We start with bespoke financial models focusing on growth, margins, and reinvestment metrics. These lay the foundation for differentiated views on key business drivers and support the thesis formation cycle. After isolating market mispricings, we identify catalysts that can narrow the gap between the internal view and the consensus view. The models, kept “chamber-ready” for minimal reaction latency when market opportunities arise, ultimately help us unpack multiples and refine embedded assumptions held by the Street.

To best understand institutional positioning, we overly this research-intensive process with a sophisticated technology stack that reviews daily option transaction data in real-time and extracts actionable information from every executed option trade on 18 different option exchanges. That encompasses roughly all 5700 listed options in the US, the equivalent of approximately 8 million daily trades and 65 million notional contracts. The overlay uses C#/Java programing language to call attention to complex, time-sensitive, multi-legged aggregate option order flow with minimal latency. Our staff pays close attention to unusual sweep, auction floor, cross block, and spread/diagonal/ratio trades, highlighting any deviation between what the equity market is indicating on any given day and what the option market might be expressing in contrast. Abnormal changes in implied volatility and open interest are instantly detected; the stack also detects clustering sweep orders and option position delta (notional shares attached) for every printed trade. Understanding market microstructure and the phenomena of “possibly better-informed edge traders” is one of our core principles.