Every figure, claim and mechanism the finished picture states, chased to a primary source. Anything that could not be chased to one is listed under “Not checked” and is deliberately kept off the screen.
The video says that research on currency markets found stop loss orders can contribute to fast, self reinforcing moves when clustered levels are triggered.
Osler states it in the abstract, in those terms:
In this paper, I provide evidence that currency stop-loss orders contribute to rapid, self-reinforcing price movements, which I call “price cascades.”
Three empirical results support it:
Most results are statistically significant for hours, though not for days. Osler notes the same mechanism may help explain the fat tails of the exchange rate return distribution.
Source: C. L. Osler, “Stop-Loss Orders and Price Cascades in Currency Markets”, Federal Reserve Bank of New York Staff Report No. 150, June 2002. https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr150.pdf
Published as: C. L. Osler, “Stop-loss orders and price cascades in currency markets”, Journal of International Money and Finance, 24(2), 2005, pp. 219 to 241. https://www.sciencedirect.com/science/article/abs/pii/S0261560604001147
The video says stop orders often cluster around obvious prices, and lists round numbers, prior highs, prior lows, breakout levels and retest levels as the places traders make similar decisions.
Osler examined the order book of a large foreign exchange dealing bank covering almost 9,700 stop loss and take profit orders placed between 1 September 1999 and 11 April 2000, in dollar yen, dollar pound and euro dollar. The distribution of requested execution rates is far from smooth:
execution rates tend to cluster, with particularly strong clusters at round numbers
The clustering patterns of the two order types differ, and that difference is the finding:
The single greatest cluster is at levels ending in 00.
Source: C. L. Osler, “Currency Orders and Exchange-Rate Dynamics: Explaining the Success of Technical Analysis”, Federal Reserve Bank of New York Staff Report No. 125, March 2001. https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr125.pdf
Published as: C. L. Osler, “Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis”, The Journal of Finance, 58(5), 2003, pp. 1791 to 1819. https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6261.00588
Note what this does not say, which the video is careful about too: the clustering is a property of where many people independently place orders, not evidence that anyone is hunting a particular account’s stop.
The video’s arithmetic claim is that trading is won by positive expectancy, meaning the average result across many trades has to be worth taking, and that a rule which reduces full losses while also reducing large winners can lower that average.
This is a definition rather than an empirical finding. Expectancy is the probability weighted average outcome per trade, conventionally written as
(win rate x average win) minus (loss rate x average loss)
so a change that lowers the average win and the loss rate at the same time moves the result in two directions at once, and the sign of the net move is an empirical question about a specific strategy rather than something that can be asserted in general. The video treats it that way: it says the tradeoff has to be measured on your own trades, and the journal test in the later chapter is how it says to measure it.
The picture therefore draws the shape of the tradeoff, the right tail of the outcome distribution being clipped while the left tail is trimmed, and prints no percentage.
Two claims in the narration could not be chased to a primary source, so no figure from either appears anywhere in the picture. The shots for those beats draw the mechanism the words describe and carry no number and no source line.
Both are consistent with the arithmetic of expectancy above, and neither is presented on screen as a measured result.