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Financial modelling and financial opinions are commonplace. And the media, YouTube, social media and other platforms may be having a good impact in creating confusion (possibly even motivating fear and greed) in society at large. Thus, an expectative model may be helpful in providing a more stable picture of financial performance via using a simpler approach based on descriptive statistics (namely, percentiles). This model may be even more interesting when updated in 'real time' (e.g., as a RealTime research project, https://doi.org/10.17605/OSF.IO/49A5F). Expectative modelling is eminently descriptive of past data. It was first trialled with epidemiological data (https://doi.org/10.17605/OSF.IO/MRJPB). However, the latter posed serious challenges due to the reliability and availability of such data. In comparison, financial data, especially from financial markets, is publicly available, largely reliable, and abundant. Thus, it may prove a better research environment for testing the model (even if it is not envisaged that it will provide any advantage to markets per se, other than descriptive data). Expectative modelling has very few assumptions, and the focus on percentiles allows to set the minimum and maximum bounds for our description at, say the 5th and 95th percentile (instead of at the minimum and maximum observed values). This range-based modelling has two main properties: On the one hand, to describe with Severity (Mayo), so that we may contain within the range highly informative (observed) behaviour by eliminating extreme data with a high probability of being exceptional, a fluke or a reporting error. On the other hand, to be attentive to Black Swans (Taleb), as the range itself tells us which (future) behaviours should not really surprise us (as opposed to those which fall outside the range). It is this expectative property of the approach that merits a preregistration, if only to mark a definite break between past data (already released) and current/future data (yet to be released).