A conceptual alternative forecasting model for alternative investments

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Dimitrios Stafylas
Konstantina Mari


In this article we present a conceptual model for forecasting purposes that can be used from fund managers or investors. Our conceptual model is a hybrid model and borrows concepts from machine learning; more specifically, from artificial neural networks (ANN) and fuzzy logic (FL). We propose the use of the nonlinear autoregressive network with exogenous inputs (NARX) which is a recurrent dynamic network, with feedback connections enclosing several layers of the network. This ANN is combined with a FL component to deal with uncertainties when considering various market conditions. The proposed conceptual forecasting model has an open architecture design so as to be extended and optimized based on investors’ needs.

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