Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market

Recently, the connection between assets in a portfolio has attracted widespread attention. How to improve the performance of a large portfolio selection from the perspective of network is still a challenging but meaningful work. To this end, we propose a novel network-augmented time-varying parametric portfolio selection model labeled as NA-TVPP. First, we construct a financial network using the least absolute shrinkage and selection operator-vector autoregression (LASSO-VAR) approach. Then, we extract two network topological characteristics and incorporate them into the time-varying parametric portfolio selection (TVPP) model to improve its performance. Finally, we apply it to construct a portfolio using the constituent stocks from the Shanghai Stock Exchange (SSE) 50 Index of China from 2010 to 2019. The empirical results illustrate the effectiveness of the NA-TVPP model in two aspects. To be specific, the NA-TVPP model outperforms several conventional portfolio selection models in terms of standard deviation, Sharpe ratio, and efficient frontier. Additionally, the stock network topological characteristics, such as degree centrality (DC) and eigenvector centrality (EC), are significant to portfolio selection through the negative effect on the weights.