  Forecasting stock returns is a challenging problem due to the highly
stochastic nature of the market and the vast array of factors and events that
can influence trading volume and prices. Nevertheless it has proven to be an
attractive target for machine learning research because of the potential for
even modest levels of prediction accuracy to deliver significant benefits. In
this paper, we describe a case-based reasoning approach to predicting stock
market returns using only historical pricing data. We argue that one of the
impediments for case-based stock prediction has been the lack of a suitable
similarity metric when it comes to identifying similar pricing histories as the
basis for a future prediction -- traditional Euclidean and correlation based
approaches are not effective for a variety of reasons -- and in this regard, a
key contribution of this work is the development of a novel similarity metric
for comparing historical pricing data. We demonstrate the benefits of this
metric and the case-based approach in a real-world application in comparison to
a variety of conventional benchmarks.
