• Understand application of valuation concepts in multi-asset investing

• Use relative value principles for active asset allocation decision making

• Apply value signals to gauge risk vs opportunity in a multi-asset context

Concepts

Valuations are an integral input for multi-asset investing.  They tell us information about expected risk and opportunity both for individual asset classes, and in relation to other assets — helping us prioritize allocations and manage portfolios. 

As an asset allocator, your job is to navigate risk, return, and opportunity-cost over typically longer-term timeframes but certainly through cycles and short-term gyrations.  Extremes in absolute and relative valuations help with identifying and proactively managing risks, as well as prioritizing opportunities through cycles and over the longer-term.

It’s not necessarily about maximizing returns, although that is an important objective, but being valuation-aware can help with what is arguably more important: protecting capital.  Indeed, it takes a 100% return to get back to square from a 50% drawdown. So a risk signal from an extreme expensive valuation reading can be just as important as an opportunity signal from an extreme cheap valuation reading.

The principles are simple, but the practice is complicated by consensus thinking (especially in the presence of peer/benchmark risk) on the way up, and fear during downturns. There is always going to be a reason to not buy when things are cheap or not to reduce exposure when things are expensive, there is always an argument to go with the flow, and always a voice in your head talking you out of doing the right thing at the right time.

That’s why the reliability, efficacy, and explainability (recall from the previous article on commodity valuations and indicator design) in valuation indicators across asset classes is critical.  You need a solid quantitative and objective valuation signal that can help you keep your head when everyone else is losing theirs.

So it’s important to build the indicators (or at least obtain visibility on valuations from a reliable third party), understand the principles behind them and appreciate the actually wide range of investment insights that a solid suite of absolute and relative value indicators and skilled interpretation can yield.

Data and Application

The same principles of indicator design apply at the high-level when it comes to building valuation indicators to help in the task of maximizing return and managing risk in constructing multi-asset portfolios.

We want indicators that provide some kind of fundamental basis for gauging whether the asset is cheap or expensive (which should indicate troughs/peaks in that asset price), and the indicator needs to work —and work over time, and ideally be easily explainable (to explain to others, but also to yourself!). 

We need to do this both in an absolute sense: i.e. is this individual asset cheap/expensive by itself? But also in the relative sense: i.e. is this asset cheap/expensive relative to another? There is also the mixed approach of valuation rankings i.e. a snapshot in time of what’s cheap, what’s expensive, and what’s in between? 

But ultimately it’s about supporting decision making: determining which assets are at most risk, have most opportunity, and prioritizing allocations – and using valuations to also inform diversification efforts (e.g. 2022 taught us that expensive bonds could not be relied on as a dampener of risk, and instead they were a source of risk).

Relative Valuations

Let’s look first at relative valuations and then individual asset class valuations and rankings.

The charts below show my absolute valuation indicators for US equities, and US treasuries (from this post). Already we can glean some insights from this pair of charts — as of the latest reading (data to 31 March 2024) stocks are expensive, and bonds are cheap. That means our future/longer-term expectations would be for stocks to record lower returns, and bonds to turn in higher returns.

We can also see the individual peaks, troughs, and extremes in valuation – and in many respects they mirror each other, given how stocks vs bonds tend to trade across the business and market cycles.. 

At the peak stocks are expensive and bonds are cheap, at the trough stocks are cheap and bonds are expensive – that makes intuitive sense (in boom times investors want risk-on/growth assets, not defensive assets, and in times of uncertainty and crisis, investors want defensive assets not risk assets.  We can use this understanding of cycles and observation of the data to help inform how we navigate cycles: when to shift allocations and go against the crowd (remember, valuations are just as much of a sentiment indicator than anything else).

But armed with the above data and indicators, we can also produce a third/synthetic asset called stocks vs bonds (the stock/bond ratio).  The relative performance line being the “price” of this third asset.

We can then simply take the spread of the stocks vs bonds valuation indicators above to generate a new valuation indicator for this “third asset”. 

Echoing the understanding from the absolute valuations, we can see that the relative value indicator provides intuitive and useful signals at peaks and troughs of the stock/bond ratio.

Using this, an asset allocator can make decisions to take the portfolio defensive when forward-looking risk is high (i.e. when the relative value indicator is high and peaking) vs rebalancing into corrections/downturns and increasing risk asset exposure when forward-looking opportunity is high (i.e. when the relative value indicator plunges to an extreme low).

Just as with individual valuation indicators, there are exceptions to the rule, and it is often trickier in practice than in concept, but the job of navigating cycles and risk vs return is made easier by having vs not having a valuation compass.  It can also be augmented by factoring in other information like macro/monetary data and tactical insights from sentiment and technicals.  But also, it’s important to note that stocks and bonds are not the only assets, and not only should we look across asset classes for opportunities, but also for prospective risks that may come “out of left field” (which may have had clues in the form of valuation extremes elsewhere…)

Multi-Asset Landscape

As noted once you have built valuation indicators for numerous asset classes, you can of course look at “third assets” and relative value indicators, but you can also use a simple ranking as a means of prioritizing research efforts, and quickly/easily spotting the extremes and lay of the multi-asset land.

The chart below is from our monthly Market Cycle Guidebook, and shows a snapshot in time (from 31 March 2024) of absolute valuation indicator levels across some of the major asset classes. 

From this we can immediately see what areas to look for opportunities (cheap) and what areas to monitor for risk (expensive). We can also from time to time see major themes (e.g. USD/US equities expensive vs EM/global assets cheap), and sentiment (e.g. government bonds cheap, risk assets expensive).

But that is just a snapshot in time, and doesn’t reveal the insights that can be gained from studying the actual time series, where it can often pay to see the historical context and trend.

So in the next few charts we will look at valuation indicators across these asset classes to provide a view of what’s possible, and demonstrate some of the historical insights and current takeaways.

Equities

As previously outlined (in posts on global equities, different valuation types), the equity valuation indicators below are based on PE ratios.  As you might guess, they largely move together for the most part, but there are clearly times where they disconnect and one market might show up significantly expensive vs its own history while others may simply be somewhat elevated or even neutral/cheap. A good example of this is the 2007 market peak, where US and developed markets were not particularly expensive, but frontier and emerging markets were trading at a significant extreme.

This example goes to show the value of looking at valuation indicators across asset classes as you can end up detecting major risks and turning points by uncovering latent and emerging risks either from within the same type of asset class (equities) or from other assets (e.g. commodities, bonds, FX, real estate).  It’s also worth highlighting that in the 2007 case, there were actually valuation extremes within US equities (e.g. small vs large, value vs growth).

One drawback to this analysis is that because we are using z-scores we are gauging the expensiveness of each asset class by itself, in reference to its own history.  This is why emerging markets for example are showing up more expensive than developed markets most recently, despite trading on similar PE multiples.  But again, even with that, using rankings can help prioritize research efforts (where to look first and in further detail + dimension).

Property

Real estate asset classes are also a good example of where detecting extremes (and peaks – i.e. the strongest signal is when a valuation indicator goes to an extreme and *then* rolls over) can yield important (macro)risk insights. The key example being again in the 2000’s where the housing market and CRE went to extreme expensive levels and then peaked and rolled over, precipitating and contributing to the global financial crisis.

But importantly, they also indicated major opportunities on the other side of that episode, especially in REITs where extreme cheap valuations were briefly found (REIT prices subsequently doubled less than a year later, and tripled soon after — which goes to show how valuations can reveal major upside opportunities).

For reference, the housing indicator is based on house prices vs incomes and rents, the CRE indicator is based on real prices, and REITs uses a composite of fundamental based valuation multiples.

Non-treasuries Fixed Income

This one shows the inflation expectations (“breakevens”) component of TIPS (simply showing whether it is high/low vs history), high yield credit spreads (inverted, such that low credit spreads are expensive, and high credit spreads are cheap), and emerging market sovereign bonds (a composite of duration and sovereign credit).

One interesting observation is how all three assets went extreme cheap in 2008/09 as panic selling hit just about anything and everything (one issue this highlights is that rebalancing and liquidity requirements contributed to self-reinforcing selling pressures across assets, and raising cross-asset correlations).  Another thing to watch for is disconnects e.g. TIPS breakevens sold-off to a greater extent and showed up cheaper than credit in 2020 and (1998/99).

