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http://hdl.handle.net/2451/28304
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| Title: | Prediction in Financial Markets: The Case for Small Disjuncts |
| Authors: | Dhar, Vasant |
| Keywords: | Small disjuncts
predictive modeling
Machine learning
Time series prediction
Financial markets |
| Issue Date: | 24-Sep-2009 |
| Series/Report no.: | CeDER-09-04 |
| Abstract: | Predictive models in regression and classification problems typically
have a single model that covers most, if not all, cases in the data. At
the opposite end of the spectrum is a collection of models each of which
covers a very small subset of the decision space. These are referred to
as “small disjuncts.” The tradeoffs between the two types of
models have been well documented. Single models, especially linear ones,
are easy to interpret and explain. In contrast, small disjuncts do not
provide as clean or as simple an interpretation of the data, and have
been shown by several researchers to be responsible for a
disproportionately large number of errors when applied to out of sample
data. This research provides a counterpoint, demonstrating that
“simple” small disjuncts provide a credible model for
financial market prediction, a problem with a high degree of noise. A
related novel contribution of this paper is a simple method for
measuring the “yield” of a learning system, which is the
percentage of in sample performance that the learned model can be
expected to realize on out-of-sample data. Curiously, such a measure is
missing from the literature on regression learning algorithms. |
| URI: | http://hdl.handle.net/2451/28304 |
| Appears in Collections: | CeDER Working Papers
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