A quant research reading list.
18 primary sources with short editorial notes. Follow a path below, or search the full list by author, title, or topic.
Start with the foundations
How portfolios, risk, and factors became the language of the field.
Learn to test claims
What happens when costs, multiple trials, and baselines enter the picture.
Inspect modern methods
Where machine learning meets return prediction and order book data.
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Recommended reading · 18 papers
These are selected for a useful idea, method, or limitation. A note explains our reason for including each one, not an endorsement of every conclusion. Links lead to primary sources; some may be behind a paywall.
Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk
William F. Sharpe
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
Riccardo Cesari, Carlo D'Adda
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
Eugene F. Fama, Kenneth R. French
The direct sequel to Sharpe: value beats the market, and the CAPM beta cannot explain it. Pairs with the 1993 three-factor paper below.
Foundations of Factor Investing
Jennifer Bender, Remy Briand, Dimitris Melas, Raman Aylur Subramanian
The clearest bridge from academic factor papers to what an index provider actually builds. Read it when you need to explain factors to someone who allocates capital.
Trading Strategies and Market Microstructure: Evidence from a Prediction Market
David M. Rothschild, Rajiv Sethi
Account-level data on 6,300 traders in the Intrade 2012 election market — a whole market ecology laid bare, including evidence of manipulation by one large trader. Rare visibility into who is actually on the other side.
Portfolio Selection
Harry Markowitz
Fourteen pages that turned investing into an optimization problem. Everything downstream — risk models, covariance shrinkage, risk parity — is an argument with this paper.
Efficient Capital Markets: A Review of Theory and Empirical Work
Eugene F. Fama
The joint-hypothesis problem lives here: you can never test market efficiency without also testing your pricing model. Worth rereading whenever a backtest looks too good.
The Pricing of Options and Corporate Liabilities
Fischer Black, Myron Scholes
Replication, not forecasting. The insight that a dynamic hedge pins down a price is the template for most modern derivatives work — and its assumptions are the standard list of what breaks in practice.
Continuous Auctions and Insider Trading
Albert S. Kyle
Where market impact comes from. Kyle’s lambda is still the unit in which execution desks think about how much your order moves the price.
Common Risk Factors in the Returns on Stocks and Bonds
Eugene F. Fama, Kenneth R. French
Size and value as priced factors. The three-factor model is the baseline any new signal has to beat before it is worth a second look.
Returns to Buying Winners and Selling Losers
Narasimhan Jegadeesh, Sheridan Titman
Momentum: the anomaly that refused to be explained away and still has not been. Note how carefully they handle overlapping holding periods — the method matters as much as the finding.
On Persistence in Mutual Fund Performance
Mark M. Carhart
Adds momentum to the three-factor model and, in doing so, explains away most of what looked like fund manager skill. A lesson in attribution before admiration.
Optimal Execution of Portfolio Transactions
Robert Almgren, Neil Chriss
The trade-off between market impact and timing risk, made tractable. If your backtest assumes fills at mid, this is the paper that tells you what you owe.
… and the Cross-Section of Expected Returns
Campbell R. Harvey, Yan Liu, Heqing Zhu
Hundreds of published factors, one multiple-testing correction, and most of them stop being significant. The single most useful paper for staying honest about your own research.
The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality
David H. Bailey, Marcos López de Prado
How to discount a Sharpe ratio for the number of trials it took to find it. Practical, and it usually deflates your favourite strategy more than you expect.
Empirical Asset Pricing via Machine Learning
Shihao Gu, Bryan Kelly, Dacheng Xiu
The reference comparison of ML methods for return prediction. The interesting result is not that trees and nets win, but that their gains come from allowing interactions and nonlinearity — not from more data.
Universal Features of Price Formation in Financial Markets: Perspectives from Deep Learning
Justin Sirignano, Rama Cont
A single deep network trained across stocks beats per-stock models, which is evidence that price formation has a universal component. The strongest argument for pooling data that we know of.
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang, Stefan Zohren, Stephen Roberts
A concrete, reproducible architecture for learning directly from raw order book state. Good starting point if you want to move past hand-engineered microstructure features.