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Feature Request

Feature request: show listing performance prediction on IPO card

Aarav Basnet

6 hours ago
3 replies

I would love to see a listing performance prediction badge or score directly on the IPO card in LaganiLabs. Something like a simple "Expected Premium: High / Medium / Low" indicator based on factors like:

- Historical oversubscription ratio of similar companies in the same sector

- Current NEPSE index momentum

- Company fundamentals (EPS, P/E ratio, debt ratio)

- PPA terms and capacity for hydropower companies

- Promoter lock-in period and public float percentage

Mockup concept:

Each IPO card could show a small badge like "High Premium" in gold or "Moderate" in blue, with a tap-to-expand breakdown showing the factors driving the prediction.

Why this would be valuable:

Right now, investors have to manually research each IPO across multiple sources (ShareSansar, Merolagani, company prospectus) to form their own view. A model-driven prediction built into the app would save hours of research and help newer investors make more informed decisions.

This does not need to be financial advice — just a data-driven indicator with a clear disclaimer.

Would other users find this useful? And is this something the LaganiLabs team is considering?

Replies

3
Roshani Tamang5 hours ago
34

This would be an incredible feature. I manually maintain a spreadsheet with these factors for every IPO I apply to. Having it built into the app would save me at least 30 minutes per IPO. Strong +1 from me.

Nitesh Yadav4 hours ago
52

Hi Aarav, this is exactly the kind of feature we have been thinking about for the next major LaganiLabs release. We are researching a data model using NEPSE historical data going back 5 years. The key challenge is the disclaimer language required by SEBON regulations. We will share more details on the Roadmap page soon.

Kiran Pokharel2 hours ago
29

As someone who comes from a data science background, I would be happy to volunteer help on the prediction model if LaganiLabs opens a beta or needs community input. I have worked with NEPSE data before and have some ideas on the modeling approach.

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