Applying neural networks for concept drift detection in financial markets

Traditional stock market analysis is based on the assumption of a stationary market behavior. The recent financial crisis was an example of the inappropriateness of such assumption, namely by detecting the presence of much higher variations than what would normally be expected by traditional models. Data stream methods present an alternative for modeling the vast amounts of data arriving each day to a financial analyst. This paper discusses the use of a framework based on an artificial neural network that continuously monitors itself and allows the implementation on a multivariate financial non-stationary model of market behavior. An initial study is performed over ten years of the Dow Jones Industrial Average index (DJI), and shows empirical evidence of concept drift in the multivariate financial statistics used to describe the index data stream.