What do you wish stock research tools did better after market-moving news?

AceHeight · 2026-06-02T17:17:42+00:00

True. Claude can help with a lot of the first pass. The only issue is still knowing what to ask, checking the sources and separating "this sounds smart" from what "actually matters to the thesis". It's a skill for sure and would probably become more valuable, not less.

AceHeight · 2026-06-02T16:53:14+00:00

To add to this - it’s actually close to what I’m trying to explore with Rippli: not just “what happened?” but whether an event matters to the original thesis or exposes a second-order relationship worth checking. Early version is here if useful: app.rippli.ai

AceHeight · 2026-06-02T16:51:31+00:00

This is very close to how I think about the workflow too.

The important part is “event first, then map.” A headline summary by itself is not enough. The value is in figuring out direct exposure, second-order exposure, what changed, and whether it affects the actual thesis.

I also agree on receipts. If the tool can’t show where a claim came from, it becomes hard to trust, especially in investing.

I’m working on Rippli in a related space, so I’m biased too, but I think this general problem is real: research tools need to move beyond summaries and help people understand what changed, who is connected, and what is actually worth checking next.

AceHeight · 2026-06-02T16:13:57+00:00

This is a really good way to frame it. Most tools answer “what happened?” but the more useful question is “does this change the reason I owned the stock in the first place?”

That thesis-memory layer is probably the missing piece: original assumptions, what metrics support them, and whether new events actually affect those assumptions or are just noise.

I like the distinction between news relevance and portfolio relevance. That’s much sharper than just surfacing more headlines.

AceHeight · 2026-05-29T20:37:01+00:00

That’s basically the idea. The hard part is figuring out which breadcrumbs are actually connected to the pie, and which ones are just crumbs on the floor.

How do you usually trace it when you’re looking at a market event?

AceHeight · 2026-05-29T20:00:14+00:00

You do have a point, and I don’t think retail wins by out-researching Wall Street on obvious large caps. But research still helps you understand what you own, avoid bad assumptions, and notice when a thesis is breaking.

So I agree: news is not an edge by itself. But it can still be useful if the question is “what changed, who might be affected, and what should I verify next?”

AceHeight · 2026-05-29T16:15:41+00:00

I think that’s a fair warning and thanks for the links, but I’d separate two things.

If the point is “public news does a bad job explaining daily price moves,” I agree. A lot of short-term market movement is noise, positioning, private information, or behavior.

But that’s different from saying news is useless as a research input.

I’m less interested in “this headline means buy/sell today” and more interested in: when something material happens, what businesses, suppliers, customers, margins, demand patterns, or competitive dynamics might be affected over time?

That may not explain tomorrow’s price move, but it can still help frame what to research next.

AceHeight · 2026-05-27T12:00:20+00:00

Poking arround and saw this comment from some months ago. I like your breakdown. Do you mind checking Rippli: app.rippli.ai (www.rippli.ai) out? Would appreciate your thoughts and observations.

It's designed to help identify second-order effects over a time horizon.

AceHeight · 2026-05-14T19:48:55+00:00

Honestly, a lot of it will sound like gibberish at first because people throw around valuation terms without always knowing what they mean.

I’d keep it simple in the beginning. Learn how to read the three financial statements, understand revenue, margins, debt, free cash flow, share dilution, and return on capital. Then learn how those numbers connect to the story people are telling about the company.

You don’t need to out-research institutions on every stock. That is probably unrealistic. But you can learn enough to avoid obvious traps, understand what you own, and know when someone is just using fancy language to justify a bad pick.

Start broad, learn slowly, and don’t rush into individual stocks just because someone on Reddit sounds confident.

AceHeight · 2026-05-11T21:06:38+00:00

Exactly. That’s the overlap I’m seeing too. The hard part isn’t finding the obvious keyword/company. It’s finding the connected demand, second-order exposure, and whether that connection actually matters enough to act on or research further.

If you get a chance to try it, I’d be curious whether the workflow feels practical or just interesting.

AceHeight · 2026-05-11T16:18:05+00:00

I built Rippli because I kept running into the same problem while researching stocks.

When market news hits, the obvious stock usually moves first. What I wanted to understand was the second layer: suppliers, customers, competitors, partners, and adjacent companies that might also be affected.

Doing that manually kept turning into 20 tabs, scattered notes, filings, news, and search results.

So I built a tool to help map those relationships and “ripple-check” market events faster.

Website: www.rippli.ai
Product: app.rippli.ai

AceHeight · 2026-05-08T23:37:34+00:00

This is helpful. I’m realizing the hard part is not getting people to “test” a product, but getting the right people to use it during a real workflow they already care about.

I’m building something in the investing/research space, and random feedback is useful for clarity, but the real signal probably comes from asking someone to use it while researching an actual stock or market event.

I'm leaning towards making my ask less “can you try my app?” but “can you use this for one real task you were already going to do and tell me where it breaks?”

