Financial interaction networks inferred from traded volumes

Autor:	Zeng, Hongli, Lemoy, Rémi, Alava, Mikko
Rok vydání:	2013
Předmět:	Quantitative Finance - Statistical Finance Condensed Matter - Disordered Systems and Neural Networks Physics - Physics and Society
Druh dokumentu:	Working Paper
DOI:	10.1088/1742-5468/2014/07/P07008
Popis:	In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, by local averaging and thresholding. This transformation introduces parameters, which must be varied to characterize the behaviour of the system. The approach is illustrated on financial data, using three inference methods -- equilibrium, synchronous and asynchronous inference -- to construct functional connections between stocks. We show that the traded volume information is enough to obtain well known results about financial markets, which use however the presumably richer price information: collective behaviour ("market mode") and strong interactions within industry sectors. Synchronous and asynchronous Ising inference methods give results which are coherent with equilibrium ones, and more detailed since the obtained interaction networks are directed. Comment: 14 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1311.3871 Zobrazit plný text záznamu View this record from Arxiv