The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?

Abstract

This paper sheds lights on the predictive power of googling investor sentiment on MENA market indexes returns from 2004 to 2018 by using a novel approach based on the thermal optimal path model, Diebold–Yilmaz Spillover Indexes and the wavelet coherence model. Thermal optimal path reveals that googling investor sentiment exhibits a lead effect for Islamic and conventional indexes returns which is influenced by political, social and economic conditions. Using Diebold–Yilmaz Spillover Indexes, we find that googling investor’s sentiment is the main net transmitter of shocks to Saudi Arabia, Egypt, Qatar, Bahrain, Oman and Jordan Islamic market indexes and the majority of conventional indexes. Further, the wavelet coherence model confirms the thermal optimal path results of the leading effect of googling investor sentiment, especially during crises periods. These results depict the robust and significant predictive power of googling investor’s sentiment to detect the behavior of investor, especially during instability periods and to predict market returns in MENA financial markets. Consequently, investor can exploit googling investor sentiment measure in portfolio investments and trading strategy in MENA financial markets.