Event-Driven Equity Underwriting with Gemini 3.1 Pro: The $APTV Spin-Off
Institutional forced selling has temporarily crushed Aptiv PLC. Here’s why Gemini 3.1 Pro predicts a rapid multiple expansion post-spin.
Ishtiaq Rahman
The Alpha-Divergence Protocol v2.0: Uncovering the $CPRX information asymmetry with Gemini 3.1 Pro.
Inside the mathematical disconnect between CPRX’s distressed multiple, its $709M cash fortress, and the verified hyper-growth of its orphan drug portfolio.
The unpriced catalyst: A Gemini 3.1 Pro-driven case for Centrus Energy ($LEU)
How we used our Alpha-Divergence Protocol to uncover a market disconnect between the Russian uranium ban and a guaranteed commercial backlog.
We applied our most successful capital markets information arbitrage methodology using OpenAI’s flagship model, GPT-5.4
Finding information asymmetry in the capital markets using GPT-5.4
Exploiting information asymmetry in the stock market with OpenAI’s flagship model, GPT-5.2
A breakdown of our recursive strategy to uncover data divergences in financial filings, including the prompt set and a new stock recommendation.
Recursive prompt engineering: how Gemini 3.0 Pro refined and executed its stock-picking playbook
We made Gemini 3.0 pro re-engineer its own deep research workflow using results from the last run.
Re-running one of our best stock-picking workflows: using GPT-5.2 to spot information asymmetry in public markets
Can GPT-5.2 find the "hidden" edge in public data again?
The Alpha-Divergence Protocol: We let Gemini-3 Pro design an autonomous stock research workflow
How we moved beyond basic prompts to build a forensic AI workflow that exploits the gap between headlines and reality, and the single high-conviction trade it uncovered.
We let GPT-5.2 design its own stock-picking methodology.
We let GPT-5.2 design a 3-prompt workflow that hunts overlooked public signals, validates catalysts in filings, links macro/micro tailwinds, and picks a single 12-month stock using scenario-weighted expected returns, landing on $MP.
We reran our most successful investment methodology using Gemini 3 Pro.
In 2023 we used GPT-4 to design a “most investable” hypothetical stock and found a real-world match that returned 117% vs 15.9% for the S&P 500. Here we rerun that process with Gemini 3 Pro and share the new thesis it surfaces.