A practical guide to applying artificial intelligence and neural networks to financial markets, covering algorithms, superintelligence theory, and normative finance.
Artificial Intelligence in Finance by Yves Hilpisch (O’Reilly, 2020) bridges two worlds that don’t often talk to each other: AI research and financial theory. The book starts with AI fundamentals, covering algorithms, types of learning (supervised, unsupervised, reinforcement), and how neural networks work as universal approximators. It includes Python code examples throughout, making abstract concepts tangible.
The middle section explores superintelligence, drawing on Nick Bostrom’s work to discuss artificial narrow intelligence, artificial general intelligence, and the control problem. While this might seem disconnected from finance, Hilpisch uses it to frame the bigger question: what happens when AI gets good enough to consistently beat financial markets?
The book then covers normative financial theories that have shaped Wall Street for decades: expected utility theory, Markowitz portfolio theory, CAPM, and arbitrage pricing theory. These chapters set up the argument that AI could challenge the efficient markets hypothesis by finding statistical inefficiencies that traditional models miss. The book is best suited for readers with some programming background who want to understand both the AI and finance sides of algorithmic trading.
Book: Artificial Intelligence in Finance Author: Yves Hilpisch Publisher: O’Reilly, 2020 ISBN: 978-1-492-05543-3
Here’s a question that bugs me. Can AI actually beat the stock market? Not in a sci-fi movie way. In a real, consistent, make-money-while-you-sleep way.
Chapter 2 of Artificial Intelligence in Finance is technically labeled a “Preface,” but it does a lot more than set the stage. Hilpisch opens with a quote from Robert Shiller asking whether financial markets will ever become truly perfect, with every asset priced correctly. It is a big question. And honestly, the way the chapter frames AI in finance around that question is what makes it interesting.
Chapter 3 of Artificial Intelligence in Finance opens with the quote about AlphaGo beating a human Go player. That event was a big deal back in 2016. People thought it would take at least another decade. It didn’t. And that sets the tone for this chapter. AI moves faster than experts predict.
This section of Chapter 3 is where things start to click. Hilpisch moves from talking about AI algorithms in general to showing how neural networks actually work. And then he drops a truth bomb that a lot of people skip over: your model is only as good as your data.
Chapter 4 of Artificial Intelligence in Finance opens with something fun: stories about AI beating humans at games. And honestly, these stories are some of the most fascinating parts of AI history. Games sound trivial, but they’re actually perfect testing grounds for intelligence. If a machine can figure out a game on its own, what else can it figure out?
So far in this chapter, Hilpisch covered AI success stories. AlphaGo, Atari games, chess engines. All impressive stuff. But now the book takes a sharp turn into much bigger questions. What happens when AI stops being narrow and starts being… everything?
Chapter 5 of Hilpisch’s book is called “Normative Finance.” And it opens with a quote from Fama and French admitting that the CAPM is built on “many unrealistic assumptions.” That’s a bold way to kick things off. Basically saying: here are the theories that shaped modern finance, and by the way, they don’t quite match reality.
The second half of Chapter 5 in Artificial Intelligence in Finance covers three theories that shaped how Wall Street thinks about investing. Mean-Variance Portfolio theory, the Capital Asset Pricing Model, and Arbitrage Pricing Theory. These ideas have been in every finance course since the 1960s. Hilpisch walks through them with actual Python code instead of just abstract math.
So this is the last post. Eight posts later, we have walked through the entire first half of Artificial Intelligence in Finance by Yves Hilpisch. AI fundamentals, neural networks, superintelligence, financial theory. A lot of ground.