The papers behind the curriculum

Portfolio Selection

Markowitz, H. · 1952

Journal of Finance, 7(1), 77–91

Founded modern portfolio theory: the mean–variance trade-off and the efficient frontier. Earned the 1990 Nobel Memorial Prize in Economics.

Connects to: Python ML · Capstone Project

Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk

Sharpe, W. F. · 1964

Journal of Finance, 19(3), 425–442

The Capital Asset Pricing Model (CAPM): expected return is linear in market beta. A cornerstone of finance. Nobel Prize 1990.

Connects to: Advanced Finance SQL · Python Pandas

The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets

Lintner, J. · 1965

Review of Economics and Statistics, 47(1), 13–37

An independent derivation of the CAPM, developed in parallel with Sharpe and Mossin.

Mutual Fund Performance

Sharpe, W. F. · 1966

Journal of Business, 39(1), 119–138

Introduced the reward-to-variability ratio — now the Sharpe ratio — the standard measure of risk-adjusted return.

Connects to: Python Foundations · Python Pandas

Efficient Capital Markets: A Review of Theory and Empirical Work

Fama, E. F. · 1970

Journal of Finance, 25(2), 383–417

Formalised the Efficient Market Hypothesis and its weak / semi-strong / strong forms — the null hypothesis every alpha strategy fights. Nobel Prize 2013.

The Pricing of Options and Corporate Liabilities

Black, F., & Scholes, M. · 1973

Journal of Political Economy, 81(3), 637–654

The Black–Scholes option-pricing formula — arguably the most influential equation in finance. Nobel Prize 1997 (Scholes & Merton).

Theory of Rational Option Pricing

Merton, R. C. · 1973

Bell Journal of Economics and Management Science, 4(1), 141–183

Generalised and rigorised option pricing (dividends, the continuous-hedging argument), cementing the theoretical foundation. Nobel Prize 1997.

The Arbitrage Theory of Capital Asset Pricing

Ross, S. A. · 1976

Journal of Economic Theory, 13(3), 341–360

Arbitrage Pricing Theory (APT) generalised the CAPM to multiple risk factors, opening the door to modern factor investing.

Option Pricing: A Simplified Approach

Cox, J. C., Ross, S. A., & Rubinstein, M. · 1979

Journal of Financial Economics, 7(3), 229–263

The binomial tree model — the most intuitive, programmable route into option pricing and the standard teaching tool.

Distribution of the Estimators for Autoregressive Time Series with a Unit Root

Dickey, D. A., & Fuller, W. A. · 1979

Journal of the American Statistical Association, 74(366), 427–431

The (Augmented) Dickey–Fuller test for stationarity — the first check before modelling any financial time series.

Prospect Theory: An Analysis of Decision under Risk

Kahneman, D., & Tversky, A. · 1979

Econometrica, 47(2), 263–291

Showed people value gains and losses asymmetrically — the foundation of behavioral finance. Kahneman won the 2002 Nobel Prize.

Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?

Shiller, R. J. · 1981

American Economic Review, 71(3), 421–436

The excess-volatility puzzle: markets swing far more than fundamentals justify, challenging pure efficiency. Nobel Prize 2013.

Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation

Engle, R. F. · 1982

Econometrica, 50(4), 987–1007

Introduced ARCH — modelling time-varying volatility (volatility clustering). Foundation of modern risk forecasting. Nobel Prize 2003.

Generalized Autoregressive Conditional Heteroskedasticity

Bollerslev, T. · 1986

Journal of Econometrics, 31(3), 307–327

GARCH generalised ARCH into the workhorse volatility model used across risk management and option pricing.

Connects to: Advanced Finance SQL

Co-integration and Error Correction: Representation, Estimation, and Testing

Engle, R. F., & Granger, C. W. J. · 1987

Econometrica, 55(2), 251–276

Cointegration: how non-stationary series can share a long-run equilibrium — the statistical engine behind pairs trading. Nobel Prize 2003.

