Current Sunspot Number
Solar Cycle Phase
Kp Index
Correlation Coefficient
Pearson correlation coefficient between monthly sunspot numbers and stock indices over the entire period.
| Index | Coefficient | Strength |
|---|---|---|
S&P 500 | ||
Dow Jones | ||
KOSPI |
Solar flares and geomagnetic storms from the past 7 days.
This is an unofficial, unconventional indicator for entertainment and educational purposes only. The sunspot-stock market correlation theory is not scientifically proven and should NOT be used as the basis for investment decisions. Past correlations do not guarantee future performance.
The sunspot-stock market correlation hypothesis suggests that the approximately 11-year solar cycle may influence economic and market cycles. While some researchers have found statistical correlations, the causation mechanism remains unproven.
The theory dates back to economist William Stanley Jevons in the 1870s, who proposed that sunspots affected agricultural output and thus economic cycles. Modern variations suggest solar activity may influence human behavior, risk appetite, or economic activity through various mechanisms.
This page plots solar activity, which rises and falls on a cycle of roughly eleven years, against stock index levels on a shared timeline. Sunspot counts come from SILSO at the Royal Observatory of Belgium and index data from FRED, and the dotted lines mark the peaks of each solar cycle. A Pearson correlation coefficient between the two series is also shown, putting a number on how closely they have moved together over the full period. A high coefficient does not mean one causes the other; the page exists to present an alternative perspective, not a claim.
The idea goes back to the economist William Stanley Jevons in the 1870s. Jevons argued that sunspots affected weather, weather affected harvests, and in an economy centred on agriculture that could translate into business cycles. Modern versions add other stories, such as geomagnetic disturbance influencing mood or risk appetite. In every version the causal mechanism remains unproven, and none of these accounts is widely accepted in the field.
The fundamental problem is sample size. With an eleven-year cycle, even the entire span of reliable market data yields only about ten independent episodes, which is nowhere near enough to separate a pattern from coincidence. Both series also carry slow long-run trends, so a high correlation can appear between them even when no relationship exists. This is an unofficial indicator offered for interest and education, and it is not a suitable basis for investment decisions.