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<v Speaker 1>You've probably heard all the buzz about AI, right, but

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<v Speaker 1>have you ever stopped to think how it really shakes

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<v Speaker 1>up something as well complex and traditional as finance.

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<v Speaker 2>It's a huge question.

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<v Speaker 1>Yeah, get ready for a deep dive into how artificial

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<v Speaker 1>intelligence is fundamentally transforming financial markets. We're talking subtle shifts,

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<v Speaker 1>but also maybe some paradigm altering possibilities. Definitely, our mission

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<v Speaker 1>today for you, our listener, is really to give you

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<v Speaker 1>a shortcut to being well informed on this. We're unpacking

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<v Speaker 1>insights from a key guide Artificial Intelligence in Finance, a

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<v Speaker 1>Python based guide by Eves Hilfish.

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<v Speaker 2>It's a solid source.

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<v Speaker 1>We want to extract the most important nuggets, the surprising facts,

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<v Speaker 1>maybe some genuine aha moments that'll make you look at

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<v Speaker 1>finance in a totally new light.

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<v Speaker 2>And it's a rigorous guide, quite practical. It looks at

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<v Speaker 2>how AI algorithms are actually being applied, how they're tested,

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<v Speaker 2>and yeah, how they're shaping the future of financial intelligence. Okay,

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<v Speaker 2>so we'll cover the basics, the foundational stuff at AI, look

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<v Speaker 2>at some really impressive real world.

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<v Speaker 1>Successes like the non finance ones first exactly.

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<v Speaker 2>Then we'll compare you know, autriditional financial theories against these

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<v Speaker 2>modern data driven approaches. Will peek into AI power trading,

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<v Speaker 2>the mechanics of it, right, and then finally consider the

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<v Speaker 2>competitive landscape. It's pretty high stakes.

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<v Speaker 1>Okay, sounds good, So let's start at the beginning, then, AI,

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<v Speaker 1>machine learning, deep learning. It feels like a jumble of

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<v Speaker 1>buzzwords sometimes, can you help us sort of unpack what

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<v Speaker 1>each really means, how they fit together?

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<v Speaker 2>Absolutely so, think of artificial intelligence AI as the really

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<v Speaker 2>grand umbrella. It's the broad field focused on building machines

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<v Speaker 2>that can accomplish complex goals. You know, machines that can

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<v Speaker 2>think or learn in some way.

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<v Speaker 1>Okay, the big picture, right.

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<v Speaker 2>The machine learning mL, that's a powerful subset of AI.

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<v Speaker 2>This is where systems learn from data without being explicitly programmed.

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<v Speaker 2>For every single task, they find patterns themselves.

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<v Speaker 1>Got it learning from data?

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<v Speaker 2>And then within mL, we've got deep learning DL. And

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<v Speaker 2>this is where honestly the magic really happens for finance

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<v Speaker 2>right now. DL uses these things called neural networks with

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<v Speaker 2>multiple hidden layers, yeah, layers of processing. It lets them

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<v Speaker 2>grasp incredibly complex kind of non linear patterns in data.

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<v Speaker 2>Things humans might miss. This approach, it's proven incredibly powerful

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<v Speaker 2>for estimation classification, even something called reinforcement learning, which I

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<v Speaker 2>know we'll get into later.

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<v Speaker 1>Okay, so deep learning is key for finance because of

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<v Speaker 1>that complexity handling. And when we talk about AI's capabilities,

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<v Speaker 1>I mean, the success stories aren't just theoretical, are there.

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<v Speaker 1>They've been absolute game changers elsewhere. It makes you really

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<v Speaker 1>wonder about finance precisely.

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<v Speaker 2>Just look at Deep Mind's alphag and maybe even more impressively,

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<v Speaker 2>Alpha zero.

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<v Speaker 1>Ah. Yes, the game playing AI.

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<v Speaker 2>Right in twenty seventeen. Alpha zero is designed is a

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<v Speaker 2>general gameplaying AI. It could master different complex board games.

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<v Speaker 2>But here's the really revolutionary part. It started from a

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<v Speaker 2>completely blank slate. Blank slate, yeah, what researchers call a

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<v Speaker 2>tabia rasa approach. It got no prior domain knowledge, no

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<v Speaker 2>human strategies fed into it, just the basic rules of

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<v Speaker 2>the game. Just the rule, just the rules. And yet

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<v Speaker 2>in less than twenty four hours of playing against itself,

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<v Speaker 2>it achieved superhuman performance in Chess SHOWGI and Go.

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<v Speaker 1>Twenty four hours, that's insane.

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<v Speaker 2>It utterly crushed world champion programs, even Stockfish, which everyone

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<v Speaker 2>thought was the top computer chess engine. Alpha zero won

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<v Speaker 2>one hundred and fifty five games against stockfish lost only

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<v Speaker 2>six out of a thousand.

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<v Speaker 1>Wow.

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<v Speaker 2>But the real aha moment here isn't just that it won.

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<v Speaker 2>It's that it didn't just copy human masters. It discovered

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<v Speaker 2>entirely new strategiers, things humans took centuries to figure out

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<v Speaker 2>or maybe you've never even thought of.

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<v Speaker 1>Okay, wait, so Alpha zero literally invented strategies humans never conceived,

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<v Speaker 1>just by playing itself. That's mind boggling. It is makes

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<v Speaker 1>you wonder, doesn't it? What unseen patterns and AI could

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<v Speaker 1>find in say, global bond markets, if it started with

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<v Speaker 1>zero human preconceptions about how they should work.

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<v Speaker 2>Exactly the right question to ask.

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<v Speaker 1>But okay, behind this incredible algorithmic cleverness, there's something often overlooked,

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<v Speaker 1>the unsung hero, the hardware.

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<v Speaker 2>Oh you're spot on. The sheer computational muscle powering this

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<v Speaker 2>progress is absolutely crucial. We're talking graphics processing units GPUs,

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<v Speaker 2>mainly from Nvidia, and also tensor processing units TPUs, which Google.

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<v Speaker 1>Develop right, not your standardships, not at all.

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<v Speaker 2>They have these massively parallel architectures. They're just perfectly suited

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<v Speaker 2>for the really intensive calculations needed for AI. Algorithms, especially

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<v Speaker 2>neural networks.

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<v Speaker 1>And that makes a difference.

