This model provides a framework for managing your exposure to U.S. equities. The two-axis model targets the intersection of real risk and perceived risk (RvP risk) to determine an appropriate strategy. We propose a strategy for each quadrant of the framework and provide the spreadsheet that details all the data points we monitor. We finish with a current assessment of the market climate and the associated recommended strategy. If you are getting whipsawed by the market, kicking yourself for not seeing the writing on the wall, or overwhelmed with too many financial news data points, this framework might be useful for managing risk.
Getting the big picture right accounts for the lion’s share of your returns, but navigating market cycles is hard. Very few people can do it well consistently. Having a framework to understand how the market works might help you to sidestep the large drawdowns, take advantage of opportunistic dislocations in the market, and manage your risk exposure better.
If you follow the markets and the financial news cycle, you are bombarded by a never-ending stream of data points about the economy, geopolitics, and company news. Trying to figure out where and how each data point fits within the big-picture macro landscape is very challenging, more so if you do not have a framework to begin with. Having a simple and logical framework and knowing where the data points fit within the framework can help to sift out the noise.
This paper will lay out a framework for navigating market cycles. While you have no control over what the market does, if you have a framework for understanding risk, you can control your own risk levels.
The framework itself evaluates the market (the U.S. equity market) on two axes. One axis measures the fundamentals (valuations and economic cycle) and the other axis measures sentiment. We refer to the former as real risk and the latter as perceived risk.
Over the long term, the price you pay for something will generally be a good predictor of your eventual returns. In the short term, the market is driven heavily by investor sentiment (essentially the collective emotions of all investors), which can change and fluctuate in an unpredictable manner from extreme optimism to extreme pessimism. This sentiment can cause valuations to become unhinged from reality, sometimes for very long periods (years, not months).
We refer to the fundamentals as the real risks (i.e., the amount that you can lose, or the difference between the price you pay and its intrinsic value); if you are a long-term investor, the primary determinant of your long-term returns is the price that you pay going in. If you overpay in a hot economy you have opened yourself up to likely experiencing losses over the long term. Conversely, if you get in when valuations are cheap in a slack economy your risk of loss is significantly less. The real risk that you take on is thus essentially locked in once you take a position. At the individual company level, that risk can change depending on actions and strategies taken by the company going forward, but at the aggregate market level, that real risk is essentially baked in once you buy.
We refer to sentiment as perceived risk. In other words, how are investors treating risk? It is based in large part on investor emotions about the outlook for future growth. Are they being cautious and selective about what they buy, or are they willy-nilly buying anything and everything regardless of price or value? Or, on the flip side, are investors just selling everything regardless of price because panic has set in? These extremes happen in almost all cycles and the extremities always seem to go on for much longer than anyone expects.
Take a look at our graphic that shows the two risks against one another and the associated market environment.
RvP Framework (Author)
On the left-hand side, we have periods when the economy is running at close to capacity, valuations are high and investors are carefree about what they buy. Think mid-2021 when it started to feel like “this time is really different and the old rules no longer apply.” Valuations were high and money was easy; we had cryptocurrency mania, NFT mania, SPAC mania, and tech mania. 30x earnings sounded reasonable in the face of historically low interest rates, which also seemed as if they would stay low forever. This turned out to be a terrible time to buy stocks.
On the right-hand side, we have periods when there is a lot of slack in the economy due to a recession or some other shock (think March 2020 with COVID shutdowns) and valuations plummeting. Investors were panicking like the world was about to end. The future was uncertain. Had we just been taken over by some plague that would forever shut down global commerce? This turned out to be a great time to be buying stocks.
It’s quite remarkable that in just a short period of under three years we went from good (2019) to terrible (2020) to exuberant (2021), and now back down significantly (2022). This has been a real roller coaster three years. Looking at the framework, in hindsight, it would have been easy to navigate, but that is never the case in real time. Nevertheless, without a framework, you are just floating in the wind.
So we have a framework that is easy to understand, logical and intuitive. The next part is quantifying the two risk parameters and laying out a positioning strategy based on where they intersect.
