Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation

Paper: arXiv 2404.08935

Abstract

In recent years, deep or reinforcement learning approaches have been applied to optimise investment portfolios through learning the spatial and temporal information under the dynamic financial market. Yet in most cases, the existing approaches may produce biased trading signals based on the conventional price data due to a lot of market noises, which possibly fails to balance the investment returns and risks. Accordingly, a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT, is proposed in this work in which multiple trading agents are created to observe and analyse the price series and directional change data that recognises the significant changes of asset prices at different levels of granularity for enhancing the signal-to-noise ratio of price series. Afterwards, by reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points. Besides, a portfolio generator is integrated into the proposed framework to fuse the spatial-temporal information and then summarise the portfolios suggested by all trading agents to produce a newly ensemble portfolio for reducing biased trading actions and balancing the overall returns and risks. The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches on three challenging data sets of DJIA, S&P 500 and CSI 300. More importantly, our proposal has potential strengths in many possible applications for future study.

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• 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 employs advanced mathematics including transformer architectures, multi-agent systems, attention mechanisms, and POMDP formulation (high math complexity). It also demonstrates strong empirical rigor with backtests on three major datasets (DJIA, S&P 500, CSI 300) and comparisons against established methods.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Develop attention-based<br>ensemble framework for<br>portfolio optimisation"] --> B["Data Preparation<br>Directional Change &<br>Price Series Data<br>(DJIA, S&P 500, CSI 300)"]

  B --> C["Multi-Agent Architecture<br>Multiple Trading Agents<br>Self-Adaptive Mechanism"]

  C --> D["Spatial-Temporal Analysis<br>Attention-based Cross-Sectional<br>Analysis Module<br>Attention-based Temporal<br>Analysis Module"]

  D --> E["Portfolio Generation<br>Ensemble Fusion of<br>Spatial-Temporal Information<br>Balance Returns & Risks"]

  E --> F["Key Outcomes<br>Improved Signal-to-Noise Ratio<br>Reduced Biased Trading Actions<br>Superior Performance vs.<br>Baseline Methods"]

Cited References (37)

1. Universal portfolios (1991)
2. On-line portfolio selection using multiplicative updates (1998)
3. PAMR: Passive aggressive mean reversion strategy for portfolio selection (2012)
4. Nonparametric kernel-based sequential investment strategies (2006)
5. A deep reinforcement learning framework for the financial portfolio management problem (2017) — link
6. Cost-sensitive portfolio selection via deep reinforcement learning (2020)
7. Relation-aware transformer for portfolio policy learning (2021)
8. DeepTrader: a deep reinforcement learning approach for risk-return balanced portfolio management with market conditions Embedding (2021)

1 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.