Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.32
In recent years, foreign exchange intervention studying has become a hot spot in finance field. To extract rules between central bank intervention and its trigger factors based on history data will be very useful for decision-making of central banks and market participants. At present, the chief research methods are all based on Econometrics to estimate the statistic characters, which are usually restricted to statistical hypothesis, and can't extract the latent rules fully. This paper starts from the viewpoint of data mining and introduces rough set theory to solve this problem. We collect the related data of Japanese intervention, construct decision table, use rough set method to extract decision rules, and execute forecast experiment of intervention based on them. Experimental result demonstrates that, this method can obtain satisfying forecast accuracy, and at the same time, it proves that the rules extracted from decision table are valid.
Foreign exchange intervention, Forecast, Rough set, Attribute reduction, Decision rules
Z. Zhang and C. Xie, "Research on Forecast of Central Bank Foreign Exchange Intervention Based on Rough Set Theory," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 23-27.