The next graphic converts the first graphic into a 10×10 quadrant. The graphic plots the two risks on an x/y axis, ranking each on a scale of 1 to 10, with 1 being low risk and 10 being high risk. The 10 by 10 matrix is divided into 4 quadrants, each one representing a stage of the typical economic cycle (1 = late cycle expansion, high valuations; 2= slowdown; 3 = recession, low valuations; 4 = recovery). The current position is highlighted by the red star, where the two risk levels intersect. Lastly, the four outside boxes prescribe how to position the portfolio in each quadrant of the matrix.
Risk framework quadrant and recommended positioning (Author)
With this, you now have a predefined strategy in advance for how to position your portfolio in each market environment – clear actionable insights. We have jumped ahead by showing you the output, but there is a process for measuring both risks as best we can to determine the location of the red star.
There are two risks here: The first is where we are in the cycle – cycle risk. The best market returns follow periods when there is a lot of slack in the economy. The worst returns occur following an economy that is running close to capacity when the economy is starting to butt up against production capacity constraints. This can also be accompanied by inflation which further confirms the capacity constraints.
We also look at the yield curve which has been a good predictor of recessions. When the yield curve is starting to invert, the short rate is rising because the Fed is trying to slow the business cycle down and the long rate is coming down or rising slower than the short rate, to reflect lower growth expectations.
Second, valuation risk represents the potential losses or upside from current valuation levels. Simply put, if you pay a high multiple for an asset you should expect lower return than if you pay a low multiple. Overpaying for an asset increases your downside risk or potential losses.
There is no generally agreed-on best method for assessing the market’s valuation. We look at four or five measures. Our own core discounted cash flow model is the most granular and provides a specific expected 10-year return and current fair value estimate.
Perceived risk is essentially a sentiment measure captured from market and other data. Opportunity in the market comes about when the market-perceived risk is different from the real risks. The most known measure is the VIX index (VIX). The VIX measures the expected volatility of an asset, but tells you nothing about the real value vs. the current price (real risk or potential downside). We look at a range of perceived risk measures including credit risk, duration risk, consumer risk, and economic growth risk, as well as things like market breadth and participation.
This is similar to the approach of other practitioners. For example, John Hussman looks at market internals, or what he used to call trend uniformity; basically, his thinking is that when investors are inclined to speculate, they are indiscriminate, so everything goes up without regard to quality. Howard Marks refers to it as taking the temperature of the market and says that these are mostly non-quantitative phenomena.
When markets have been rising for some time investors tend to project that as normality going forward, and become complacent about risk. Buyers become indiscriminate and buy everything. Once a bull market turns to risk-off, sellers start dumping stocks that show any sign of weakness.
Similarly, toward the end of bear markets, investors have become so risk averse because they cannot see the day when things will turn around. Sellers become indiscriminate and sell everything. Toward the end of a bear market, buyers will start cherry-picking the stocks with good value.
Detecting these shifts is important. These shifts can get shielded from view by the larger indices, so we need to look inside the market to detect these trends. We look at three things: One, is the market generally risk-on, risk-off, or somewhere in between? Two, is this risk increasing or decreasing? Three, are investors discriminate or indiscriminate in selecting assets or sectors?
In total there are about 75 data points we evaluate to arrive at our two risk scores and we consolidate them into a spreadsheet. The spreadsheet dashboard shows each of the items we measure and track for each risk category. All of this data is publicly available, but there are a few proprietary component models which are highlighted in grey. It is not possible to explain each item in the spreadsheet in detail, but we have tried to label each item so that it is clear what we are measuring. The headline items are shaded in red or green. (If you have a specific question, please ask it in the comments section and I will try to answer it.)
Market state dashboard (Author)
The spreadsheet is separated into real risks on the left, perceived risks on the right, and the middle contains risks that could be in either or both categories.
We assign a rank to each item that we measure on a scale of 1 to 10, with 10 being the highest risk. This ranking process is somewhat subjective, but generally tries to assess the level of risk of each item relative to its historical values. It is part art, part science. The box in the middle shows risks that could be both real and perceived risks. For example, margin debt/GDP reflects a real risk inherent in too much leverage, and also reflects that investors perceive the risk to be low so they are willing to run up their margin balances. The final ranking for Real risk and Perceived risk is an average of these individual items.
Once you have the final risk measures, then it becomes an art of deciding how to adjust your exposure based on the level of these measures which we laid out in the previous 4-quadrant graphic.
