Multi-Agent Model Based Proactive Risk Management For Equity Investment (Forthcoming in “Engineering Applications of Artificial Intelligence”)

Abstract

Developing and applying new artificial intelligence (AI) techniques in finance has become popular and one of the growing areas. Although many studies focus on return prediction and do not pay much attention to price formation, revealing its mechanism is essential in risk management, particularly in proactive risk management for investment to improve the performance. Thus, this paper introduces a novel multi-agent model, which is able to explain how agents’ portfolio rebalances determine the market price dynamics to clarify the price formation by applying a state space model.
The technical novelty is the effective integration of state space modeling and fuzzy logic into a multi-agent model with four types of typical investors and their fuzzy trading strategies. By using the estimated unobservable fund flows of each trader in the model, this work proposes a new proactive warning signal. As a result, the signal improves both the risk and return of the investment in the Japanese and United States equity markets. Our findings indicate that the agents’ estimated fund flows driving asset prices help us to avoid a market crash, reduce the risk and improve the return in investment practice.

This paper is available at https://www.sciencedirect.com/science/article/pii/S0952197623008850?dgcid=author