Why do the stories always come after the prices?

Every evening, financial media explains with total confidence why the market did what it did. Every morning, it predicts with equal confidence. Course 1 taught you gentle suspicion of tidy stories; this lesson gives the suspicion structure — and a healthy news diet to replace what it removes.

The narrative production line

Journalists face an impossible daily brief: the index moved, readers demand a reason, deadline's at six. So a reason is found — always. On a −0.8% day the headline is "markets slide on rate fears"; had it closed +0.8% with the same facts available, "markets shrug off rate fears" was ready. The facts didn't choose the story; the closing print did. Once you see this inversion — price first, explanation reverse-engineered after — you can't unsee it.

Narratives are real forces anyway

Here's the nuance that keeps this from becoming cynicism: stories genuinely MOVE markets even when they explain them badly. A narrative that recruits believers ("AI changes everything", "housing never falls") creates real buying, real prices, real booms — and, when the story exhausts its recruits, real reversals. The professional posture is two-sided: skeptical of narratives as explanations, respectful of them as forces. Track what stories the crowd believes; just don't mistake them for analysis.

The healthy news diet

Everything in this unit compresses into four habits:

1. Calendars before commentary — scheduled facts (earnings, macro prints, ex-dates) explain more than opinion columns.
2. Primary sources over retellings — the company's own release beats nine paraphrases of it.
3. Reaction over prediction — what price and volume DID (your Unit 1–2 skills) is data; what anyone says happens next is content.
4. Materiality filter always on — two real items beat twenty skimmed headlines.

In the data

The crowd's tone can be scored. A sentiment feed reads each day's news about a company and gives the day a score from -1 (gloomy) to +1 (glowing) — and, critically, a count of the articles the score was built from. Here are five days of Apple's:

A +0.8 built on two articles and a +0.8 built on two hundred are the same number describing entirely different things; reading the score without the count is how a narrative gets mistaken for a measurement.

Try it now

1. Find the day's market-recap headline — the latest market-wide items from the news feed are below, or use whatever source you read. Ask: would the opposite headline have worked with the same facts?

2. Weigh a mood before you believe it, with the five days of Apple's scores above. Average the five scores the plain way, then average them again weighting each day by its article count, and subtract one from the other. The gap is how much the quiet days — here a Saturday scored off seventeen articles — were shouting in your first number.

3. Take one narrative currently everywhere. Write one sentence FOR it as a force ("believers are buying X") and one AGAINST it as an explanation.

4. Unit checkpoint ahead. Then the course's final unit — the one almost nobody teaches: when the data itself deserves your suspicion.