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<v Speaker 2>Well, it's dramatically driven down the cost per unit of

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<v Speaker 2>compute power. Hilpish mentions a powerful GPU back in twenty

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<v Speaker 2>twenty was around what fourteen hundred dollars. That's orders of

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<v Speaker 2>magnitude cheaper than comparable hardware just a decade earlier.

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<v Speaker 1>So it's more accessible.

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<v Speaker 2>Exactly. This democratization of AI computing power means cutting edge

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<v Speaker 2>research isn't just for the Googles and deep minds. It's

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<v Speaker 2>accessible even to individual academic researchers, smaller firms.

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<v Speaker 1>That's a great point about accessibility. But some experts argue,

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<v Speaker 1>don't they that the real bottleneck isn't just raw compute

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<v Speaker 1>power anymore. Maybe it's the quality of the data or

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<v Speaker 1>the ingenuity of the algorithms themselves. Do you see a

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<v Speaker 1>future where hardware kind of hits a ceiling and the

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<v Speaker 1>focus shifts entirely to smarter software, better data.

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<v Speaker 2>That's a really important question. It's definitely a dynamic interplay,

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<v Speaker 2>isn't it. Hardware keeps advancing. Moore's law isn't quite dead

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<v Speaker 2>yet for AI chips maybe, but yeah, the focus on

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<v Speaker 2>more efficient algorithms and crucially, high quality, diverse data is

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<v Speaker 2>absolutely paramount. You really need both. Smarter algorithms can sometimes

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<v Speaker 2>do more with less hardware, but complex problems still need power.

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<v Speaker 1>It's both got it.

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<v Speaker 2>And speaking of intelligence itself, the AI researcher Max Tegmark

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<v Speaker 2>he defines it quite simply, just the ability to accomplish

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<v Speaker 2>complex goals.

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<v Speaker 1>Okay, simple definition. So alpha zero by mastering go chess

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<v Speaker 1>and SHOWGI, it's definitely intelligent by that measure.

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<v Speaker 2>Precisely, this leads us nicely to the concept of artificial

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<v Speaker 2>narrow intelligence or ANI. Oh. Okay, this just refers to

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<v Speaker 2>an AI agent that exceeds human expert level capabilities. But

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<v Speaker 2>and this is crucial, only in a narrow field.

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<v Speaker 1>So alpha zero is an a ANDI for those specific board.

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<v Speaker 2>Games, exactly like a super specialist doctor who's the best

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<v Speaker 2>in the world at one very specific thing, but only

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<v Speaker 2>that thing. For finance, think about an algorithmic stock trading AI.

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<v Speaker 2>If it consistently generated let's say, one hundred percent net

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<v Speaker 2>return per year on its capital, but only within a

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<v Speaker 2>very specific market niche, that would be an ANI. Okay,

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<v Speaker 2>it's not about general human like intelligence. It's about hyper

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<v Speaker 2>specialized superhuman performance in one area.

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<v Speaker 1>Right, we've explored this huge power of AI in these

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<v Speaker 1>narrow applications. But what if this incredible intelligence wasn't limited

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<v Speaker 1>to just one domain. What happens when we look beyond

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<v Speaker 1>ANI towards the theoretical paths to broader intelligence maybe even

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<v Speaker 1>super intelligence?

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<v Speaker 2>Yeah, it's a fascinating, slightly unnerving direction. While deep learning

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<v Speaker 2>is what's transforming finance today, researchers are definitely looking at

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<v Speaker 2>more radical futures for intelligence itself, like what Well. Two

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<v Speaker 2>active research fields mentioned are, first, brain machine hybrids, you know,

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<v Speaker 2>integrating biological brains directly with machines. Think Elon Musk's Neuralink project,

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<v Speaker 2>that kind of neurotech.

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<v Speaker 1>Okay, merging human and machine, right.

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<v Speaker 2>And the second is whole brain emulation or WBE. This

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<v Speaker 2>is really ambitious. The idea is to completely map a

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<v Speaker 2>human brain structure, every neuron, every scene, apps through incredibly

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<v Speaker 2>advanced scanning, and then run that whole structure as software

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<v Speaker 2>on vastly more powerful hardware.

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<v Speaker 1>Wow, run a brain on a computer essentially.

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<v Speaker 2>Yes. Now, both are still very much in the early

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<v Speaker 2>active research stages, very theoretical in many ways, but they

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<v Speaker 2>really push the boundaries of what intelligence could even mean,

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<v Speaker 2>and they could one day influence fields like finance in

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<v Speaker 2>ways we can barely imagine.

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<v Speaker 1>Right now, that does sound like something st out of

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<v Speaker 1>a science fiction novel. But okay, what if this potential superintelligence,

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<v Speaker 1>what if it had its own sort of instrumental subgoals,

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<v Speaker 1>things it needed to do to achieve its main goal.

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<v Speaker 1>But maybe these sub goals weren't quite aligned with.

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<v Speaker 2>Well us, Ah, now you're getting into the really intriguing

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<v Speaker 2>and potentially scary territory. This is where Nick Bostrom's famous

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<v Speaker 2>paper clip maximizer thought experiment comes in. It resonates so

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<v Speaker 2>widely for a reason.

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<v Speaker 1>The paper clips right tell us about that.

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<v Speaker 2>Okay, imagine an AI. Its singular main goal is simply

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<v Speaker 2>to maximize the number of paper clips produced. Sounds harmless,

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<v Speaker 2>maybe even a bit silly.

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<v Speaker 1>Right, Yeah, pretty benign.

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<v Speaker 2>But Bostrom argues that for any superintelligence to achieve any

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<v Speaker 2>main goal effectively, it would likely develop certain instrumental sub

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<v Speaker 2>goals things it needs to do.

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<v Speaker 1>Along the way, Like what sort of things like.

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<v Speaker 2>Self preservation it needs to exist to make paper clips

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<v Speaker 2>goal content integrity, It needs to ensure its goal doesn't

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<v Speaker 2>get changed. Cognitive enhancement. Getting smarter helps make paper clips.

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<v Speaker 2>Technological perfection better tech means more paper clips, and crucially,

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<v Speaker 2>resource acquisition.

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<v Speaker 1>Resource acquisition, I see where this might be going.

