.
But I bet it would not have felt so good less than 2 years later in March 2009 after a 53% drawdown.

With hindsight waiting for a better moment to enter the long equity trade would have been phenomenally better. If you had waited to buy in March 2009 instead (I know, ludicrous cherry picking, but just about any time around then was great) then your returns would have been a total of over 300%.


Conclusion

The outlook for equities from the perspective of high nominal GDP or high earnings growth look rather limited. Earnings is near record highs as a share of GDP and we are at the stage of the cycle where wages are rising instead.

If we rely on a PE expansion to make us optimistic, we need to be comfortable buying at levels which previously have been associated with a “bubble”.

We can perhaps consider equities being good value compared to bonds, but we must then remember that yields are too low given fundamentals and the termination of QE.

If you are happy to hold them for a decade and do not worry too much about drawdowns, then I come up with an expected annual return of 6.5%. This is higher than bonds right now but perhaps waiting for a better entry level will turn out to be a better strategy.

Framework for Equity Valuation Part III Earnings Outlook

We explored in the last post (Framework for Equity Valuation Part II – Equity Drivers) how earnings as a share of GDP can be an important driver of medium term equity returns.


What is the market expecting earnings to be?

The chart below shows the difference between what the PE ratio is today and what it is expected to be in a year’s time (i.e. a measure of what analysts expect total earnings growth to be). Currently it shows that equity analysts are predicting a 20% increase in corporate profits. This implies that although the current PE ratio may be high, it will be brought down by rapidly rising earnings.

I find this chart is the best explanation of the Trump rally. Analyst earnings expectations rose immediately and this is temporarily reflected in a higher PE ratio. Once the earnings come through we will see that the rise in equities was driven by earnings not by animal spirits. Assuming the analysts’ earnings optimism is correct of course.

What does this mean for earnings over GDP?

I will leave aside for now views on how effective Trump will be at increasing growth and just look at the confidence level implied in market prices. It is all too easy with controversial political figures and issues for analysis to become infected with partisan assumptions and desires which lead to worse decisions.

The first point to note is that taking analysts expectations of a 20% earnings increase, this would imply earnings as a share of GDP will immediately rebound to all-time highs (dotted red line in the chart we used previously). We have seen drops in E/GDP of that magnitude before during recessions but never an increase and this seems an odd stage of the cycle to expect it.

Is this forecast consistent with other data?

Another useful way to use national income data is split the economy into just 2 parts – Wages and Profits.

The National Income Accounts (NIPA) data is used a lot more by economists than it is by market participants. To give some context, it was particularly useful to use during the late stages of the dot com bubble, as it showed that reported earnings were far in excess of the profits seen in the national accounts. This implied some form of earnings inflation and potentially even fraud, which actually did come to light later in 2002. The chart below shows how the reported earnings diverged for 4 years before coming back in line very sharply. Checking reported data and forecasts for simple internal consistency can be surprisingly rewarding. It is best not to assume that analysts have done this for you.

Using the National Income Accounts data, we can construct a chart of the respective shares of national income for wages and profits. As you can see, there is a clear and logical inverse relationship between wages and profits as a share of GDP.

We know that wages have finally been rising again recently and all forecasts are that this will continue. So how can we have nominal GDP of 4%, wages rising at least 2.5% and profits rising 20%.

Quick answer – we can’t.

Long answer – it requires some heroic assumptions in other parts of the national accounts which I won’t go into here.

Scenario – if we assume corporate earnings will rise by 20% over the next 12 months and allow the other components of GDP (including proprietors’ income) to grow at 4%, we can solve for wages and we get an increase of just 0.7%. Rather different from the 2.5% current seen in average hourly earnings.

Summary

There is a great deal of optimism among analysts for the outlook of corporate earnings. It is hard to reconcile that with some basic arithmetic from the national accounts. When nominal GDP growth is moderate and wages are accelerating, it is hard to also get record increases in corporate profits. If earnings do not rise as rapidly as anticipated then to be optimistic on the S+P you need to be positive on the prospects for PE expansion. I will look at that next.

