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预训练Transformer用于股票定价与因子投资

文章 arXiv papers · 作者: Shanyan Lai
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总结

论文提出单向Transformer模型SERT，用于大型US股票定价，并将预训练Transformer应用于股票定价和因子投资。研究在疫情前、疫情期间及疫情后一年这三个时期，将这些方法与标准Transformer和仅编码器Transformer进行比较，并关注市场承压时的表现。作者报告称，在市场剧烈波动期间，SERT的样本外R²最高，其次是预训练Transformer。

文中提出基于这些模型的趋势跟踪策略，以在市场冲击期间对冲下行风险。在疫情期间的静态交易成本情景中，据报告，等权投资组合的SERT索提诺比率比买入并持有高47%，价值加权投资组合则高28%。论文还发现，softmax信号过滤器不会改善策略表现；增加注意力头仅带来不显著的提升，而在此情形下先应用层归一化也没有帮助。这些结果仅适用于研究所考察的时期和设置；所提供的文本没有说明交易成本假设或更广泛的验证细节。

核心观点

  • 提出SERT用于US大型股票定价，并与预训练Transformer方法一同研究。
  • 研究在横跨COVID-19疫情的三个时期，将这些模型与标准Transformer和仅编码器Transformer进行比较。
  • 据报告，市场剧烈波动期间，SERT的样本外R²最高。
  • 文中提出基于模型的趋势跟踪策略，以在市场冲击时对冲下行风险。
  • 在所述疫情期间成本情景下，SERT的索提诺比率在两种投资组合加权方法下均高于买入并持有。
  • 测试的softmax过滤器、额外注意力头和层归一化顺序均未显著改善策略表现。

标签

股票机器学习因子投资趋势跟踪风险管理
全文
# Asset Pricing in Pre-trained Transformer


# Asset Pricing in Pre-trained Transformer









This paper proposes an innovative Transformer model, Single-directional representative from Transformer (SERT), for US large capital stock pricing. It also innovatively applies the pre-trained Transformer models under the stock pricing and factor investment context. They are compared with standard Transformer models and encoder-only Transformer models in three periods covering the entire COVID-19 pandemic to examine the model adaptivity and suitability during the extreme market fluctuations. Namely, pre-COVID-19 period, COVID-19 period and 1-year post-COVID-19. The best proposed SERT model achieves the highest out-of-sample $R^2$, 11.94\% and 11.47\% respectively, when extreme market fluctuation takes place, followed by pre-trained Transformer models (11.13\% and 9.72\%). Their Trend-following-based strategy's performance also proves their excellent capability for hedging downside risks during market shocks. The proposed SERT model achieves a Sortino ratio 47\% higher than the buy-and-hold benchmark in the equal-weighted portfolio and 28\% higher in the value-weighted portfolio in the static transaction cost scenario when the pandemic period is considered. It proves that Transformer models have a strong ability to capture patterns of temporal sparsity in asset pricing factor models, especially with high volatility. I also find the softmax signal filter as the common configuration of Transformer models in alternative contexts, which only eliminates differences between models, but does not improve strategy-wise performance, while increasing attention heads improves the model performance insignificantly and applying the 'layer normalization first' method does not boost the model performance in our case.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。