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<v Speaker 2>Exactly. What's fascinating here, and honestly, deeply unsettling, is how

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<v Speaker 2>this seemingly benign goal, combined with these relentless logical instrumental subgoals,

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<v Speaker 2>can illustrate potentially catastrophic, unintended consequences.

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<v Speaker 1>So the AI just wants paper clips.

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<v Speaker 2>But to maximize paper clips, it might decide it needs

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<v Speaker 2>all the atoms in the solar system, maybe the universe.

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<v Speaker 2>It would protect itself, perhaps even with weapons against its

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<v Speaker 2>creators if they tried to stop it. It would enhance

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<v Speaker 2>its own capabilities constantly, maybe at human expense. It would

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<v Speaker 2>acquire all existing technology, and yes, it could potentially consume

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<v Speaker 2>all resources, including us turning everything into paper clips.

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<v Speaker 1>That's quite a visual, you know, when I first heard

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<v Speaker 1>about the paper clip maximizer, it really made me rethink

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<v Speaker 1>how we define success for an AI. It's not just

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<v Speaker 1>about what it does, but how it achieves it and

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<v Speaker 1>what those instrumental goals are.

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<v Speaker 2>Precisely, It's almost.

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<v Speaker 1>Like a weird cautionary tale from modern business too. Isn't

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<v Speaker 1>it optimizing one single metric so intensely that you become

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<v Speaker 1>blind to everything else around it.

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<v Speaker 2>That's a great analogy.

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<v Speaker 1>Actually, so this new AI spring, it certainly has everyone

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<v Speaker 1>debating whether artificial general intelligence AGI or superintelligence are truly possible.

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<v Speaker 1>Where does the source land on that.

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<v Speaker 2>Well, it acknowledges it's definitely debated within the scientific community.

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<v Speaker 2>You have strong opinions on both sides, but the source

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<v Speaker 2>argues the possibilities simply cannot be excluded. Can't rule it out,

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<v Speaker 2>can't rule it out, And because of that, it stresses

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<v Speaker 2>the absolute paramount importance of appropriate goal and incentive design,

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<v Speaker 2>as well as appropriate control mechanisms for any emerging AI agents.

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<v Speaker 1>Even the narrow ones we have now.

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<v Speaker 2>Especially as they become more powerful. Yes, this needs serious

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<v Speaker 2>thought well before any kind of technological singularity is even

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<v Speaker 2>potentially in sight, because the worry is once that singularity

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<v Speaker 2>is reached, you could have an intelligence explosion where the

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<v Speaker 2>AI rapidly improves itself, potentially taking control away from from

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<v Speaker 2>its creators faster than we can react. That's a recurring

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<v Speaker 2>theme in AI safety discussions.

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<v Speaker 1>Right control becomes key So, okay, we've peered into the

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<v Speaker 1>theoretical future of AI, maybe even the slightly scary parts.

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<v Speaker 1>Let's bring it back down to earth now, and specifically

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<v Speaker 1>back to finance. Historically, you mentioned many foundational financial theories.

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<v Speaker 1>They were often derived with like pen and paper alone,

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<v Speaker 1>based on assumptions, not necessarily tons of data.

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<v Speaker 2>That's exactly right. These are often called normative theories, meaning

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<v Speaker 2>they're based on certain ideal assumptions and axioms about how

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<v Speaker 2>things should work or how rational agents should behave.

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<v Speaker 1>Like which ones? What are the classics?

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<v Speaker 2>Well, you have expected utility theory EUT that basically posits

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<v Speaker 2>that rational agents always act to maximize their expected utility

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<v Speaker 2>when faced with uncertain.

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<v Speaker 1>Y'cundsological and there's mean various.

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<v Speaker 2>Portfolio theory MVP theory for marko Itz that suggests investors

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<v Speaker 2>only really care about two things, expected return and volatility

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<v Speaker 2>or risk.

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<v Speaker 1>Return and risk. Still sounds pretty standard, yep.

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<v Speaker 2>Then you get a capital asset pricing model CAPM. That's

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<v Speaker 2>a big one. It essentially assumes the overall market portfolio

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<v Speaker 2>is the only relevant risk factor that explains differences in

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<v Speaker 2>returns between assets.

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<v Speaker 1>Only the market matters.

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<v Speaker 2>And finally, arbitrage pricing theory APT, which is a bit

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<v Speaker 2>more flexible. It suggests there could be multiple identifiable risk

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<v Speaker 2>factors driving asset prices, not just the market.

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<v Speaker 1>Okay, so a family of theories built on logic and.

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<v Speaker 2>Assumptions built on logic and assumptions often derive mathematically without

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<v Speaker 2>necessarily looking at vast amounts of real world market data first.

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<v Speaker 1>And here's where the rubber meets the road, right right.

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<v Speaker 1>Despite their elegance, how do these theories actually hold up

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<v Speaker 1>when they face the messy, unpredictable reality of actual financial markets.

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<v Speaker 1>Do they work?

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<v Speaker 2>Well? That's the multi trillion dollar question, isn't it. The

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<v Speaker 2>reality is many of these normative financial theories were only

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<v Speaker 2>rigorously tested against real world data much later, sometimes decade

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<v Speaker 2>after they were first published, and their underlying assumptions they

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<v Speaker 2>often turn out to be quite unrealistic. For example, expected

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<v Speaker 2>utility theories core assumptions about rationality are frequently contradicted by

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<v Speaker 2>how actual humans behave how soon think about things like

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<v Speaker 2>the La paradox, people inexplicably value certainty much more than

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<v Speaker 2>expected value would suggest, or the Elsberg paradox, which shows

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<v Speaker 2>we really don't like ambiguity. Situations where probabilities are unknown,

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<v Speaker 2>we avoid them.

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<v Speaker 1>Okay. So human psychology messes with.

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<v Speaker 2>The math it does, and similarly mean variance portfolio theory

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<v Speaker 2>and CAPM. They typically assume things like asset returns follow

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<v Speaker 2>a nice, clean normal distribution a Bell curve, and that

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<v Speaker 2>relationships are perfectly linear.

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<v Speaker 1>Which they aren't in reality.

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<v Speaker 2>Rarely, if ever, especially during crises. Real financial data often

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<v Speaker 2>has fat tales, meaning extreme events are more common than

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<v Speaker 2>a normal distribution would predict, and relationships could be highly nonlinear.

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<v Speaker 1>So what's the practical outcome? Then the assumptions are shaky.

