BAYESIAN NETWORKS FOR FINANCIAL MARKET SIGNALS DETECTION

In order to model and explain the dynamics of the market, different types and sources of information should be taken into account. We propose to use a Bayesian Network as a quantitative financial tool for market signals detection. We combine and incorporate in the model, accounting, market, and sentiment data. The network is used to describe the relationships among the examined variables in an immediate way. Furthermore, it permits to identify in a mouse-click time scenarios that could lead to operative signals. An application to the analysis of S&P 500 index is presented.