Eric Sager Luxenberg

selected publications

1. 

   Exponentially Weighted Moving Models

   Eric Luxenberg, and Stephen Boyd

   2024

   Bib PDF

   @misc{luxenberg2024ewmm,
     author = {Luxenberg, Eric and Boyd, Stephen},
     title = {Exponentially Weighted Moving Models},
     year = {2024},
   }

2. 

   Disciplined Saddle Programming

   Philipp Schiele, Eric Luxenberg, and Stephen P. Boyd

   Transactions on Machine Learning Research 2024

   Bib PDF

   @article{schiele2024dsp,
     title = {Disciplined Saddle Programming},
     author = {Schiele, Philipp and Luxenberg, Eric and Boyd, Stephen P.},
     journal = {Transactions on Machine Learning Research},
     issn = {2835-8856},
     year = {2024},
     url = {https://openreview.net/forum?id=KhMLfEIoUm},
   }

3. 

   Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization

   Eric Luxenberg, Philipp Schiele, and Stephen Boyd

   Journal of Optimization Theory and Applications 2024

   arXiv Bib PDF

   @article{robust_bond,
     title = {Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization},
     author = {Luxenberg, Eric and Schiele, Philipp and Boyd, Stephen},
     year = {2024},
     journal = {Journal of Optimization Theory and Applications},
   }

4. 

   Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization

   Eric Luxenberg, Philipp Schiele, and Stephen Boyd

   Computational Economics 2024

   Bib PDF

   @article{luxenberg2024cptopt,
     title = {Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization},
     author = {Luxenberg, Eric and Schiele, Philipp and Boyd, Stephen},
     journal = {Computational Economics},
     year = {2024},
     pages = {1--21},
     publisher = {Springer},
   }

5. 

   Strategic Asset Allocation with Illiquid Alternatives

   Eric Luxenberg, Stephen Boyd, Misha Beek, and 2 more authors

   In Proceedings of the Third ACM International Conference on AI in Finance 2022

   Abs Bib PDF

   We address the problem of strategic asset allocation (SAA) with portfolios that include illiquid alternative asset classes. The main challenge in portfolio construction with illiquid asset classes is that we do not have direct control over our positions, as we do in liquid asset classes. Instead we can only make commitments; the position builds up over time as capital calls come in, and reduces over time as distributions occur, neither of which the investor has direct control over. The effect on positions of our commitments is subject to a delay, typically of a few years, and is also unknown or stochastic. A further challenge is the requirement that we can meet the capital calls, with very high probability, with our liquid assets. We formulate the illiquid dynamics as a random linear system, and propose a convex optimization based model predictive control (MPC) policy for allocating liquid assets and making new illiquid commitments in each period. Despite the challenges of time delay and uncertainty, we show that this policy attains performance surprisingly close to a fictional setting where we pretend the illiquid asset classes are completely liquid, and we can arbitrarily and immediately adjust our positions. In this paper we focus on the growth problem, with no external liabilities or income, but the method is readily extended to handle this case.

   @inproceedings{10.1145/3533271.3561769,
     author = {Luxenberg, Eric and Boyd, Stephen and van Beek, Misha and Cao, Wen and Kochenderfer, Mykel},
     title = {Strategic Asset Allocation with Illiquid Alternatives},
     year = {2022},
     isbn = {9781450393768},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi-org.stanford.idm.oclc.org/10.1145/3533271.3561769},
     doi = {10.1145/3533271.3561769},
     booktitle = {Proceedings of the Third ACM International Conference on AI in Finance},
     pages = {249–256},
     numpages = {8},
     keywords = {model predictive control, illiquid alternatives, endowment model, convex optimization},
     location = {New York, NY, USA},
     series = {ICAIF '22},
   }