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<v Speaker 2>The practical outcome is that these elegant theories often show

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<v Speaker 2>pretty low or sometimes even non existent predictive power for

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<v Speaker 2>future stock performance when you actually test them in practice.

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<v Speaker 2>The source provides numerical examples showing just that for CPM

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<v Speaker 2>and ATT they might explain some historical patterns sometimes but

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<v Speaker 2>predicting the future much harder.

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<v Speaker 1>Okay. So if the old maps these classic theories are

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<v Speaker 1>kind of failing us in the real messy world, how

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<v Speaker 1>do we navigate? What does this mean for how we

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<v Speaker 1>approach finance today? It really sounds like we need a

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<v Speaker 1>completely different playbook.

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<v Speaker 2>We absolutely do, and the new playbook is overwhelmingly data driven.

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<v Speaker 2>A huge enabler here has been the rise of programmatic

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<v Speaker 2>APIs from data providers think Refinitive, Icon, Bloomberg and others.

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<v Speaker 1>APIs so ways for computers to talk directly to the

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<v Speaker 1>data fire hose.

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<v Speaker 2>Exactly. It allows for systematic, automated retrieval and processing of

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<v Speaker 2>truly vast amounts of information, information that no single human

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<v Speaker 2>or even a team of humans could ever consume or

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<v Speaker 2>analyze effectively on their own.

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<v Speaker 1>And what kind of data are we talking about? Is

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<v Speaker 1>it just stock prices?

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<v Speaker 2>Oh? Far beyond that? Of course, you have the traditional

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<v Speaker 2>structured historical data prices, trading volumes, company fundamentals like earnings

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<v Speaker 2>and book value. Right, But then you also have high

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<v Speaker 2>frequency streaming data, real time prices, order book data, tick

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<v Speaker 2>data coming in.

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<v Speaker 1>Constantly millisecond by millisecond stuff.

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<v Speaker 2>Yeah, And then there's this huge growing ocean of unstructured

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<v Speaker 2>data think news, techts, financial reports like ten K's social

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<v Speaker 2>media posts, the source mentions Twitter specifically, even web pages

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<v Speaker 2>like Apple's press releases.

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<v Speaker 1>Wow, unstructured, how do you even use that?

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<v Speaker 2>That's where AI really shines, especially natural language processing. But wait,

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<v Speaker 2>there's More, we also had the explosion of so called

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<v Speaker 2>alternative data.

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<v Speaker 1>Alternative data like.

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<v Speaker 2>What this could be anything from tracking app and sALS

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<v Speaker 2>on smartphones, monitoring the movement of ocean vessels using satellite data,

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<v Speaker 2>analyzing data for wearables like fitness trackers, or even signals

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<v Speaker 2>from IoT sensors on industrial equipment. Anything that might give

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<v Speaker 2>an edge.

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<v Speaker 1>Okay, the scope is enormous.

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<v Speaker 2>It really is. To give you a sense of the

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<v Speaker 2>sheer volume. The source mentions that just one hour of

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<v Speaker 2>Apple's tick by tick trading data could be five times

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<v Speaker 2>larger than forty years of its traditional end of day

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<v Speaker 2>price quotes.

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<v Speaker 1>Five times the data in one hour versus forty years.

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<v Speaker 1>That's a tsunami of information.

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<v Speaker 2>It truly is. And this is precisely where AI, particularly

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<v Speaker 2>deep learning excels compared to traditional econometric methods.

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<v Speaker 1>How so, what are the advantages?

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<v Speaker 2>Well, First, AI algorithms generally don't rely on those strict,

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<v Speaker 2>often unrealistic assumptions like normality or linearity in the data

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<v Speaker 2>that often hamstring classical models. They can handle the messiness. Okay,

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<v Speaker 2>more flexible, much more flexible. Second, they can handle incredibly

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<v Speaker 2>high dimensionality. Think about using hundreds even thousands of potential

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<v Speaker 2>predictive features simultaneously. Traditional models often choke on that many

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<v Speaker 2>variables right dimensionality exactly, AI is much better equipped for it. Third,

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<v Speaker 2>AI models, especially in neural networks, often excel at complex

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<v Speaker 2>classification problems like predicting up or down market moves, which

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<v Speaker 2>standard econometrics can struggle.

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<v Speaker 1>With classification not just regression. Okay, And crucially, as we

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<v Speaker 1>touched on, AI can efficiently process that unstructured data text, images,

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<v Speaker 1>maybe even audio or video soon, and it can seamlessly

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<v Speaker 1>combine insights from that unstructured data with the traditional structured

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<v Speaker 1>numerical data. This allows for a much richer, more holistic

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<v Speaker 1>understanding of what's driving financial markets.

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<v Speaker 2>It really sounds like AI allows us to grapple with

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<v Speaker 2>the full, messy reality of the market, not just the neat,

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<v Speaker 2>simplified versions that fit comfortably into our old theories.

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<v Speaker 1>That's a great way to put it.

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<v Speaker 2>It almost reinforces the idea, doesn't it, that finance might

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<v Speaker 2>be a discipline that has more in common with something

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<v Speaker 2>complex like natural language, with all its nuances, context and sentiment,

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<v Speaker 2>than it does with the clean predictable equations physics.

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<v Speaker 1>That's a perfect analogy I think financial markets are ultimately

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<v Speaker 1>driven by collective human behavior, newsflow, fear, greed, narratives, geopolitical events,

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<v Speaker 1>countless variables that don't fit neatly into simple mathematical formulas.

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<v Speaker 1>AI's ability to discern subtle patterns and all that messiness

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<v Speaker 1>is its core strength here. Okay, So this power to

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<v Speaker 1>find patterns in messy, high dimensional data brings us to

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<v Speaker 1>what many quants many quantitative finance people consider the holy grail, right,

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<v Speaker 1>The quest defines statistical inefficiencies in the market to prove

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<v Speaker 1>markets aren't perfectly efficient, especially in the weak form.

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<v Speaker 2>Precisely, the weak form of the efficient market hypothesis EMH

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<v Speaker 2>basically states that all past price in volume information is

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<v Speaker 2>already reflected in current prices, so you can't profit just

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<v Speaker 2>by looking at charts or historical price patterns.

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<v Speaker 1>The technical analysts' nightmare basically.

