Learning Financial Networks with High-frequency Trade Data.
Learning Financial Networks with High-frequency Trade Data.
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DOI：
10.1080/26941899.2023.2166624
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Kara Karpman;Suman S. Basu;D. Easley;Sanghee Kim
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文献类型：
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作者：
Kara Karpman;Suman S. Basu;D. Easley;Sanghee Kim
金融网络通常通过将标准时间序列分析应用于以低频收集的基于价格的经济变量来估计（例如，每日或每月的股票回报率或已实现的波动率）。这些网络用于风险监测和研究金融市场的信息流动。高频率的日内交易数据集可以利用高分辨率信息，为网络联系提供更多的见解。然而，这样的数据集提出了重大的建模挑战，由于其异步的性质，复杂的动态和非平稳性。为了应对这些挑战，我们使用随机森林来估计金融网络，这是一种最先进的机器学习算法，可以提供出色的预测精度，而无需昂贵的超参数优化。我们网络中的边缘是通过使用一家公司的微观结构指标来预测市场指标变化的迹象（例如另一家公司的已实现波动率）来确定的。我们首先研究了2007-09年美国金融危机之前网络连接的演变。我们发现，网络的密度在2007年最高，与2006年的雷曼兄弟公司的高度连接。第二个分析企业之间的联系的性质表明，较大的公司往往提供更好的预测能力比较小的公司，定性的发现与以前的作品在市场微观结构的文献。
Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, complex dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests, a state-of-the-art machine learning algorithm which offers excellent prediction accuracy without expensive hyperparameter optimization. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure such as the realized volatility of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature.