A novel ARMA- GARCH-Sent-EVT-Copula Portfolio model with investor sentiment

GARCH-EVT-Copula is proved to have some advantages in improving the accuracy of predicting returns. Investor sentiment described by four indicators (market-turnover ratio, advance-decline ratio, new-highs/lows ratio, and Arms index) attracts increasing attention from researchers. In view of these two factors, firstly, we construct the ARMA-GARCH-Sent (AGS) model with investor sentiment indicator by principal component analysis (PCA). Secondly, considering the advantages of extreme value theory (which can deal with extreme deviations in the value of the probability distribution), we present the ARMA-GARCH-Sent-EVT (AGSE) model to describe the daily logarithmic return series of stocks. In addition, the Copula model is used to construct the multivariate distribution of daily logarithmic stock return series to capture their asymmetric and nonlinear characteristics. Furthermore, we propose an ARMA-GARCH-Sent-EVT-Copula (AGSEC) portfolio model with Copula. In order to highlight the advantages of our model, we make a comparative analysis of three models: the original ARMA-GARCH-Copula (AGC) model, the ARMA-GARCH-Sent-Copula (AGSC) model and the AGSEC model. Finally, we use the data of SHSE (Shanghai Stock Exchange) and SZSE (Shenzhen Stock Exchange) for empirical analysis and compare the dynamic portfolio strategies of the three models. The results show that our model AGSEC with investor sentiment is superior to the other two models, namely, it has higher return rates under the same constraint conditions, which means investor sentiment can assist investors in better navigating market dynamics and plays an increasingly important role in shaping portfolio performance and risk management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.