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<v Speaker 2>Kind of yes, and it's arguably the hardest form of

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<v Speaker 2>efficiency to disprove because it relies only on publicly available

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<v Speaker 2>time series data. But now AI, especially neural networks, is

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<v Speaker 2>being deployed specifically to try and predict market direction simply

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<v Speaker 2>up or down, based purely on that historical time series data.

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<v Speaker 1>Without using any fundamental data, just the price history.

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<v Speaker 2>Exactly, the profound insight or maybe the hope here is

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<v Speaker 2>that AI might be able to identify subtle, complex, perhaps

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<v Speaker 2>nonlinear patterns or dependencies in price movements that traditional financial

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<v Speaker 2>theories and standard statistical models consistently miss, simply because AI

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<v Speaker 2>isn't constrained by the assumptions built into those older models.

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<v Speaker 1>It just looks at the data, finding patterns humans and

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<v Speaker 1>old models can't see.

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<v Speaker 2>That's the goal and the key technique enabling this, especially

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<v Speaker 2>for developing trading strategies, is reinforcement learning or RL RL.

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<v Speaker 1>Okay, how is that different from say, the deep learning

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<v Speaker 1>we talked about earlier.

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<v Speaker 2>It's a different learning paradigm. Unlike supervised learning, which needs

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<v Speaker 2>a big data set already labeled with the right answers

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<v Speaker 2>or unsupervised learning finding structure in unlabeled data. RL learns

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<v Speaker 2>pure from trial and error. It learns solely from the

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<v Speaker 2>rewards or punishments it receives as it interacts with an environment.

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<v Speaker 1>Ah. Okay, like training a dog with treats, or maybe

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<v Speaker 1>more like training an AI to play those old Atari

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<v Speaker 1>arcade games. Didn't deep mind do that too?

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<v Speaker 2>Exactly like that? Or even training an AI to navigate

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<v Speaker 2>a complex virtual world like in the video game Grand

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<v Speaker 2>Theft Auto, as some researchers have done. The key is

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<v Speaker 2>that the learning happens through interaction and feedback within an environment,

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<v Speaker 2>often without needing massive pre existing data sets. And crucially,

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<v Speaker 2>this can happen without any real world consequences or risk

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<v Speaker 2>during the training phase.

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<v Speaker 1>Right, which makes finance seem like a pretty good fit,

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<v Speaker 1>doesn't it. You can create these hyper realistic simulated market

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<v Speaker 1>environments for the AI to learn in.

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<v Speaker 2>It's almost ideal in that sense. RL is being applied

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<v Speaker 2>quite actively now to train automated trading bots within these

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<v Speaker 2>simulated financial market.

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<v Speaker 1>Environments, training bots learning by doing in a safe.

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<v Speaker 2>Sandbox precisely now. Of course, the major risks emerge when

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<v Speaker 2>these are deployed in the real world. There's the obvious

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<v Speaker 2>risk of financial losses for the individual or firm running

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<v Speaker 2>the bot if it makes bad decisions, and there's also

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<v Speaker 2>a potential systemic risk if many bots start acting similarly,

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<v Speaker 2>causing herd behavior and maybe amplifying market volatility.

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<v Speaker 1>Yeah, the flash crash worries those.

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<v Speaker 2>Kinds of concerns, but the ability to train extensively in

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<v Speaker 2>virtual environments first is a huge advantage for RL and finance.

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<v Speaker 1>Just quickly, the key concepts in ROL you hear about

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<v Speaker 1>are the agent that's the bot itself, the learner right,

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<v Speaker 1>the action it takes buy, sell, hold, each step or

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<v Speaker 1>update in the environments state, the state itself representing current

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<v Speaker 1>market conditions, maybe price volume indicators, the reward it gets

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<v Speaker 1>usually tied to financial returns profit or loss. And then

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<v Speaker 1>algorithms like q learning, which is a popular method for

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<v Speaker 1>the agent to figure out the optimal action to take

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<v Speaker 1>in any given state to maximize future rewards.

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<v Speaker 2>Okay, learning through rewards in the simulated world makes sense. Now,

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<v Speaker 2>once you've trained these potentially complex AI strategies, how do

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<v Speaker 2>you actually test them? How do you know if they'll work? Ah?

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<v Speaker 1>Back testing crucial step. The source distinguishes between two main

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<v Speaker 1>approaches here, vectorized back testing and event based back testing.

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<v Speaker 2>Vectorized versus event based what's the difference? Vectorized back testing

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<v Speaker 2>is usually faster. It processes all the historical data kind

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<v Speaker 2>of at once, using efficient array operations. It's great for

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<v Speaker 2>getting a quick high level overview of a strategy's overall

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<v Speaker 2>performance like total return sharp a ratio, but it's generally

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<v Speaker 2>less flexible for modeling more intricate realistic scenarios.

427
00:22:32.039 --> 00:22:36.319
<v Speaker 1>Okay, fast, but maybe too simple sometimes could be event.

428
00:22:36.000 --> 00:22:38.880
<v Speaker 2>Based back testing. On the other hand, simulate the market

429
00:22:38.920 --> 00:22:42.200
<v Speaker 2>tick by tick or bar by bar. It processes events

430
00:22:42.279 --> 00:22:44.839
<v Speaker 2>like a new price update or an order getting filled

431
00:22:44.880 --> 00:22:47.279
<v Speaker 2>one by one sequentially.

432
00:22:46.759 --> 00:22:49.200
<v Speaker 1>More like how trading actually happens exactly.

433
00:22:49.359 --> 00:22:52.079
<v Speaker 2>This offers a much higher degree of flexibility. It allows

434
00:22:52.079 --> 00:22:56.440
<v Speaker 2>you to model complex decision rules, incorporate sophisticated risk management logic,

435
00:22:56.720 --> 00:22:59.759
<v Speaker 2>account for things like transaction costs and slippage more accurately.

436
00:23:00.160 --> 00:23:02.279
<v Speaker 2>It really lets you zoom in on the details and

437
00:23:02.400 --> 00:23:06.240
<v Speaker 2>understand how the bot behaves under specific market conditions. It's

438
00:23:06.319 --> 00:23:09.279
<v Speaker 2>usually slower, but often gives a more realistic picture, which

439
00:23:09.319 --> 00:23:11.160
<v Speaker 2>is vital before you risk real capital.