Commodities and Currencies

On currencies and commodities, we can also find some clues for major macro/asset allocation shifts e.g. the peak in the USD around 2000 occurred at a time where US stocks (and tech stocks in particular) were significantly outperforming (hence greater demand for USD) – and commodities/EM/hard assets were underperforming and significantly cheap. 

Likewise the trough in USD valuations and peak in EMFX in the early 2010’s coincided with the peak in commodities and EM (when both were expensive) and trough in US assets (and trough in US vs global relative value).

This highlights how you can gain confirming information by looking at valuations across asset classes and across time — this helps in building conviction, identifying risks and opportunities, and rotating allocations accordingly.

For reference, the FX value indicators are based on deviation from PPP and real effective exchange rate levels vs history, while the gold indicator uses M2 and inflation adjusted gold prices and gold-driver value information, and the commodities indicator on real prices and trend/mean reversion.

Overall, as you can see, applying the concepts and principles to actual data it’s clear that valuations can be a very important source of information for asset allocators and active investors. Not only do they help identifying risks and opportunities within asset classes, but also across asset classes through relative value, ranking, and cross-asset/macro risk detection.

The Best and Worst Setups

To sum-up the data and application section, the table below presents the best and worst possible setups from an opportunity vs risk perspective across the key categories of valuation information in the multi-asset context.

For example, the best upside opportunity in say emerging market equities would be when they show up cheap vs history, cheap vs peers (and cheaper than usual vs peers), and that EMFX/commodities (macro-similar) also show up as cheap, and the USD (opposite) shows up as expensive.

Meanwhile, the worst downside risk setup for say US equities would be when they show up expensive vs history, expensive vs peers (and more expensive than usual vs peers), USD and credit also expensive (macro-similar), and bonds cheap (alternative/opposite).

As you can see there are more dimensions to the valuation discussion than what typically gets presented. This table or something like it can be used as a helpful check-list (indeed, I advocate using checklists and ratings across factors to help frame and integrate various sources of information to aid in decision making).

Perspectives

In this section I turn to my mostly institutional investor following on LinkedIn to add some practitioner perspectives on the matter and as a prompt to consider some of the other practical issues in application.

Importance of Valuations in Multi-Asset Investing

You can probably guess that the majority of practitioners find valuations valuable.  A small but material cohort say it’s the most important thing (probably longer-term minded investors, who are less focused on short-term fluctuations and have the mandate and latitude to take more forward thinking and at times deeply contrarian positions).

Yet, also a material amount say it’s only somewhat important.  This perhaps is more from portfolio managers who have shorter-term performance measurement windows who are perhaps more benchmark aware, more peer aware, and maybe have less tolerance for short-term fluctuations and less latitude to take patient positions.  Most likely they instead rely more on tactical factors to help with timing and avoiding short-term value traps (not to mention avoiding lagging peers, lagging the benchmark, and upsetting clients!)

Source: Callum Thomas (LinkedIn), Topdown Charts

Use of Valuations by Asset Allocators

Again, this survey shows that the significant majority of asset allocators use valuations in decision making, and as you would expect given what we discussed in this article, most use both absolute and relative valuations to help manage risk vs return and prioritize allocations.

I think it would probably be risky to rely only on absolute or relative value, rather than both but it would be even more risky and frankly foolish to not consider valuations at all (unless the investment process is more set-and-forget or more externally imposed, or perhaps pure trend-following). 

As we saw in the data and application section, not having visibility on absolute and relative value information would have potentially meant missing out on major opportunities, getting blind-sided by major risks, missing important correlation shifts, engaging in sub-optimal diversification, and ultimately poor outcomes for investors and clients.  So I think unless there is a compelling reason (or substitute) to valuation information, it’s borderline irresponsible to not be valuation-aware.

Source: Callum Thomas (LinkedIn), Topdown Charts

Reflections

While there are some drawbacks and challenges to using valuations, for example getting the timing right, pulling the trigger when it’s time to act (vs internal and external pushback), and navigating other short-term complexities (such as benchmark/peer/client related risks and pressures) — the value of valuations far outweigh any costs or limitations.

As noted above I think it would be foolish and irresponsible not to at least be aware of valuations as an active investor or custodian of other people’s money. Valuations help identify risks and opportunities, especially in a multi-asset context. They also help detect possible shifts in asset class behavior (correlations, volatility), emerging macro themes (as similar asset class valuations shift together), sentiment extremes (by looking at defensive vs risk asset valuations), and even opportunities in “third assets“ (aka synthetic assets such as the stock/bond ratio, or credit vs government bonds, emerging vs developed equities, value vs growth, and others).

From a practical standpoint they also help prioritize research efforts, reveal hidden risks, build conviction levels, and optimize portfolio allocations. So I highly recommend investors at all levels seek to understand and gain visibility on valuations, particularly in the multi-asset context.

Key Points

• The same basic concepts of valuation signals apply to every asset class.

• Absolute valuation indicators can yield important insight into risk and opportunity for a single asset class, but may also have meaningful insights for other assets.

• It is therefore important, and possible, to develop valuation indicators for the major asset classes of direct relevance, and asset classes that may bring in important indirect information across time and cycles.

• Relative valuations can thus also help detect opportunities/risks, uncover information about shifts in correlations, and prioritize capital allocation.

• Ranking absolute value scores can be very helpful in spotting risks/opportunities, and prioritizing research efforts, albeit may sometimes not be a direct apples-to-apples comparison.  So as always, nuance, thoughtfulness, and grasp of detail is important in analysis and application.

—

Best regards
Callum Thomas

Head of Research and Founder of Topdown Charts

Twitter: https://twitter.com/Callum_Thomas
LinkedIn: https://www.linkedin.com/in/callum-thomas-4990063/

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• Understand valuation concepts for the stockmarket

• Compare and contrast the different indicators and approaches

• Use valuation indicators to identify risk and opportunities in the stock market

Concepts

Following on from my previous posts on using valuation signals to navigate the cycle, this article presents a look at a listing of different valuation indicators for the stockmarket. The purpose is to provide a reference of the various different methods, to reiterate the principles for market cycle analysis, and provide further worked examples for indicator design and input selection.

As a brief refresher, the reason for studying valuation signals is to attempt to identify which stage we’re at in the market cycle, to help form a directional view, and to understand the risk vs opportunity outlook.

The visual below shows how to interpret value signals at a high level — there is contrarian information at extremes (e.g. extreme expensive = at risk/sell signal vs extreme cheap = opportunity/buy signal), meanwhile not ignoring the momentum information through the range (e.g. cheap can get cheaper, wait for the turn).

For active asset allocation, market timing, and risk management — valuation signals can hence been tremendously valuable.

But due to the various strengths, weaknesses, distortions, nuances, and shifts over time, we do need to be thoughtful on indicator design and input selection when it comes to building up an actionable picture.

The visual below presents a summary from the post on the peculiar case of Commodity Valuation Indicator Design.

You want an indicator that is ideally explainable (makes sense), works over time (consistent signal), and sensible (actually gives the right signal and helps clarify vs confuse). You also ideally want it to be mean-reverting (travels within a well-defined range, rather than trends, and ideally oscillates enough to present actionable signals rather than just going to one level and staying there).

It’s important to keep both of these visuals in mind as we go through the various indicators below. Think: how does this indicator help me navigate the cycle, and does it meet the criteria of a good indicator? Even if the indicator fails, there can still be some merit in occasionally checking and tracking a wider set of indicators, while relying on a few core series.

Before we get into the data, I will add one point of clarification. This post does not address methods such as discounted cash flow valuation models, analyst point estimates, comps or other point in time appraisals (such as might be used in corporate finance, stock selection, venture capital and private equity). These methods are out of scope for this article because we are focused on market cycle analysis, and we need a consistent, objective, quantitative source of information that evolves with the cycle, and can ideally be easily sourced and updated.

But I would note that the ultimate level of valuations from those types of analysis will rise and fall with the broader market tides — which we attempt to discern with these indicators. And indeed one way to think about valuations is as the tides of the market (e.g. high tide = high valuations, low tide = low valuations — know the tides, know the changing of the tides), whereas by contrast other sources of information like sentiment and technicals can be used to determine the waves (to maintain the oceanic metaphor).

Data and Application

With that all said, let’s get into the data and take a look at a not-entirely-exhaustive (but pretty close to it — write-ins will be accepted and added) listing of stockmarket valuation indicators.

Trailing PE: Price divided by the trailing 12-month’s sum of earnings.

