MAVIS: A Multiagent Value Investing System

Abstract

Portfolio management is a challenging task where humans have to make decisions under uncertainty. Since usually humans tend to avoid unknown risk, in general they don't maximize their utility function when managing a portfolio. This fact favours using an automated trading system for portfolio management. In this work, we propose an automated trading system using multiagent systems. We use fundamental and cluster analysis to select the stocks, and additionally we employ a financial distress prediction model to estimate companies financial health. We also optimize the portfolio for different investor's utility functions. Comparing our approach's results to a benchmark, we have obtained higher return values and lower risks; moreover, the approach was profitable even when we have added brokerage fees.