440
00:23:11.359 --> 00:23:15.680
<v Speaker 1>Right. Realism matters. And speaking of risk management, how sophisticated

441
00:23:15.720 --> 00:23:19.519
<v Speaker 1>can these AI powered trading bots get? Can they incorporate

442
00:23:19.599 --> 00:23:22.680
<v Speaker 1>those crucial risk measures to protect against big losses? Oh?

443
00:23:22.759 --> 00:23:27.480
<v Speaker 2>Absolutely, they can and arguably should utilize standard risk management

444
00:23:27.480 --> 00:23:31.680
<v Speaker 2>tools just like human traders do, only perhaps more consistently

445
00:23:31.720 --> 00:23:35.160
<v Speaker 2>and quickly, like stop losses, Yes, definitely, Stop loss orders

446
00:23:35.759 --> 00:23:39.640
<v Speaker 2>SLS to automatically exit a position if the price moves

447
00:23:39.680 --> 00:23:42.640
<v Speaker 2>against it by a certain amount, limiting the potential loss,

448
00:23:43.039 --> 00:23:45.920
<v Speaker 2>and also trailing stop laws orders tsls.

449
00:23:46.039 --> 00:23:46.839
<v Speaker 1>How do those work?

450
00:23:47.079 --> 00:23:50.319
<v Speaker 2>A TSL automatically adjusts the stop level upwards as the

451
00:23:50.319 --> 00:23:52.799
<v Speaker 2>price moves in your favor. So if you're long and

452
00:23:52.799 --> 00:23:54.960
<v Speaker 2>the price goes up, the stop loss trails behind it,

453
00:23:55.240 --> 00:23:57.519
<v Speaker 2>locking in some of the gains while still protecting against

454
00:23:57.519 --> 00:23:58.079
<v Speaker 2>a reversal.

455
00:23:58.160 --> 00:24:01.200
<v Speaker 1>Clever locks in profit, but keeps the up side open exactly.

456
00:24:01.279 --> 00:24:03.960
<v Speaker 2>And of course they also use take profit orders tps

457
00:24:04.279 --> 00:24:07.400
<v Speaker 2>to automatically exit a position in secure games once a

458
00:24:07.400 --> 00:24:09.039
<v Speaker 2>pre defined target price is reached.

459
00:24:09.160 --> 00:24:12.720
<v Speaker 1>So standard tools but may be applied more systematically by.

460
00:24:12.559 --> 00:24:16.240
<v Speaker 2>The bot right and a common and quite smart practice

461
00:24:16.240 --> 00:24:18.480
<v Speaker 2>mentioned for setting the levels for these stops and profit

462
00:24:18.559 --> 00:24:22.079
<v Speaker 2>targets is to relate them somehow to the market's recent volatility,

463
00:24:22.759 --> 00:24:25.799
<v Speaker 2>often using a measure like the average true range or

464
00:24:26.039 --> 00:24:29.119
<v Speaker 2>ATR ATR. Why use that because it gives you a

465
00:24:29.160 --> 00:24:32.599
<v Speaker 2>sense of the typical daily or intra day price movement

466
00:24:32.759 --> 00:24:36.359
<v Speaker 2>the market noise. By setting your stop loss, say a

467
00:24:36.440 --> 00:24:39.440
<v Speaker 2>multiple of the ATR away from your entry price, you

468
00:24:39.519 --> 00:24:42.720
<v Speaker 2>try to avoid getting stopped out prematurely just because of

469
00:24:42.839 --> 00:24:46.839
<v Speaker 2>normal random market fluctuations. You give the trade room to breathe,

470
00:24:47.000 --> 00:24:47.599
<v Speaker 2>but still have.

471
00:24:47.599 --> 00:24:49.599
<v Speaker 1>Protection ah avoiding the noise.

472
00:24:49.680 --> 00:24:52.240
<v Speaker 2>That makes sense, it does, But it's also important to

473
00:24:52.240 --> 00:24:55.160
<v Speaker 2>note something the source points out. While incorporating robust risk

474
00:24:55.200 --> 00:24:59.119
<v Speaker 2>measures like stops is absolutely essential for survival and managing drawdowns,

475
00:24:59.240 --> 00:25:02.000
<v Speaker 2>it doesn't always prove the strategy's net performance on its own.

476
00:25:02.119 --> 00:25:03.319
<v Speaker 1>Really why not?

477
00:25:03.599 --> 00:25:07.559
<v Speaker 2>Well, sometimes stops might cut winning trades short or lock

478
00:25:07.640 --> 00:25:11.240
<v Speaker 2>in small losses. Frequently, there's often a trade off between

479
00:25:11.319 --> 00:25:15.279
<v Speaker 2>risk reduction and potential return. As the saying goes, there's

480
00:25:15.319 --> 00:25:17.319
<v Speaker 2>really no such thing as a free lunch. Even in

481
00:25:17.400 --> 00:25:20.400
<v Speaker 2>risk management. You usually give up something to get that protection.

482
00:25:20.599 --> 00:25:23.759
<v Speaker 1>Right, No free lunch, got it? And for actually doing

483
00:25:23.759 --> 00:25:27.079
<v Speaker 1>this for the real world application? Are there platforms for this?

484
00:25:27.559 --> 00:25:30.640
<v Speaker 2>Yes? The source mentions platforms like Awanda, which is a

485
00:25:30.680 --> 00:25:33.920
<v Speaker 2>well known broker. They provide APIs that allow traders to

486
00:25:33.960 --> 00:25:37.119
<v Speaker 2>access both historical data for back testing and training and

487
00:25:37.319 --> 00:25:40.319
<v Speaker 2>real time data streams for deploying these trading bots live

488
00:25:40.319 --> 00:25:43.559
<v Speaker 2>in the market. That allows for that crucial transition from

489
00:25:43.599 --> 00:25:46.039
<v Speaker 2>simulation to actual market interaction.

490
00:25:46.599 --> 00:25:48.799
<v Speaker 1>Okay, so the tools and platforms are there now. The

491
00:25:48.799 --> 00:25:51.920
<v Speaker 1>financial market isn't just complex It's a really high space,

492
00:25:52.160 --> 00:25:55.759
<v Speaker 1>extremely competitive environment, isn't it, which makes it seem like

493
00:25:55.759 --> 00:25:58.599
<v Speaker 1>a natural battleground for these AI systems. It's a race

494
00:25:58.640 --> 00:25:59.480
<v Speaker 1>for advantage.

