more art than hard science
“The naïve approach to Data Mining in finance assumes that somebody can provide a cookbook instruction on “how to achieve the best result”. Some publications continue to foster this unjustified belief. In fact, the only realistic approach proven to be successful is providing comparisons between different methods showing their strengths and weaknesses relative to problem characteristics (problem ID) conceptually and leaving for user the selection of the method that likely fits the specific user problem circumstances. In essence this means clear understanding that Data Mining in general, and in finance specifically, is still more art than hard science.”
“It has been shown that the financial data are not random and that the efficient market hypothesis is merely a subset of a larger chaotic market hypothesis (Drake and Kim, 1997). This hypothesis does not exclude successful short term forecasting models for prediction of chaotic time series (Casdagli and Eubank, 1992).
Data Mining does not try to accept or reject the efficient market theory. Data Mining creates tools, which can be useful for discovering subtle short-term conditional patterns and trends in wide range of financial data. This means that retraining should be a permanent part of data mining in finance and any claim that a silver bullet trading has been found should be treated similarly to claims that a perpetuum mobile has been discovered.”
Data Mining and Knowledge Discovery Handbook
(source: http://www.books24×7.com/)
