Multi-Effect Decompositions for Financial Data Modeling
Abstract
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.
Cite
Text
Wu and Moody. "Multi-Effect Decompositions for Financial Data Modeling." Neural Information Processing Systems, 1996.Markdown
[Wu and Moody. "Multi-Effect Decompositions for Financial Data Modeling." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/wu1996neurips-multieffect/)BibTeX
@inproceedings{wu1996neurips-multieffect,
title = {{Multi-Effect Decompositions for Financial Data Modeling}},
author = {Wu, Lizhong and Moody, John E.},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {995-1004},
url = {https://mlanthology.org/neurips/1996/wu1996neurips-multieffect/}
}