Strengths/Weaknesses/Nuances: This is the standard PE ratio that you have probable seen and heard of already. Conceptually it is a good starting point — comparing price vs fundamentals (and earnings is probably about as fundamental as it gets when it comes to fundamentals for stocks). It’s a useful indicator in that it’s often higher than usual around market peaks, and lower than usual around market troughs. But the main weakness is that during a recession earnings declines can mean that you get nonsense readings just due to volatility in the denominator. It also, as with many absolute value indicators like PE ratios, can go through structural changes (and sometimes trends rather than mean reverts) in part due to changing macroeconomic conditions (e.g. higher/lower inflation and bond yields) and even index composition changes.

Forward PE: Price divided by analyst consensus estimates of the next 12-month’s sum of earnings (in this case using the gold standard of analyst consensus data from I/B/E/S (LSEG)).

Strengths/Weaknesses/Nuances: This is the same concept as the trailing PE, but using estimated future earnings. This can be helpful when the past 12-months of earnings are not representative of what the next 12-months might be (e.g. in the case of a recession/downturn in earnings or anticipated acceleration in earnings). One issue is that the analysts estimates are just that — estimates, and are subject to being wrong. But from a market analyst standpoint, it provides us with an objective and quantitative datapoint which we can track over time.

CAPE (Cyclically Adjusted Price/Earnings Ratio): Sometimes referred to as the Shiller PE, calculated by taking the real (CPI deflated) price divided by trailing 10-year average real (CPI deflated) earnings.

Strengths/Weaknesses/Nuances: This version of the PE ratio corrects for earnings volatility by taking a more stable anchor (by using the average of earnings over a 10-year period) — the intention is to smooth out the cyclical ups and downs in earnings. Where this indicator runs into trouble is if you get a major up or down shift in earnings e.g. if earnings were elevated for a number of years artificially due to a credit bubble and then come down going forward (this might make it appear cheaper than it actually is), or e.g. if a new industry with structurally higher earnings rises to dominate the index (this might make it appear more expensive than it actually is).

PPE (Price to Peak Earnings): This indicator divides price by the trailing maximum earnings figure (both in nominal terms).

Strengths/Weaknesses/Nuances: Parsimony likers will enjoy this indicator — it gives basically the same signal as the CAPE (actually slightly better: gives a more accentuated peak and trough signal). Basically it’s just another way of correcting for earnings volatility, and intuitively it is appealing if you think that the recent peak in earnings is probably more representative of near-term potential earnings. The weakness would be if you had a bubble in the fundamentals or encountered a long period of economic stagnation, where the peak would not be representative going forward.

PEG (P/E to Growth Ratio): This indicator attempts to adjust the PE ratio for earnings growth, I have shown 2 approaches — the Forward PE vs 3-year analyst consensus earnings growth estimate, and the Forward PE vs the consensus Long-Term Earnings Growth estimates.

Strengths/Weaknesses/Nuances: Conceptually it’s an interesting idea, and it attempts to correct for what I noted before about potential shifts in earnings growth rates. In practice it is highly flawed and unusable — and does not provide any real signal improvement relative to alternatives. One of the main issues with it is selecting an objective and consistent figure for the growth rate, here I have used analyst consensus estimates, but these can swing around due to volatility in base comparators and swings in analyst sentiment. Conceptually interesting, practically not practical. Do not use.

Price to Cash Earnings: Similar to the PE ratio but using Cash Earnings as the denominator (funds from operations).

Strengths/Weaknesses/Nuances: Gives a slightly less volatile picture than the PE ratio given the earnings figure is adjusted for depreciation etc and hence a more accurate representation of the ongoing actual recurring operating earnings. Interestingly, it gave more expensive/peak signals at the actual market peak or final stages before the 2000 peak and 2021 peaks. One issue is that it does seem to trend through the entire period in the chart …so it’s difficult to use it for an outright all-time high/low expensive/cheap interpretation — as opposed to more expensive vs cheaper than usual. That is often an issue with absolute valuation metrics like this, but there are ways and means of dealing with this (see my thoughts and approach in the reflections section).

Price to Book Ratio: Price vs book value (net assets).

Strengths/Weaknesses/Nuances: Similarly, the price to book ratio provides good signals due to the generally less volatile nature of the book value figure. One issue is changing composition of the index e.g. for the US the rising weight of sectors (like internet and software businesses) which are generally capital-light. However, one could have made the same argument near the peak of the dot com bubble (“it’s different this time“ — and sometimes it is), and there is a lesson in there. Be alert to extremes, even if they can be ‘explained‘, and be alert to changing sector composition for context …as well as signals.

EV/EBITDA: Enterprise Value (market cap + value of net debt) divided by EBITDA (earnings before interest, tax, depreciation, amortization).

Strengths/Weaknesses/Nuances: This is a popular metric for comps in corporate finance (capital raising, M&A), as a stockmarket valuation indicator it is on par with the PE ratio, and shares similar flaws (can give a wrong signal during times of volatility in the EBITDA figure).

Revenue Multiple: Price divided by sales (also showing enterprise value to sales ratio).

Strengths/Weaknesses/Nuances: The price to sales or revenue multiple can be useful when a company has negative earnings (e.g. early stage/growth companies), it is often used in corporate finance comps — particularly M&A where the acquiror might be able to run a different cost structure and hence a higher earnings margin. But on that note, it does ignore changes in earnings margins, and hence can under/over-state things if earnings margins undergo a structural shift.

Price to M2 Money Supply: S&P500 index divided by US M2 money supply.

Strengths/Weaknesses/Nuances: This is one of those newer indicators, which as far as I can tell came about during the post-pandemic stimulus era when the markets were awash with stimulus and liquidity, and hence the thinking with this indicator is that the market price level should reflect the economy-wide liquidity level.

Market Cap vs GDP: Stock market capitalization as a percentage of nominal GDP.

Strengths/Weaknesses/Nuances: I’ve never been a fan of this indicator, I think it’s because one day long ago I was an analyst at a stock exchange operator — I was pulling together this stat for the exchange and global peers… as an indicator for capital market development. Think about that: if you have a thriving and well developed capital market, then more of the economy will be listed on the exchange and represented by the stock market (so a higher figure might just mean you have better capital markets, and a rising figure might mean improving coverage and function of your capital markets). Having said that, it has historically been very high at some of the major peaks, and very low at the bottoms — so there is some merit and info to it, but I would not use it as a primary indicator.

Equity Risk Premium: My method = 10-year earnings yield (average trailing 10 year earnings figure divided by price) minus 10-year real yield (10-year treasury yield minus the 10-year CPI compound annual growth rate).

Strengths/Weaknesses/Nuances: This indicator basically attempts to contextualize the PE ratio (inverted to find the earnings yield (i.e. 1/PE ratio)) for the level of interest rates. Conceptually the higher it is the greater the premium over and above treasuries investors require to take on equity risk. In other words, it gives a buy signal at extreme highs (when you get a greater premium for taking on equity risk), and sell signal at extreme lows (when you get a smaller or negative premium for taking on equity risk) — particularly relative to bonds (useful for asset allocation).

Earnings Yield vs Bond Yield: Earnings yield minus 10-year treasury yield (I have shown the earnings yield from the CAPE, trailing, and forward PEs).

Strengths/Weaknesses/Nuances: This indicator likewise compares equity yields to bond yields, albeit with no inflation adjustment. Similarly, buy signals come from elevated readings, sell signals come from low/negative levels. The main problem with this one is, much like the absolute value signals, it goes through long periods above or below zero — so you can’t make a hard and fast rule (e.g. if you said sell when ever it’s negative, you would have been out of the market through most of the 90’s bull market). As with all of these indicators, the thing to watch for is outright extremes, and significant (rapid or large) moves (e.g. compare and contrast the 2000 dot com peak vs 2009 financial crisis low).

Earnings Yield vs Cash Rate: Earnings yield minus 3-month T-Bill yield (again, I have shown the indicator for CAPE earnings yield, trailing 12-month earnings yield, and next 12-months earnings yield).

Strengths/Weaknesses/Nuances: Similar to the price vs M2 ratio, this one has some intuitive appeal given that higher cash rates present two types of headwinds to stocks — opportunity cost (higher rates, especially relative to equity yields, mean the opportunity cost of holding risky stocks vs safe high-yielding cash is higher), and tighter monetary policy (and thus the brakes being applied to the economy, which may lead to lower confidence, recession risk, and lower earnings). So a negative earnings yield vs cash rate spread would signal increased downside risk for stocks (and vice versa).