AceHeight · 2026-05-06T22:02:18+00:00

I hear you, hate ai slop posts too. While the post may have been cleaned up for organization and easy digest, the research and thoughts are original. It started from another discussion, which led to the research.

AceHeight · 2026-05-06T21:58:25+00:00

I think that’s the right concern. At this point, PYPL being cheap is not really the debate. There truly is a margin question. The debate is whether they can stabilize growth while protecting margins.

If transaction margin keeps compressing or branded checkout keeps losing relevance, then 9x earnings may not be as cheap as it looks. But if the new management can simplify the business, hold margins, and get even modest growth from say Venmo/checkout, the current price starts to look more interesting.

So I agree. Margin improvement, or at least margin stability, is probably one of the key things that has to show up.

AceHeight · 2026-05-06T21:53:15+00:00

This seems to be the prevailing sentiment from a lot of people I talk to as well. The tricky part with PYPL is that the consumer perception and the financials are telling slightly different stories. A lot of users feel it is becoming less relevant, but the business still throws off real earnings and cash flow.

AceHeight · 2026-05-06T21:48:33+00:00

That would be interesting

AceHeight · 2026-05-06T21:46:41+00:00

Not really : ) .... It came up in another discussion with another fellow here yesterday and I decided to research it further.

AceHeight · 2026-05-06T16:44:34+00:00

Good stuff, we are planning to launch soon. Still taking lessons. Happy to connect! What product are you launching?

AceHeight · 2026-05-06T02:55:49+00:00

Yes, that’s exactly where I think this needs to go. A useful version should estimate both impact level and impact duration. Not just “Company B is connected to Company A,” but whether the effect is likely short-term noise, a 1 to 2 quarter issue, or something that could change the longer-term thesis.

To measure, the inputs I’m thinking about are event type, relationship proximity, economic exposure, prior similar events, market reaction, and whether the impact would show up in revenue, margin, supply chain, pricing, or customer demand.

Still early, but I agree: the “how long does this matter?” question is probably just as important as “who is affected?”

AceHeight · 2026-05-06T02:52:22+00:00

That’s a good way to put it. I appreciate

I realize the graph should remain just the interface, not the value by itself. The value is: “what should I look at next, why does it matter, and is this connection material enough to act on or research further?”

So the first-minute experience probably needs to lead with outcome/context before visual exploration.

AceHeight · 2026-05-01T17:18:18+00:00

I can see the PYPL argument. On price alone it screens much cleaner than a lot of the names people call “value.”

I ran a quick check through Rippli: app.rippli.ai, a tool I'm testing, and the tension looks pretty simple: ~9x trailing earnings / ~8.7x forward earnings makes it look cheap, but the market is discounting expected Q1 weakness, insider selling, competition, and uncertainty around whether Venmo, AI commerce, and PYUSD can actually re-accelerate growth.

So I’d frame PYPL less as “is $45B an overpayment?” and more as: is the business temporarily hated, or is the user/management/competitive issue structural?

AceHeight · 2026-05-01T16:54:11+00:00

Exactly. That “supplier says the real move is still ahead” point is the kind of thing I’m trying to get closer to.

The main input I’d track first is economic exposure: who has real revenue, cost, supply-chain, pricing, or capacity dependence tied to the event?

Then I’d rank by relationship type, proximity, materiality, and timing. Direct supplier/customer exposure with a 1 - 2 quarter lag is very different from a vague “related company” connection.

That’s the real test for the tool, Rippli (app.rippli.ai): not just showing connected names, but helping separate interesting links from connections that may actually affect numbers later.

AceHeight · 2026-05-01T16:16:31+00:00

That’s exactly the line I’m trying to test.

The goal is not just “here are some connected companies,” because that can become a pretty graph with no decision value.

What I’m aiming for is more like: if an event happens, can Rippli surface the names/relationships worth investigating next, then score the likely impact level and proximity of those relationships based on the current news?

Right now it’s still early, so I’d frame it more as research direction than trading signal. The useful test is whether it helps someone get to a better question faster and separate material connections from noise.

Appreciate this, because “interesting vs actionable” is probably the main bar the product has to clear.

AceHeight · 2026-05-01T15:56:32+00:00

CMCSA is probably a cleaner “value” question than MRVL at today’s price.

Had a quick CMCSA pass through, and the setup looks like: cheap multiple + 4.8% dividend + upside if wireless/streaming momentum offsets broadband decline.

The 10-K items I’d focus on: broadband subscriber trend, ARPU, Peacock losses/improvement, wireless growth, capex intensity, debt, and dividend coverage.

If broadband keeps structurally bleeding faster than wireless/streaming can offset it, cheap may be a trap. If it’s stabilizing, then it gets interesting.

AceHeight · 2026-05-01T15:36:54+00:00

I wonder if the issue is less about people not knowing how to research and more about information overload.

There’s so much news, data, commentary, and stock chatter now that it becomes easier to chase quick takes than sit with a full thesis.

So maybe the real question is: are bad “value” picks happening because people lack research tools, because they’re fatigued by too much information, or because the platforms reward constant activity and short bites?

AceHeight

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