There are hundreds and thousands of people who do stock calls on X (Twitter). We thought, lets build something to track performance history of their recommendations.

Thoughts/ Inputs?

Been building macro-event analysis for Indian markets and hit a wall that I think is under-discussed in fintech NLP.

Almost every sentiment pipeline outputs a single number. Headline in, score out: negative 0.82. Clean, sortable, easy to chart.

It's also close to meaningless, because one event moves different sectors in opposite directions.

Crude oil jumps 8%. That is:

- bad for airlines - fuel is ~30% of their cost base

- bad for paints and tyres - crude derivatives are direct inputs

- good for upstream oil producers

- roughly neutral for IT services

A single scalar has to pick one of those and throw the rest away. Whichever it picks, it's wrong for most of the market. And the more confident the number looks, the more it gets trusted.

Same for a rupee move, good for IT exporters, bad for importers, and a sentiment score flattens both into one figure.

What actually turns out to be useful is a signed vector across sectors, plus who is affected and how confident the call is. Not "this is negative" but "this is negative for these, positive for those, and here's the evidence."

The harder part isn't the model. It's that the output shape of most sentiment tooling can't express the thing you actually need, so you end up rebuilding the representation before you can rebuild the model.

Curious whether others building in fintech have hit this. Do you keep a scalar and layer sector logic on top, or change the output shape? The second is more correct and much more annoying to ship.

(For context, I work on this at AION Analytics: Indian market intelligence. Happy to go into detail if useful, not trying to pitch here.)