Open research community
The community layer for quantitative finance.
QuantHQ is a place to read quant research, argue about it, and publish your own. Everything we put out shows its working.
What we study — drag a node to pull the map apart.
What you actually get.
A reading list, not a link dump
18 papers that are actually worth the evening — from the equilibrium arguments the field rests on to what deep learning has really established about price formation. Each one says why it made the list.
Open the reading listPeople reading the same things
The group is where papers get posted, picked apart, and occasionally demolished. Quants, engineers, and students, mostly arguing about method.
Join the groupSomewhere to put your work
Written something worth reading — a replication, a negative result, a note on a method that keeps going wrong? Send it over and we will publish it.
Get in touchStart with these.
Three of the 18 papers on the list. Not summaries — the reason each one is still worth your time.
- Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk The CAPM. Read it for the argument, not the result: Sharpe derives a single price of risk from equilibrium alone, and every factor model since is a fight about what he left out.
- A Simple Approach to the Theory of Asset Prices Prices assets as bundles of moment-characteristics without assuming expected utility. A useful antidote if you have only ever seen pricing derived one way.
- The Value Premium and the CAPM The direct sequel to Sharpe: value beats the market, and the CAPM beta cannot explain it. Pairs with the 1993 three-factor paper below.
Shorter notes from the work.
- Regime Detection for Factor Rotation: A Practical Guide Market regimes change, and so should your factor exposures. Learn practical techniques for regime detection and how to apply them to systematic factor rotation strategies.
- Prompt Engineering for Financial AI: Best Practices Effective prompts are the difference between hallucinations and actionable insights. Learn prompt engineering best practices for financial AI applications, from sentiment analysis to document understanding.
- Deflated Sharpe Ratios: How to Account for Multiple Testing If you test 100 factor variants and report the best one, your Sharpe is inflated. Learn how to use deflated Sharpe ratios to correct for multiple testing bias.
- Why Quant Research Should Be Open The case for doing quantitative research in the open — and why secrecy is overrated for most alpha.
You'll probably fit if…
- You're starting out A student or career-switcher trying to get into quant, tired of being sold a course. Read what the field actually reads.
- You can already build An engineer or data scientist who can ship a model but wants to know which published results survive contact with real data.
- You do this for a living A working quant who wants to argue about method with people outside your own desk.
Come argue about method with us.
The community runs on LinkedIn. It is free, it is public, and nobody is selling you a course.
Curious what the research tooling looks like? The lab has interactive demos — everything there runs on simulated sample data.