Explanation of binarized time series by a behavioral economic approach

The aim of this paper is to reveal the relations between time scales and time series properties by concentrating on information requisite for speculators using a genetic learning model of investor sentiment. For this purpose, first we identify the conditions to describe investor sentiment using a variety of parameters of genetic algorithm. Then we calculate auto-correlations and conditional probabilities using the estimated models in the first step. Our results show that both the amount and quality of information for the agents determine the time series properties. This implies that the preciseness of information which speculators permit depends on their time scales.