Note that, unlike many market timing models that provide a binary outcome of being in or out of the market, our quadrant provides a more nuanced strategy. The way you use this framework will be different for each investor, depending on their investment horizon and own unique tax circumstances and whether you invest in mostly indices or individual stocks.
For example, if you are holding a portfolio of long-term core companies that provide you with a nice stream of income from dividends, you may not want to interfere with those positions other than trimming or adding on extreme irrational price behavior. For example, if you have a 100% gain on a position and the tax rate is say 20% if you sell, that means the stock would need to drop by at least 10% for you to replace the holding and be no worse off than before. You would have to be fairly confident the market is going to drop by more than 10% to rationalize that decision.
The matrix and spreadsheet in this paper are current as of Oct. 22, 2022, and would accompany this summary report.
Cycle stage – the best time to buy stocks is when there is a lot of slack in the system; the worst time to buy is when there is no slack in the system – i.e., running close to capacity.
Currently, all capacity utilization indicators we follow indicate little slack in the system. This is further confirmed by high inflation. Separately, the slope of the yield curve (10-year to 3-month) is almost inverted, signaling possible recession.
Valuations are currently high, even after a 20% correction from the highs of last year. In fact, one could argue that the rise in long-term interest rates from 1.5% at the market peak to now 4.2% on the ten-year bond implies that current valuations are even more extreme than they were before the 20% drop.
Based on our own DCF model, we project a 10-year total return for the S&P 500 (SP500) of 2.54%. The S&P 500 would need to drop another 28% to deliver an equity risk premium of 3% above the current 10-year bond yield. The Shiller CAPE was at 28 at the end of Q2 and the Shiller CAPE EY (excess yield above 10-year bonds inflation-adjusted) was at 3.21. The average EY for the past 50 years has been 3.8. If we update the Shiller data for the current 10-year bond yield, the Shiller EY comes down to 1.91%. For the EY to get to the 50-year average would require the S&P500 to drop down to 2,500 or about 31% lower than today. The as-reported P/E ratio of the S&P 500 stands at 19.69. If you think about a normalized equity risk premium of about 3%, then fair value P/E ratios should be closer to 15 (3% + 4% GS10 = 7% yield required ~ 15x P/E). This would also imply a reduction in the S&P 500 of about 25%.
With the 10-year yield currently above 4%, all the measures we track point to a similar 25% – 30% overvaluation of the S&P 500 at 3,750. Given the above, we assign a real risk rating of 8.5 on a scale of 1 to 10.
Volatility measures across a broad range of ETFs are mostly printing around the 90th percentile (based on data going back to 2004). Consumer risk is high. Interest rate risk is high. Growth expectations are low. The most common measure, the VIX, is at 33 – which is high. Clearly, it is a risk-off environment. Furthermore, volatility is still rising as measured by the 10-day averages being higher than the 30-day averages.
While selling does not appear to be as indiscriminate as it was three months ago it is still mostly indiscriminate. We are seeing some signs of selective buying into the non-interest-sensitive defensive sectors of healthcare, communication services, and consumer staples. The one bright spot that has not cracked open yet is corporate bond spreads, which still remain fairly tight even though absolute yields have risen in line with government bonds. If this dam breaks we can expect a sharp rise in perceived risk and a drop in equities.
Given this backdrop, we assign a perceived risk rating of 7.8 on a scale of 1 to 10.
The intersection of these two risk measures puts us in Quadrant 2, which calls for a continued volatile environment. Exercise extreme caution; keep some dry powder and put cash to work opportunistically on dislocations in long-term watch list companies. The market is still overvalued by 25%+ even at these levels.
Keep an eye on long-term interest rates. They have been the main driver of the correction so far, as reflected in low P/E ratios. We expect they could still go a little higher over the next six months before trending back down to the 3%-3.5% range.
You’ll also want to keep an eye on corporate bond spreads. We have not yet seen a significant deterioration in the earnings component. If earnings deteriorate, which we think they will due to profit margin pressures, then it becomes more likely we will trend closer to fair value. The first sign that earnings are in trouble will likely show up in corporate bond spreads which as mentioned have so far held up well.
If you are looking to nibble opportunistically on drawdowns, there are some sectors of the market that already have more realistic valuations, small caps for example as well as REITs.
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Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.