Machine Learning Trading
GARCH Volatility Forecasting
Volatility is not constant and it is not random — turbulent days cluster together. GARCH is the standard way of turning that regularity into a forecast.
Wiki learning path
AI and Quantitative Finance
Explore financial data, machine learning and AI research methods, including model applications, practical limitations and common mistakes.
32 guides. Start with the introductory topics; follow related links inside each guide.
Machine Learning Trading
Why Machine Learning Predicts Volatility, Not Price Tops
The same model that fails completely at calling the top can be genuinely good at telling you how wide the range will be. The difference is not effort — it is the structure of the data.
Machine Learning Trading
Understanding Quantitative Confidence Scores
An agreement label describes a comparison between methods. It is not automatically a confidence interval or a verified probability of being correct.
AI Fundamentals in Finance
Artificial Intelligence in Finance Explained
AI now touches nearly every corner of finance, from fraud alerts to trading systems. Here's what the term actually covers — and what it doesn't.
AI Fundamentals in Finance
Machine Learning in Finance Explained
Machine learning models find patterns in financial data that traditional formulas can't — here's how they actually work, in plain English.
AI Fundamentals in Finance
AI vs Traditional Financial Models
Statistical forecasting has driven finance for decades. Here's what actually changes — and what doesn't — when machine learning enters the picture.
AI Fundamentals in Finance
How Banks Use Artificial Intelligence
From catching fraud in milliseconds to underwriting loans, banks have quietly built AI into most of their core operations. Here's where it actually shows up.
AI Fundamentals in Finance
How Hedge Funds Use AI
Quant funds were early adopters of machine learning long before the current AI wave — here's what it actually does inside a trading operation.
AI Stock Analysis
AI Stock Analysis Tools Explained
A growing category of software uses AI to scan filings, sentiment, and market data for investors. Here's what these tools actually do, in vendor-neutral terms.
AI Stock Analysis
Best AI Stock Analysis Tools
Rather than ranking specific products, here's how to compare categories of AI investing tools by what they're actually built to do.
AI Stock Analysis
How AI Analyzes Stocks
AI models can read a decade of filings or a day of headlines in seconds. Here's what data they actually process, and how it turns into a signal.
AI Stock Analysis
AI Stock Prediction Explained
AI models can attempt to forecast a probability distribution of outcomes from historical patterns — but that's a different thing than knowing what a stock will do tomorrow. Here's the honest mechanics.
AI Stock Analysis
Can AI Predict Stock Prices?
Short answer: not reliably, and no system has a proven durable edge. Here's why, in plain terms — and what AI is genuinely good for instead.
AI Stock Analysis
AI vs Human Stock Analysts
AI processes more filings and data points than any analyst could alone. Judgment, context, and accountability are a different matter — here's how the two actually compare.
AI Stock Analysis
Using ChatGPT for Stock Analysis
Large language models can summarize filings and explain concepts quickly, but they have real blind spots for stock research. Here's how to use them well — and where to be careful.
Machine Learning Trading
Machine Learning in Trading Explained
Quant desks feed market data into statistical models to look for patterns a human analyst would miss. Here's what that pipeline actually looks like, and why it's harder than it sounds.
Machine Learning Trading
Algorithmic Trading Explained
Algorithmic trading replaces a human clicking "buy" with code that decides what, when, and how much to trade. Here's how those systems are built, executed, and kept in check.
Machine Learning Trading
AI Trading Models Explained
"AI trading model" covers a wide range of techniques, from decades-old regressions to modern deep learning. Here's what the main families are and where each one is actually used.
Machine Learning Trading
Machine Learning Models Used in Finance
Regression, decision trees, neural networks, and ensembles show up constantly in financial machine learning. Here's what each one actually does, in plain terms.
Machine Learning Trading
Deep Learning in Trading
Neural networks can, in principle, learn patterns in price data without being told what to look for. Here's how that actually works in practice, and where it tends to fall short.
Machine Learning Trading
Reinforcement Learning in Trading
Instead of predicting a single number, reinforcement learning trains an agent to make a sequence of trading decisions by rewarding good outcomes and penalizing bad ones. Here's how that setup actually works.
Machine Learning Trading
NLP in Finance: Sentiment, Earnings Calls and Limits
Learn how natural language processing turns news, earnings calls and filings into research signals, and where sentiment models can mislead.
AI Financial Data Applications
AI Fraud Detection Explained
Every card swipe, wire transfer, and login attempt now passes through a model that scores how suspicious it looks — here's how that scoring actually works, and what it can't do.
AI Financial Data Applications
AI Risk Management Explained
Credit decisions, trading limits, and capital reserves all rest on risk models — here's how machine learning changes what those models can see and how fast they can adapt.
AI Financial Data Applications
AI Portfolio Management Explained
Robo-advisors turned portfolio construction into software — here's what the algorithms actually automate, and where a human still needs to be in the loop.
AI Financial Data Applications
AI Sentiment Analysis in Finance
Every earnings call, headline, and social post carries a tone — here's how models turn that tone into a number, and what that number can and can't tell you.
AI Financial Data Applications
Alternative Data in Finance: Sources, Uses and Limits
Learn how satellite imagery, card spending and web traffic inform investment research, with examples of noisy proxies, data costs and limitations.
Advanced AI & Markets
Large Language Models in Finance
Large language models can read a filing, an earnings call transcript, and a research note faster than any human — here's what that actually means, and where the output still needs a second pair of eyes.
Advanced AI & Markets
Generative AI in Finance Explained
Generative AI doesn't just crunch numbers — it drafts text, summarizes documents, and produces first passes of work that used to be entirely manual. Here's where that's actually showing up across finance.
Advanced AI & Markets
AI Agents in Finance
An AI agent doesn't just answer a question — it plans a sequence of steps, pulls data on its own, and works toward a goal. Here's how that shows up in research, monitoring, and decision support.
Advanced AI & Markets
AI Financial Analysts Explained
A growing category of AI tools is built to replicate parts of what an equity research analyst does — gathering data, spotting patterns, drafting notes. Here's what they actually do, and what they don't.
Advanced AI & Markets
Future of AI in Investing
Nobody knows exactly how AI will reshape investing over the next decade. This is a discussion of plausible directions worth thinking about — not a forecast, and not investment advice.