Paper: arXiv 2401.13126

Abstract

This work discusses the benefits of constrained portfolio turnover strategies for small to medium-sized portfolios. We propose a dynamic multi-period model that aims to minimize transaction costs and maximize terminal wealth levels whilst adhering to strict portfolio turnover constraints. Our results demonstrate that using our framework in combination with a reasonable forecast, can lead to higher portfolio values and lower transaction costs on average when compared to a naive, single-period model. Such results were maintained given different problem cases, such as, trading horizon, assets under management, wealth levels, etc. In addition, the proposed model lends itself to a reformulation that makes use of the column generation algorithm which can be strategically leveraged to reduce complexity and solving times.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper uses advanced mixed-integer programming and a rolling horizon framework with column generation (high math), while also presenting numerical experiments with real-world data and providing reproducible code for backtesting (high empirical rigor).

Research Flowchart

  flowchart TD
  A["Research Goal: Optimize Transition<br>for Small-Medium Portfolios"] --> B["Methodology: Dynamic Multi-Period<br>Model with Turnover Constraints"]
  B --> C["Inputs: Market Data,<br>Wealth Levels, Trading Horizons"]
  C --> D["Computation: Column Generation<br>Algorithm for Efficiency"]
  D --> E["Comparison: vs. Naive<br>Single-Period Model"]
  E --> F["Outcome: Higher Terminal<br>Wealth & Lower Costs"]
  F --> G["Conclusion: Robust across<br>various market conditions"]