Price Expectations for Financial Markets : Randomness and Signal Detection

We examine contributions that noise can make to the objective of detecting signal in agent expectations for price in financial markets. Although contrary to most assumptions on exogenous noise in financial markets as increasing both risk and uncertainty in the detection of signal, a basis for the contribution that noise can have to agent objectives in signal detection through stochastic resonance (SR) is well-documented across disciplines. After reviewing foundations for the micro-processing of expectations, we offer a multicomponent model of networked agents that includes a component of bounded rational processing and a component that has been cited as generating “herding” behavior in financial markets. We then consider the effects of random disturbances that are common in financial markets on the detection of signal in expectations for price. The models that we implement in a signal-to-noise ratio directly investigate a process in stochastic resonance through which exogenous noise can increase efficiency in detecting signal. Results with both deterministic and stochastic forms of the proposed model support SR as a process in which randomness can contribute to the recovery of signal in agent expectation. We further report AUROC predictive models that indicate the sensitivity of the occurrence of SR to parameters of a model of agent expectations