Update on market views

FX

USD

On May 30th I wrote a post explaining why I was bearish the USD (Framework for FX valuation – where is the USD heading?). Value seemed to me to be the most important driver and so I had paired a short USD view against a basket of currencies which I considered the most undervalued i.e. CAD, AUD, EUR, NOK and SEK.

This has been working very well so far.

However currently I do not think the USD has moved enough to warrant a reduction in my conviction level, as the long-term potential for the move is still far greater.

GBP

I have been very bearish on sterling for a long time. I may write a post explaining the key reasons in more detail, but essentially I see GBP as highly vulnerable for many fundamental reasons and it is heading towards a large negative trade shock with Brexit. This results in a heightened potential for a calamitous drop.

My 5th May post (Who loses more from Brexit? The UK or EU?) is where I laid out why the UK has far more to lose then the EU from Brexit. Since then sterling has performed very poorly vs. my basket of undervalued currencies (AUD, EUR, CAD, NOK and SEK.)

I retain my bearish view of the currency but am aware that it is hard to justify looking solely at historic value. My view for continued weakness rests on my view that Brexit will happen and very bad economic consequences will follow.

If there is a political earthquake leading to a U-turn, or perhaps the UK ends up in some permanent transition limbo within the single market, then I would revisit my view.

Equities

In early June I laid out why I was bearish on US equities. Since then they have barely moved. My views have not really changed but I intend to update my analysis given some interesting data releases since then. Most importantly wage growth has continued to disappoint which means that there remains an opportunity for corporate earnings to keep rising, despite lacklustre GDP growth.

Fixed Income

I looked at the front end of US fixed income on May 16th and noted that the continued slow drift lower in rates had been a good trade and that it could be about to change.

There has been no sign of a change at all in the market i.e. a very slow move to lower rates continues. This is not a trade I have any appetite to hold either side of.

The story is similar in the long end with little movement in any direction.

With mediocre growth and no acceleration in wage growth, there is no catalyst but my concerns with overall valuation mean I have no desire to own any fixed income at these levels.

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 FX valuation – where is the USD heading?

Here is a metaphor to explain how to approach a situation when there are conflicting potential drivers of an asset.

You are sitting on a small yacht, drifting in the sea. You want to know in which direction you will drift: onto those scary rocks or safely away from them. There are two potential factors which could be very important – the wind and the tide. The wind is the one you will be most aware of; but the tide could perhaps be very important even though it is less clear what it is doing.

If you ask for help you may find advice split into 2 camps. Those who believe that the wind is always the critical factor and those who believe that the tide is always the critical factor. This ideological split is not very helpful because there is no consistent answer to this problem. Sometimes the wind will matter more, sometimes the tide will matter more. You have to use your knowledge and judgement to decide how to incorporate those factors.

As for asset markets, this is a useful metaphor for many macro markets from fixed income, to equities to foreign exchange. A clear and important example of this today is how to position ourselves in the USD.

FX Model

Tide – Value (proxy is the real effective exchange rate)

Wind – Relative monetary policy.

Monetary Policy (Wind)

Most FX strategy I have read over the past few months has been bullish the USD. The most common argument relates to divergence of monetary policy. The Fed is raising rates when few other countries are, and with Trump these expectations became even stronger.

The relationship between the movement of a currency and the relative interest rates in those countries is a good one. If we look at the last decade of the Euro vs the US dollar then we see what a great first approximation it is.

But in financial markets, it is often a mistake to assume that something you have found that works well for a period is always reliable. It is not possible to treat macroeconomics or investing as having “Laws” in this sense.

If we look at the prior decade for the Euro, then the monetary policy model is terrible.

Value (Tide)

The other first approximation model I want to look is value. For this post, I will take the real effective exchange rate (REER) as a simple proxy.

It is clear from this chart that assuming a mean-reverting tendency in the REER would not have been at all useful in the past 3 years. The USD has been above its average value and heading higher strongly.

Maybe value only matters at extremes? Taking a longer view, adding bands of +/- 10% gives us a sense of how far the USD can move before a value constraint starts to be meaningful.

If you take the model for the yacht as the same conditions last time you went sailing, when there was not much tide and the wind blew you safely away from the rocks, this is not sensible. It would be particularly dangerous if there is a rip tide and you are ignoring it by assuming that a light breeze will determine your path.

Why is everyone talking bullishly about the USD?

  • Recently value has been a terrible model.
    The US dollar is expensive and going higher
  • Rates differential has been a great model
  • Simple extrapolation means that people believe rates will continue to be the best model in the future

Signs this may be happening now

The recent strengthening of the euro is often “explained” with reference to a potential change in QE from the ECB. But rates have not moved. So perhaps we are seeing the influence of a force we have not had to pay attention to recently. Value.