Dividend Yield: Trailing 12-month’s dividends divided by price.

Strengths/Weaknesses/Nuances: The dividend yield similarly rises and falls with the market cycle — spiking during crashes and downturns, and declining as the market rises. From a dividend income investor’s perspective buying at the bottom when yields are high is a dream come true (as long as the dividends continue!). But one flaw with this is the rise and rise of buybacks. Companies in the US increasingly return cash to shareholders via buybacks, so that’s why one reason why in the previous fixed income spread charts we use the earnings yield rather than the dividend yield (and changing payout ratios also matter).

PPT Ratio: Saving the best and most controversial until last, this one is a combination of 3 pure priced based signals (I call it the PPT Ratio — Pure Priced Transformation), a 3-year mean reversion factor (price vs 3-year moving average), 5-year mean reversion factor, and the deviation of log price vs trend (i.e. de-compounded, and de-trended). It takes an equal-weight of the z-scores of each. It follows a similar, but different, approach to that used for the commodity value indicator.

Strengths/Weaknesses/Nuances: The main strength of this indicator is that it does what it is — valuation indicator; i.e. it gives a consistent, reliable, sensible signal as to whether the market is stretched to the upside or downside (which is ultimately why we look at valuations from a market cycle analysis standpoint). The main downside is that it does not directly incorporate any fundamental anchor (except to the extent to which you believe that stock prices already reflect all of the fundamentals). But that’s why we look at multiple data points and indicators to gather the fullest picture and color. In practical terms I would use an indicator like this for actionable signals, while reviewing the other indicators above for context-building.

Perspectives

Unlike the previous posts, my plan with this one is to post the article first and then go to social media market to get some views — I’ll be posting some surveys where we’ll run-off the various indicators to find the consensus on what everyone’s favorite valuation indicator is. Stay tuned for updates (I will post the results here in a couple week’s time) and check out my socials for the surveys [LinkedIn and Twitter(/X) + feel free to comment here!].

Reflections

As you can see from this large but not exhaustive listing, there are actually a lot of different options and inputs you can use to gauge the valuation picture for the stockmarket. Each has its own strengths and weaknesses, and I would say none offer an infallible holy grail panacea. But we look at, track, and study valuations as one of a few key sources of information as we try to build up a full picture. Building up the picture within the valuation lens, but also as part of a broader spectrum of information like macro, sentiment, technicals.

If I had to pick some favorites, I would say the CAPE, price to peak earnings, earnings yield vs cash rate, the ERP, and the pure-price transformation indicator. I think there is merit in combining signals, and I will go into that in more detail in a future post, but I also think even if you go and come up with some sort of a holy grail be-all-and-end-all indicator, there is still going to be merit in having visibility of the components so that you don’t miss any important shifts and changes.

The macro-market cycle rhymes, but it is always different in some details — different macro regimes, different technology trees, different geopolitical paradigms, different social trends, and differing length of economic expansion and contraction. Knowing how things change and how these changes can influence valuation indicators and signals is important. But also knowing what stays the same — the principles, is important for keeping a level data-driven head, especially in times of great greed and fear (be thoughtful when others are thoughtless).

Key Points

• Valuation indicators can help us navigate risk vs opportunity through the cycle.

• The ideal indicator should be explainable, sensible, and reliable — it should do what it’s supposed to do and do that consistently.

• Ideally you want an indicator that is objective and quantitative, that moves logically with the cycle, and is easily sourced and updated.

• Most valuation indicators attempt to contextualize and compare the current level of price vs some sort of quantitative fundamental anchor.

• Some indicators are not that useful despite the attention they get, and no indicator should be used as a standalone. Best practice involves looking at multiple inputs and sources of information; and being thoughtful in application and interpretation.

—

Best regards
Callum Thomas

Head of Research and Founder of Topdown Charts

Twitter: https://twitter.com/Callum_Thomas
LinkedIn: https://www.linkedin.com/in/callum-thomas-4990063/

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• Understand valuation concepts for commodity markets

• Understand indicator design in non-traditional asset classes

• Apply principles to identify risk and opportunities in commodities

Concepts

Following on from my previous post introducing the concept of valuations for market timing and risk management, this post focuses on the application of value signals for navigating the cycles in commodity markets.

The same basic concept applies – we want to buy low (cheap) and sell high (expensive). It’s of course more nuanced in practice, but that’s the key objective and the core purpose of valuation indicators.

When it comes to commodities, figuring out a reliable and sensible valuation indicator can be quite challenging.  For equities it’s comparatively simple: e.g. just use some variation of the PE ratio (and maybe the equity risk premium, price to book ratio, relative PE ratios, etc), maybe with some transformations, but typically using well-established, conventional, mostly uncontroversial and publicly available data.

For commodities it is difficult because there is often no readily available fundamental anchor against which to gauge or standardize and compare price (across time).

So this post is as much about concepts and application, as it is about indicator design and input selection.

Firstly, one reason for even looking at commodities in the first place as an asset allocator is that commodity markets tend to follow repeating cycles – just as stocks and bonds do.  This makes them ripe for position taking, but as an added benefit they can act as a useful diversifier, sometimes even a hedge – and often run out of sync with bond/stock cycles.

The chart below shows this playing out in practice, specifically by looking at the cycles evident in valuation indicators for stocks, bonds, and commodities.

As you can probably tell from the chart commodities present another option for diversification.  And by the way, in my view – from a multi-asset portfolio management standpoint you should basically ignore historical correlations and look only at the current forward looking risk vs return menu.

Here’s why:

As I noted in the first post on valuations, bonds were expensive in 2021, that was a key reason why they were a source of risk rather than a diversifier of risk (in contrast to the long-term correlation stats which show bonds as a negatively correlated diversifier – not in 2022 though!).  Commodities meanwhile were cheap – the macro scenario that unfolded in 2022 of course wildly favored commodities over stocks and bonds, but the clues or conditions that lead to commodities outperforming and diversifying against the drop in stocks AND bonds were in the valuation signals.

This tells us about the utility of valuations, the dynamic nature of correlations, and the need to think rather than just rely on simple headline statistics. And by the way, to be clear, I see valuations as a tool to support thinking, not a substitute for thinking.

That example and the chart above also highlight the value of having more levers to pull (e.g. as opposed to just stocks vs bonds).  Indeed, a value-driven investor will want to have a wide universe of assets at their disposal, and a systematic process of identifying high/low extremes in valuations to maximize their longer-term chances of success (where success = higher returns + lower/avoided risk), and obtain an outright edge over those just allocating to stocks and bonds. So commodities are certainly worth looking at.

But back onto the issue of valuations for commodities, we need a quantitative anchor that tells us what we need to know in terms of the forward-looking risk vs opportunities…

The solution is to start from the principle that a valuation indicator should be high/expensive when the market is high/peaking, and low/cheap when the market is low/bottoming.  In that respect we need to be pragmatic as practitioners – to the extent that you might even readily reject an otherwise fundamentally/theoretically sound indicator if it does not give a reliable and sensible signal. Similarly, you adopt and integrate signals that may rank lower on economic logic (the reason why it works may be less clear, less easy to explain), but rank highly on the sensible + reliable front.

Let’s spell those characteristics out:

Sensible = it gives the correct signal (expensive at highs, cheap at lows)

Reliable = it acts more or less the same way through time

Explainable = makes intuitive or theoretical sense why it works

The visual below summarizes it – it has to start with sensibility.  If the signal doesn’t say sell (expensive) at the highs, and doesn’t say buy (cheap) at the lows, then it is quite simply useless and unhelpful.  But it also has to be reliable… it’s no good if the signal repeatedly fails to correctly identify market peaks and troughs, or if it breaks down entirely and stops working (this does happen). 

Finally, it helps if it’s explainable… partly because eventually you might be asked to explain it!  But the other thing is you’ll probably start from the explainable end most of the time during the indicator design process (e.g. think about what makes sense, and then go get the data and see if it actually works).

Overall, coming up with an indicator and using it really is the same basic process for stocks or any other asset.  But we do need to exercise a greater deal of pragmatism and creativity as an analyst in the case of relatively non-conventional assets like commodities – while at the same time deeply understanding the mechanics of your indicators, and the usual + any other limitations it might have.

