A generic feature of many complex systems is the presence of collective periodic oscillations of their interacting elements. It is important to stress that collective periodic motions may bring us information about the hidden structure of a corresponding system. In this article we discuss a method to infer the structure of a human organization from participant behavior. Recently various human activities can be observed directly with high resolution, and collected data are stored by computers due to the development of Information and Communication Technology. Specifically we focus on the foreign exchange market. All market participants in the foreign exchange market communicate through electronic brokering systems and find others who are able to exchange currencies with them. The database includes 15 kinds of currency pairs consisting of 10 major currencies: EUR (Euro), NZD (New Zealand Dollar), CAD (Canadian Dollar), SEK (Swedish Krone), AUD (Australian Dollar), USD (United States Dollar), GBP (British Pound), NOK (Norwegian Krona), JPY (Japanese Yen), and CHF (Swiss Franc) for a period from 1st to 29^th September 2000. By means of periodgram estimator we conduct spectral analysis on quotation frequencies defined as the number of quotations per minute. As a result of empirical investigations a group structure of characteristic periodic decisions of market participants is found. In order to explain this finding at the microscopic level the agent-based model of a financial market in which N market participants trade M financial commodities is considered. If different sources of periodic information exist then the relationship among these characteristic periodic behaviors may be associated with a special structure where market participants perceive such information in the foreign exchange market. The periodic exogenous information is thought of as periodic stimuli which market participants perceive through computer terminals as part of the electronic brokering system. From numerical simulation it is found that the signal-to-noise ratio of behavioral frequencies is a function with an extreme value of uncertainty of agents’ perception. Namely stochastic resonance occurs in the agent-based model. This result proposes the hypothesis that the signal-to-noise ratio is related to variation of behavioral parameters of the market participants.

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