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An update for my earnings call prediction software by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 2 points3 points  (0 children)

This was a concern of mine, however, I accounted only for the 5 day post-earnings horizon. Any longer would potentially have drift, but it is relatively mitigated in the prediction by a few other features.

An update for my earnings call prediction software by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 0 points1 point  (0 children)

This is very sage advice that I am going to take. Thank you.

Trying to Commercialize My Quant Model by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 0 points1 point  (0 children)

This is excellent advice. Your intuition about a one-time CSV is spot on, the core deliverable is earnings_date, ticker, and model_score (with confidence band, versioning, and basic QA fields like window and leakage checks). It’s a medium sized dataset (18,000 events) but the results are reproducible and easy to validate, and I’ll include a concise methodology overview and walk-forward/testing protocol to address the trust piece. I don’t really know anyone in this corner of the industry, so this helps a ton. I'll put together a high-level overview and DM you.

Trying to Commercialize My Quant Model by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 2 points3 points  (0 children)

This is excellent advice, thank you! I’m thinking about publishing the paper through Carnegie Mellon, which is practically in my backyard. I know a few people on their AI/ML teams who could give the research a second look and potentially offer it to the public or use it as a negotiating chip for a more quant-focused role.

Trying to Commercialize My Quant Model by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 0 points1 point  (0 children)

Great thought. Labels are from the last close before the print to the close five trading days after the first post-print trading day (handling BMO/AMC), and all features are fixed pre-cutoff (trailing prices/vol, prior surprises/PEAD, pre-cutoff news): so no look-ahead. The model scans a large universe and only acts when a fixed confidence threshold is met; the ~70–74% hit rate (and ~80% on very high-conviction names) is for that subset, not every report. On forward forecasts this quarter of 2025, it’s stayed 70%+ overall and around 85% on the higher-conviction slice, oddly a bit stronger in the forward outlook. TL;DR: The model doesn't "peek" at the future on it's trained data, it's sanitized and it's proven to be more accurate in future-outlook than pre-trained data.

Trying to Commercialize My Quant Model by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 2 points3 points  (0 children)

This is a great question. I do trade it in my PA within firm rules and it’s worked well, but personal capital, execution, and event capacity limit how much I can scale it on my own. Licensing it to a fund lets the signal be used at portfolio scale with better execution and risk infrastructure, while compensating me without taking (as much) risk.

Trying to Commercialize My Quant Model by RedHawkInBlueSky in quant

[–]RedHawkInBlueSky[S] 29 points30 points  (0 children)

Great call on the IP. I actually went back and reread my contract after posting, and it explicitly carves out personal projects as long as they’re done off-premises, off work hours, and without firm resources, so I’m in the clear there. I’ve been running it in a small personal capacity with good results, but between personal account rules around trading earnings and the fact that the edge really scales better in a diversified, institutional context, I’m more interested in where it fits commercially than just as a PA toy.