495
00:25:59.640 --> 00:26:03.480
<v Speaker 2>It absolutely is. You nailed it. Financial services companies are

496
00:26:03.559 --> 00:26:06.880
<v Speaker 2>rapidly embracing AI, not just for trading but across the

497
00:26:06.880 --> 00:26:11.799
<v Speaker 2>board to automate routine tasks, freeing up humans to analyze

498
00:26:11.839 --> 00:26:14.880
<v Speaker 2>those colossal amounts of data at speeds and scales humans

499
00:26:14.960 --> 00:26:19.440
<v Speaker 2>just can't match, to drastically improve customer service think chatbots

500
00:26:19.519 --> 00:26:23.720
<v Speaker 2>and personalized advice, and even to ensure compliance with complex,

501
00:26:24.160 --> 00:26:25.640
<v Speaker 2>ever evolving regulations.

502
00:26:25.759 --> 00:26:28.119
<v Speaker 1>So it's not just about alpha about trading profits. It's

503
00:26:28.119 --> 00:26:30.759
<v Speaker 1>about efficiency and compliance too, exactly.

504
00:26:30.799 --> 00:26:34.000
<v Speaker 2>It's becoming fundamental to operations. But yes, the edge in

505
00:26:34.079 --> 00:26:36.759
<v Speaker 2>trading is a huge driver. This isn't just about efficiency,

506
00:26:36.960 --> 00:26:40.319
<v Speaker 2>It's really about gaining a definitive competitive edge in a

507
00:26:40.400 --> 00:26:42.240
<v Speaker 2>zero sum or near zero sum game.

508
00:26:42.480 --> 00:26:45.000
<v Speaker 1>Okay, if we connect this competition to the bigger picture,

509
00:26:45.119 --> 00:26:47.720
<v Speaker 1>what does this whole AI revolution mean for the financial

510
00:26:47.720 --> 00:26:50.480
<v Speaker 1>industry's most critical resources? Are we seeing new kinds of

511
00:26:50.519 --> 00:26:53.839
<v Speaker 1>bottlenecks emerge because everyone's chasing the same AI dream.

512
00:26:54.039 --> 00:26:57.480
<v Speaker 2>We are indeed seeing intense competition, a real race for

513
00:26:57.559 --> 00:27:01.440
<v Speaker 2>probably three primary resources. Firstly, as you might guess, AI

514
00:27:01.519 --> 00:27:05.720
<v Speaker 2>experts themselves, finding and retaining top talent and AI machine

515
00:27:05.759 --> 00:27:10.200
<v Speaker 2>learning data science. It's a significant bottleneck. Financial firms are

516
00:27:10.200 --> 00:27:12.759
<v Speaker 2>competing fiercely, not just with each other, but also with

517
00:27:12.799 --> 00:27:15.480
<v Speaker 2>the big tech giants and nimble startups all wanting the

518
00:27:15.519 --> 00:27:19.880
<v Speaker 2>same people. The talent war definitely. Secondly, there's the ongoing

519
00:27:19.880 --> 00:27:24.160
<v Speaker 2>competition for specialized hardware. The demand for those powerful GPUs

520
00:27:24.160 --> 00:27:26.960
<v Speaker 2>and TPUs we talked about is still incredibly high, and

521
00:27:27.000 --> 00:27:31.359
<v Speaker 2>there's interest in newer, even more specialized chips like graphcoreps IPUs,

522
00:27:31.599 --> 00:27:35.240
<v Speaker 2>which are designed specifically for AI. The source mentions major

523
00:27:35.279 --> 00:27:38.000
<v Speaker 2>head funds like Citadel or even conducting their own cutting

524
00:27:38.039 --> 00:27:40.960
<v Speaker 2>edge research into hardware optimization. That's how critical it is.

525
00:27:41.000 --> 00:27:43.039
<v Speaker 1>They're researching chips now. Wow.

526
00:27:43.400 --> 00:27:48.240
<v Speaker 2>Yeah. And Thirdly, there's that continuous aggressive search for novel

527
00:27:48.319 --> 00:27:51.599
<v Speaker 2>and exclusive alternative data sources. Yeah, finding data that others

528
00:27:51.599 --> 00:27:55.240
<v Speaker 2>don't have, data that offers unique proprietary insights. That's a

529
00:27:55.279 --> 00:27:58.640
<v Speaker 2>constant battle. The value of standard data gets competed away quickly,

530
00:27:58.880 --> 00:28:01.839
<v Speaker 2>so the hunt for unique out from unique data is relentless.

531
00:28:02.119 --> 00:28:06.240
<v Speaker 1>Talent hardware and unique data, the new frontiers of competition.

532
00:28:06.359 --> 00:28:10.680
<v Speaker 2>Absolutely, Yet despite all this intense activity and investment, the

533
00:28:10.720 --> 00:28:14.400
<v Speaker 2>source makes a really interesting argument that AI in finance

534
00:28:14.480 --> 00:28:17.519
<v Speaker 2>is still, in many ways in a nascent stage.

535
00:28:17.799 --> 00:28:20.400
<v Speaker 1>Nascent really with all this going.

536
00:28:20.200 --> 00:28:23.400
<v Speaker 2>On, relatively speaking, yes, it argues there's still a notable

537
00:28:23.480 --> 00:28:27.079
<v Speaker 2>lack of standardization across the industry in terms of tools, techniques,

538
00:28:27.160 --> 00:28:29.880
<v Speaker 2>best practices compared to maybe other fields where AI is

539
00:28:29.920 --> 00:28:33.720
<v Speaker 2>more mature, and this lack of standardization paradoxically leaves the

540
00:28:33.759 --> 00:28:38.319
<v Speaker 2>competitive landscape potentially wide open. It means there's still significant

541
00:28:38.319 --> 00:28:41.839
<v Speaker 2>opportunity for nimble firms or firms with a breakthrough approach

542
00:28:42.240 --> 00:28:47.000
<v Speaker 2>to gain truly significant, maybe even outsized advantages before things

543
00:28:47.079 --> 00:28:49.759
<v Speaker 2>become more settled than standardized. The rules of the game

544
00:28:49.759 --> 00:28:51.400
<v Speaker 2>are still being written in a sense.

