Surveys

• Universal Portfolios: how to (not) get rich, blog by Sebastian Pokutta. Recommended.
• Lai & Yang 2022 – A survey on gaps between mean-variance approach and exponential growth rate approach for portfolio optimization
• Li & Hoi 2014, Online portfolio selection: a survey.
• Fernholz 2002, Stochastic portfolio theory.

Papers

• Chopra & Ziemba 1993, The effect of errors in means, variances and covariances on optimal portfolio choice
  • “MV optimization is very sensitive to errors in the estimates of the inputs” and “For investors with moderate to high risk tolerance, the cash equivalent loss for errors in means is an order of magnitude greater than that for errors in variances or covariances.”
• Boyd et al. 2017 – Multi-period trading via convex optimization
• DeMiguel, Garlappi, Uppal 2009, Optimal vs. naive diversification: how inefficient is the 1/N portfolio?.
  • “… we conclude that of the strategies from the optimizing models, there is no single strategy that always dominates the 1/N strategy in terms of Sharpe ratio.”
• Cover 1991, Universal portfolios.
• Wong 2016, Universal portfolios in stochastic portfolio theory
• Cuchiero, Schachermayer, Wong 2018, Cover’s universal portfolio, stochastic portfolio theory, and the numéraire portfolio
• Kardaras & Robertson 2021, Ergodic robust maximization of asymptotic growth
• Tsang, Sit, Wong 22, Adaptive robust online portfolio selection.
  • This paper is clearly laid out. The authors propose a method that adaptively chooses a rebalancing penalty and an ellipsoidal uncertainty set by matching the choice of the best performing of a group of “experts” with fixed choices of the rebalancing penalty / uncertainty set size. A good candidate for implementation.

Misc

• McQuarrie, Stocks for the Long Run?

Robustness

• Duchi 2020 – Understanding and mitigating the tradeoff between robustness and accuracy