Non-stationary time series are commonly found in financial applications. Added to the complexity are the time-varying nature and non-linearity of accurate models for describing the dynamic behavior of these financial time series. We extend the techniques of cointegration to handle time-varying, non-linear relationship between a time series (“news”) and its casually affected time series. The predictability of daily return, as related to the NASDAQ indexes and to a possible NASDAQ-GEM relationship, is investigated based on a proposed “news” model for dynamic changes. The effectiveness and robustness of neural network models for handling non-linearity is compared with linear least-squares estimation.