Modeling investor sentiment jumps using deep reinforcement learning with a Hawkes cross-excitation modeling approach

News sentiment is different from the true investor sentiment, and there is a conductive process of information flow from news sentiment to the latent investor sentiment and vice versa. This study aims to develop a methodology to estimate the latent effect between the investor sentiment jumps and the market return jumps using a multivariate Hawkes process along with a deep reinforcement learning algorithm. We achieve this goal through a three-step process: (i) identify the baseline intensity among the events of news sentiment and market return by a multivariate Hawkes process; (ii) estimate the hidden effect that drives the movement of events of news sentiment and market return from the baseline intensity via deep reinforcement learning; (iii) reveal the interaction mechanism among the true investor sentiment and the market return that is responsible to the latent investor sentiment. This approach can be broadly applied to analyzing many phenomena in finance and economics where latent events are non-stationary and can not be observed directly.