About the only certainty in the stock market is that, over the long haul, over performance turns into under performance and vice versa. Is there a pattern to this movement? Let’s apply some simple regression analysis (see footnote) to the question.
Here’s a chart of the S&P Composite stretching back to 1871.
The chart shows the real (inflation-adjusted) monthly average of daily closes. We’re using a semi-log scale to equalise 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 1.71%.
The Bearish View
The peak in 2000 marked an unprecedented 157% overshooting of the trend — about double the overshoot in 1929. The index had been above trend for nearly 18 years. It dipped about 9% below trend briefly in March of 2009, but at the beginning of April 2011 it is 45% above trend. In sharp contrast, 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, it would be hovering around 900. If the index should decline over the next few years to a level comparable to previous major bottoms, it would fall to the low 400s.
The Bullish Alternative
A critical factor for the reliability of a regression analysis of stock prices over many decades is the accuracy of the inflation adjustment. The Bureau of labour Statistics (BLS) has been actively tracking inflation since 1919 and has estimated inflation rates back to 1913 using data on food prices. In 1982, however, the BLS began incorporating changes to the Consumer Price Index (CPI), which is used to calculate inflation. These changes have resulted in much lower “official” inflation rates than would have been the case if the method of calculation had remained consistent.
At his www.shadowstats.com website, Economist John Williams publishes an “Alternate CPI” employing the earlier BLS method. Here is a chart that illustrates the significant difference between these two calculation methods.
The change is astonishing. The adjustments to post-1982 data alter the slope of the regression and impacts the variance from the trend across the entire time frame, dramatically so in the last two decades. The slope drops from an annualized growth rate of 1.71% with official CPI to 1.38% with the alternate CPI. In this view, the S&P 500 has been below trend since the end of 2007. The 2009 bear market low saw the monthly average index price drop to 58% below the trend, which puts us in the territory of those secular market troughs. The current price is about 41% below trend.
My opinion is that the optimum method for calculating consumer prices is probably somewhere between the revised BLS method and the historic method preserved by Williams. Ordinarily for a long-term regression analysis, consistency would be preferable, which may lend some credibility to the alternate CPI chart. However, government policy, the Federal Funds Rate, interest rates in general and decades of major business decisions have been fundamentally driven by the official BLS inflation data, not the alternate CPI. For this reason I think the bullish alternative is misleading.
The more I study long-term economic and market trends, the less I believe this alternate-adjusted regression analysis.
Check back next month for another update.
Footnote on Calculating Regression: The regressions on the Excel charts above are exponential regressions to match the logarithmic vertical axis. I used the Excel Growth function to draw the lines. The percentages above and below the regression are the 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, the monthly average of daily closes for March was 1304.49. The Growth function value for the month was 898.91. Thus, 1304.49 divided by 898.91 minus 1 equals 45.12%, which I rounded to 45%.
Footnote on the S&P Composite: For readers unfamiliar with this index, see this article for some background information.
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