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In Chapter IX, Gann objects to buying on a downward scale because the trader adds holdings while the first position is losing. His objection concerns exposure, not whether the arithmetic average falls. A lower average entry can be correct arithmetic and still describe a more vulnerable account. Use an explicitly synthetic example. A trader buys 100 shares at $100, then another 100 at $80. The total cost is $18,000 and the average entry is $90. At the second purchase price, the position still has a $2,000 unrealized loss. Buying more did not recover any money. It only changed how many shares participate in the next move. If price then falls to $70, the combined loss is $4,000. Without the second purchase, the original holding would lose $3,000. The added lot therefore creates another $1,000 loss on that next decline. A useful worksheet separates average entry, current loss, and loss per further dollar of decline. This makes the temptation clear: the break-even price moves closer while the account becomes more sensitive to an adverse move.

Try it

Synthetic case: 100 shares at $100 plus 100 at $80. What are average cost, loss at $80, and loss per additional $1 decline?

Limits

This is position arithmetic, not proof that every planned staged investment fails. It excludes fees and taxes and does not recommend a trade.

Source

W. D. Gann, Truth of the Stock Tape (1923), Book II, Chapter IX, Methods of Operating, Buying and Selling on a Scale, printed p. 45. Primary source: Internet Archive scan of the original printed book; passage cross-checked against archived OCR and local Gann Study Group transcription. Synthetic examples and modern calculations are editorial, not historical Gann trades.

In the manuscript of Are Investors Moonstruck? Lunar Phases and Stock Returns, Kathy Yuan, Lu Zheng, and Qiaoqiao Zhu examine 48 countries from January 1973 to July 2001 and report annualized return differences of 3 to 5 percent between full-Moon and new-Moon windows in two global portfolios. Those are the authors' historical sample results, not a forecast and not a return per lunation. The most instructive detail is the structure of the experiment. Lunar phase is defined by the Moon's ecliptic longitude relative to the Sun, so a full Moon is one shared event for every country, not a separate treatment drawn 48 times. Local sessions differ, but the clock is common. Here is a deliberately artificial demonstration of what that means. Copy one eight-day return series into 48 country columns and average them. The average equals the original series and its variation has not shrunk at all, whereas an independence assumption would divide the standard deviation by the square root of 48. Copies contain no new information. The paper handles this with panel-corrected standard errors that allow contemporaneous correlation across countries. That is the reading lesson: counting countries is not the same as counting independent evidence. The causal bridge from phase to mood to returns remains unproved, and the authors say the relation could be spurious.

Try it

Put the synthetic returns 0.01, -0.02, 0.03, -0.01, 0.02, -0.03, 0.015, -0.005 in one spreadsheet column and duplicate it 48 times. Compare the standard deviation of the daily average with the original. Compute what independence would predict and explain why that is wrong for exact copies.

Limits

The manuscript was read but its data were not acquired or re-estimated, and no post-2001 persistence is established. The synthetic example is not the paper's result. The study does not validate zodiac, aspect, or planetary-sector claims or establish mood as the cause.

Source

Yuan, Zheng, and Zhu, Are Investors Moonstruck? Lunar Phases and Stock Returns (manuscript), Abstract; section 3, Data; panel-corrected standard error discussion on manuscript page 4. Empirical finance scholarship read from the author-hosted manuscript; USNO defines lunar phase by ecliptic longitude difference.