Grey-Box Methods in Forecasting Financial Markets

We first present methods of data analysis in defining stochastic mathematical models suitable for use in forecasting financial markets. With the purpose of multi-period portfolio selection via model predictive control, we focus on input-output model structures. By capturing cause-and-effect dynamic behaviors these models exhibit improved fidelity in simulation. Second we present a probabilistic approach for augmenting the identified models with auxiliary speculative/subjective information derived from analyst and regulatory reports. The technique is an application of the Kalman filter and can be interpreted as a logical extension — to a multi-period framework — of the well-known single-period Black-Litterman approach from portfolio optimization.