Turnover, account value and diversification of real traders: evidence of collective portfolio optimizing behavior

Lachapelle, David Morton de; Challet, Damien

doi:10.1088/1367-2630/12/7/075039

Cited by 19 publications

(1 citation statement)

References 25 publications

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“…Recent research has related aggregated market trading volume with search queries in Google or Yahoo considered as a macroscopic field [5][6][7], but individual activity data is not easily accessible for research purposes. Just a few papers have been published with this sort of data [8][9][10][11] and none of them focuses on the relationship between microscopic and macroscopic levels. In this regard, the main purpose of the paper is to answer the question about how do nonexpert investors make decisions and whether their actions are a response to a given macroscopic field with rather unique data from an Spanish investment firm.…”

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confidence: 99%

Volatility polarization of non-specialized investors' heterogeneous activity

Gutiérrez-Roig,

Perelló

2013

Preprint

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Financial markets provide an ideal frame for studying decision making in crowded environments. Both the amount and accuracy of the data allows to apply tools and concepts coming from physics that studies collective and emergent phenomena or self-organised and highly heteregeneous systems. We analyse the activity of 29 930 non-expert individuals that represent a small portion of the whole market trading volume. The very heterogeneous activity of individuals obeys a Zipf's law, while synchronization network properties unveil a community structure. We thus correlate individual activity with the most eminent macroscopic signal in financial markets, that is volatility, and quantify how individuals are clearly polarized by volatility. The assortativity by attributes of our synchronization networks also indicates that individuals look at the volatility rather than imitate directly each other thus providing an interesting interpretation of herding phenomena in human activity. The results can also improve agent-based models since they provide direct estimation of the agent's parameters.

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