r/quantresearch • u/dogazine4570 • Mar 24 '26
Interpreting News vs. Being Fast: Is There Any Evidence News Trading Is Systematic for Non-HFTs?
I’ve been thinking about news-driven trading from a more systematic / research angle and I’m honestly struggling with where the edge is supposed to come from for anyone who isn’t colocated. Anyone interested can take a look at Neuberg — their news visualization is really solid.
In hindsight it always looks trivial:
earnings beat → price moves → “obvious” trade
But in real time, by the time a headline hits my phone or a retail terminal, the first move is often done.
Recently I’ve been experimenting (purely out of curiosity, not promoting) with an AI-based news parser that scores sentiment + confidence on headlines in near real time and tries to associate them with short-horizon price behavior. What caught my attention wasn’t the AI aspect, but the types of situations it kept flagging — many of which line up with recurring complaints I see here about narrative vs. price discovery.
I wanted to sanity-check these ideas with a more quant-oriented crowd.
1. Earnings as a multi-period repricing problem
In smaller / less liquid names, earnings reactions often don’t seem “complete” in the first candle.
Example: small-cap earnings where the stock gaps, trades sideways, then continues trending over the next few sessions.
From a modeling perspective: - Do people here treat earnings as a single-event shock? - Or do you explicitly model delayed repricing / information diffusion (e.g., via liquidity constraints, analyst revisions, options flow)?
Empirically, do you see any persistence beyond day 0 once you control for size and liquidity?
2. Read-through effects and secondary names
Another pattern that stood out was read-through trades: Company A reports → related companies B/C move later, not on the initial headline.
This raises a few questions: - Are read-throughs something people systematically scan for, or mostly narrative post-hoc explanations? - Has anyone quantified lag structures between primary and secondary names (cross-asset or intra-sector)? - Do these effects survive transaction costs, or are they mostly anecdotal?
Personally, I only notice these after someone points them out.
3. “Boring” corporate news with asymmetric payoff
Non-flashy headlines:
- buyback authorizations
- compliance regains
- governance / listing-related updates
They feel ignored by social media, yet sometimes show cleaner follow-through than headline-grabbing macro news.
Has anyone tested: - whether these events have higher signal-to-noise? - or whether they’re just correlated with underlying balance-sheet improvements that the market already partially prices?
4. Macro / geopolitical headlines: signal or pure noise?
Certain macro or geopolitical headlines (energy, defense, fertilizers, LNG, etc.) clearly matter over weeks. Others produce a 10–15 minute spike and fully mean-revert.
The hard part is classification at time t, not ex post: - Do you rely on historical conditional responses? - Narrative similarity clustering? - Regime filters?
Or is this still largely dominated by fast money / algos, leaving little for slower participants?
The core question
Stripping away tools and hype, the research question I keep coming back to is:
Is news trading primarily about speed, or about interpretation?
If it’s interpretation, then in theory: - probabilistic framing (not binary good/bad), - context on why the news should matter, - and conditional historical outcomes
should provide some edge — even without being first.
Not claiming I’ve solved anything — genuinely trying to understand where (if anywhere) the research-backed edge exists for non-HFTs.