So let’s look at the data, and how it works in practice…

Data and Application

The chart on the left below shows the GSCI Commodities index and the composite valuation indicator for commodities.  On that note, just to clarify in this case we are looking at commodities at the aggregate or asset class level rather than individual commodities.

As you can easily see, the indicator hits extreme expensive (high) levels when the market is high/peaking, and extreme cheap (low) levels when the market is low/troughing.  The usual principles for using valuation indicators apply: the biggest risks and opportunities are found at extremes, and it’s important to be aware of the momentum information through the range (e.g. cheap can get cheaper, expensive can go higher).

But onto the more interesting part – the components. 

Firstly though, the reason why we use more than one input is we want to maximize signal vs noise, and enhanced the overall sensibility and reliability of the composite indicator.  The approach I use is to apply a simple unweighted average (giving each indicator an equal vote).

Into the detail, the first input is the commodities index deflated by the producer price index; this can be thought of as a fundamental-adjacent indicator: it has some explainability because all-else-equal commodity prices over the long-run shouldn’t wildly outpace the cost of production.  Thus on a real price basis, commodities should be, and are in practice: mean-reverting.

The second indicator looks at commodities vs stocks, this one ranks lower on explainability, but brings in useful signal shaping and helps strengthen the value signal for commodities vs stocks – as the next best risk-asset alternative.

The Log/Trend factor firstly takes the natural logarithm (to correct for the exponential growth of commodity prices over the longer-term), what’s left is a more uniformly trending series, and then we can just use a trend reversion model (i.e. (LN-price)/linear regression implied level based on the latest date).  One way to explain this one would be that because of supply constraints on earth and growing human population – over the long-term commodities should trend up… but there will be cycles around that trend due to demand (+speculation) cycles and longer-term supply investment cycles.  So any major deviation from underlying trend should be a signal to suppliers to adjust supply, and hence price will eventually move to reflect to that adjustment (with the usual principle of market overreaction applying).

The 3-year mean-reversion series is simply the latest price vs the 3-year rolling average.  This one works well in terms of sensibility and reliability – it also strengthens the composite signal (so there is basically a 4^th factor in composite indicator design which we could call signal amplification).  In terms of explainability, again it is less-obvious, but if we think about commodity prices as reflecting most of the fundamental information, then if prices deviate significantly up/down vs the past 3 years that’s unusual and contains information.  Perhaps most importantly, producers will probably be making decisions informed by that typical price level vs today’s price in terms of supply investment decisions, and consumers who are price sensitive may alter purchasing decisions – all of which will have an influence on the subsequent path of prices.

Basically on the explainability front it all comes back to the same principle for commodities, and we can distil much of this into a single visual.  Commodity prices go through fairly clear price cycles due to the underlying cycles in supply and demand.  Importantly, price sends signals to suppliers to either increase supply or pullback on supply growth – those actions then influence the future path of prices.  Or more simply, the cure to high prices is high prices (and vice versa).

Ultimately though, while we might be able to debate the explainability of each component, the composite indicator’s reliability and sensibility ranks high enough (it just works) that we can use this as an anchor to help set directional bias, identify risks and opportunities, and compare against other asset class valuations in managing portfolio level risk and return trade-offs.

Starting with that valuation anchor point we can then go and observe other factors like what’s happening with supply growth, how is demand tracking, what’s going on with monetary policy and currencies, and rounding that out with sentiment/positioning and technicals.

That in a nutshell is how to build maximum conviction and clarity in navigating risk vs opportunity in commodity markets.

Perspectives

In this section I turn to my mostly institutional investor following on LinkedIn to add some practitioner perspectives on the matter and as a prompt to consider some of the other practical issues in application.

Do you even invest in commodities?

A large proportion of those who responded noted that they do on a regular basis as a matter of course invest in commodities. Indeed anecdotally, a lot of asset allocators started making strategic allocations to commodities after/during the 2000’s super cycle – partly as a diversifier/hedge against inflation risk, but also due to attractive historical returns (it ticked a lot of boxes in the traditional mean-variance portfolio optimizations).

But aside from that, and I would say including that group, there is a large portion who I would say will opportunistically take risk in commodities (whether from the long or short side). And again that speaks to the opportunities (and risks) in commodities as they run through cycles.

A smaller portion say not now, not ever – anecdotally I know some won’t invest in commodities for philosophical reasons (e.g. ESG concerns), and/or perception reasons (don’t want to be seen to be punting risk in non-traditional assets).  But there’s also practical reasons e.g. lack of access to appropriate and cost effective exposure (commodities are different from stocks or bonds), or lack of proper analytical infrastructure and expertise.

Source: Callum Thomas (LinkedIn), Topdown Charts

Valuations for Commodities

Indeed, on that note, looking at the prevalence of those who use valuation signals for commodities – it’s quite small, only about a quarter.  From my discussions with clients, a big part of this is a lack of availability of established and sound indicators.  Few people have done the work on this, and I would say I’m one of the few to have worked and thunk about this extensively (and actually applied it over the past decade+).  This article is basically the culmination of that work.

There is also a view that you simply can’t value commodities, and it’s true you can’t use a discounted cash flow model or net-present value, but as I have shown – you can come up with indicators that give a consistent bounded signal as to whether the current price level is stretched in either direction.  And that’s the thing, when it comes to non-traditional assets, you need to adapt and adopt non-traditional analytical methods (based on sound principles that work across markets and time).

Source: Callum Thomas (LinkedIn), Topdown Charts

Reflections

When it comes to commodities, the core underlying principles for using valuation indicators is the same.  The objective of buy low (cheap), sell high (expensive) is the same when it comes to identifying opportunities and managing risk.  Meanwhile the pitfalls, limitations, and nuance are equally important to understand and factor in – just as with any indicator and strategy.

And while commodities are a unique and somewhat non-traditional asset class, the principles of indicator design are basically the same – you want an indicator that works (gives the right signal, and does so over and over again), and ideally you want an indicator that makes sense and has some economic logic to it.

Understanding these principles and objectives, and then understanding the unique drivers and characteristics of commodities, it’s entirely possible to come up with sound, sensible, and highly useful valuation indicators for this unique asset class.  And the benefits of doing so can be significant for those with the latitude and toolkit to venture into this space.

Lastly, in case you are wondering (since I have looked at this mostly at the asset class level), yes – you can apply the same principles and techniques from this article to individual commodities …which I have done (46 of them), albeit it does require nuance and attention to detail as some individual commodities require slightly different approaches (and have varying historical data availability).

But here’s what we can do once we have an indicator for all of them – breadth analysis!  Just as looking at the trend across components of a stock index can help bring in extra insight for the stock market, looking at the trend across individual commodity valuations can reinforce the asset class view.

The chart on the left shows the upper/lower quartile and median valuation across those 46 commodities over time.  The chart on the right shows the proportion of commodities showing up as either extreme (more than 1 Standard Deviation) cheap vs expensive. 

Using these further charts would have helped raise conviction and signal strength on valuations at some of the major peaks and troughs over the past few decades.  This is the principle of confirmation – to be clear, you want to be wary of “confirmation bias”, but as an analyst you need to build up and gather confirming evidence (and note any evidence that does not confirm, or even contradicts the core thesis).

Overall, the more tools and charts you can develop or acquire, the greater confidence, clarity, and conviction you can have in your conclusions.

Key Points

• The same basic concepts of valuation signals apply to commodities (there are cycles, the objective is to buy low (cheap), sell high (expensive)).

• Valuation signals can be used to identify risks and opportunities in commodities, and relative to other assets, across cycles.

• The key concepts for indicator design are: sensibility (does it work), reliability (does it work over time), and explainability (can you explain why it works).

• It is useful to combine indicators into a composite signal to maximize signal vs noise, and to improve the performance of the indicator (sensible + reliable).

• Taking a non-traditional approach to non-traditional asset classes, but informed by the same basic principles for traditional assets classes, can help generate valuable insights for market cycle analysis and investment decision making.

—

Best regards
Callum Thomas

Head of Research and Founder of Topdown Charts

Twitter: https://twitter.com/Callum_Thomas
LinkedIn: https://www.linkedin.com/in/callum-thomas-4990063/

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• Understand the difference between absolute vs relative value

• Apply understanding of value signals to global equities and explore new concepts

• Grasp nuance, challenges, merits of value signal applications for global equities (particularly as it pertains to countries/regions)

Concepts

In this article we build on the concepts from the previous post introducing valuations (how to use valuation signals in navigating risk and return through the cycle) — looking specifically at global equities and applying what we learned in the previous post, but also explore several new concepts and issues when it comes to using valuations to navigate risk and return within global equities and across the cycle.

