Novel Financial Network Models Using Neuro Correlations and Applications

This work introduces a novel approach to study financial networks and portfolio optimization by addressing the limitations of Pearson correlation, which only captures linear relationships between assets. The novelty of this study is to propose pairwise neuro correlations, which emphasize nonlinear relationships using non-linear neural network autoregressive (NNAR) models. The methodology involves, modeling non-stationary/non-linear stock prices using ARIMA and NNAR models, defining neuro correlations as the correlations of the innovations from NNAR models and constructing adjacency matrices for financial networks using neuro correlations. The study employs network-based community detection method to uncover hidden market structures and applies them to portfolio selection. Experimental results demonstrate that combining neural networks of stock prices and financial networks (for capturing asset relationships) enhances portfolio optimization, yielding portfolios with improved cumulative returns. This approach highlights the strengths of both techniques: neural networks' ability to model non-linearity and financial networks' ability to capture the relationship structure of assets in modeling complex financial relationships.