Applications of Data Mining in E-Business and Finance by Z.-H. Zhou

By Z.-H. Zhou

The appliance of information Mining (DM) applied sciences has proven an explosive progress in increasingly more diverse parts of commercial, executive and technological know-how. of crucial company components are finance, particularly in banks and insurance firms, and e-business, reminiscent of net portals, e-commerce and advert administration services.In spite of the shut courting among study and perform in facts Mining, it's not effortless to discover details on essentially the most vital concerns serious about actual global software of DM know-how, from enterprise and knowledge realizing to assessment and deployment. Papers frequently describe learn that used to be built with out considering constraints imposed by means of the motivating software. while those matters are taken into consideration, they're usually now not mentioned intimately as the paper needs to specialize in the strategy. for that reason wisdom which may be beneficial if you want to practice a similar strategy on a similar challenge isn't shared. The papers during this ebook deal with a few of these matters. This publication is of curiosity not just to facts Mining researchers and practitioners, but in addition to scholars who desire to have an concept of the sensible concerns serious about information Mining.

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The time range is from June 1996 to August 2006 and comprises about 2500 trading days. Five technical trading strategies are applied in a series of tests described below. 1. The details of the other four trading strategies are not discussed here. Interested readers can refer to [18,19] for further information. The typical settings of various trading strategies are also obtained from [18]. Besides, the simple buy-and-hold strategy (BH) is also tested for comparison purpose. During the experiments, the trading strategies are always applied to a security/index with an initial capital of AU$10,000 for a security or AU$1,000,000 for an index.

References [1] E. Acar and S. Satchell, editors. Advanced Trading Rules. Butterworth-Heinemann, Oxford, 2 edition, 2002. -H. Park and S. H. Irwin. What do we know about the profitability of technical analysis? Journal of Economic Surveys, 2006. [3] R. Sullivan, A. Timmermann, and H. White. Data-snooping, technical trading rule performance, and the bootstrap. Journal of Econometrics, 54:1647–1691, 1999. [4] B. Kovalerchuk, E. Vityaev, and E. Vityaev. Data Mining in Finance: Advances in Relational and Hybrid Methods.

Generally speaking, if a certain attribute is totally correlated to the target attribute, then the diversification of target attribute can be speculated by it. 38 Y. Dong-Peng et al. 5 algorithm, we know that the split on each node of the decision tree is carried out on the attribute which has the largest information gain ratio, that is to say, the attribute of each node represents the factor that influences the target attribute the most. The closer a certain attribute is to the root node, the more it can influence the target attribute.

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