Most retail trading starts from the chart: find a pattern in past prices, bet on it continuing. Most of those patterns are noise wearing a costume. And most of what scrolls across a financial news terminal has the same problem in a different outfit. A CEO's hot take, an analyst upgrade, a story about a competitor, a macro print (a scheduled economic release like a jobs report), a geopolitical headline: all of these move prices, and all of them matter to somebody. They are also continuous, fuzzy-edged, and impossible to define a population over. You cannot usefully study "the basket of all CEO tweets in 2023," because the universe is unbounded and the relevant features are unstructured.
This course starts somewhere else entirely: the calendar. Earnings dates, index rebalances, lock-up expirations, offering filings, SPAC deadlines. In Price as Consensus: Information vs. Expectation we argued that systematic trading lives on the information side of the ledger. This lesson sharpens that into a test you can apply to anything the market throws at you.
The Three-Part Test
When this course says "event," we mean something with three properties.
Recurring. The same kind of event happens again and again, across many companies and many years. Earnings arrive every quarter for every listed company. Nearly every IPO comes with a lock-up. Offerings are filed by the hundreds every year. Recurrence is what gives you a population rather than an anecdote: you are never studying one event, you are studying the class.
Dated. The event happens, or is recorded, at a specific moment, and often the date is known well in advance. Lock-up expirations are written into the IPO prospectus, the disclosure document that accompanies a listing, months ahead. SPAC merger votes are scheduled. An offering is priced at a single timestamp. There is no fuzziness about when the thing occurred, and the date is what allows a study to be constructed: day zero is unambiguous.
Disclosed. The event leaves a paper trail. There is an 8-K, a Form 4, a registration statement, or some equivalent document that records what happened, when, and in what amount. Disclosure is what makes the event visible across the entire universe of listed companies, not just the ones you happen to be watching.
Strip out everything that fails any part of the test and you are left with a manageable list: corporate actions, financings, insider transactions, registration statements, redemption windows, earnings. These are the raw materials of the discipline, and the rest of this course works through them one class at a time.
If Everyone Can See It Coming
An objection should be forming. These events are public. The dates are published. Anyone can look them up.
If everyone can see the event coming, how can there be any edge left in it?
This is one of the more counterintuitive things in markets, and the answer comes straight from the last lesson: the event is public, but the behavior it forces is lopsided.
Take the cleanest example. When a stock is added to the S&P 500, every fund tracking the index must buy it, in size, on a known effective date. Not because the managers think it is cheap: because their mandate says so. The seller on the other side of that trade is being paid for providing liquidity to someone who has to transact.
The same structure repeats everywhere once you look for it. Earnings announcements concentrate an entire quarter of information into one timestamp, and option market makers, the dealers who stand ready to buy and sell options all day, must price that uncertainty before knowing the answer. Lock-up expirations release a known quantity of shares from insiders who have been waiting, sometimes desperately, to sell. De-SPAC completions, the moment a SPAC closes its merger and becomes an operating company, hand redeemable shares to arbitrage funds whose entire plan is to exit immediately.
In every case, somebody in the room is not trading on opinion. They are trading on obligation. Awareness of the date gives you nothing by itself, because everyone has it. What the date gives you is a precise place to look for flows that do not care about price, and for pricing of the event's magnitude that can stay systematically off because the natural counterparties are constrained, or the opportunity is too small for the biggest desks, or the flow is simply unpleasant to warehouse.
Queryable Is the Load-Bearing Word
Put the three properties together and you get the word this whole course rests on. An event class that is recurring, dated, and disclosed is queryable.
Queryable means you can write a procedure that walks the entire universe of US-listed companies, asks "did this event happen here?", and gets a clean answer for every name. You can run that procedure over any historical window where the data exists. You can run it in batch, produce a study, and store the result. And the same procedure can run tomorrow morning against today's filings and hand you the active candidates.
Compare that to anything in the discretionary pile. "Was the CEO's tweet bullish?" requires human judgement on every single name. "Did the macro print surprise?" requires picking a definition of surprise, and the definition is itself a discretionary choice. "Is the chart bullish?" is not a query at all; it is a Rorschach test. The events in this course do not have that ambiguity. A registration statement was filed or it was not. Lock-up day was August 14 or it was not. The merger closed on April 22 or it did not.
Here is why the word is load-bearing. If you can query an event across the whole universe, you can stop doing the thing that loses most traders money: trying to be smarter than the market about one name at a time. Instead you study the population. Did the last several hundred of these events, taken together, drift a particular direction afterward? By how much, over what horizon, with what tails? If the answer is yes and the effect survives costs, you do not need to be right about any individual name. You need the population to keep behaving like the population.
You are not predicting a stock. You are identifying a population effect and harvesting it.
The intelligence lives in the design of the query and the discipline of the process. The actual trading is mechanical, and that is a feature: publicly known events give no advantage to awareness alone, so the edge lives in querying every event, running every study the same way, and sizing every name by the same rule. The procedure works because every step is boring.
That procedure has exactly four steps, and the next lesson states them precisely, because you will run them for the rest of the course.
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