Academic Bibliography
Complete citations for research referenced on the Financial Astrology pages. All links verified as of January 2026.
How We Classify Evidence Strength
- Strong: Published in peer-reviewed journals, replicated across multiple studies/countries, 100+ citations
- Moderate: Peer-reviewed but fewer replications, or institutional working papers (e.g., Federal Reserve)
- Weak: Limited citations, single study, or preliminary findings
- Disputed: Conflicting findings across studies, or effects opposite to theoretical predictions
Strong Evidence
“Lunar Cycle Effects in Stock Returns”
Dichev, Ilia D. & Janes, Troy D. (2003)
The Journal of Private Equity, 6(4), pp. 8-29
Abstract
This paper studies the relation between lunar phases and stock market returns. We find strong lunar cycle effects in stock returns. Specifically, returns in the 15 days around new moon dates are about double the returns in the 15 days around full moon dates. This pattern of returns is pervasive; we find it for all major U.S. stock indexes over the last 100 years and for nearly all of the 24 other countries we examine.
Why We Cite This
Foundational study documenting lunar cycle effects in equity markets across 100 years of US data and 24 countries.
“Are Investors Moonstruck? Lunar Phases and Stock Returns”
Yuan, Kathy; Zheng, Lu & Zhu, Qiaoqiao (2006)
Journal of Empirical Finance, 13(1), pp. 1-23 | DOI: 10.1016/j.jempfin.2005.06.001
Abstract
This paper investigates the relation between lunar phases and stock market returns of 48 countries. The findings indicate that stock returns are lower on the days around a full moon than on the days around a new moon. The magnitude of the return difference is 3% to 5% per annum.
Why We Cite This
Major cross-country study confirming lunar effects across 48 nations with rigorous controls for calendar anomalies.
“Stock Returns and the Weekend Effect”
French, Kenneth R. (1980)
Journal of Financial Economics, 8(1), pp. 55-69 | DOI: 10.1016/0304-405X(80)90021-5
Abstract
This paper examines two alternative models of the process generating stock returns. Under the calendar time hypothesis, the process operates continuously and generates expected returns that are the same for each day. Under the trading time hypothesis, expected returns are generated only during active trading. The evidence strongly supports the trading time hypothesis and documents the "Monday Effect" where returns from Friday close to Monday close are significantly negative.
Why We Cite This
Seminal paper documenting the weekend effect, one of the most studied market anomalies. Over 2,100 citations.
“Equity Returns at the Turn of the Month”
McConnell, John J. & Xu, Wei (2008)
Financial Analysts Journal, 64(2), pp. 49-64 | DOI: 10.2469/faj.v64.n2.11
Abstract
We document that a significant portion of the total monthly return accrues on the last and first trading days of the month (the "turn-of-the-month" effect). The pattern is remarkably consistent across 31 countries.
Why We Cite This
Documents turn-of-month effect across 31 countries with consistent evidence.
“Size-Related Anomalies and Stock Return Seasonality: Further Empirical Evidence”
Keim, Donald B. (1983)
Journal of Financial Economics, 12(1), pp. 13-32 | DOI: 10.1016/0304-405X(83)90025-9
Abstract
This paper documents the January Effect - the phenomenon where small-cap stocks exhibit abnormally high returns in January, particularly in the first week.
Why We Cite This
Foundational paper on the January Effect and small-cap anomalies.
Moderate Evidence
“Playing the Field: Geomagnetic Storms and International Stock Markets”
Krivelyova, Anna & Robotti, Cesare (2003)
Federal Reserve Bank of Atlanta Working Paper, 2003-5a
Abstract
We investigate the effect of geomagnetic storms on daily stock market returns. Geomagnetic storms have significant negative effect on the following day's stock returns for a large set of international equity markets. 75% of storms caused 30-80% increase in hospitalization for mood-related conditions.
Why We Cite This
Federal Reserve research documenting geomagnetic storm effects on global equity markets, with proposed mood-based mechanism.
“Sunspots, GDP and the Stock Market”
Modis, Theodore (2007)
Technological Forecasting and Social Change, 74(8), pp. 1508-1514 | DOI: 10.1016/j.techfore.2007.01.004
Abstract
This paper documents correlations between US GDP and sunspot activity, as well as between the Dow Jones Industrial Average and sunspots. The 11-year solar cycle appears to correlate with economic activity.
Why We Cite This
Documents correlation between solar cycles and economic indicators including stock market performance.
“Robust Global Mood Influences in Equity Pricing”
Dowling, Michael & Lucey, Brian M. (2008)
Journal of Multinational Financial Management, 18(2), pp. 145-164 | DOI: 10.1016/j.mulfin.2007.06.002
Abstract
We investigate the robustness of weather and geomagnetic storm mood effects using a large sample of international stock market indices.
Why We Cite This
Cross-validates geomagnetic effects across international markets.
Weak or Disputed Evidence
“Long Live Hermes! Mercury Retrograde and Equity Prices”
Qi, Yue; Wang, Hong & Hang, Xueying (2021)
Shanghai Jiao Tong University Finance Research / Auckland University of Technology
Abstract
Stock market returns are annually 3.33% lower during Mercury Retrograde periods than in other periods. Effect found across 48 countries.
Why We Cite This
One of few academic studies on Mercury retrograde effects in financial markets.
“Mercury Retrograde Effect in Capital Markets: Truth or Illusion?”
Murgea, Aurora (2016)
Timisoara Journal of Economics and Business, 9(1), pp. 49-61
Abstract
Found lower return volatility during Mercury retrograde periods, inconsistent with astrological theories which predict chaos, but consistent with reduced trading activity.
Why We Cite This
Provides mixed evidence - finds effect but opposite direction from astrological prediction.
Important Notes
- 1.Statistical significance does not equal trading profits. Transaction costs, slippage, and market impact can eliminate paper gains.
- 2.Effect sizes are often small. A 3-5% annual difference sounds large but translates to basis points per trade.
- 3.Publication bias exists. Studies finding no effect are less likely to be published.
- 4.Past performance does not predict future results. Many documented anomalies have weakened or disappeared after publication.
- 5.Citation counts are approximate. Sourced from Google Scholar and may change over time.