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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14171
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| Title: | DISCOVERING INTERESTING PATTERNS FOR INVESTMENT DECISION MAKING WITH
GLOWER C - A GENETIC LEARNER OVERLAID WITH ENTROPY REDUCTION |
| Authors: | Dhar, Vasant
Chou, Dashin
Provost, Foster |
| Issue Date: | Jan-2000 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-00-02 |
| Abstract: | Prediction in financial domains is notoriously difficult for a number of
reasons. First, theories tend to be weak or non-existent, which makes
problem formulation open-ended by forcing us to consider a large number
of independent variables and thereby increasing the dimensionality of
the search space. Second, the weak relationships among variables tend to
be nonlinear, and may hold only in limited areas of the search space.
Third, in financial practice, where analysts conduct extensive manual
analysis of historically well performing indicators, a key is to find
the hidden interactions among variables that perform well in
combination. Unfortunately, these are exactly the patterns that the
greedy search biases incorporated by many standard rule algorithms will
miss. In this paper, we describe and evaluate several variations of a
new genetic learning algorithm (GLOWER) on a variety of data sets. The
design of GLOWER has been motivated by financial prediction problems,
but incorporates successful ideas from tree induction and rule learning.
We examine the performance of several GLOWER variants on two UCI data
sets as well as on a standard financial prediction problem (S&P500
stock returns), using the results to identify and use one of the better
variants for further comparisons. We introduce a new (to KDD) financial
prediction problem (predicting positive and negative earnings
surprises), and experiment withGLOWER, contrasting it with tree- and
rule-induction approaches. Our results are encouraging, showing that
GLOWER has the ability to uncover effective patterns for difficult
problems that have weak structure and significant nonlinearities. |
| URI: | http://hdl.handle.net/2451/14171 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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