First, as a refresher, the same concepts generally apply when it comes to global equities. Simply put, we want to buy low (when stocks are cheap), and sell high (when stocks are expensive).

But as noted, while valuation signals have their strongest information about risk and opportunities at extremes, through the range it’s almost more of a momentum signal – and you need to grasp how the signal from valuations will change as the cycle progresses.  The chart below shows how this plays out at the conceptual level.

It can make as much sense to avoid something that is cheap – but has strong downward momentum (typically reinforced by deteriorating macro/fundamentals), as it might to stay invested in something that is expensive – but has strong upward momentum (again, typically reinforced by macro/fundamentals; and often referred to as growth when it’s on the way up).

It’s important to review this conceptual cycle map when it comes to valuation signals in absolute terms, but the same kind of concepts are also relevant when it comes to *relative valuations*.

So far in talking valuations we mean it in an absolute sense – is the asset/market cheap or expensive outright (often in relation to the historical tendency).  But another important lens in valuations is that of relative value – how one asset is looking relative to another.

This is particularly relevant for global equities because one of the key considerations aside from the high-level asset allocation decision (e.g. allocating to global equities vs cash or bonds), is how you allocate to global equities on a country and/or regional or group level.

To compare and contrast the two ways of looking at valuations, consider the quadrant below.

Adding in a second dimension to the way we think about valuations can support investment decisions, help avoid risk, while also bringing in important information on opportunity-cost (or benchmark-aware insights).

For example, consider the lower panel of the table: often times when you get a global market correction just about every country/region lights up as cheap in absolute terms as investor panic drives down prices further than the deterioration in fundamentals justifies.  Again, the over-reaction creates a value opportunity.  But here’s the thing, when looking at the global equities universe – yes maybe every market rises from that cheap valuation starting point, but not all markets are created equal: some will rise further and faster than others. 

We can find clues to that in relative value – those markets that get beaten down further than others: creating a large valuation gap vs peers ultimately creates the opportunity for greater upside vs peers over the longer-term. 

It’s really the same concept as absolute valuations, but basically for a new synthetic asset whose price is the relative performance of the two markets being compared, and whose valuation indicator is based on the relative valuation level of the two markets (e.g. is market x trading at a premium or discount vs market y on a PE ratio basis?). 

Data and Application

These two approaches are demonstrated with data below, using Global excluding-US (ex-US) Equities as the absolute value example, and global ex-US vs US equities as the relative value example.

The first most apparent thing is the extremes in the indicators lining up with peaks and troughs in the price series, i.e. the moment of max risk vs opportunity.  The second standout is the apparent cycles at play, which demonstrates in the real world how the conceptual cycle map works in practice. 

Albeit, also standing out are some of the exceptions and complications. And it is important to note how the conceptual model is a lot messier when applied in the real world.  This does speak in part to the limitations of value signals – especially if you end up over-relying on valuations (or any other one signal). But even if there are exceptions and even if the real world doesn’t quite fit precisely with the conceptual framework, it is still vital to have core guiding principles; and to have an underlying intuition around the way things should work.

Just as you are never going to get it right 100% of the time when it comes to making decisions about an unknowable and uncertain future, valuation indicators are not going to work precisely to plan 100% of the time.  But getting it right “most of the time” is enough both in terms of investment decision making (especially when risk is controlled, and upside allowed to run), and indicator reliability. 

And actually over the longer-term valuation signals do provide useful information on forward returns.  The scatter plots below show the level of the PE10 ratio and subsequent 10-year price changes (annualized), for as many countries as the data is available and for the longest period of data availability across the emerging market and developed market universes. 

In both cases, the slope of the line and distribution of dots is about as you would expect – generally higher subsequent returns at the lower valuation levels, and generally lower subsequent returns at the higher valuation levels.

Interestingly, the results are more compelling when it comes to relative valuations and relative returns – which are shown below, using the same approach (but this time looking at individual country PE10 discount/premium vs global, and subsequent relative returns [spread] vs global).

So again, while there are exceptions to the rules (and we should seek to understand why those exceptions come about, and protect against the pitfalls of those by bringing in more pieces of the puzzle e.g. monetary policy changes, cycles in fundamentals, and structural shifts) – we should still focus on the rule, the concept, and therefore derive useful signals from the data.

Practitioner Perspectives

In this section I turn to my mostly institutional investor following on social media to add some practitioner perspectives on the matter and as a prompt to consider some of the other practical issues in application.

Use of Relative Value

In this survey the question was do people use relative value in their investment process – and the answer is basically 90% yes (which should tell you something about its merits!).  Most people look at relative value both from a snapshot in time and relative to history basis.  Let me explain the difference with an example.

Source: Callum Thomas (LinkedIn), Topdown Charts

The table below shows the PE10 ratio for US, Developed ex-US, and Emerging Markets.  In the bold is the latest observation (12-Feb-2024) – from that data we get information about the ranking of the 3 markets: EM is the cheapest, US is the most expensive.  The relative value discount is shown in the lower rows (e.g. EM vs USA – EM trading at a 57% discount vs US).  Those are snapshots.  The second column in light italics shows the long-term average (maximum available data).  With the historical context we can see that the EM vs US discount of 57% is greater than the typical discount of 38% (so it is a larger discount than usual: which is important to note as it makes the cheap relative value signal more meaningful).

So this is what we mean by a one-off snapshot right now, as opposed to looking at how the current relative value signal compares vs history (and we looked at that visually in the global ex-US vs US chart earlier).

Global vs US Valuation Gap

As we saw earlier (and in the table above), global ex-US equities are trading at a deep discount vs US equities.  This is something I have dug into in detail in my own work, so I wanted to see what the perspectives were on it – do people see it as the opportunity that it seems to be, or is there skepticism..

Interestingly, most people saw it indeed as an opportunity – this makes sense given what we’ve talked about and what the data shows.

Source: Callum Thomas (LinkedIn), Topdown Charts

BUT: quite a number of people saw it as an example of a value trap – which is a situation where valuation levels e.g. go to a cheap level, and stay cheap – “cheap for a reason”.  The logic behind this would be something such as ‘because US equities (big tech) has superior growth prospects, they should trade at a permanently higher valuation vs the rest of the world’.  And the truth is, value traps do happen.  Sometimes you get structural shifts (see the reflections section below for examples).

But equally, a material amount of folks selected “not real” or something else, and there were plenty of comments… e.g. one fact to consider is the changing sector and country weightings within global equities.   Over time, market cap weighted indexes will drift out of the worst performers and into the winners.  In practice this has seen the US index skewing heavily into big tech, and global skewing comparatively more into old cyclicals. 

These sector differences do have an impact on the true/adjusted relative value picture, and it is worth making adjustments (I have ran the numbers, and for what it’s worth, there still is a value discount, albeit slightly less-so when you adjust for sector differences).  But at the same time, most investors are actually allocating based on indexes, and it is the index that you are buying/selling – not the sector-adjusted version.  Still, two important nuances to consider.

Relative Value Efficacy

In this one I wanted to get a sense of the perception around relative value – we got some insight into this matter in the previous survey, but this one asks it more directly.  The consensus is that it sometimes works (probably a fair assessment, certainly given the fact it works better for longer-term timeframes e.g. the scatter plot earlier showing a clear link between relative value premium/discount and subsequent 10 year returns, and also given the potential for structural shifts, and impact of composition changes).

Interestingly, just over 1/5 said it doesn’t work.  I would say if you based your opinion on the track record of relative value, and especially in the case of global vs US, you might easily come to that view.  The global vs US valuation discount has persisted for about a decade now (to a greater or lesser degree), and yet there has not been any major turnaround in the US vs global relative performance line.  So if we are focused on recent history, and shorter-term timeframes, you might indeed say it doesn’t work.  Meanwhile I would place myself somewhere between saying it works well (based on the data), and sometimes works (given the nuance and exceptions).

Source: Callum Thomas (LinkedIn), Topdown Charts

So even though there are exceptions to the rules, and a healthy range of opinions on the current global relative value landscape, clearly practitioners find merit in using relative value signals.

