Ai and stock market

Stock Market, Pick and Shovel, Artificial Intelligence #Australia

ByLaurel Papworth

A practical framework for using AI as a thinking partner in investment analysis: moving from market trends to hidden dependencies, scenario planning, and testing assumptions.

Most AI investment questions start with “What stock should I buy?” This video explores an alternative approach: using AI to analyse trends, uncover hidden supply chains, explore scenarios, and test assumptions before making decisions.

Directional Signals, Needle Facts, and Option Space: A Framework for Thinking About Markets with AI

Most people approach AI and the stock market in the same way they approach financial news: “What should I buy?” The problem is that both analysts and AI tend to surface the same obvious trends; the headlines everyone is already talking about. When AI answers that question, it usually produces directional signals: the visible momentum of the market. In the AI sector right now, that might mean companies like NVIDIA or Cisco — the names dominating media narratives and investor attention. But markets are shaped by shared stories, and when everyone follows the same narrative, those signals quickly become crowded and self-reinforcing.

A more useful way to work with AI is to treat it as a structured thinking partner rather than a prediction engine. In this video, I demonstrate a four-step framework for exploring investments with AI: starting with directional signals (the obvious trends), moving to needle facts (the overlooked suppliers and dependencies beneath the headlines), then exploring option space through scenario analysis, and finally testing your assumptions. The goal is not to outsource judgement to the model, but to use AI to surface hidden structures, challenge your thinking, and help you understand an industry more deeply before making any decision.

Video of AI and the Stock Market – Direction Signals, Needle Facts and More

Transcript of Beyond the Headlines: Using AI to Explore Investment Structures

DIRECTIONAL SIGNALS FOR STOCK MARKET (00:00:00.20) My name is Laurel Papworth, and I thought I’d go through with you how AI can help and will help in the future with the stock market and investment. I’m going to stick in my stream, which is about AI, and you can apply it, the structured prompt and the scaffolding, to any sector that you’re interested in. And it’s not about investment. This is about how to work with AI as a thinking partner. So a lot of people would ask AI, “Oh, there’s a lot of talk about AI at the moment. What stock should I invest in?” And AI will do what every, I guess, financial paper will do, financial media or any analyst article, anything like that. It will go with the headlines, and it will use what’s called directional signals for trends. What are the trends in investing in AI? And it’s going to come back and say, I don’t know, “NVIDIA. Very popular”. And the thing with the market is that it’s a shared narrative. So as the narrative moves towards NVIDIA, it becomes self-fulfilling. And then once the tide turns and it moves away, same thing. So a directional signal is a trend. Tell me what’s obvious. Tell me what the media is talking about. Tell me what the analysts are talking about that thing. If you don’t frame it within an analysis… If you don’t frame it within analysts and media commentary, then it’s difficult to move on to the next part. Which we’re going to do now.

NEEDLE FACTS FOR STOCK MARKET (00:01:48.12) So the second question that I would ask once it had come up with NVIDIA and maybe Cisco for their quantum computing networking stuff is called needle facts. And if directional signals is, tell me what the bleeding obvious is, the needle facts would be, tell me the stuff I’m not thinking about. Tell me the stuff that isn’t clearly stated but can be inferred. Tell me the stuff that doesn’t really appear except as a footnote in an addendum, like what am I missing? What’s being missed here? And this is where you find the little gems. So in the terms of AI, you might ask for something that’s second or third level. What specific non-visible structural dependencies are there that I could invest in? For example, which glass, ceramics, materials, specialty materials, provide the fabrication material? So it’s not the rare earth minerals, it’s the next level on. It’s the little that goes into a chip, that goes onto a card, that goes into a machine, that goes into a data centre, that becomes AI later on. There’s a company in Japan that makes a glad wrap, a saran wrap, and all of NVIDIA chips use it. So they’re a supplier, a vendor to NVIDIA. So there’s a flow on effect. If NVIDIA is doing well, these guys will do well as well. And in fact, I think they’re fully sold out for the next two years. And they have a waitlist. So does the company that makes the cleaning fluid for the chips. So does another company, Toto, that makes bidets, and they also make something else to do with fabrications. So once you’ve got directional signals, which is the framing of the main shift towards NVIDIA or Cisco or something like that, and then you have needle facts, which are there’s this substrata of an ecosystem of smaller companies that are doing extremely well.

OPTIONS SPACE (00:04:04.22) The next question is, what is the options space for the next three to five years? So we build our story, our narrative, the human narrative on prior information. And so does AI. AI uses tokens and data sets and stuff like that to build its story out. So the options base, which is the third one, is going to be looking at a tree of thoughts style prompt as opposed to chain of thoughts. You’re used to chain of thoughts. That’s tell me step by step how to get from here to here, step by step by step. Tree of thoughts are, give me the options. It’s an “if this, then that” type of scenario where you’re running along the branches saying, what if NVIDIA fails and it’s a bubble? What if utilities become overly regulated? What if this happens? What if that happens? And the AI is brain is big enough to hold all that stuff if you articulate the scaffolding of the prompt correctly. So what are the realistic scenarios that could unfold in the next three to five years? What if FAB construction slows down? What if geopolitical changes around regulations and tariffs and things like that impact these Japanese and Taiwanese companies that are making the bits and bobs that go into American, European, and Australian data centres, and what if there is a continued AI CapEx search or there isn’t. So it knows to start to play with the weighting, and you’re effectively using options-based to create tension because AI doesn’t hold tension. Humans do. So we’re creating a liminal space for the AI to hold tension and then to think through, “Oh, what if this happens?”

TESTING HYPOTHESIS IN STOCK MARKET AI (00:05:55.14) And then the fourth one is testing. I wish the people that pitch stuff to me would put this through, their pitches through the testing mechanism. But what if I’m wrong? What if I’ve got a basic assumption wrong? What if I’m missing something critical that should be in here? What If things change dramatically, what would invalidate my hypothesis that the substrata glass and ceramic companies that support AI will have a boom? What if the AI cycle cools? I’m not saying we head into another winter or bust, but what if for one reason or another, everybody starts to back off, gets a bit of cold feet? It happens. It tends not to happen TOO much in emergent tech. A lot of people talk about it. By the time there is the backing off. It’s so truly ingrained(normalised) that the market has stabilised. But testing your hypothesis and telling the to not support you, but to give you critiques and criticism, be gentle with me, though, is really super useful.

OUTPUT FORMAT (00:07:10.00) I would tell the model to keep each of those four sections separate. The directional signal, which is the headline stuff, the needle facts, which are the little known gems, the option space, which is the “if this, then that” tree of thoughts scenario building, And the fourth one, which is testing. I would say definitely keep them separate. We really want to go from trend noise to structural substrate, to scenario building, to testing our hypotheses. So that we’re not just saying, “I got a bit of pocket money, what should I invest in?” And we’re moving towards a much more risk averse foundational strategy.

AI MENTOR for INVESTING IN STOCK (00:07:59.04) On one last note I just want to remind people that Warren Buffett said he wouldn’t invest in any industry that he didn’t understand fully. I think he said that and airlines, but that if he didn’t understand the industry, he wouldn’t invest. And I understand that. I probably even agree with him. But if you use AI as a thinking partner rather than as a stock prediction oracle, or the Delphic oracle of stock markets, it will help you think through and understand better what you are investing in and what the risk assessments are, what the salience is of the headlines, all of that kind of stuff. Anyway, I hope you found it useful, and I’ll see you in the next video. Thanks. Bye.

This is not investment advice but how to consider thinking about investing with AI as a mentor.

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