A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

The internal structure of a complex system can manifest itself with correlations among its components. In global business, the interactions between different markets cause collective lead–lag behavior having special statistical properties which reflect the underlying dynamics. In this work, a cybernetic system of combining the vector autoregression (VAR) and genetic algorithm (GA) with neural network (NN) is proposed to take advantage of the lead–lag dynamics, to make the NN forecasting process more transparent and to improve the NN’s prediction capability. Two business case studies are carried out to demonstrate the advantages of our proposed system. The first one is the tourism demand forecasting for the Hong Kong market. Another business case study is the modeling and forecasting of Asian Pacific stock markets. The multivariable time series data is investigated with the VAR analysis, and then the NN is fed with the relevant variables determined by the VAR analysis for forecasting. Lastly, GA is used to cope with the time-dependent nature of the co-relationships among the variables. Experimental results show that our system is more robust and makes more accurate prediction than the benchmark NN. The contribution of this paper lies in the novel application of the forecasting modules and the high degree of transparency of the forecasting process.