A novel view of suprathreshold stochastic resonance and its applications to financial markets

Abstract

We introduce an original application of Suprathreshold Stochastic Resonance (SSR). Given a noise-corrupted signal, we induce SSR in effort to filter the effect of the corrupting noise. This will yield a clearer version of the signal we desire to detect. We propose a financial application that can help forecast returns generated by big orders. We assume there exist return signals that correspond to big orders, which are hidden by noise from small scale traders. We induce SSR in an attempt to reveal these return signals.