Is there a basis for believing this?
The number of confounders you need to control for before you could confidently not make this assumption is staggering.
For example if market data operated on a significant lag in Brazil for amateur investors, but didn't in the UK, this would represent a structural disadvantage which might make it impossible to beat the market except through dumb luck in Brazil.
It wouldn't mean it's definitely possible to beat the market in the UK, but it would be something one would want to control for in a study which makes a global prediction based on data from a sample in one nation.
However political events, as I understand you to mean, are one of the things most traders are specifically backing themselves to predict in a futures market.
So my question is: what's the basis for believing that there are structural impediments to retail day traders which are present in Brazil but not present in the rest of the world? Everyone in this thread is absolutely certain they exist, but nobody seems to be able to outline what disadvantages are present for a retail Brazilian futures trader which are not present in the UK outside of red herrings like "Brazil is less stable".
Day traders may well believe they can predict political events in Brazil effectively. And yet, there may be some quality about Brazilian political events that impacts the cohort of Brazilian day traders systematically differently than say Korean political events affect Korean day traders.
For example, maybe a new cabinet member was just appointed in Brazil who likes to leak new political announcements early to an old business partner with connections at several large banks and trading shops, and so regular day traders are at a significant disadvantage in just this one market.
Or maybe during the time period of the study, there were political protests that disproportionately involved young people, meaning the average age (and perhaps skill level) of the observed cohort of traders was artificially too high just in the window of time of the study. Or any number of possible things like this.
You can’t assume these effects don’t exist when looking at different cohorts that have real reasons why they might not be probabilistically equivalent to a more general population. You have to actually account for the possible sources of confounding (in this case the country of the exchange), either by proposing some prior counterfactual model, or by collecting data across different cohorts and explicitly controlling for the confounder with whatever type of model you are fitting.
To be clear: none of this requires you to know in advance what the confounders actually are, and indeed in most causal inference models you literally cannot know what they are. The best you can hope is that you measures things like, e.g. country of exchange or age of the traders, that correlates with the hidden confounders well enough that they allow you to control for the confounding effects in your model.
It is not a thing in professional statistics to expect researchers to already know the sources of confounding before postulating that there could be sources of confounding that need to be controlled prior to believing the results are generalizeable.
Biologist: "Whoa, you see that in the distance? That's amazing! All cows in Scotland are brown!"
Physicist: "Hold on -- all we know at this point is that there's a population of cows in Scotland that's brown."
Mathematician: "No, all we know is, there exists at least one cow in Scotland such that this side is brown."
With that said, I agree that it's an excessively narrow sample for the kind of conclusion they're trying to draw.
That being said, this seems like a double-standard.