Framework for Equity Valuation Part II – Equity Drivers

In previous posts, I have written up ideas on primary drivers and first approximations for fixed income and foreign exchange markets, and I now want to continue with equity markets (Click here for Framework for valuing equities Part 1). This is a little more complex to explain so will take a few posts to go through the steps.

Most equity analysis I read starts from the bottom up. The analyst knows a lot about individual companies or sectors and will extrapolate from there to the broader index. Or if some macro analysis is performed, it assumes some form of “conventional wisdom” such as high growth means higher equities.

I want to start with a top-down equity valuation and so I find a useful way to begin is to break the equity price into components. Then I can compare equities to GDP and the economy, things that I am familiar with already.

This framework can be applied to all equity markets but will start here with the US and the S+P 500.

Breakdown of the Equity Price

Using some very simple algebra:

Price = (Price / Earnings) * Earnings

Price = (Price / Earnings) * (Earnings / Nominal GDP) * Nominal GDP


Focus on the Components

Eventually, I will I look at how these variables relate to each other, but first let’s start examining each in turn:

  1. Nominal GDP Long Term Diver
  2. Earnings Medium Term Driver
  3. PE Ratio Medium and Short Term Driver
  1. Nominal GDP 

In the long-run, Nominal GDP is the only thing that matters for equity prices.
Nominal GDP is 35 times bigger than it was in 1961 and the S+P Index price is 37 times higher. Fundamentally, if you are a long-term investor and just stay long equities, then the rising tide of growth will lift you to large compounded returns over the decades.

However, if your horizon is less than decades, nominal GDP is not such a clear driver of equity returns. In the very short run and even in the medium term of say 2 years, nominal GDP moves far less than equity prices do.

Follow-up Question “Does outlook for nominal GDP matter now for investment decisions?”

See later post

  1. Earnings as a share of GDP

Below is the chart from 1967 to now. Over a given 2-year horizon, we have seen material movements in S&P trailing EPS over GDP. Many are interesting, but the recent financial crisis moves stand out the most. Earnings were clearly volatile but amid the talk of bubbles, panic, and recovery of confidence, how important were they versus other drivers?


Example – Financial Crisis and recovery

During financial crisis earnings dominated.
For all the talk of animal spirits and how equity markets were highly erratic and emotional, the “boring” fundamentals of corporate earnings explained practically all price movements.

Follow-up Question “Outlook for earnings?”

See later post

  1. PE Ratio – Medium and Short term driver

Short term

In the short-run by definition PE ratio is the only thing that matters.
GDP data and earnings releases are only quarterly and so, for most days, the only thing that could have changed is the PE ratio.

You may argue that these daily changes in PE ratio are explainable and even predictable as they are driven by

i) Change in expectations of earnings

ii) Change in expectations of nominal GDP

iii) Change in yields in other substitutable markets

iv) Change in yield demanded from equities due to change in risk preferences or change in perception of risk

Radio and TV programmes are filled with a succession of strategists and pundits all required to “explain” yesterday’s market movement and these factors are therefore reached for repeatedly.

Unfortunately, these short-term changes are all too easy to explain away, given the limited range of explanations that are permitted. But you can tell that these “explanations” are also very hard to predict. The driver that is sometimes assumed is that the PE ratio change is due to rational updating of forecasts of GDP growth and corporate earnings. But markets are too volatile and erratic for that explanation to be compelling and talking about animal spirits is more natural.

Medium term

From the chart below you can see that the PE ratio is broadly unchanged over the past 50 years i.e. it is not at all a long-term driver of equities. But it is also clear that with a range of 7 to 30 it can have a huge impact on medium term price movements.