Reflections

By now I think it’s quite clear that there are potentially highly valuable signals to glean from both absolute and relative valuations in the global equities context.  They can help inform us on the risk vs opportunity outlook both in absolute terms (will the market go up or down), and in relative terms (will a certain market out/under-perform vs peers).

The conceptual framework of buy low (cheap), sell high (expensive), and momentum through the range, also applies well – as we saw in the data and applications section.  So I would say valuation signals are an important source of information for investors in global equities, especially for asset allocators, but also for those involved in global equity stock-picking (being aware of where the maximum risk vs opportunity lies across the country/regional landscape).

But, as with many things, application in the real world is often nuanced, complicated, and features many exceptions.  This is not a reason to ignore or discard value signals, and indeed, the set of charts below shows examples of several countries who saw a clear tendency for mean reversion over time, and a relatively consistent range (and stable upper/lower bound).

These paragons of valuation mean reversion (which is a key concept for value signal design – the indicator should ideally travel in a typical range, and should say cheap at bottoms, expensive at tops) show again why it’s worth paying attention to as an important source of information.

However, as alluded to, there are exceptions and nuance – the examples below show structural breaks and shifts in the typical range.  In some instances e.g. Japan, you went from a lower range, to a higher range, and then back to a lower range.  These shifts can be caused by changes in market composition, macro regimes (different growth/inflation/interest rate levels), structural changes in the economy and political backdrop (e.g. emergence of new industries/tech, worsening or improving governance), and can especially be an issue in smaller markets where even a single company or sector can significantly shift things.

Again, we don’t ignore/discard value signals because of this, but rather we pay attention to what can cause such exceptions, shifts, and changes in signal purity over time.  In this way, we can still obtain significant advantages in navigating risk vs return, while being well-informed on the limitations and prospective pitfalls of overreliance on any one signal. 

And finally, even when there are shifts like those above, the extremes (particularly where the indicator hits an extreme, and then turns the corner) continue to offer important insights.  Furthermore, I will discuss ways to correct for some of these issues in a future note on indicator design.

In the meantime, an important set of concepts and tools to add to your analytical set.

Key Points

• Valuation signals are useful for global equity investors and asset allocators in gauging forward-looking risk and opportunity both in an absolute and relative sense, and across the cycle.

• The concept of buy low (cheap) and sell high (expensive) works for absolute returns (market goes up/down) and relative returns (one market under/out-performs vs another market).

• Overlaying relative vs absolute value signals can provide a fuller picture and add further insight to inform investment decisions.

• There are exceptions and nuance to the conceptual rules, such as structural shifts in the typical range, timing, and changing market composition.

• It is important to understand the limitations and avoid overreliance on a single signal, but still draw on the valuable insights that value signals can offer.

—

Best regards
Callum Thomas

Head of Research and Founder of Topdown Charts

Twitter: https://twitter.com/Callum_Thomas
LinkedIn: https://www.linkedin.com/in/callum-thomas-4990063/

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• Understand the concepts of using valuation indicators in market cycle analysis.

• Interpret & apply valuation signals in investment strategy and decision making.

• Be able to use valuations to reduce risk, and see/seize opportunities.

Concepts

Valuation signals are perhaps one of the most important methods of understanding the stage and progression of the market cycle for investors. Valuation signals are useful for identifying opportunities and setting future risk vs return expectations. 

The purpose of a valuation signal is to inform your underlying directional bias: if the signal says the asset is cheap, you should be bullish all else equal, and if the signal says the asset is expensive, you should be ready for downside risk.  But it doesn’t stop there.

When we talk about valuations in the market cycle analysis context, we are not talking about what is the specific right or fair price at a moment in time as such, but rather we are most interested in whether the valuation signal today is high (expensive) or low (cheap) vs its historical context.

The reason is that valuations go through cycles where they typically peak at expensive levels, and trough at cheap levels.  This is driven by the various repeating underlying cycles in crowd psychology, monetary policy, and asset specific fundamentals.

It is the progression of these cycles — especially to extremes — where the forward-looking opportunities and risks are generated. 

Conceptually, the best time to buy is when valuations are driven to extreme cheap levels due to investor panic, pessimistic sentiment, slumping expectations about the macro/fundamental outlook.  Typically this phase is at least initially grounded in very real deterioration in the fundamentals and macro backdrop.  Overall, the downshift and cyclical trough in valuations is an (over)reaction to negative developments.

On the other hand, conceptually the riskiest time to own an asset is when valuations are driven to extreme expensive levels due to investor euphoria, optimistic sentiment, overconfident expectations about the future and over-extrapolation of favorable conditions.  These conditions are likewise grounded-in or at least start from very real improvement in the fundamentals and normally start from a supportive macro backdrop.  Likewise, it is an (over)reaction to positive developments.

Indeed, while there are usually clear and compelling macro/fundamental cycles underlying the evolution of valuations — valuation indicators can basically be thought of as a confidence barometer.  High/expensive valuations indicate bullish investor confidence, while low/cheap valuations indicate bearish investor confidence.  This is particularly true at extremes.

Ultimately, the key concept and operation of valuation signals is to support the efforts towards the ideal of “buy low, sell high” (and/or “sell high, buy low”) — through the cycle.  Ideally they are grounded in some objective fundamental metric, and provide an objective source of information, with logical (make sense fundamentally) and consistent signals (send the right signal at the right time, over time).

Although valuation signals tend to be most powerful and useful in informing longer-term risk vs return expectations (based on the actual data, and based on how long cycles tend to run), they can also have important tactical/short-term insights at extremes. 

Meanwhile, between the extremes valuations also have their limitations as the informational content drops as valuation signals approach neutral.  But one subtlety to grasp is that while level is clearly important, direction is also an important piece of context.  As with most things, application is more nuanced than the high-level concept — the nuanced conceptual model below adds further color.

Thinking about this visual it really emphasis the cyclical context and nuance.  For example, cheap valuations can get cheaper (as you move through the cycle) and potentially stay cheap (value traps, longer-running cycles).  Likewise, expensive valuations can go further and stay higher than you expect (structural shifts, market bubbles). 

Valuations are a key piece of the puzzle, but it is only one piece of the puzzle – and cycle-aware investors will want to bring in further cyclical context to raise conviction and confidence.  So, once you have established an underlying directional bias based on the valuation signal, you can then build out the rest of the picture with other factors to raise conviction and fine-tune timing. 

In this sense, valuations can be thought of as a foundational signal for active investors.

Data and Application

This section takes it from concepts and theory to data and application to make the concepts more concrete and show you how to use them with real world application.

The first example looks at my valuation indicator for US equities. I will get into specific methods on indicator design in a later post, but the key point to note when using a valuation indicator is conceptually it should be giving an expensive/elevated signal around market peaks, and it should be giving a cheap/low signal around market bottoms. 

The valuation indicator in the chart below is based on PE ratios, and in that sense is anchored in fundamentals, and hence provides an objective and economically logical signal.  It also looks the way it should and gives the right signal over time (signal consistency). 

The statistics (scatter plot) also confirm its merits (higher subsequent long-term returns are more common when valuations are lower, and vice versa).

And that highlights an important point.  Valuation signals generally provide the most useful information for longer-term investment horizons — this is useful for long-term investors, but also for those with shorter timeframes looking for an underlying directional bias.  Beyond that though, if you eyeball the chart, there are clear instances where extremes in the indicator provided useful tactical insights – especially around market bottoms.

When it comes to practical application, as noted, there are nuances — if we look at some of the specific examples for US equities:

• 1987 – the crash was flagged by the extreme expensive reading, but you did not get time to wait for the indicator to turn down.  In that instance, high valuations were an important risk signal, but you had to act quick.

• 1997-2000 – in this instance expensive valuations did forewarn of the downturn and long period to reclaim the all-time high, but in contrast the signal remained in extreme expensive territory for 4 years.  In that example, it was necessary to wait for the cycle to turn, and lean heavier on other factors.

• 2021 – expensive valuations flagged the peak in advance, but understanding the macro/policy backdrop was key to identifying the catalyst and fine-tuning exact timing.

• Major Bottoms (2002/03, 2008/09, 2020) – in these three instances, the indicator sounded a loud buying signal, which was highly useful from a tactical standpoint, and interestingly enough gave longer-term investors only a relatively brief window to act.

So clearly, there is useful information here, but there is nuance to it, and although we can get highly useful risk management information from valuation signals it can be risky to rely on just one signal.

