Insights on AI disruption, equity research, SEC filing analysis, and smarter investing.
IDC says 3.7x per dollar. IBM says 25% of initiatives pay off. MIT says 95% of pilots fail. How all three are true, and how to budget accordingly.
Mid-market companies are too small for a Chief AI Officer and too big for a free-for-all, so AI ownership defaults to nobody. Here are the four realistic fixes.
By the time you read a quarterly filing, the numbers are weeks old. Here is how that reporting lag works and how investors close the gap with timelier data.
Pharma stocks trade on pipelines, patent cliffs, and trial readouts more than on current earnings. Here is a practical framework for analyzing them.
How sentiment analysis extracts signal from earnings calls, SEC filings, news, and social media, plus a simple workflow for tracking language changes yourself.
Cutting staff to fund AI destroys the knowledge and goodwill the rollout needs. The evidence favors programs that hold headcount flat and redeploy time.
Markets overreact to bad news and misprice good companies. Here is a practical process for separating temporary problems from businesses that are genuinely broken.
A practical guide to reading 13-F filings: what funds must disclose, what stays hidden, and how to track position changes without chasing stale trades.
How the SEC treats crypto and digital-asset disclosures, and a practical checklist for judging whether a company is telling you the truth about its exposure.
MIT found 95% of GenAI pilots deliver no measurable P&L return. Automating a broken workflow just speeds up the mess. Map, prune, and standardize first.
SaaS companies live or die on ARR, net dollar retention, LTV/CAC, and the Rule of 40. Here is what each one really tells you and how to read the benchmarks.
Most financial models are built backward from a conclusion. How to build ones that forecast instead, using base rates, scenarios, and honest scorekeeping.
