Managing Risk Behavior on an Evolutionary Market -- A Risk Limits and Value-at-Risk Measures Approach

The goal of this paper is to develop a system able to coordinate a trader in optimizing a stock market portfolio in order to improve the profitability of a short or medium time period investment. The system is able to classify the risk and quantifies its effect on an investment based on sentiment analysis, certain characteristics, the traders confidence level, and by measuring the potential loss over a certain period of time. The system id also able to create certain types of portfolios based on different methods of computing the investment risk and on the associated level of confidence. We proposed a multi-agent system that uses sentiment analysis, volatility, Monte Carlo simulation, trust models and risk models/limits in order to choose the appropriate mix of investments in order to minimize the risk and maximize the gain on a stock portfolio taking in consideration also the traders possibilities to enter in an investment. A prototype was developed on which we validated our research.