Ensembling Portfolio Strategies for Long-Term Investments: A Distribution-Free Preference Framework for Decision-Making and Algorithms

Paper: arXiv 2406.03652

Abstract

This paper investigates the problem of ensembling multiple strategies for sequential portfolios to outperform individual strategies in terms of long-term wealth. Due to the uncertainty of strategies’ performances in the future market, which are often based on specific models and statistical assumptions, investors often mitigate risk and enhance robustness by combining multiple strategies, akin to common approaches in collective learning prediction. However, the absence of a distribution-free and consistent preference framework complicates decisions of combination due to the ambiguous objective. To address this gap, we introduce a novel framework for decision-making in combining strategies, irrespective of market conditions, by establishing the investor’s preference between decisions and then forming a clear objective. Through this framework, we propose a combinatorial strategy construction, free from statistical assumptions, for any scale of component strategies, even infinite, such that it meets the determined criterion. Finally, we test the proposed strategy along with its accelerated variant and some other multi-strategies. The numerical experiments show results in favor of the proposed strategies, albeit with small tradeoffs in their Sharpe ratios, in which their cumulative wealths eventually exceed those of the best component strategies while the accelerated strategy significantly improves performance.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a formal, distribution-free framework using online learning theory and combinatorial analysis, indicating high mathematical sophistication, while the experiments utilize a 27-year real market dataset (CRSP) with specific performance metrics like Sharpe ratios and cumulative wealth, demonstrating substantial empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Ensemble multiple portfolio strategies<br>for long-term investment robustness"] --> B{"Problem:"}
  B --> C["No distribution-free framework<br>for combining strategies"]
  C --> D["Methodology:<br>Novel Preference Framework"]
  D --> E["Key Process:<br>Define investor preference<br>between decisions"]
  E --> F["Construction:<br>Combinatorial strategy<br>free from statistical assumptions"]
  F --> G["Experiments:<br>Test on financial data<br>vs. baselines"]
  G --> H["Outcome:<br>Proposed strategy exceeds<br>best component wealth"]
  H --> I["Outcome:<br>Accelerated variant shows<br>significant performance boost"]

Cited References (23)

1. MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model (2023) — link
2. An integrated approach of ensemble learning methods for stock index prediction using investor sentiments (2023) — link
3. An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction (2022) — link
4. Dynamic stock-decision ensemble strategy based on deep reinforcement learning (2022) — link
5. Open Problem: Fast and Optimal Online Portfolio Selection (2020)
6. A comprehensive evaluation of ensemble learning for stock-market prediction (2020) — link
7. Online Portfolio Selection (2018) — link
8. OSQP: an operator splitting solver for quadratic programs (2017) — link

0 of 23 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.