No abstract

We show that a simple mood-separable preference in a network study of stock returns captures a variety of stylized facts regarding stocks' provisional (ab)normal behavior. These behaviors are articulated in a multi-state complete Euclidean network model that specifies the existence, direction and magnitude of a self-organized dynamics for each individual stock during abnormal market moods. In the empirical setting, we apply suggested model along with two established visual approaches (MDS and AHC) for benchmark purposes. Results reveal different levels of erratic return dynamics for each stock and the entire market in different abnormal market moods. We model and interpret these self-organized dynamics as evidence of stocks' and market's bipolar behavior.