Example – 1980s

In the 1980s, for all the talk of the transformation of the US economy through the Reagan/Volker years combined with the “Greed is Good” era unlocking corporate value through increased efficiency, it was neither high GDP growth nor rising earnings that dominated the dramatic rise in equity prices. In fact, earnings as a share of GDP fell during this period and it was the rise in the PE ratio that drove prices higher.

One can think of this as a yield effect from the fall in inflation and the subsequent drop in bond yields. Lower bond yields drove yields lower in all asset classes, including property and equities. Lower yields mean higher prices and so we saw a huge bull market, commonly mis-explained by deregulation and improved business management.

Example – 2013 to now

 

Over the past 4 years, the dominant driver has again been the PE ratio. Despite more confidence in the recovering economy, earnings as a share of GDP has not risen.

Again there has been a the yield effect with QE reducing yields in the bond market (https://appliedmacro.com/2017/05/23/framework-for-valuing-fixed-income-long-end/) and this has slowly filtered into other asset classes, such as equities, reducing yields and increasing prices.


Follow-up Question “Outlook for PE ratio?”

See later post

Conclusion

The framework of separating nominal GDP, earnings and PE ratio is helpful in describing what have been the historic drivers of equity markets. What we can do next is look at the current outlook for each of these drivers and from that the outlook for US equity markets.

Framework for valuing equities Part I- Compared to bonds

A useful framework for considering one investment is to compare it with another, you can then do analysis to decide if you prefer one to the other. This is of course relative value and if the benchmark asset is government debt, this is a solid place to start.

The “Fed Model”

The “Fed Model” is that the stock market yield is related to the yield on long-term government bonds. Like so many models, it has fallen into disrepute seems to come more from its misuse over the years as opposed to its intrinsic failings.

Expected Returns for Equities and Bonds

A way to start thinking about this model is to start with the expected returns on the two investments, equities and bonds. Consideration of the spread of returns and the distribution around the expected return can come later.


Bonds

Expected return for US government bonds in nominal terms is as easy as it gets – yield to maturity.
I will ignore the remote possibility of a default on the debt.

Equities
Expected returns for equities is harder; there is a choice of possible yields, with none necessarily equating to the eventual return.

  • Dividend yield
    Problematic given that dividend policy is a management decision. Microsoft’s decision not to issue dividends was not a good indicator of its total return.
  • Earnings yield
    More sensible i.e. E/P (or just PE ratio inverted).
  • Earnings yield + Inflation

Considering we are using historical earnings, to get a future value we could add an inflation component given that earnings would be expected to rise along with inflation, in the long run.

Testing the expected returns model for Equities

Back-testing expected returns to 10 year actual returns, the US equity market shows surprisingly good results, especially post WW2. This makes intuitive sense as one would expect that buying equities with a lower PE or when inflation is higher would produce better returns. But the strength of the relationship is eye-catching, implying that current earnings do on average provide a good guide to expected equity total returns.

If you come from a purely “efficient markets” view of the world, this may seem blindingly obvious with equity value as simply the present value of the earnings stream. But bear in mind that earnings yield (E/P) is not a yield in the same way that bonds have a yield, unless you make an argument where the word “assume” occurs very frequently.


Expected Returns for Equities versus Bonds

Given that we are happy with our model of expected returns for both equities and bonds, we can move on to comparing one versus the other.

The model for expected return of equities over bonds would look like

We can use data from end 2016 to get actual numbers

This difference/expected return is often called an equity risk premium (ERP).

We can now back-test its use in predicting if the equity market will actually outperform the bond market. Chart below again shows pretty decent relationship – but can we say how good?

expected vs actual

Given the nature of the data we should not perform a regression, and instead here is a truth table for the data back to 1950.

With ex ante premium (i.e. model) above 2%, then equities outperform bonds 93% of the time.
With it below 2%, then equities only outperform 37% of the time. That is a pretty solid result.

Summary

This investigation that equities look cheaper than bonds. If this is the only model you use then the clear imperative is to buy equities now. Before I make my mind up, I want to think about fixed income valuation next.