Global Portfolio Optimization

Black, F., & Litterman, R. · 1992

Financial Analysts Journal, 48(5), 28–43

The Black–Litterman model blends an investor’s subjective views with market-equilibrium returns, fixing the instability of naive mean–variance optimisation.

Common Risk Factors in the Returns on Stocks and Bonds

Fama, E. F., & French, K. R. · 1993

Journal of Financial Economics, 33(1), 3–56

The three-factor model (market, size, value) that became the empirical workhorse of asset pricing and the benchmark for measuring alpha.

Connects to: Python ML

Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency

Jegadeesh, N., & Titman, S. · 1993

Journal of Finance, 48(1), 65–91

Documented the momentum anomaly — past winners keep winning — one of the most robust and replicated effects in finance.

Connects to: Capstone Project

Regression Shrinkage and Selection via the Lasso

Tibshirani, R. · 1996

Journal of the Royal Statistical Society: Series B, 58(1), 267–288

The Lasso (L1 regularisation) — automatic feature selection that tames the high-dimensional predictor sets common in quant signals.

Connects to: Python ML

On Persistence in Mutual Fund Performance

Carhart, M. M. · 1997

Journal of Finance, 52(1), 57–82

Added a momentum factor to Fama–French, creating the four-factor model used to judge fund manager skill.

Coherent Measures of Risk

Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. · 1999

Mathematical Finance, 9(3), 203–228

Defined the axioms a sensible risk measure must satisfy — and showed Value-at-Risk fails subadditivity, motivating Expected Shortfall (CVaR).

Optimization of Conditional Value-at-Risk

Rockafellar, R. T., & Uryasev, S. · 2000

Journal of Risk, 2(3), 21–41

Showed CVaR can be minimised with linear programming, making tail-risk-aware portfolio optimisation tractable.

Random Forests

Breiman, L. · 2001

Machine Learning, 45(1), 5–32

Random forests — robust, low-tuning ensembles that remain a strong baseline for return prediction and classification.

Connects to: Python ML

Data Structures for Statistical Computing in Python

McKinney, W. · 2010

Proceedings of the 9th Python in Science Conference (SciPy), 56–61

The paper that introduced pandas and the DataFrame — the backbone of every Python finance workflow.

Connects to: Python Pandas

Scikit-learn: Machine Learning in Python

Pedregosa, F., et al. · 2011

Journal of Machine Learning Research, 12, 2825–2830

The reference for scikit-learn — the consistent, accessible ML API used throughout the ML track.

Connects to: Python ML

Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance

Bailey, D. H., Borwein, J., López de Prado, M., & Zhu, Q. J. · 2014

Notices of the AMS, 61(5), 458–471

Why most backtested strategies fail live: with enough trials you can always find a spurious winner. Essential reading before trusting any backtest.

Connects to: Python ML · Capstone Project

A Five-Factor Asset Pricing Model

Fama, E. F., & French, K. R. · 2015

Journal of Financial Economics, 116(1), 1–22

Extended the model with profitability and investment factors — the modern factor zoo’s reference point.

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. · 2016

Proceedings of the 22nd ACM SIGKDD, 785–794

Gradient-boosted trees at scale — the model that dominates tabular ML competitions and many production quant pipelines.

Empirical Asset Pricing via Machine Learning

Gu, S., Kelly, B., & Xiu, D. · 2020

Review of Financial Studies, 33(5), 2223–2273

The benchmark study comparing ML methods for return prediction — neural nets and trees beat linear models out of sample.

Array Programming with NumPy

Harris, C. R., et al. · 2020

Nature, 585, 357–362

The definitive NumPy reference — the vectorised array engine underneath pandas, scikit-learn and SciPy.

SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python

Virtanen, P., et al. · 2020

Nature Methods, 17, 261–272

SciPy’s optimisation, statistics and linear-algebra routines power portfolio optimisation and statistical tests in the curriculum.

Connects to: Python ML