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"]

Cited References (19)

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  2. N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting (2022) — link
  3. Multi- Period Trading via Convex Optimization (2017) — link
  4. Deep learning for forecasting stock returns in the cross-section (2018)
  5. Survey of stock market prediction using machine learning approach (2017) — link
  6. Systematic analysis and review of stock market prediction techniques (2019) — link
  7. Gurobi Optimizer Reference Manual (2023) — link
  8. Organizational Commitment Profiles and Job Satisfaction among Greek Private and Public Sector Employees (2007) — link
and 11 more
  1. Portfolio optimization with linear and fixed transaction costs (2007) — link
  2. Portfolio Selection (1952) — link
  3. CAPITAL ASSET PRICES : A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK * (1964) — link
  4. Linear and Mixed Integer Programming for Portfolio Optimization (2015) — link
  5. Optimization of conditional value-at-risk (2000) — link
  6. Breaking Down the Direct Indexing Investment Process (2022) — link
  7. Liquidity regimes and optimal dynamic asset allocation (2020) — link
  8. Dynamic Trading with Predictable Returns and Transaction Costs : Dynamic Trading with Predictable Returns and Transaction Costs (2013) — link
  9. A Transition Model for Portfolio Revision (1967) — link
  10. PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming (2022) — link
  11. Smart “predict, then optimize” (2022)

0 of 19 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.