01 · Entry — Is this earning worth it?
A risk-quality signal that gets ahead of the move — not a hunch.
Earnings intelligence
EarningSpy turns institutional and academic strategy into a clear method: When to enter, when to exit, and how much to risk. All built around post-earnings drift — a market anomaly documented for 50 years.
What we do
Earnings look like a coin flip. EarningSpy breaks them down into a measurable risk profile — and finds the cases where the potential reward clearly outweighs the risk taken.
A risk-quality signal that gets ahead of the move — not a hunch.
If you're already in, we tell you whether to hold or exit before the print.
The position size the asymmetry justifies — calculated, not guessed.
Behind each answer: Pre- and post-earnings snapshots, risk metrics calibrated to the earnings window, and machine-learning models. See the method →
After an earnings announcement, the price tends to keep drifting in the direction of the surprise — for up to 60 days. It's a market anomaly documented in academic research for five decades. EarningSpy is built to identify it and exploit it systematically.
We recalibrate the model every quarter. It only gets better with time.
From the Earnings Brief
We measure the anomaly before the announcement and verify the result after. That cycle is what trains the prediction.
Historical post-earnings drift
| Trim. | 3d | 30d | 60d |
|---|---|---|---|
| 25.Q4 | 19.60% | 37.38% | 67.68% |
| 25.Q3 | 4.35% | -1.49% | -13.16% |
| 25.Q2 | -9.08% | -3.71% | 18.22% |
The surprise, vs. consensus
Change in fundamentals
The calendar
Click any company for its full risk and volatility profile.
Who we are
EarningSpy began as a master's thesis: A rigorous review of whether post-earnings drift is still a live anomaly, which sectors it concentrates in, and how to detect it. What started as an academic question became a system to answer it in practice — quarter after quarter, on the real market.
The founder
Alberto José Rincones
Over ten years as a software engineer in fintech and financial infrastructure, and an active trader of his own capital. He holds an MBA and a master's in finance from Universidad Torcuato Di Tella, where his 2025 thesis is the direct origin of the EarningSpy model. Post-Earnings Announcement Drift (PEAD): A review of the anomaly and computation of NYSE abnormal returns .
EarningSpy exists to bring that rigor — institutional and academic — to the independent, sophisticated investor.
EarningSpy is still being built. Leave your email and be among the first to trade the next earning with method.
In development
We're tuning the model. Leave your email and be among the first to trade an earning with method.
We'll be in touch as soon as EarningSpy is available. Thanks for your interest.