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

• URL: http://arxiv.org/abs/2406.03652v2
• Date: Thu, 06 Feb 2025 11:58:31 GMT
• Title: Ensembling Portfolio Strategies for Long-Term Investments: A Distribution-Free Preference Framework for Decision-Making and Algorithms
• Authors: Duy Khanh Lam,
• Abstract summary: This paper investigates the problem of ensembling multiple strategies for sequential portfolios to outperform individual strategies in terms of long-term wealth. We introduce a novel framework for decision-making in combining strategies, irrespective of market conditions. We show results in favor of the proposed strategies, albeit with small tradeoffs in their Sharpe ratios.
• Score: 0.0
• License:
• 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.

Related papers

• EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
  We propose explicit policy optimization (EPO) for strategic reasoning. EPO provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior. Experiments across social and physical domains demonstrate EPO's ability of long-term goal alignment.
  arXiv  Detail & Related papers  (2025-02-18T03:15:55Z)
• Indefinite causal order strategy nor adaptive strategy does not improve the estimation of group action [53.64687146666141]
  We consider estimation of unknown unitary operation when the set of possible unitary operations is given by a projective unitary representation of a compact group. We show that indefinite causal order strategy nor adaptive strategy does not improve the performance of this estimation when error function satisfies group covariance.
  arXiv  Detail & Related papers  (2025-01-16T06:00:57Z)
• Sequential Portfolio Selection under Latent Side Information-Dependence Structure: Optimality and Universal Learning Algorithms [0.0]
  We show that a dynamic strategy, which forms a portfolio based on perfect knowledge of the dependence structure and full market information over time, may not grow at a higher rate infinitely often than a constant strategy. We show that a random optimal constant strategy almost surely exists, even when a limiting growth rate for the dynamic strategy does not.
  arXiv  Detail & Related papers  (2025-01-12T03:49:47Z)
• Stratify: Unifying Multi-Step Forecasting Strategies [0.0]
  Stratify is a framework that addresses multi-step forecasting and unifying existing strategies. In over 84% of 1080 experiments, novel strategies in Stratify improved performance compared to all existing ones. Our results are the most comprehensive benchmarking of known and novel forecasting strategies.
  arXiv  Detail & Related papers  (2024-12-29T16:06:46Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• On solving decision and risk management problems subject to uncertainty [91.3755431537592]
  Uncertainty is a pervasive challenge in decision and risk management. This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
  arXiv  Detail & Related papers  (2023-01-18T19:16:23Z)
• Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies [0.0]
  Our paper proposes a new approach for developing long-term investment strategies using an ensemble of evolutionary algorithms and a deep learning model. Our methodology focuses on building long-term wealth by improving systematic investment planning (SIP) decisions on Exchange Traded Funds (ETF) over a period of time.
  arXiv  Detail & Related papers  (2022-03-24T15:39:05Z)
• Optimal Strategies of Quantum Metrology with a Strict Hierarchy [3.706744588098214]
  We identify the ultimate precision limit of different families of strategies, including the parallel, the sequential, and the indefinite-causal-order strategies. We provide an efficient algorithm that determines an optimal strategy within the family of strategies under consideration.
  arXiv  Detail & Related papers  (2022-03-18T06:14:56Z)
• Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors [91.3755431537592]
  This article develops a framework for learning optimal strategies in real-world matching markets. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. We prove that participants can be better off with multi-stage matching compared to single-stage matching.
  arXiv  Detail & Related papers  (2021-02-13T19:25:52Z)
• Learning Strategies in Decentralized Matching Markets under Uncertain Preferences [91.3755431537592]
  We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space. We derive optimal strategies that maximize agents' expected payoffs.
  arXiv  Detail & Related papers  (2020-10-29T03:08:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.