Detecting asset value dislocations in multi-agent models of market microstructure

Abstract

Consider a financial market participant observing the trade flow of an asset traded through a limit order book. Trades are driven by an agent-based model where individual agents observe the trading decisions of previous agents, as well as their private signal on the value of the asset and then execute a trading decision. Given trading decisions of agents, how can a market observer detect a shock to the underlying value of the traded asset? The distribution of shock times is assumed to be phase-type distributed to allow for a general set of change time probabilities beyond geometric change times. We show that this problem is equivalent to change detection with social learning. We provide structural results that allow the optimal detection policy to be characterized by a single threshold policy.

Partially Observed Markov Decision Processes: Structural Results

“…Recall that in classical social learning after some finite time k, all agents choose the same action and the public belief freezes resulting in an information cascade; see [22,66] and [58,59] for a financial application.…”