论文标题
通过图形自动编码器了解股市不稳定
Understanding stock market instability via graph auto-encoders
论文作者
论文摘要
了解股票市场不稳定是财务管理中的一个关键问题，因为从业人员试图预测资产共同发展的破坏，这使投资组合的价值快速而毁灭性地崩溃。这些共同体的结构可以描述为一张图表，其中公司由节点表示，边缘捕获其价格变动之间的相关性。在了解财务稳定性和波动性预测方面，学习及时的共同运动崩溃指标（表现为图形结构中的修改）至关重要。我们建议使用图形自动编码器（GAE）的边缘重建精度作为资产之间空间均匀连接的指标，根据财务网络文献，我们将其用作推断市场波动的代理。在2015 - 2022年期间，我们对标准普尔500指数的实验表明，较高的GAE重建误差值与较高的波动率相关。我们还表明，通过添加提议的度量，可以改善样本外挥发性的自回归建模。我们的论文为金融机器学习的文献做出了贡献，尤其是在了解股票市场不稳定的情况下。
Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid and devastating collapses in value. The structure of these co-movements can be described as a graph where companies are represented by nodes and edges capture correlations between their price movements. Learning a timely indicator of co-movement breakdowns (manifested as modifications in the graph structure) is central in understanding both financial stability and volatility forecasting. We propose to use the edge reconstruction accuracy of a graph auto-encoder (GAE) as an indicator for how spatially homogeneous connections between assets are, which, based on financial network literature, we use as a proxy to infer market volatility. Our experiments on the S&P 500 over the 2015-2022 period show that higher GAE reconstruction error values are correlated with higher volatility. We also show that out-of-sample autoregressive modeling of volatility is improved by the addition of the proposed measure. Our paper contributes to the literature of machine learning in finance particularly in the context of understanding stock market instability.
