When to efficiently rebalance a portfolio

Paper: arXiv 2308.08745

Authors: Masayuki Ando, Masaaki Fukasawa

Abstract

A constant weight asset allocation is a popular investment strategy and is optimal under a suitable continuous model. We study the tracking error for the target continuous rebalancing strategy by a feasible discrete-in-time rebalancing under a general multi-dimensional Brownian semimartingale model of asset prices. In a high-frequency asymptotic framework, we derive an asymptotically efficient sequence of simple predictable strategies.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a rigorous theoretical framework using multi-dimensional Brownian semimartingale models and high-frequency asymptotics to optimize discrete portfolio rebalancing. While high in mathematical sophistication and novelty regarding tracking error efficiency, it lacks empirical backtesting and real-world data validation.

Research Flowchart

  flowchart TD
    A["Research Goal: Optimize portfolio tracking error under discrete rebalancing"] --> B["Inputs: Multi-dimensional Brownian semimartingale asset price model"]
    B --> C["Methodology: High-frequency asymptotic framework"]
    C --> D["Process: Analyze trade-off between target continuous strategy & discrete timing"]
    D --> E["Outcome: Derive asymptotically efficient sequence of simple predictable strategies"]