The second example looks at bonds (US treasuries, focused on the 10-year Treasury), and helps illustrate the concept and application of valuation signals for a different asset class.  As with stocks, the bond valuation indicator also does more or less what it’s supposed to in terms of flagging downside risk at the peaks, and signaling cheap valuations at market troughs. The scatter plot also adds statistical evidence to the intuition, with lower valuation scores being consistent with higher 10-year subsequent returns, and vice versa.

Indeed, from an active asset allocation or multi-asset investing standpoint, it is critical to develop valuation indicators for each asset class in order to identify moments of extreme risk both in absolute terms for each individual asset, but also as a whole, and on a relative/ranked basis (n.b. I will cover cross-asset applications in greater detail in a future post).

Indeed, one useful aspect of having valuation indicators for both stocks and bonds was the rare moment in 2022 where both stocks and bonds fell materially: shattering at least temporarily the 60/40 investing paradigm.

This was preceded by *both* stocks and bonds showing up as extreme expensive in 2021 – something that has not happened before, and something that conceptually should be very rare given the differing drivers for each asset.  In that instance, you could have effectively considered cash as cheap by comparison, especially given the combined 60/40 portfolio view of valuations rising to one of the most extreme expensive readings on record (indicating elevated risk of adverse future returns).

As it turned out, given the lack of offset from bonds (which are typically expected to be a diversifying asset), the following year was the second worst total return for the 60/40 portfolio in over 150 years. And if you think about it — if your bond valuation signal is saying extreme expensive, you can’t look at that asset to perform its usual capital preservation role when it itself is at risk.

In this way, valuation signals can provide advance warning to even seemingly novel and far tail risk scenarios, and present important information on the risk vs opportunity set at both the individual asset level as well as at the portfolio/asset allocation level.

Practitioner Perspectives

In this section I turn to my mostly institutional investor following on social media to add some practitioner perspectives on the matter and as a prompt to consider some of the other practical issues in application.

Importance of Valuations

When it comes to how people look at valuations, most (50%) said it was a “very important” part of their investment process, while few said that it was either the most important thing (mostly fundamental value investors) or not at all important (mostly market technicians and quants).  Personally, I would agree, it’s a very important and useful input, but given the benefit of also incorporating other inputs and limitations of valuations – I wouldn’t call it the single most important thing.

Source: Callum Thomas (LinkedIn), Topdown Charts

Why Use Valuations

In terms of why people use valuations, the overwhelming consensus is because valuation signals provide objective/quantitative info – and this is important, it’s one of the key strengths of valuations and what they are supposed to do.  They’re supposed to provide a cool, clear, logical signal… and often times the most important signal comes when crowd psychology is at its most extreme, when grand narratives have taken over, and your will to do the right thing (as suggested by the valuation signal) is at an all-time low, because it’s typically a contrarian position.  So in those times, the value of an objective signal is critical.

Indeed, at the major market bottoms identified in the charts above, most people would have talked themselves out of buying as the prototypical fear, uncertainty, doubt/doom messages and emotions pervaded vs evidence and logic.

But let’s also give passing reference to the last option in the survey – perception. This is vital for those acting as custodians of other people’s money.  You need to not only have a good process that performs, but you need to be able to communicate that process and instill confidence in your stakeholders/clients (especially relevant for when things don’t work… and you never get it right 100% of the time!). 

This is about appearing sensible, but also being sensible by having a process that is not just based on the fear of underperforming vs peers, not just chasing the latest hot trend and being overrun by recency bias.  A repeatable evidence-based process is key to survival and long-term success.  Important and interesting points to ponder.

Source: Topdown Charts (LinkedIn)

The Challenge with Valuations

As noted, there are a number of limitations and short-comings when it comes to using valuation signals for investment decision making and market cycle analysis.  Most people noted that getting the timing right is the biggest issue – and this was quite clear in the data & application section earlier. 

It’s why you need to be as much of a historian as a statistician when applying these signals, but also why you need to build up the bigger picture with puzzle pieces (for example, bringing in additional information from monetary policy settings, economic cycle data, and technical analysis are typically what it takes to improve the timing and conviction-level).

But interestingly, there were also quite a number of respondents citing lack of patience (alluding to the point that valuations have their strongest reliability in influencing longer-term returns).

Figuring out the right metric to use also featured highly — which indeed is an issue, as there are many different options when it comes to valuation indicators, and each with their own strengths/weaknesses, and then also as with all indicators: maximizing signal vs noise. Structural changes meanwhile were also front of mind (which refers to situations where the typical range and level of e.g. a PE ratio may move to a semi-permanent new higher or lower range). 

Source: Callum Thomas (LinkedIn), Topdown Charts

So it’s clear, even among practitioners that despite the usefulness and importance of valuations, there are challenges and nuance in their application that need to be well-understood by those who would use them.

Reflections

Tying it all together, I tend to think of valuations as one of the most important pieces of the puzzle, it helps set your directional bias and should guide your longer-term expectations around risk vs return -- as well as reminding you not to get too carried away in the short-term gyrations of greed and fear.

I often see criticisms levelled at valuations (usually by those with a vested interest or existing bias; which they are attempting to confirm) — that because they are most useful for longer-term horizons, most investors can’t use them and/or should ignore them.  As you might guess if you’ve read this far, I would say that’s wrong from a number of standpoints.

Firstly, equipped with the nuanced conceptual model, the statistical evidence, and the right indicators, investors can not only draw important tactical insights at the extremes (at either end of the cycle) – but even use valuations as a compass and torchlight for navigating through the cycle.

Yet I would also say a pure valuation-driven approach could conceptually work, provided you have the patience and risk tolerance.  For example by underweighting/selling the most expensive assets, overweighting/buying the cheapest, and letting mean-reversion sort things out over time.

Hidden in that thought is also the importance of knowing your objectives, constraints, and your own psychology… and ensuring that your information sources align with that.  One of the biggest sources of disagreement and confusion in markets is when a short-term trader attempts to convince a long-term investor of their viewpoint or what they should do – it’s almost never productive as the two individuals are playing different games, with different objectives, different parameters, and differing strengths/edge.  So: know yourself; and know the game you are playing.

Back onto the utility of valuations though, another approach could involve only taking action on the most extreme readings in valuations. Taking relatively few positions, and perhaps only acting once every few years as the strongest signals typically only come that often (or even less frequently), but then taking sizeable positions when they do come.

So there are a number of ways to incorporate and utilize valuation signals, but practically speaking, I still think of it as just one piece of the puzzle.  The below table shows how it fits in with how I run multi-asset investment strategy at Topdown Charts.

Based on my experience in the funds management industry and as an independent investment strategist serving a wide range of institutional investors – I would say “best practice” for active investment strategy (particularly in the asset allocation space) is being grounded in valuation and building the puzzle picture up from there.  

Focusing on that metaphor, typically you need multiple puzzle pieces in order to get the full picture.

When it comes to investment strategy, having a fuller picture helps you raise conviction levels (which determines your threshold to action – whether you actually take a position, and how significant the position would be), and aids in finessing timing *through the cycle* -- because it’s not just about reacting to the extremes, but navigating the often long and large spaces between.

Indeed, as noted in the concepts section, valuation signals take on a different meaning and weighting at different stages of the cycle. 

So overall, I would say that valuations are the best place to start for cycle-aware investors in forming the risk vs return picture. And again, valuations are one of the purest measures of where we are in the cycle, from a fundamental, monetary, and crowd psychology perspective.

Key points

• Valuations provide an objective source of information that can generate important insight into the stage of the cycle and the risk vs opportunity outlook.

• In concept, valuations help investors “buy low (cheap), and sell high (expensive)”.

• In practice, valuations provide useful evidence-based information across asset classes and cycles, but care needs to be taken in their use given limitations/nuance.

• Limitations and challenges include first and foremost getting the timing right (the nuanced model), getting the timeframe right (they have a greater influence on longer-term returns), and then getting the indicator and wider context right.

• Best practice when it comes to using valuation signals is to be aware of the cycle, be aware of past scenarios, let valuations guide your underlying directional bias – but after all as just one of a number of puzzle pieces for building up the larger picture (and conviction level).

—

Best regards
Callum Thomas

Head of Research and Founder of Topdown Charts

Twitter: https://twitter.com/Callum_Thomas
LinkedIn: https://www.linkedin.com/in/callum-thomas-4990063/

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