Nonlinearity or Structural Break? - Data Mining in Evolving Financial Data Sets from a Bayesian Model Combination Perspective

Much work in the exploration of data mining has been focused on quality data. Data non-stationary is prevalent in reality and is further complicated by model uncertainty. The emphasis on organizational impact and benefit maximization of data mining urges us to develop models that are easy to be understood by managerial decision makers. To provide a better solution to these application challenges, we propose a new approach integrating Bayesian structural break models with change point detection methods to chronologically ordered observations. We apply our approach to three exchange rate predictions. Our approach incorporates both structural break and model uncertainty explicitly. It not only has a clear intuitive appeal but also has a fairly firm statistical foundation. The benchmark comparison shows strong empirical evidence that our approach could match the approximating ability of neural networks in mining data with structural break. Furthermore, comparing to neural networks, our approach provides better interpretability.