Mining Multiple Time Series Co-Movements

In this paper, we propose a new model, called co-movement model, for constructing financial portfolios by analyzing and mining the co-movement patterns among multiple time series. Unlike the existing approaches where the portfolios' expected risks are computed based on the co-variances among the assets in the portfolios, we model their risks by considering the co-movement patterns of the time series. For example, given two financial assets, A and B, where we know that whenever the price of A drops, the price of B will drop, and vice versa. Intuitively, it may not be appropriate to construct a portfolio by including both A and B concurrently, as the exposure of loss will be increased. Yet, such kind of relationship can not always be captured by co-variance(i.e traditional statistics). Apart from manipulating the risk, our proposed co-movement model also alters the computation of the portfolio's expected return out of the traditional perspective. Existing approaches for computing the portfolio's expected return are to combine the expected return of each individual asset and its contribution in the portfolio linearly. This formulation ignores the dependence relationship among assets. In contrast, our co-movement model would capture all dependence relationships. This can mimic the real life situation much better than the traditional approach. Extensive experiments are conducted to evaluate the effectiveness of our proposed model. The favorable experimental results indicate that the co-movement model is highly effective and feasible.