Blog - Investing Notes

January 29, 2008 - The Sunspot Cycle and Stock Returns (Testing Charles Nenner)

A reader asks:

"Have you had the opportunity to evaluate Charles Nenner as an equity and commodities forecaster?"

Charles Nenner is self-described as "the talk of Wall Street in 2006 with his 'triple call' on the market." However, we found hardly any references to him in the online business media. We therefore listened to a Bloomberg-sponsored presentation/discussion of his methods. Dr. Nenner cites in that presentation a specific key indicator for equity returns, sunspot activity, that we can test. Using monthly sunspot counts from the National Geophysical Data Center (NGDC) and contemporaneous monthly S&P 500 index data for January 1950 through November 2007 (695 months), we find that...

As background, the referenced presentation summarizes Charles Nenner's approach to predicting the behavior of financial markets, as follows:

• Charles Nenner is "a natural pattern recognizer." His analysis is not a science, it is an art, although it is as complicated as rocket science.
• He uses three principal tools to analyze financial markets:
  1. A technical model based on about 200 indicators (such as moving averages, put-call ratios, advance-decline ratios, insider sales, up-down volume ratios). This tool generally prevails when his other tools disagree.
  2. Empirical cycles (distances between tops and bottoms) over multiple horizons, refined via Fourier analysis. Cycles are as reliable as airplanes, reflecting consistent human interpretations of events that are impervious to learning. Cycles will always work because politicians and economists do not want to believe that economies are deterministic (and constituents therefore not in need of their services). Longer cycles are generally more powerful than shorter ones. Cycles indicate direction, but not level. Other methods determine level via measuring upward and downward price momentum.
  3. Elliott wave analysis.
• He believes that the sunspot cycle correlates strongly with equity markets via the predictable effects of magnetic field disturbances on investors. High sunspot activity produces exuberance. The sunspot cycle indicates that the bull market will top in 2013.

The assertion that Dr. Nenner's work is more art than science suggests that no one can independently and systematically replicate his forecasts. We can, however, test his belief in the predictive power of the sunspot cycle for equity returns.

NGDC states that:

"Sunspot counts rise and fall approximately every 11.1 years. The cycle, though, is not symmetrical, for the spot count takes on the average about 4.8 years to rise from a minimum to a maximum and another 6.2 years to fall to a minimum once again. The largest annual mean number...occurred in 1957."

The following chart compares the monthly sunspot count to the contemporaneous monthly closing levels for S&P 500 index (log scale) over the entire sample period. Visual inspection reveals no consistent relationship between the two series.

For a closer look, we compare sunspot activity to monthly changes in the S&P 500 index.

The following scatter plot relates the change in the S&P 500 index over a month to the sunspot count during that month. If there is a positive relationship, returns should generally be higher when sunspot activity is higher. Visual inspection, however, indicates no relationship. The Pearson correlation is -0.07 and the R-squared statistic is 0.004, indicating that monthly sunspot count explains less than 1% of monthly stock returns. Nor do we find a relationship between sunspot activity during a month and S&P 500 index returns in any of the subsequent three months.

As a separate test, we rank months according to sunspot activity and calculate average monthly returns for the S&P 500 index over approximately equal-size subsamples based on ranges of sunspot activity. The average monthly return on the index over the entire sample period is 0.7%. The following table shows no systematic variation in returns across ranges of sunspot activity. If anything, results indicate that low sunspot activity may be more bullish than low sunspot activity.

Might the trend in sunspot activity, rather than its absolute level, predict stock returns?

The next scatter plot relates the change in the S&P 500 index over a month to the change in sunspot activity over that same month. In order to show the shape of the main distribution, we have deleted 23 observations with changes in sunspot activity greater than 200%. Including these 23 observations does not affect the statistics of the distribution.

Visual inspection indicates no relationship. The Pearson correlation is 0.01 and the R-squared statistic is 0.000, indicating that changes in sunspot count during a month explain nothing about stock returns that month. Nor are there relationships between change in sunspot activity during a month and stock returns in any of the subsequent three months.

As a separate test, we rank months according to change in sunspot activity and calculate average monthly returns for the S&P 500 index over approximately equal-size subsamples based on ranges of fluctuation in sunspot activity. As noted above, the average monthly return on the index over the entire sample period is 0.7%. The following table shows no systematic variation in returns across ranges of fluctuation in sunspot activity. If anything, results indicate that a decrease in sunspot activity may be more bullish than an increase in sunspot activity.

In summary, evidence does not support Charles Nenner's belief in a relationship between sunspot activity and stock returns (intermediated by magnetic effects on investors).

For related research, see Blog Synthesis: Calendar Effects.