Buy the Rumor, Sell the News: When Is News Priced In?

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "Buy the Rumor, Sell the News: When Is News Priced In?".

Jane: The gist The news-aligned move sits before and at publication,

Tom: First, who's behind it and why it matters.

Title and authors: Jane: So, what do they actually find? The summary suggests that the news-aligned move—the actual price change related to a piece of news—sits before and right at the time of publication. They also look at rumor-flagged events and find that for those, the rumor day captures everything, while any confirmation after that doesn't add anything new.

Tom: That’s a big finding because it confirms one side of that old saying. The cumulative move in the news direction by the close of publication day is two point eight times its value twenty days later for all signed events, according to the paper <ref:2608.14014#pg2>.

Lu: And they introduce a way to measure this: they built this three-layer infrastructure involving tagged events, story structure, and a benchmark for publicity. The tagging process uses an Azure-hosted GPT teacher that labels a sample with seventeen event tags and five attributes, and then a small distilled classifier extends those labels to the full corpus at ninety-five percent fidelity <ref:2608.14014#pg1>.

Meng: Ninety-five percent fidelity is high, but I wonder how robust that distillation is when you’re dealing with such varied financial language. What's the cost of building that infrastructure?

Lalam: The paper says they achieved this pipeline with an estimated ninety-five percent corpus-wide teacher fidelity at a total cost of under twenty dollars for teacher labels, which is pretty low for this kind of work.

Jane: And that’s just the mechanics. But the core result is more interesting than just the setup. They also find that markets underreact to numbers but overreact to stories when you remove a placebo group of comparable stocks that drift with or without news, which is a key finding in Section two of their work <ref:2608.14014#pg2>.

Tom: That’s what I was hoping for. It suggests that we need to focus on the narrative structure rather than just the raw numbers when building our models.

The paper's summary: Jane: The paper doesn't just stop at those findings; they suggest a few ways to improve how we use this information for forecasting. They say that for building news-conditioned forecasting systems, durable information is the kind of event, the fact and intensity of coverage, and its width.

Lu: They propose ordering these event lines by measured per-tag, per-attribute, per-size drift priors rather than relying on source reputation heuristics. This means we should prioritize how much a specific tag or attribute actually influences drift over just trusting which news outlet is known for good stuff.

Meng: So instead of using some high-level reputation score, you use the actual measured drift data to decide what feature to focus on? That’s a practical shift for deployment.

Tom: Right. And they suggest encoding text as tagged, flagged event lines—tag, scheduled, rumor—instead of just sentiment scalars. They argue that the tag and flags carry the durable information better than just reading how positive or negative something sounds in a sentence.

Lalam: That makes sense from a modeling standpoint because it lets you know exactly which structural features matter and how fast that information decays over time. It shifts us away from relying on noisy sentiment signals.

Jane: And they also point out that the background-drift correction can be used to reinterpret market data by subtracting the placebo group, which helps remove apparent asymmetries between good and bad news and eliminates size gradients in post-event drift.

Tom: So, if you're building a system, you use this correction to filter out noise caused by general coverage patterns that look like reversals when they aren't real. That’s a useful tool for cleaning up the signals we feed into our models.

The paper's improvements: Jane: So, wrapping up the "Buy the Rumor, Sell the News: When Is News Priced In?" paper, we see that for news-conditioned forecasting, it’s not just about direction. It’s about durability and width—how much coverage there is and what kind of event it is.

Tom: The main idea is that direction gets priced in at publication, but the presence and width of the news flow persist over time. That's what we need to capture in our event lines instead of just sentiment scores.

Lu: The authors conclude that the exploitable object in news flow isn't necessarily direction, but presence and width. The focus shifts to how news is priced, and the direction is spent by the closing bell.

Meng: From an engineering standpoint, this means we should be structuring our input data around these tagged event lines rather than just relying on raw sentiment scalars for our models. That’s a concrete way to improve what we feed into production systems.

Lalam: I'm confident in the paper's answer because they provided a reproducible recipe for labeling those large corpora, and their final tag map shows that quantified fundamental tags continue to drift while soft news reverses, which is a really solid structural finding.

Jane: It’s clear this work gives us a better lens for understanding how we can build systems that actually learn from the structure of information flow rather than just the surface-level sentiment. That’s what we have today with this discussion on "Buy the Rumor, Sell the News: When Is News Priced In?".

Tom: Yeah. We’ll be looking out for how these structural insights translate into actual predictive models soon.

Conclusion: Jane: So we've looked at "Buy the Rumor, Sell the News: When Is News Priced In?" and it seems like for forecasting, we need to focus on how much coverage there is and what kind of event it is, not just direction.

Tom: That’s right. The paper shows that direction gets priced in at publication, but the presence and width of the news flow persist over time. We have to capture that structure instead of just sentiment scores for our models.

Lu: They conclude that the exploitable object in news flow isn't necessarily direction, but presence and width. The focus shifts to how news is priced, and the direction is spent by the closing bell.

Meng: From an engineering standpoint, this means we should be structuring our input data around these tagged event lines rather than just relying on raw sentiment scalars for our models. That’s a concrete way to improve what we feed into production systems.

Lalam: I'm confident in the paper's answer because they provided a reproducible recipe for labeling those large corpora, and their final tag map shows that quantified fundamental tags continue to drift while soft news reverses, which is a really solid structural finding.

Jane: It’s clear this work gives us a better lens for understanding how we can build systems that actually learn from the structure of information flow rather than just the surface-level sentiment. That’s what we have today with this discussion on "Buy the Rumor, Sell the News: When Is News Priced In?".

Tom: Yeah. We’ll be looking out for how these structural insights translate into actual predictive models soon.

Lu: I'm curious to see if this event-time approach applies when we look at things that are inherently spatial or temporal, like in some of the work on three dee generation or action modeling.

Meng: Yeah, because when you’re shipping something, you have to worry about what breaks at scale. This paper gives us a better way to structure data before we even start building the model architecture.

Lalam: I'm ready for whatever comes next, but this research on event-time pricing seems like it sets a really strong foundation for how we think about information flow in these complex AI systems.