Multi-effect Decompositions for Financial Data Modeling

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Lizhong Wu, John Moody


High frequency foreign exchange data can be decomposed into three components: the inventory effect component, the surprise infonnation (news) component and the regular infonnation component. The presence of the inventory effect and news can make analysis of trends due to the diffusion of infonnation (regular information component) difficult. We propose a neural-net-based, independent component analysis to sep(cid:173) arate high frequency foreign exchange data into these three components. Our empirical results show that our proposed multi-effect decomposition can reveal the intrinsic price behavior.