论文标题
用于基于注意力的资产分配的投资组合变压器
Portfolio Transformer for Attention-Based Asset Allocation
论文作者
论文摘要
传统的金融资产分配方法始于回报预测，然后是确定最佳资产权重的优化阶段。在预测步骤中犯的任何错误都会降低资产权重的准确性，从而降低整体投资组合的盈利能力。此处介绍的投资组合变压器（PT）网络避免了预测资产回报的必要性，而是直接优化了Sharpe比率，Sharpe比率是经过实践广泛使用的风险调整的性能度量。 PT是一种新颖的端到端投资组合优化框架，灵感来自自然语言处理中注意机制的众多成功。 PT凭借其完整的编码器架构，专门的时间编码层和门控组件，具有很高的能力，可以在投资组合资产之间学习长期依赖性，因此可以更快地适应不断变化的市场状况，例如COVID-19-19的大流行。为了证明其鲁棒性，将PT与其他三个不同数据集的其他算法（包括当前基于LSTM的最新技术状态）进行了比较，结果表明它提供了最佳风险调整的性能。
Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The Portfolio Transformer (PT) network, introduced here, circumvents the need to predict asset returns and instead directly optimizes the Sharpe ratio, a risk-adjusted performance metric widely used in practice. The PT is a novel end-to-end portfolio optimization framework, inspired by the numerous successes of attention mechanisms in natural language processing. With its full encoder-decoder architecture, specialized time encoding layers, and gating components, the PT has a high capacity to learn long-term dependencies among portfolio assets and hence can adapt more quickly to changing market conditions such as the COVID-19 pandemic. To demonstrate its robustness, the PT is compared against other algorithms, including the current LSTM-based state of the art, on three different datasets, with results showing that it offers the best risk-adjusted performance.