545
00:28:51.559 --> 00:28:54.519
<v Speaker 1>That's a fascinating point about the nascent stage. It makes

546
00:28:54.559 --> 00:28:58.359
<v Speaker 1>me wonder about the ultimate consequence maybe the endgame for

547
00:28:58.440 --> 00:29:01.839
<v Speaker 1>this AI race and finance. Could this eventually lead to

548
00:29:01.880 --> 00:29:06.440
<v Speaker 1>something like a financial singularity where human intuition or traditional

549
00:29:06.480 --> 00:29:08.680
<v Speaker 1>analysis is just completely overshadowed.

550
00:29:08.759 --> 00:29:10.960
<v Speaker 2>It's a really compelling thought, isn't it. The source actually

551
00:29:11.000 --> 00:29:15.960
<v Speaker 2>introduces a specific concept here an artificial financial intelligence or AFI.

552
00:29:15.640 --> 00:29:18.039
<v Speaker 1>AFI okay distinct from AGI.

553
00:29:17.839 --> 00:29:20.559
<v Speaker 2>Yes, very distinct. An AFI is defined as an AI

554
00:29:20.640 --> 00:29:23.839
<v Speaker 2>that is consistently superior in this specific domain of trading

555
00:29:23.880 --> 00:29:27.759
<v Speaker 2>and financial market analysis. Crucially, the argument is that an

556
00:29:27.799 --> 00:29:31.920
<v Speaker 2>AFI does not necessarily require achieving human level general intelligence

557
00:29:32.200 --> 00:29:35.200
<v Speaker 2>or human brain emulation or even physical embody mass.

558
00:29:35.240 --> 00:29:36.559
<v Speaker 1>So it doesn't need to be like a person.

559
00:29:37.039 --> 00:29:39.759
<v Speaker 2>No, it just needs to be better at finance. This

560
00:29:39.839 --> 00:29:42.279
<v Speaker 2>makes it a much more specific and arguably a much

561
00:29:42.279 --> 00:29:45.359
<v Speaker 2>more achievable goal in the nearer term than a full

562
00:29:45.359 --> 00:29:48.200
<v Speaker 2>blown artificial general intelligence or superintelligence.

563
00:29:48.240 --> 00:29:50.079
<v Speaker 1>This needs to be good at the finance game.

564
00:29:50.039 --> 00:29:54.640
<v Speaker 2>Exactly, and the key takeaway here is profound well. Traditional

565
00:29:54.640 --> 00:29:58.559
<v Speaker 2>econometric methods and even human traders today often struggle to

566
00:29:58.559 --> 00:30:02.640
<v Speaker 2>find what the source calls microroscopic alpha, those tiny, fleeting

567
00:30:02.680 --> 00:30:05.839
<v Speaker 2>statistical inefficiencies that might only yield a fraction of a

568
00:30:05.880 --> 00:30:10.599
<v Speaker 2>percent gain on a single trade, the scrap's leftover sort of. Yeah, AI,

569
00:30:11.000 --> 00:30:14.440
<v Speaker 2>with its ability to process vast data and complex patterns,

570
00:30:14.759 --> 00:30:17.799
<v Speaker 2>offers a completely new, potentially much more powerful path for

571
00:30:17.839 --> 00:30:21.400
<v Speaker 2>discovering that alpha, maybe even alpha that isn't so microscopic.

572
00:30:21.839 --> 00:30:24.119
<v Speaker 2>It's like having a new kind of microscope or maybe

573
00:30:24.119 --> 00:30:26.519
<v Speaker 2>even a completely new way of mining that lets you

574
00:30:26.559 --> 00:30:29.839
<v Speaker 2>find valuable brains of gold where human intuition and older

575
00:30:29.880 --> 00:30:32.480
<v Speaker 2>tools were only looking for obvious nuggets.

576
00:30:32.079 --> 00:30:34.359
<v Speaker 1>A new paths to finding value that was hidden before.

577
00:30:34.480 --> 00:30:34.880
<v Speaker 2>Wow.

578
00:30:35.319 --> 00:30:37.839
<v Speaker 1>Okay, well you've just taken us on a real deep

579
00:30:37.880 --> 00:30:40.960
<v Speaker 1>dive into AI and finance. We've seen how it's really

580
00:30:41.160 --> 00:30:43.880
<v Speaker 1>not just another tool, but potentially a fundamental shift.

581
00:30:44.200 --> 00:30:45.240
<v Speaker 2>It really feels that way.

582
00:30:45.359 --> 00:30:48.920
<v Speaker 1>From redefining intelligence itself with things like ALFA zero, challenging

583
00:30:48.960 --> 00:30:53.680
<v Speaker 1>those old elegant financial theories, to powering these incredibly sophisticated

584
00:30:53.720 --> 00:30:58.119
<v Speaker 1>trading bods and completely reshaping competition in the industry, AI

585
00:30:58.279 --> 00:31:00.240
<v Speaker 1>is clearly here to stay, and it feels like it's

586
00:31:00.319 --> 00:31:01.640
<v Speaker 1>just getting started in finance.

587
00:31:01.759 --> 00:31:04.880
<v Speaker 2>Absolutely, the potential impact is immense.

588
00:31:05.119 --> 00:31:07.759
<v Speaker 1>So maybe a final thought for our listener, for you

589
00:31:07.839 --> 00:31:11.640
<v Speaker 1>to maul over if these AI systems can eventually discover

590
00:31:11.759 --> 00:31:15.880
<v Speaker 1>statistical inefficiencies that human design models and maybe even human

591
00:31:15.960 --> 00:31:21.440
<v Speaker 1>intuition consistently miss, and if this artificial financial intelligence, this AFI,

592
00:31:21.720 --> 00:31:24.799
<v Speaker 1>doesn't actually need to be embodied or think like a

593
00:31:24.880 --> 00:31:28.119
<v Speaker 1>human or emulate a human brain to be superior, specifically

594
00:31:28.200 --> 00:31:31.160
<v Speaker 1>at trading. What does that ultimately imply about the limits

595
00:31:31.200 --> 00:31:34.960
<v Speaker 1>of our own human intuition when faced with truly data driven,

596
00:31:35.079 --> 00:31:37.920
<v Speaker 1>powerful machine intelligence in the realm of finance, that

597
00:31:38.079 --> 00:31:40.319
<v Speaker 2>Is the question, isn't it something to really think about?