Conclusion

Macro investing is hard. The world is complex and confusing.  Over the years I have noticed many people fall into one of two traps

  1. Become fixed in a single view of how the world works and happily ignore or rationalise away contrary information
  2. Form a fluid view of the world which adapts to a model which can make the most sense of their recent experience.

The time we can make the most money from markets is when they are the most wrong. This can happen people are using the wrong model.

Models – How do computers play chess?

Chess computers have been good enough to beat me my whole life. It took until 1997 for them to beat a reigning World Champion, when Deep Blue beat Kasparov. They can now comfortably beat all the best players in the world. But development of Go computers has been far slower, it was only this year that AlphaGo defeated Lee Sodol, the 9 dan professional. What is the difference?

The most common reason given for why Go is harder, is that it is more complicated. In chess, there is a choice of 20 first moves, in Go the choice is 361. So in Go, the permutations and, as a result, possible games are far higher than in chess. Since computers play these games by simulating permutations, it makes intuitive sense that this is easier in chess than Go.

This argument is logical but highly misleading. The problem is that BOTH games are insanely complex and unsolvable by brute force. Imagine I am trying to move a pair of large rocks. One weighs 100 tonnes. The other weighs 10,000 tonnes. Is it sensible to say that the second is 100 times harder to move? Or simply that both are unmovable. Degrees of impossibility is not a very useful concept.

There is a more important difference between the two games. In chess we can build a simple model that acts as an excellent first approximation to evaluate who is winning. Just count the material and use a simple scoring of queen= 9 pawns, rook = 5 pawns etc. to come up with a single total for both sides. The one with the higher number is winning. This is how beginners think of the game, the aim is to grab material. Thus, it is easy to code a simple model to get the computer started. Once this first order approximation is worked out then second order models can be added such as pawn structure, space advantage or use of open files. In Go there is no such simple evaluation metric and how they managed to programme a computer to win is a fascinating topic and likely a separate post on AI.

A good first order approximation often gets you a decent way to a solution. If you don’t have this, you may have trouble finding a solution that doesn’t take an infinite amount of time to solve, as the early versions of Go computers found.

This has an interesting link to the way I approach financial markets and economics. I think it is most important to spend time thinking about appropriate first order approximations to help with the general understanding of what is really going on. But the influences around us often obscure this, for example from news or complex analysis.


Reminiscences of a Stock operator

I first read this book when I was 17 but it took me many years of trading and painful experiences to realise that the character who had the most to teach me was Partridge. He is the older, experienced trader who whenever presented with a stock tip by an excited young trader would always reply

“Well, this is a bull market, you know!” as though he were giving you a priceless talisman wrapped inside a million-dollar accident-insurance policy. And of course, I did not get his meaning.


Useful trading models

In economics and finance, it is the development and understanding of models of a first approximation that are the most useful and the most important. This is primarily the method I am using for models of asset market pricing described in other posts. Far too much effort and time is spent on far more “complex” analysis and models, which often focus on second or third order drivers by assuming away the first order ones. The “news” constantly blaring out on cable TV is at best a focus on factors causing minute differences in asset prices. At worst, it is just distracting white noise. Precise directions for the last 100m of your trip are a not much use if you are not sure which town you are going to. It is far better to be approximately right than to be precisely wrong.

Trade Ideas

A common type of trade idea proposed to me will be in this form:

  1. There is a recent development or upcoming event which matters for the Australian dollar (substitute in any other market) e.g. a piece of economic data
  2. We should buy/sell it

What is rarely done however, is considering how important this driver is in context. Commonly the idea is logical but essentially rests on the idea that the current market price is already correctly priced. This approach fits well with many people’s education in which assumptions of efficient markets are often embedded without them realising. The reason that these trade ideas often fail is that the new information will only dominate the market movements if and only if the more important drivers of the currency are correctly priced. Instead of assuming the market is fairly priced, I would prefer to question whether this first order approximation is appropriate before moving on.

Australian Dollar Example

To use an Australian dollar example, the value of the currency doubled between 2001 and 2008

It did not rise like this because of a succession of incremental pieces of random news which happened to cumulate in a massive movement. It happened because the currency was by first order approximation far too cheap. A useful first order approximation model for currencies is Purchasing Power Parity (indices are freely available and calculated by the OECD). The chart below shows that the PPP of the AUD was very steady at around 0.70 cents. In 2001 it was very cheap, and when it approached parity it was very expensive. Capturing these kinds of move is where I spend my time and historically where I have made my biggest profits.

Conclusion

I have learned to focus on the bigger picture and look for large market movements. In my experience, these are most likely to happen when the market price is a long way from a good first approximation model. I therefore put time into building these first approximation models across asset classes as I have briefly described so far in fixed income, and will follow up with ones on currencies and equities. Just as in chess, a good understanding of a first approximation model can get you a long way. Focusing on very new information or complex models which are actually third order features, while neglecting the first order drivers, only leads to confusion and major mistakes.