Achitouv Ixandra
Institut des Systèmes Complexes ISC-PIF, CNRS, Paris, France.
PLoS One. 2025 May 9;20(5):e0319985. doi: 10.1371/journal.pone.0319985. eCollection 2025.
Applying a network analysis to stock return correlations, we study the dynamical properties of the network and how they correlate with the market return, finding meaningful variables that partially capture the complex dynamical processes of stock interactions and the market structure. We then use the individual properties of stocks within the network along with the global ones, to find correlations with the future returns of individual S&P 500 stocks. Applying these properties as input variables for forecasting, we find a 21[Formula: see text] improvement on the R2score in the prediction of stock returns on long time scales (per year), and 3[Formula: see text] on short time scales (2 days), relative to baseline models without network variables. These findings highlight the potential of integrating network-based variables into stock return prediction models, which could enhance forecasting accuracy and provide a deeper understanding of market dynamics. This approach could be valuable for both investors and researchers seeking to model and predict stock behaviour in complex financial networks.
通过对股票收益相关性应用网络分析，我们研究了网络的动态特性以及它们与市场收益的相关性，发现了一些有意义的变量，这些变量部分地捕捉了股票相互作用和市场结构的复杂动态过程。然后，我们利用网络中股票的个体特性以及全局特性，来寻找与标准普尔500指数成分股未来收益的相关性。将这些特性作为预测的输入变量，相对于没有网络变量的基线模型，我们发现在长期时间尺度（每年）上预测股票收益时，R2分数提高了21[公式：见正文]，在短期时间尺度（2天）上提高了3[公式：见正文]。这些发现凸显了将基于网络的变量整合到股票收益预测模型中的潜力，这可以提高预测准确性，并提供对市场动态的更深入理解。这种方法对于寻求在复杂金融网络中建模和预测股票行为的投资者和研究人员都可能有价值。
