We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to $40\%$ improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.
翻译:我们提出了一个模型,利用机器学习,从基于链接和节点的金融网络特征预测市场关联结构。为此,市场结构以动态资产网络为模型,通过量化全球主要市场指数中各个公司组成群体资产价格回报的基于时间的共同变动,对基于时间的共同变动进行量化。我们用三种不同的网络过滤方法,即动态资产图(DAG)、动态最小覆盖树(DMST)和动态阈值网络(DTN)来估计市场结构。实验结果显示,拟议的模型可以预测市场结构,预测性能高,比基于时间的不变相关基准改进40美元。非双向关联特征与所有所研究的市场传统上使用的双向关联措施相比非常重要,特别是在对股票市场结构的长期预测中。为DAX30、EUROSTOXX50、FTSE100、HANGSENG50、NASDAQ100和NIFTY50市场指数的股票组成者提供了证据。调查结果有助于改进投资组合的选择和风险管理方法,这些方法通常依赖后向组合组合矩阵来估计风险。