Bond Yields Crank Higher and Markets Sell Off

Tom Gentile

Posted in

By: Tom Gentile
October 25th, 2023

6 mins read

Originally published via our newsletter previously. Subscribe for early access!

10-year bonds yields went up to their highest level not seen since 2007.  This was also true for the 5-year.

Equity and Bond traders pore over the economic data as it is that data the Fed uses in determining where they set interest rates.

When you hear from the financial news networks stocks are lower due to fresh new highs in bond yields and you wonder why? What’s the correlation let me off up this tidbit of education.

Higher bond yields are a negative to the stock market. When this happens, it is an indication tighter monetary policy and lower growth expectations are in store for stocks/companies.

Higher yields could lead to a stall or reduction in profits the companies generate and or lead to them reducing or not distributing a dividend.

This may in turn then lead to investors selling off their shares and the price of the security drops.

It comes down to where the best return of one’s capital can be achieved.

You can attribute the inverse relation between the two because they are both competing for your capital. If investors like stock prices and believe the price growth in those will be better than the returns bonds can produce for their account that is where the money flow goes. One can diversify in both, but it comes down to where the majority of capital is going.

The next page shows the two charts one for SPY (representing stocks) and TNX representing bonds.

Tom Gentile
C1P: Chief 1-Percenter

Market in Focus: SPY and TNX: Inverse Correlation Between the Two

SPY and TNXL An Inverse Correlation
SPY and TNXL An Inverse Correlation

From the Desk of a CMT – Sector View

Part of my stock market process includes assessing the broad averages, then the major sectors. This helps acknowledge what is happening in the markets versus what I think should be happening in them.

Even when an economic data release seems to be bullish or bearish in nature, there are no guarantees the market will move accordingly. There are so many other factors at play and do not underestimate the importance of two important ones:

  1. Other financial markets including bonds, currencies, and commodities, and
  2. Market structure impacts (automated institutional trading).

Starting out a little differently, let’s look at the correlations across four primary market indices:

  • Dow Jones Industrial Average
  • S&P 500 Index
  • Nasdaq
  • Russell 2000

After navigating to the correlation analyzer (Stocks à Stock Analysis à Correlations), you can select individual symbols or lists to compare, types of correlations, and the data to assess. In figure 1, the same list (Broad Market ETF: DIA, SPY, QQQ, IWM) was used twice so that each component of the list had a correlation value to the other components in it. Daily percent change was the data type used.

One key takeaway from a correlation analysis is that you don’t want to allocate dollars to two securities or more with the intention of diversifying while using securities with strong positive correlations (moving towards +1.0).

Although the first analysis used four indices, you can use stock lists, individual stocks or ETFs, or any other symbol available in Tom’s Option Tools, (

Figures 2 and 3 test correlations on:

  • The Broad Market ETF list and the Sector SPDRS list [XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY, SPY, XLRE, XLC] 
  • The Sector SPDR list on itself.

Since the settings were the same (with the exception of the lists to check), just the tables are provided for these next two tests.

If you were to dig into the individual component lists for any of the indices or ETFs you should expect strong positive correlations for the higher weighted stocks (intuitive), but you may also see some stocks not as strongly correlated as expected. This is particularly true for indices with more components. 

The main point of this exercise is that you have some good tools at your disposal, so take a look at what’s happening broadly across the market. It is likely not an analysis you’ll perform weekly, but it’s good to do it periodically to get a stronger sense of what’s driving changes in your portfolio.

Figure 1: Correlation Analysis for DIA, SPY, QQQ, and IWM (10/17/2023) Note:
Figure 1: Correlation Analysis for DIA, SPY, QQQ, and IWM (10/17/2023) Note:
  • DIA to SPY and SPY to QQQ are notable strong correlations
  • IWM is the index that will behave differently than the others on a given market day
Figure 2: Sector SPDR ETF versus Broad Market ETFs (10/17/2023)
Figure 2: Sector SPDR ETF versus Broad Market ETFs (10/17/2023)
Figure 3: Sector SPDR ETF versus Sector SPDR ETF (10/17/2023)
Figure 3: Sector SPDR ETF versus Sector SPDR ETF (10/17/2023)

If a top weight in your portfolio is the S&P 500, adding XLC, XLK, XLY, or XLI will not be the best diversifiers. You’d want to start from the end of the SPY row to observe the sectors that have no or low correlation to it.

To assess the sectors, we’ll use the Close % Change over the last month (Stocks à Stock Rankers à Close % Change), then two of the more bullish names with no to low positive correlation and find in the money (ITM), short-term calls to see how they perform together and versus the S&P 500.

The Number of Trading Days selected for this ranker was 20 with Bullish and Bearish Rankings. The results appear in figure 4.

Figure 4: Sector SPDR ETF Rankings for Close % Change, Bearish to Bullish (10/17/2023)
Figure 4: Sector SPDR ETF Rankings for Close % Change, Bearish to Bullish (10/17/2023)

Note that XLC is out of order in this list and is actually the top-performing sector over a one-month basis. 

 Since XLE’s correlations to the other sectors is no/low, XLE and XLV will be used. Since the ranking was completed using one month, let’s simply look at November expirations for both ETFs. Despite both ETFs being lower than one month ago, the shorter-term trend for each is up when viewing:

  • 10-day exponential moving average (EMA) for XLV
  • 10-day & 20-day EMAs for XLE

Let’s go 3% ITM for each, which is 88 for XLE (91.13) and 127 for XLV (130.99). Consider using realized volatility data to set a different moneyness value for two.

Figures 5 and 6 provide the risk graphs for the two bullish case studies, and the primary goal is to monitor how two relatively stronger, uncorrelated (0.199) sectors behave over the next month.

Figure 5: 1 Nov 17 XLE 88 Call at 4.73 Midpoint (10/17/2023)
Figure 5: 1 Nov 17 XLE 88 Call at 4.73 Midpoint (10/17/2023)
Figure 6: Nov 17 XLV 127 Call at 4.78 Midpoint (10/17/2023)
Figure 6: Nov 17 XLV 127 Call at 4.78 Midpoint (10/17/2023)

Despite the similar premium, note the spread for the XLV call is much larger after the close on 10/17/2023.

Each single call is under the $500 max loss and will be held until Nov 16th, the day before expiration. If this were a true set-up rather than observation, prices for an expected gain and loss would be provided for each underlying. The back test data for each will be viewed next article.

Since this article is written a couple of days before you can run your own scans, consider viewing different time periods for the Close % Change and adjusting the time to expiration accordingly.

Clare White, CMT

Thanks, Clare!

Tom Gentile
C1P: Chief 1-Percenter


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