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Citations

Primary academic sources for the theory and validation pages. All identifiers below are direct DOIs or stable journal URLs.

Foundational

  • [@markowitz1952] Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91. DOI: 10.1111/j.1540-6261.1952.tb01525.x

  • [@merton1972] Merton, R. C. (1972). An Analytic Derivation of the Efficient Portfolio Frontier. Journal of Financial and Quantitative Analysis, 7(4), 1851-1872. DOI: 10.2307/2329621

Estimation error

  • [@michaud1989] Michaud, R. O. (1989). The Markowitz Optimization Enigma: Is 'Optimized' Optimal? Financial Analysts Journal, 45(1), 31-42. DOI: 10.2469/faj.v45.n1.31

  • [@kan2007] Kan, R., & Zhou, G. (2007). Optimal Portfolio Choice with Parameter Uncertainty. Journal of Financial and Quantitative Analysis, 42(3), 621-656. DOI: 10.1017/S0022109000004129

  • [@marchenko1967] Marchenko, V. A., & Pastur, L. A. (1967). Distribution of eigenvalues for some sets of random matrices. Mathematics of the USSR-Sbornik, 1(4), 457-483. DOI: 10.1070/SM1967v001n04ABEH001994

Shrinkage

  • [@ledoit2003] Ledoit, O., & Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603-621. DOI: 10.1016/S0927-5398(03)00007-0

  • [@ledoit2004] Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2), 365-411. DOI: 10.1016/S0047-259X(03)00096-4

  • [@chen2010] Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. (2010). Shrinkage Algorithms for MMSE Covariance Estimation. IEEE Transactions on Signal Processing, 58(10), 5016-5029. DOI: 10.1109/TSP.2010.2053029

Black-Litterman

  • [@black1992] Black, F., & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal, 48(5), 28-43. DOI: 10.2469/faj.v48.n5.28

  • [@he1999] He, G., & Litterman, R. (1999). The Intuition Behind Black-Litterman Model Portfolios. Goldman Sachs Investment Management Research. DOI: 10.2139/ssrn.334304

  • [@idzorek2005] Idzorek, T. M. (2005). A Step-by-Step Guide to the Black-Litterman Model: Incorporating User-Specified Confidence Levels. In Forecasting Expected Returns in the Financial Markets, Academic Press, 17-38. DOI: 10.1016/B978-075068321-0.50003-0

Robust and CVaR

  • [@goldfarb2003] Goldfarb, D., & Iyengar, G. (2003). Robust Portfolio Selection Problems. Mathematics of Operations Research, 28(1), 1-38. DOI: 10.1287/moor.28.1.1.14260

  • [@rockafellar2000] Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21-41. DOI: 10.21314/JOR.2000.038

  • [@ben-tal2009] Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. DOI: 10.1515/9781400831050

Validation benchmarks

  • [@demiguel2009] DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal Versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy? The Review of Financial Studies, 22(5), 1915-1953. DOI: 10.1093/rfs/hhm075

  • [@pedregosa2011] Pedregosa, F. et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830. URL: https://www.jmlr.org/papers/v12/pedregosa11a.html