Detecting and forecasting economic regimes in multi-agent automated exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict market trends. The agent can use this information for tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We present methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models. We show how this model combined with real-time observable information is used to identify the current dominant market condition and to forecast market changes over a planning horizon. Market changes are forecast via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and next day (supporting tactical decisions), while the Markov process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.