  This paper considers a statistical signal processing problem involving agent
based models of financial markets which at a micro-level are driven by socially
aware and risk- averse trading agents. These agents trade (buy or sell) stocks
by exploiting information about the decisions of previous agents (social
learning) via an order book in addition to a private (noisy) signal they
receive on the value of the stock. We are interested in the following: (1)
Modelling the dynamics of these risk averse agents, (2) Sequential detection of
a market shock based on the behaviour of these agents. Structural results which
characterize social learning under a risk measure, CVaR (Conditional
Value-at-risk), are presented and formulation of the Bayesian change point
detection problem is provided. The structural results exhibit two interesting
prop- erties: (i) Risk averse agents herd more often than risk neutral agents
(ii) The stopping set in the sequential detection problem is non-convex. The
framework is validated on data from the Yahoo! Tech Buzz game dataset.
