Sequential Detection of Market shocks using Risk-averse Agent Based Models

TL;DR

This paper models risk-averse agent behaviors in financial markets to detect market shocks sequentially, revealing that risk aversion influences herd behavior and the detection process, validated on real-world data.

Contribution

It introduces a novel risk measure-based social learning model and formulates a Bayesian change point detection for market shocks.

Findings

Risk averse agents herd more often than risk neutral agents.

The stopping set in detection is non-convex.

Framework validated on Yahoo! Tech Buzz dataset.

Abstract

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.