AI as an Attention Filter: Using Sector-Based Triggers to Avoid Chasing Dogs
How AI can help investors focus at the right moments by monitoring sector stress instead of reacting to individual stock moves.
This note explores how AI can be used as an attention filter rather than a stock-picking tool. By linking alerts to sector-level stress instead of individual names, the goal is to focus investors at the right moments without mechanically chasing the weakest stocks.
I’ve been thinking a lot about how people try to buy stocks during drawdowns — and why the mechanics often work against them. This note lays out a simple framework I’m using for software stocks, and how I’m experimenting with alerts created by CHAT GPT to provide me with information about when I should carefully analyze a sector and perhaps start selling or buying individual stocks.
This is not about predicting bottoms or automating trades, and I do eschew automatic limit orders on individual stocks. The purpose of the proposed system is to let the investor know it is now time to pay attention.
My first practical example of this approach is being applied to software stocks.
Why software is interesting right now
The software sector has gone through a broad, grinding drawdown after years of strong performance, largely because of new competition from AI startups.
In many cases, prices have fallen not because businesses broke, but because valuations compressed and sentiment shifted. That distinction matters.
High‑quality software companies tend to share a few traits: recurring revenue, high switching costs, durable customer relationships, and strong free cash flow. When prices fall across the whole sector, short‑term weakness is often cyclical rather than structural.
Those periods — when the sector is out of favor but business quality remains intact — are the environments I want to engage.
But I don’t want to put limit orders on individual stocks.
Why limit orders often fail in practice
A common strategy is to place limit orders and wait. In practice, this often leads to bad outcomes.
Static limit orders tend to fill first in the weakest names or during company‑specific negative events. Over time, this biases accumulation toward stocks that are falling for idiosyncratic reasons rather than broad market pressure.
Especially in software, where dispersion between strong and weak franchises can widen quickly, this approach increases the risk of buying “dogs” rather than leaders experiencing temporary stress.
Use the sector to gate attention, not individual stocks
Instead of anchoring decisions to individual price moves, I prefer to use sector conditions to decide when to engage.
For software, that means watching the sector itself and asking a simple question:
Is weakness broad and sentiment‑driven, or is this just noise in a single name?
When the sector is under pressure, individual stock declines are more likely to reflect risk‑off behavior rather than deteriorating fundamentals. That’s when it makes sense to review high‑quality names and consider action.
This flips the usual logic: sector stress creates opportunity selectively, not mechanically.
Where alerts actually help
The real value of alerts isn’t execution — it’s attention.
Most people don’t want (or need) to stare at markets all day. The goal is to be notified when conditions might justify scrutiny, not to be told what to do.
Alerts are rare and scheduled, not constant • They provide context, not instructions • Silence is the default state. In other words, alerts exist to say “this might be worth a look”, not “act now.”
The current alert framework
Right now, the framework is intentionally simple.
Morning sector context Shortly after the market opens, I get a brief snapshot of the software sector: how it’s trading versus the prior close, where it sits in its longer‑term range, and whether the day appears routine or notable.
This answers one question: Can I ignore the sector today, or should I keep it on my radar?
Pre‑close status check About 30 minutes before the close, I review where the sector and a small set of tracked stocks finished the day. This helps frame overnight exposure and whether broader conditions are improving, worsening, or unchanged.
There is currently only one alert example in use. Additional variations may be added later, but the emphasis will remain on restraint.
It may be desirable to create a midday alert or an alert that goes once and only once if a certain level of volatility is reached. I am thinking about this approach but don’t want to spam myself.
What this is — and what it isn’t
This system does not automate trades. It does not chase volatility. And it does not attempt to time exact bottoms.
It does impose structure:
Sector conditions determine when to look • Stock quality determines what to buy • Alerts reduce emotional and ad‑hoc decision‑making
Volatility becomes an input, not a trigger.
A note on delivery
A practical note before closing.
ChatGPT today functions best as a reasoning and context layer, not as a brokerage-style notification system. Alerts appear in-app and can generate email notifications that link back to the analysis stream. This is sufficient to prompt review and attention, but it is not the same as a real-time SMS or trading-platform alert.
In time, the logic behind these alerts can become more nuanced without becoming more frequent. Rather than responding to singleday moves, triggers could incorporate patterns such as several consecutive down days, cumulative weakness over a one- to two-week window, or broader measures of stress like proximity to recent lows. The intent would remain the same: not to prompt action, but to indicate that conditions are sufficiently unusual to justify a deeper review.
As AI tools mature and gain access to richer market and fundamental data, their usefulness in this role should improve. Better inputs can help distinguish routine volatility from more meaningful dislocations. That said, even with imperfect data and basic triggers, the value of this framework comes less from precision than from discipline — creating a structured reason to engage, rather than relying on constant monitoring or ad-hoc reactions.
Why I’m sharing this
I think many investors intuitively understand these ideas but struggle to operationalize them. Alerts are usually either noisy or useless. Limit orders are mechanical but blind to context.
Combining a sector‑driven framework with disciplined alerts is one way to bridge that gap.
I’m still refining this approach, and there’s more to explore — additional alert types, better delivery mechanisms, and different sector applications. For now, the goal is modest but important: turn market stress into a reasoned opportunity rather than a reactive moment.
More to come.
Next: why this same alert logic applies even more cleanly to bank stocks — and how sector-level stress can matter more than individual price moves in financials.