Developing An Attention-Based Ensemble Learning Framework for Financial
  Portfolio Optimisation
Zhenglong Li Vincent Tam
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.