This article was originally written by Doug Short. From 2016-2022, it was improved upon and updated by Jill Mislinski. Starting in January 2023, AP Charts pages will be maintained by Jennifer Nash at Advisor Perspectives/VettaFi.
Quick take: At the end of February the inflation-adjusted S&P 500 index price was 117% above its long-term trend, up 5% from January.
About the only certainty in the stock market is that, over the long haul, overperformance turns into underperformance and vice versa. Is there a pattern to this movement? Let's apply some simple regression analysis (see footnote below) to the question.
Below is a chart of the S&P composite stretching back to 1871 based on the real (inflation-adjusted) monthly average of daily closes. We're using a semi-log scale to equalize vertical distances for the same percentage change regardless of the index price range.
The regression trendline drawn through the data clarifies the secular pattern of variance from the trend — those multi-year periods when the market trades above and below trend. That regression slope, incidentally, represents an annualized growth rate of 1.93%.
At the time, the peak in 2000 marked an unprecedented 116% overshooting of the trend — substantially above the overshoot in 1929. In recent years, we have seen the index get as high as 179% above the trend (2021). The index has been above trend for nearly three decades, with one exception: it dipped below trend for almost a year from October 2008 to August 2009, getting as low as 25% below trend.
At the beginning of March 2023, it is 117% above trend. The major troughs of the past saw declines in excess of 50% below the trend. If the current S&P 500 were sitting squarely on the regression, its value would be 1880.
Incidentally, the standard deviation for prices above and below trend is about 44%. Here is a close-up of the regression values with the regression itself shown as the zero line. I've highlighted the standard deviations in red. We can see that the early 20th-century real price peaks occurred at around the second deviation. Troughs prior to 2009 have been more than a standard deviation below trend. After experiencing third standard deviations, the trend is now back down to levels last seen in 2000.
S&P 500 Snapshot: Down 2.67% from Last Week
This article was originally written by Doug Short. From 2016-2022, it was improved upon and updated by Jill Mislinski. Starting in January 2023, AP Charts pages will be maintained by Jennifer Nash at Advisor Perspectives/VettaFi.
The S&P 500 snapped a four day losing streak on Thursday, but ultimately ended the week down 2.67% from last Friday. Today's close is the lowest for the index in over a month. The index is up 3.4% YTD and is 17.23% below its record close.
The U.S. Treasury put the closing yield on the 10-year note, as of February 17, at 3.95% which is above its record low (0.52% on 8/4/2020). The 2-year note is at 4.78%. See our latest Treasury Snapshot here.
Here's a snapshot of the index going back to 2013.
A perspective on drawdowns
Here's a snapshot of record highs and selloffs since the 2009 trough. Note the recent selloffs in 2022.
Here's a table with the number of days of a 1% or greater change in either direction and the number of days of corrections (down 10% or more from the record high) going back to 2013.
Here is a more conventional log-scale chart with drawdowns highlighted:
Here is a linearly scaled version of the same chart with the 50- and 200-day moving averages.
A perspective on volatility
For a sense of the correlation between the closing price and intraday volatility, the chart below overlays the S&P 500 since 2007 with the intraday price range. I've also included a 20-day moving average to identify trends in volatility.
ETFs associated with the S&P 500 include: iShares Core S&P 500 ETF (IVV), SPDR S&P 500 ETF Trust (SPY), Vanguard S&P 500 ETF (VOO), and SPDR Portfolio S&P 500 ETF (SPLG).
Footnote on calculating the regression: The regression on the Excel chart above is an exponential regression to match the logarithmic vertical axis. I used the Excel growth function to draw the line. The percentages above and below the regression are calculated as the real average of daily closes for the month in question divided by the growth function value for that month minus 1. For example, if the monthly average of daily closes for a given month was 2,000. The growth function value for the month was 1,000. Thus, the former divided by the latter minus 1 equals 100%.
Footnote on the S&P composite: For readers unfamiliar with this index, see this article for some background information.
