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Abstract

The authors 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 multistate 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, the authors apply suggested model along with 2 established visual approaches (multidimensional scaling and agglomerative hierarchical clustering) for benchmark purposes. Results reveal different levels of erratic return dynamics for each stock and the entire market in different abnormal market moods. The authors model and interpret these self-organized dynamics as evidence of stocks’ and market’s bipolar behavior.

Item Type:	Article
Authors/Creators:	Ajirlou, AI and Esmalifalak, H and Esmalifalak, M and Behrouz, SP and Soltanalizadeh, F
Keywords:	complete euclidean network, alpha measure, stocks’ bipolar behavior, market’s bipolar behavior
Journal or Publication Title:	The Journal of Behavioral Finance
Publisher:	Routledge
ISSN:	1542-7560
DOI / ID Number:	10.1080/15427560.2018.1508022
Copyright Information:	Copyright 2019 The Institute of Behavioral Finance. This is an Accepted Manuscript of an article published by Taylor & Francis Group in The Journal of Behavioral Finance on 24/01/2019, available online: http://www.tandfonline.com/10.1080/15427560.2018.1508022
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